diff --git a/Assignment Colab/.ipynb_checkpoints/Kiberu_Davis-checkpoint.ipynb b/Assignment Colab/.ipynb_checkpoints/Kiberu_Davis-checkpoint.ipynb
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@@ -0,0 +1,3122 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "   <div class = 'alert alert-info'>\n",
+    "\n",
+    "## ?? This means I need to see more\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "<div class = 'alert alert-success'>\n",
+    "\n",
+    "## This represents a good job done\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "<div class = 'alert alert-danger'>\n",
+    "\n",
+    "## This is danger!\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Outline:\n",
+    "  1. Import required packages\n",
+    "  2. Reading in Datasets\n",
+    "  3. Data exploration\n",
+    "  4. Descriptive statistics for the data\n",
+    "  5. Class distribution of the data \n",
+    "  6. Checking for correlation between the different attributes\n",
+    "  7. Cheking for skewdness of the data\n",
+    "  8. Understanding Data with visualization\n",
+    "  9. Preparing Data for Machine Learning\n",
+    "  10. Feature selection\n",
+    "  11. Predictions by algorithm\n",
+    "  12. Improving performance with Ensembles\n",
+    "  13. Comparing Machine Learning Algorithms used\n",
+    "  \n",
+    "  \n",
+    "<div class = 'alert alert-success'>\n",
+    "Great summary, really helpful!\n",
+    "</div>\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Import required packages\n",
+    "For this assignment, the required packages and modules are;\n",
+    "* numpy;for computing scientific/mathematical data.\n",
+    "* pandas; for data wrangling and manipulation\n",
+    "* seaborn; for statistical data visualization\n",
+    "* matplotlib.pyplot;  for plots\n",
+    "\n",
+    "\n",
+    "<div class = 'alert alert-success'>\n",
+    "\n",
+    "Great job!\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
+   },
+   "source": [
+    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
+    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
+    "# For example, here's several helpful packages to load in \n",
+    "\n",
+    "import numpy as np # linear algebra\n",
+    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Input data files are available in the \"../input/\" directory.\n",
+    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
+    "\n",
+    "import os\n",
+    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
+    "    for filename in filenames:\n",
+    "        print(os.path.join(dirname, filename))\n",
+    "\n",
+    "# Remove unnecessary warnings\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "# Any results you write to the current directory are saved as output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np # linear algebra\n",
+    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Reading in Datasets\n",
+    "Comma Seperated Variables(CSV) data files are read into DataFrame objects using Pandas' read_csv() method.\n",
+    "Two datasets are read in: \n",
+    "* The train set assigned to train\n",
+    "* The test set assigned to test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
+    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
+   },
+   "outputs": [],
+   "source": [
+    "# Reading in Datasets\n",
+    "train = pd.read_csv(\"../AMP Data Sets/AMP_TrainSet.csv\")\n",
+    "test = pd.read_csv(\"../AMP Data Sets/Test.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Data exploration\n",
+    "To get a feel of the data, we visualize a few of the rows(instances) and columns(attributes) of the data using the `head()` function which shows the first five rows.\n",
+    "\n",
+    "a) We the check for the dimensions of the data using the `shape` property of the data which returns a tuple indicating number of rows and columns.\n",
+    "\n",
+    "b) We can then explore the names of the various attributes using the columns property and then check for the data type of each attribute using the `dtypes` property.\n",
+    "\n",
+    "c) We can also check for missing values (NAs and NaNs) using the `isnull()` function\n",
+    "\n",
+    "\n",
+    "<div class = 'alert alert-success'>\n",
+    "\n",
+    "Great job!\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>FULL_Charge</th>\n",
+       "      <th>FULL_AcidicMolPerc</th>\n",
+       "      <th>FULL_AURR980107</th>\n",
+       "      <th>FULL_DAYM780201</th>\n",
+       "      <th>FULL_GEOR030101</th>\n",
+       "      <th>FULL_OOBM850104</th>\n",
+       "      <th>NT_EFC195</th>\n",
+       "      <th>AS_MeanAmphiMoment</th>\n",
+       "      <th>AS_DAYM780201</th>\n",
+       "      <th>AS_FUKS010112</th>\n",
+       "      <th>CT_RACS820104</th>\n",
+       "      <th>CLASS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.951</td>\n",
+       "      <td>74.842</td>\n",
+       "      <td>0.975</td>\n",
+       "      <td>-3.663</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.282</td>\n",
+       "      <td>73.444</td>\n",
+       "      <td>5.661</td>\n",
+       "      <td>1.041</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5.405</td>\n",
+       "      <td>0.931</td>\n",
+       "      <td>71.595</td>\n",
+       "      <td>0.957</td>\n",
+       "      <td>-4.011</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.600</td>\n",
+       "      <td>68.222</td>\n",
+       "      <td>6.537</td>\n",
+       "      <td>1.453</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5.5</td>\n",
+       "      <td>5.405</td>\n",
+       "      <td>0.873</td>\n",
+       "      <td>73.595</td>\n",
+       "      <td>0.961</td>\n",
+       "      <td>-2.512</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.593</td>\n",
+       "      <td>69.444</td>\n",
+       "      <td>4.934</td>\n",
+       "      <td>1.722</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4.167</td>\n",
+       "      <td>0.895</td>\n",
+       "      <td>66.250</td>\n",
+       "      <td>0.999</td>\n",
+       "      <td>-1.362</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.614</td>\n",
+       "      <td>67.222</td>\n",
+       "      <td>4.316</td>\n",
+       "      <td>1.382</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.5</td>\n",
+       "      <td>8.537</td>\n",
+       "      <td>0.932</td>\n",
+       "      <td>64.720</td>\n",
+       "      <td>0.979</td>\n",
+       "      <td>-2.091</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.616</td>\n",
+       "      <td>72.944</td>\n",
+       "      <td>4.540</td>\n",
+       "      <td>1.539</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   FULL_Charge  FULL_AcidicMolPerc  FULL_AURR980107  FULL_DAYM780201  \\\n",
+       "0          5.0               0.000            0.951           74.842   \n",
+       "1          4.0               5.405            0.931           71.595   \n",
+       "2          5.5               5.405            0.873           73.595   \n",
+       "3          5.0               4.167            0.895           66.250   \n",
+       "4          7.5               8.537            0.932           64.720   \n",
+       "\n",
+       "   FULL_GEOR030101  FULL_OOBM850104  NT_EFC195  AS_MeanAmphiMoment  \\\n",
+       "0            0.975           -3.663          0               0.282   \n",
+       "1            0.957           -4.011          1               0.600   \n",
+       "2            0.961           -2.512          0               0.593   \n",
+       "3            0.999           -1.362          0               0.614   \n",
+       "4            0.979           -2.091          0               0.616   \n",
+       "\n",
+       "   AS_DAYM780201  AS_FUKS010112  CT_RACS820104  CLASS  \n",
+       "0         73.444          5.661          1.041      1  \n",
+       "1         68.222          6.537          1.453      1  \n",
+       "2         69.444          4.934          1.722      1  \n",
+       "3         67.222          4.316          1.382      1  \n",
+       "4         72.944          4.540          1.539      1  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Viewing the first five rows of the data\n",
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>FULL_Charge</th>\n",
+       "      <th>FULL_AcidicMolPerc</th>\n",
+       "      <th>FULL_AURR980107</th>\n",
+       "      <th>FULL_DAYM780201</th>\n",
+       "      <th>FULL_GEOR030101</th>\n",
+       "      <th>FULL_OOBM850104</th>\n",
+       "      <th>NT_EFC195</th>\n",
+       "      <th>AS_MeanAmphiMoment</th>\n",
+       "      <th>AS_DAYM780201</th>\n",
+       "      <th>AS_FUKS010112</th>\n",
+       "      <th>CT_RACS820104</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>3.704</td>\n",
+       "      <td>0.873</td>\n",
+       "      <td>73.519</td>\n",
+       "      <td>0.987</td>\n",
+       "      <td>-4.833</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.382</td>\n",
+       "      <td>74.556</td>\n",
+       "      <td>7.225</td>\n",
+       "      <td>1.234</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.444</td>\n",
+       "      <td>0.892</td>\n",
+       "      <td>62.444</td>\n",
+       "      <td>0.931</td>\n",
+       "      <td>-0.584</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.320</td>\n",
+       "      <td>56.056</td>\n",
+       "      <td>4.942</td>\n",
+       "      <td>1.853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.901</td>\n",
+       "      <td>47.000</td>\n",
+       "      <td>1.039</td>\n",
+       "      <td>-5.664</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.164</td>\n",
+       "      <td>47.000</td>\n",
+       "      <td>5.969</td>\n",
+       "      <td>1.174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.5</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.869</td>\n",
+       "      <td>69.222</td>\n",
+       "      <td>0.982</td>\n",
+       "      <td>-5.423</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.010</td>\n",
+       "      <td>69.222</td>\n",
+       "      <td>5.462</td>\n",
+       "      <td>1.138</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-4.0</td>\n",
+       "      <td>21.591</td>\n",
+       "      <td>1.061</td>\n",
+       "      <td>71.682</td>\n",
+       "      <td>0.976</td>\n",
+       "      <td>-2.002</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.758</td>\n",
+       "      <td>66.000</td>\n",
+       "      <td>5.582</td>\n",
+       "      <td>1.453</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   FULL_Charge  FULL_AcidicMolPerc  FULL_AURR980107  FULL_DAYM780201  \\\n",
+       "0          4.0               3.704            0.873           73.519   \n",
+       "1          4.0               4.444            0.892           62.444   \n",
+       "2          2.0               0.000            0.901           47.000   \n",
+       "3          4.5               0.000            0.869           69.222   \n",
+       "4         -4.0              21.591            1.061           71.682   \n",
+       "\n",
+       "   FULL_GEOR030101  FULL_OOBM850104  NT_EFC195  AS_MeanAmphiMoment  \\\n",
+       "0            0.987           -4.833          0               0.382   \n",
+       "1            0.931           -0.584          0               0.320   \n",
+       "2            1.039           -5.664          0               0.164   \n",
+       "3            0.982           -5.423          0               2.010   \n",
+       "4            0.976           -2.002          0               2.758   \n",
+       "\n",
+       "   AS_DAYM780201  AS_FUKS010112  CT_RACS820104  \n",
+       "0         74.556          7.225          1.234  \n",
+       "1         56.056          4.942          1.853  \n",
+       "2         47.000          5.969          1.174  \n",
+       "3         69.222          5.462          1.138  \n",
+       "4         66.000          5.582          1.453  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### a) Data dimensions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((3038, 12), (758, 11))"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Checking for number of rows and columns in the datasets\n",
+    "train.shape, test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### b) Data type for each attribute"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['FULL_Charge', 'FULL_AcidicMolPerc', 'FULL_AURR980107',\n",
+       "       'FULL_DAYM780201', 'FULL_GEOR030101', 'FULL_OOBM850104', 'NT_EFC195',\n",
+       "       'AS_MeanAmphiMoment', 'AS_DAYM780201', 'AS_FUKS010112', 'CT_RACS820104',\n",
+       "       'CLASS'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# First lets take a look at the column names.\n",
+    "train.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['FULL_Charge', 'FULL_AcidicMolPerc', 'FULL_AURR980107',\n",
+       "       'FULL_DAYM780201', 'FULL_GEOR030101', 'FULL_OOBM850104', 'NT_EFC195',\n",
+       "       'AS_MeanAmphiMoment', 'AS_DAYM780201', 'AS_FUKS010112',\n",
+       "       'CT_RACS820104'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FULL_Charge           float64\n",
+       "FULL_AcidicMolPerc    float64\n",
+       "FULL_AURR980107       float64\n",
+       "FULL_DAYM780201       float64\n",
+       "FULL_GEOR030101       float64\n",
+       "FULL_OOBM850104       float64\n",
+       "NT_EFC195               int64\n",
+       "AS_MeanAmphiMoment    float64\n",
+       "AS_DAYM780201         float64\n",
+       "AS_FUKS010112         float64\n",
+       "CT_RACS820104         float64\n",
+       "CLASS                   int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Next, determine the data type for each column\n",
+    "train.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FULL_Charge           float64\n",
+       "FULL_AcidicMolPerc    float64\n",
+       "FULL_AURR980107       float64\n",
+       "FULL_DAYM780201       float64\n",
+       "FULL_GEOR030101       float64\n",
+       "FULL_OOBM850104       float64\n",
+       "NT_EFC195               int64\n",
+       "AS_MeanAmphiMoment    float64\n",
+       "AS_DAYM780201         float64\n",
+       "AS_FUKS010112         float64\n",
+       "CT_RACS820104         float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### c) Missing values\n",
+    "Missing values include the standard `NaN` and `Na` "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FULL_Charge           0\n",
+       "FULL_AcidicMolPerc    0\n",
+       "FULL_AURR980107       0\n",
+       "FULL_DAYM780201       0\n",
+       "FULL_GEOR030101       0\n",
+       "FULL_OOBM850104       0\n",
+       "NT_EFC195             0\n",
+       "AS_MeanAmphiMoment    0\n",
+       "AS_DAYM780201         0\n",
+       "AS_FUKS010112         0\n",
+       "CT_RACS820104         0\n",
+       "CLASS                 0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check for missing values.\n",
+    "train.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.isnull().sum().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 758 entries, 0 to 757\n",
+      "Data columns (total 11 columns):\n",
+      "FULL_Charge           758 non-null float64\n",
+      "FULL_AcidicMolPerc    758 non-null float64\n",
+      "FULL_AURR980107       758 non-null float64\n",
+      "FULL_DAYM780201       758 non-null float64\n",
+      "FULL_GEOR030101       758 non-null float64\n",
+      "FULL_OOBM850104       758 non-null float64\n",
+      "NT_EFC195             758 non-null int64\n",
+      "AS_MeanAmphiMoment    758 non-null float64\n",
+      "AS_DAYM780201         758 non-null float64\n",
+      "AS_FUKS010112         758 non-null float64\n",
+      "CT_RACS820104         758 non-null float64\n",
+      "dtypes: float64(10), int64(1)\n",
+      "memory usage: 65.2 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Another way to check for null values and datatypes is using the .info() function. \n",
+    "test.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1a113afc50>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1728x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualization to check for missing data\n",
+    "import missingno as msno\n",
+    "msno.bar(train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above plot indicates that there are no missing values in the `train` dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In summary, the train data has 3028 instances and 12 attributes while the test dataset has 758 instances and 11 attributes. \n",
+    "The data types for each instance are numerical i.e. integers(int64) and floating point numbers(float64).\n",
+    "There are no missing values in the datasets.\n",
+    "\n",
+    "\n",
+    "<div class = 'alert alert-success'>\n",
+    "\n",
+    "Great job!\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Descriptive statistics for the data\n",
+    "For descriptive statistics, a summary is obtained using the `describe()` function.\n",
+    "The statistics summary show the counts for each attribtute, the mean, standard deviation, the minimum value for numerical atrributes, the 25th, 50th and 75th percentile for each numeric attribute andthe maximum value.\n",
+    "\n",
+    "\n",
+    "<div class = 'alert alert-info'>\n",
+    "\n",
+    "## What do we learn from this step?\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>FULL_Charge</th>\n",
+       "      <th>FULL_AcidicMolPerc</th>\n",
+       "      <th>FULL_AURR980107</th>\n",
+       "      <th>FULL_DAYM780201</th>\n",
+       "      <th>FULL_GEOR030101</th>\n",
+       "      <th>FULL_OOBM850104</th>\n",
+       "      <th>NT_EFC195</th>\n",
+       "      <th>AS_MeanAmphiMoment</th>\n",
+       "      <th>AS_DAYM780201</th>\n",
+       "      <th>AS_FUKS010112</th>\n",
+       "      <th>CT_RACS820104</th>\n",
+       "      <th>CLASS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2.060237</td>\n",
+       "      <td>8.521520</td>\n",
+       "      <td>0.971410</td>\n",
+       "      <td>73.668760</td>\n",
+       "      <td>0.994007</td>\n",
+       "      <td>-2.432927</td>\n",
+       "      <td>0.088545</td>\n",
+       "      <td>15.683233</td>\n",
+       "      <td>73.650828</td>\n",
+       "      <td>5.911361</td>\n",
+       "      <td>1.235255</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3.819929</td>\n",
+       "      <td>7.586652</td>\n",
+       "      <td>0.107413</td>\n",
+       "      <td>8.527489</td>\n",
+       "      <td>0.031333</td>\n",
+       "      <td>1.707223</td>\n",
+       "      <td>0.284133</td>\n",
+       "      <td>11.575665</td>\n",
+       "      <td>9.166092</td>\n",
+       "      <td>0.693689</td>\n",
+       "      <td>0.210012</td>\n",
+       "      <td>0.500082</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-16.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.684000</td>\n",
+       "      <td>42.750000</td>\n",
+       "      <td>0.866000</td>\n",
+       "      <td>-10.432000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.041000</td>\n",
+       "      <td>42.778000</td>\n",
+       "      <td>3.533000</td>\n",
+       "      <td>0.785000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.516000</td>\n",
+       "      <td>0.895000</td>\n",
+       "      <td>68.294000</td>\n",
+       "      <td>0.974000</td>\n",
+       "      <td>-3.606000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.587500</td>\n",
+       "      <td>67.556000</td>\n",
+       "      <td>5.459250</td>\n",
+       "      <td>1.082000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>7.143000</td>\n",
+       "      <td>0.963000</td>\n",
+       "      <td>74.059500</td>\n",
+       "      <td>0.994000</td>\n",
+       "      <td>-2.296500</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.988500</td>\n",
+       "      <td>73.697000</td>\n",
+       "      <td>5.925500</td>\n",
+       "      <td>1.184000</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>13.158000</td>\n",
+       "      <td>1.041000</td>\n",
+       "      <td>79.343750</td>\n",
+       "      <td>1.011000</td>\n",
+       "      <td>-1.283250</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>26.807750</td>\n",
+       "      <td>79.778000</td>\n",
+       "      <td>6.382000</td>\n",
+       "      <td>1.351000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>46.667000</td>\n",
+       "      <td>1.451000</td>\n",
+       "      <td>101.682000</td>\n",
+       "      <td>1.196000</td>\n",
+       "      <td>3.576000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>51.280000</td>\n",
+       "      <td>103.167000</td>\n",
+       "      <td>8.662000</td>\n",
+       "      <td>2.192000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       FULL_Charge  FULL_AcidicMolPerc  FULL_AURR980107  FULL_DAYM780201  \\\n",
+       "count  3038.000000         3038.000000      3038.000000      3038.000000   \n",
+       "mean      2.060237            8.521520         0.971410        73.668760   \n",
+       "std       3.819929            7.586652         0.107413         8.527489   \n",
+       "min     -16.000000            0.000000         0.684000        42.750000   \n",
+       "25%       0.000000            2.516000         0.895000        68.294000   \n",
+       "50%       2.000000            7.143000         0.963000        74.059500   \n",
+       "75%       4.000000           13.158000         1.041000        79.343750   \n",
+       "max      30.000000           46.667000         1.451000       101.682000   \n",
+       "\n",
+       "       FULL_GEOR030101  FULL_OOBM850104    NT_EFC195  AS_MeanAmphiMoment  \\\n",
+       "count      3038.000000      3038.000000  3038.000000         3038.000000   \n",
+       "mean          0.994007        -2.432927     0.088545           15.683233   \n",
+       "std           0.031333         1.707223     0.284133           11.575665   \n",
+       "min           0.866000       -10.432000     0.000000            0.041000   \n",
+       "25%           0.974000        -3.606000     0.000000            5.587500   \n",
+       "50%           0.994000        -2.296500     0.000000           14.988500   \n",
+       "75%           1.011000        -1.283250     0.000000           26.807750   \n",
+       "max           1.196000         3.576000     1.000000           51.280000   \n",
+       "\n",
+       "       AS_DAYM780201  AS_FUKS010112  CT_RACS820104        CLASS  \n",
+       "count    3038.000000    3038.000000    3038.000000  3038.000000  \n",
+       "mean       73.650828       5.911361       1.235255     0.500000  \n",
+       "std         9.166092       0.693689       0.210012     0.500082  \n",
+       "min        42.778000       3.533000       0.785000     0.000000  \n",
+       "25%        67.556000       5.459250       1.082000     0.000000  \n",
+       "50%        73.697000       5.925500       1.184000     0.500000  \n",
+       "75%        79.778000       6.382000       1.351000     1.000000  \n",
+       "max       103.167000       8.662000       2.192000     1.000000  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Obtain summary statistics\n",
+    "train.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>FULL_Charge</th>\n",
+       "      <th>FULL_AcidicMolPerc</th>\n",
+       "      <th>FULL_AURR980107</th>\n",
+       "      <th>FULL_DAYM780201</th>\n",
+       "      <th>FULL_GEOR030101</th>\n",
+       "      <th>FULL_OOBM850104</th>\n",
+       "      <th>NT_EFC195</th>\n",
+       "      <th>AS_MeanAmphiMoment</th>\n",
+       "      <th>AS_DAYM780201</th>\n",
+       "      <th>AS_FUKS010112</th>\n",
+       "      <th>CT_RACS820104</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>758.000000</td>\n",
+       "      <td>758.000000</td>\n",
+       "      <td>758.000000</td>\n",
+       "      <td>758.000000</td>\n",
+       "      <td>758.000000</td>\n",
+       "      <td>758.000000</td>\n",
+       "      <td>758.000000</td>\n",
+       "      <td>758.000000</td>\n",
+       "      <td>758.000000</td>\n",
+       "      <td>758.000000</td>\n",
+       "      <td>758.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2.073879</td>\n",
+       "      <td>8.945091</td>\n",
+       "      <td>0.973046</td>\n",
+       "      <td>73.821891</td>\n",
+       "      <td>0.994212</td>\n",
+       "      <td>-2.397922</td>\n",
+       "      <td>0.084433</td>\n",
+       "      <td>15.570067</td>\n",
+       "      <td>73.844204</td>\n",
+       "      <td>5.904492</td>\n",
+       "      <td>1.250189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>4.230615</td>\n",
+       "      <td>7.814449</td>\n",
+       "      <td>0.110676</td>\n",
+       "      <td>8.029524</td>\n",
+       "      <td>0.032370</td>\n",
+       "      <td>1.597138</td>\n",
+       "      <td>0.278219</td>\n",
+       "      <td>11.362589</td>\n",
+       "      <td>8.915193</td>\n",
+       "      <td>0.656911</td>\n",
+       "      <td>0.218102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-13.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.699000</td>\n",
+       "      <td>47.000000</td>\n",
+       "      <td>0.889000</td>\n",
+       "      <td>-7.844000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.060000</td>\n",
+       "      <td>47.000000</td>\n",
+       "      <td>3.843000</td>\n",
+       "      <td>0.841000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>-0.500000</td>\n",
+       "      <td>2.721750</td>\n",
+       "      <td>0.894000</td>\n",
+       "      <td>68.740250</td>\n",
+       "      <td>0.973000</td>\n",
+       "      <td>-3.457250</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.709000</td>\n",
+       "      <td>68.346000</td>\n",
+       "      <td>5.471250</td>\n",
+       "      <td>1.096000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>7.500000</td>\n",
+       "      <td>0.965000</td>\n",
+       "      <td>74.069500</td>\n",
+       "      <td>0.994000</td>\n",
+       "      <td>-2.238000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>15.057000</td>\n",
+       "      <td>73.646000</td>\n",
+       "      <td>5.935500</td>\n",
+       "      <td>1.188000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>14.230250</td>\n",
+       "      <td>1.053500</td>\n",
+       "      <td>79.284750</td>\n",
+       "      <td>1.013000</td>\n",
+       "      <td>-1.306250</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>25.290250</td>\n",
+       "      <td>80.069250</td>\n",
+       "      <td>6.375250</td>\n",
+       "      <td>1.378500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>44.118000</td>\n",
+       "      <td>1.431000</td>\n",
+       "      <td>102.929000</td>\n",
+       "      <td>1.182000</td>\n",
+       "      <td>2.017000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>50.098000</td>\n",
+       "      <td>102.929000</td>\n",
+       "      <td>7.588000</td>\n",
+       "      <td>2.283000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       FULL_Charge  FULL_AcidicMolPerc  FULL_AURR980107  FULL_DAYM780201  \\\n",
+       "count   758.000000          758.000000       758.000000       758.000000   \n",
+       "mean      2.073879            8.945091         0.973046        73.821891   \n",
+       "std       4.230615            7.814449         0.110676         8.029524   \n",
+       "min     -13.000000            0.000000         0.699000        47.000000   \n",
+       "25%      -0.500000            2.721750         0.894000        68.740250   \n",
+       "50%       2.000000            7.500000         0.965000        74.069500   \n",
+       "75%       4.000000           14.230250         1.053500        79.284750   \n",
+       "max      30.000000           44.118000         1.431000       102.929000   \n",
+       "\n",
+       "       FULL_GEOR030101  FULL_OOBM850104   NT_EFC195  AS_MeanAmphiMoment  \\\n",
+       "count       758.000000       758.000000  758.000000          758.000000   \n",
+       "mean          0.994212        -2.397922    0.084433           15.570067   \n",
+       "std           0.032370         1.597138    0.278219           11.362589   \n",
+       "min           0.889000        -7.844000    0.000000            0.060000   \n",
+       "25%           0.973000        -3.457250    0.000000            5.709000   \n",
+       "50%           0.994000        -2.238000    0.000000           15.057000   \n",
+       "75%           1.013000        -1.306250    0.000000           25.290250   \n",
+       "max           1.182000         2.017000    1.000000           50.098000   \n",
+       "\n",
+       "       AS_DAYM780201  AS_FUKS010112  CT_RACS820104  \n",
+       "count     758.000000     758.000000     758.000000  \n",
+       "mean       73.844204       5.904492       1.250189  \n",
+       "std         8.915193       0.656911       0.218102  \n",
+       "min        47.000000       3.843000       0.841000  \n",
+       "25%        68.346000       5.471250       1.096000  \n",
+       "50%        73.646000       5.935500       1.188000  \n",
+       "75%        80.069250       6.375250       1.378500  \n",
+       "max       102.929000       7.588000       2.283000  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Class distribution of the data\n",
+    "The data being used has a `CLASS` attribute.\n",
+    "We therefore have to determine the number of clases, their labels/values and distribution of observations within these classes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Checking for the number of classes in the data\n",
+    "train['CLASS'].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 0])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Checking for the class values\n",
+    "train['CLASS'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are 1519 instances in class 1 and 1519 instances in class 0.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1a12045c50>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# There are two classes i.e 1 and 0.\n",
+    "# checking for number of instances in each class \n",
+    "class1 = train.groupby('CLASS').size()[0].sum()\n",
+    "class0 = train.groupby('CLASS').size()[1].sum()\n",
+    "print(\"There are %s instances in class 1 and %s instances in class 0.\" % (class1, class0))\n",
+    "# Visualizing the distribution of the data in the different classes\n",
+    "train.groupby('CLASS').size().plot(kind='bar', color=('skyblue', 'pink'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Classes 1 and 0 have equal instances and as such our observations are balanced.  \n",
+    "Due to this uniform distribution, chances of algorithm bias are reduced.\n",
+    "\n",
+    "<div class = 'alert alert-success'>\n",
+    "\n",
+    "Great job!\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Checking for correlation between the different attributes\n",
+    "Correlation helps us determine how the different attributes relate with each other. It also helps us when deciding which attributes to select especially in the presence of highly correlated attributes in which case one of the two attributes would be sufficient.\n",
+    "\n",
+    "The `corr` function is used with Pearson's correlation method since most of the attributes are continous with the exception of `NT_EFC195` and `CLASS`.\n",
+    "\n",
+    "<div class = 'alert alert-success'>\n",
+    "\n",
+    "Great job! Good use of markdown.\n",
+    "\n",
+    "</div>\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>FULL_Charge</th>\n",
+       "      <th>FULL_AcidicMolPerc</th>\n",
+       "      <th>FULL_AURR980107</th>\n",
+       "      <th>FULL_DAYM780201</th>\n",
+       "      <th>FULL_GEOR030101</th>\n",
+       "      <th>FULL_OOBM850104</th>\n",
+       "      <th>NT_EFC195</th>\n",
+       "      <th>AS_MeanAmphiMoment</th>\n",
+       "      <th>AS_DAYM780201</th>\n",
+       "      <th>AS_FUKS010112</th>\n",
+       "      <th>CT_RACS820104</th>\n",
+       "      <th>CLASS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>FULL_Charge</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.612996</td>\n",
+       "      <td>-0.490977</td>\n",
+       "      <td>-0.434603</td>\n",
+       "      <td>-0.058725</td>\n",
+       "      <td>-0.283758</td>\n",
+       "      <td>0.088068</td>\n",
+       "      <td>0.355477</td>\n",
+       "      <td>-0.365374</td>\n",
+       "      <td>-0.090570</td>\n",
+       "      <td>0.232929</td>\n",
+       "      <td>0.534602</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FULL_AcidicMolPerc</th>\n",
+       "      <td>-0.612996</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.794796</td>\n",
+       "      <td>0.541481</td>\n",
+       "      <td>0.115201</td>\n",
+       "      <td>0.513344</td>\n",
+       "      <td>-0.143168</td>\n",
+       "      <td>-0.431590</td>\n",
+       "      <td>0.449621</td>\n",
+       "      <td>0.002334</td>\n",
+       "      <td>-0.213543</td>\n",
+       "      <td>-0.598816</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FULL_AURR980107</th>\n",
+       "      <td>-0.490977</td>\n",
+       "      <td>0.794796</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.548253</td>\n",
+       "      <td>0.346139</td>\n",
+       "      <td>0.462712</td>\n",
+       "      <td>-0.169540</td>\n",
+       "      <td>-0.426097</td>\n",
+       "      <td>0.456260</td>\n",
+       "      <td>0.032958</td>\n",
+       "      <td>-0.403599</td>\n",
+       "      <td>-0.584111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FULL_DAYM780201</th>\n",
+       "      <td>-0.434603</td>\n",
+       "      <td>0.541481</td>\n",
+       "      <td>0.548253</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.010118</td>\n",
+       "      <td>0.334778</td>\n",
+       "      <td>-0.090058</td>\n",
+       "      <td>-0.408793</td>\n",
+       "      <td>0.894191</td>\n",
+       "      <td>0.055915</td>\n",
+       "      <td>-0.326792</td>\n",
+       "      <td>-0.554838</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FULL_GEOR030101</th>\n",
+       "      <td>-0.058725</td>\n",
+       "      <td>0.115201</td>\n",
+       "      <td>0.346139</td>\n",
+       "      <td>0.010118</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.319157</td>\n",
+       "      <td>-0.230417</td>\n",
+       "      <td>-0.160269</td>\n",
+       "      <td>-0.029085</td>\n",
+       "      <td>0.040480</td>\n",
+       "      <td>-0.151935</td>\n",
+       "      <td>-0.260470</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FULL_OOBM850104</th>\n",
+       "      <td>-0.283758</td>\n",
+       "      <td>0.513344</td>\n",
+       "      <td>0.462712</td>\n",
+       "      <td>0.334778</td>\n",
+       "      <td>0.319157</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.230561</td>\n",
+       "      <td>-0.336297</td>\n",
+       "      <td>0.275640</td>\n",
+       "      <td>-0.452769</td>\n",
+       "      <td>0.155304</td>\n",
+       "      <td>-0.453287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NT_EFC195</th>\n",
+       "      <td>0.088068</td>\n",
+       "      <td>-0.143168</td>\n",
+       "      <td>-0.169540</td>\n",
+       "      <td>-0.090058</td>\n",
+       "      <td>-0.230417</td>\n",
+       "      <td>-0.230561</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.178683</td>\n",
+       "      <td>-0.036844</td>\n",
+       "      <td>0.145924</td>\n",
+       "      <td>0.080898</td>\n",
+       "      <td>0.260702</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AS_MeanAmphiMoment</th>\n",
+       "      <td>0.355477</td>\n",
+       "      <td>-0.431590</td>\n",
+       "      <td>-0.426097</td>\n",
+       "      <td>-0.408793</td>\n",
+       "      <td>-0.160269</td>\n",
+       "      <td>-0.336297</td>\n",
+       "      <td>0.178683</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.322378</td>\n",
+       "      <td>0.025580</td>\n",
+       "      <td>0.171524</td>\n",
+       "      <td>0.693552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AS_DAYM780201</th>\n",
+       "      <td>-0.365374</td>\n",
+       "      <td>0.449621</td>\n",
+       "      <td>0.456260</td>\n",
+       "      <td>0.894191</td>\n",
+       "      <td>-0.029085</td>\n",
+       "      <td>0.275640</td>\n",
+       "      <td>-0.036844</td>\n",
+       "      <td>-0.322378</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.045562</td>\n",
+       "      <td>-0.256060</td>\n",
+       "      <td>-0.437168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AS_FUKS010112</th>\n",
+       "      <td>-0.090570</td>\n",
+       "      <td>0.002334</td>\n",
+       "      <td>0.032958</td>\n",
+       "      <td>0.055915</td>\n",
+       "      <td>0.040480</td>\n",
+       "      <td>-0.452769</td>\n",
+       "      <td>0.145924</td>\n",
+       "      <td>0.025580</td>\n",
+       "      <td>0.045562</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.445284</td>\n",
+       "      <td>0.033432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CT_RACS820104</th>\n",
+       "      <td>0.232929</td>\n",
+       "      <td>-0.213543</td>\n",
+       "      <td>-0.403599</td>\n",
+       "      <td>-0.326792</td>\n",
+       "      <td>-0.151935</td>\n",
+       "      <td>0.155304</td>\n",
+       "      <td>0.080898</td>\n",
+       "      <td>0.171524</td>\n",
+       "      <td>-0.256060</td>\n",
+       "      <td>-0.445284</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.267652</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CLASS</th>\n",
+       "      <td>0.534602</td>\n",
+       "      <td>-0.598816</td>\n",
+       "      <td>-0.584111</td>\n",
+       "      <td>-0.554838</td>\n",
+       "      <td>-0.260470</td>\n",
+       "      <td>-0.453287</td>\n",
+       "      <td>0.260702</td>\n",
+       "      <td>0.693552</td>\n",
+       "      <td>-0.437168</td>\n",
+       "      <td>0.033432</td>\n",
+       "      <td>0.267652</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    FULL_Charge  FULL_AcidicMolPerc  FULL_AURR980107  \\\n",
+       "FULL_Charge            1.000000           -0.612996        -0.490977   \n",
+       "FULL_AcidicMolPerc    -0.612996            1.000000         0.794796   \n",
+       "FULL_AURR980107       -0.490977            0.794796         1.000000   \n",
+       "FULL_DAYM780201       -0.434603            0.541481         0.548253   \n",
+       "FULL_GEOR030101       -0.058725            0.115201         0.346139   \n",
+       "FULL_OOBM850104       -0.283758            0.513344         0.462712   \n",
+       "NT_EFC195              0.088068           -0.143168        -0.169540   \n",
+       "AS_MeanAmphiMoment     0.355477           -0.431590        -0.426097   \n",
+       "AS_DAYM780201         -0.365374            0.449621         0.456260   \n",
+       "AS_FUKS010112         -0.090570            0.002334         0.032958   \n",
+       "CT_RACS820104          0.232929           -0.213543        -0.403599   \n",
+       "CLASS                  0.534602           -0.598816        -0.584111   \n",
+       "\n",
+       "                    FULL_DAYM780201  FULL_GEOR030101  FULL_OOBM850104  \\\n",
+       "FULL_Charge               -0.434603        -0.058725        -0.283758   \n",
+       "FULL_AcidicMolPerc         0.541481         0.115201         0.513344   \n",
+       "FULL_AURR980107            0.548253         0.346139         0.462712   \n",
+       "FULL_DAYM780201            1.000000         0.010118         0.334778   \n",
+       "FULL_GEOR030101            0.010118         1.000000         0.319157   \n",
+       "FULL_OOBM850104            0.334778         0.319157         1.000000   \n",
+       "NT_EFC195                 -0.090058        -0.230417        -0.230561   \n",
+       "AS_MeanAmphiMoment        -0.408793        -0.160269        -0.336297   \n",
+       "AS_DAYM780201              0.894191        -0.029085         0.275640   \n",
+       "AS_FUKS010112              0.055915         0.040480        -0.452769   \n",
+       "CT_RACS820104             -0.326792        -0.151935         0.155304   \n",
+       "CLASS                     -0.554838        -0.260470        -0.453287   \n",
+       "\n",
+       "                    NT_EFC195  AS_MeanAmphiMoment  AS_DAYM780201  \\\n",
+       "FULL_Charge          0.088068            0.355477      -0.365374   \n",
+       "FULL_AcidicMolPerc  -0.143168           -0.431590       0.449621   \n",
+       "FULL_AURR980107     -0.169540           -0.426097       0.456260   \n",
+       "FULL_DAYM780201     -0.090058           -0.408793       0.894191   \n",
+       "FULL_GEOR030101     -0.230417           -0.160269      -0.029085   \n",
+       "FULL_OOBM850104     -0.230561           -0.336297       0.275640   \n",
+       "NT_EFC195            1.000000            0.178683      -0.036844   \n",
+       "AS_MeanAmphiMoment   0.178683            1.000000      -0.322378   \n",
+       "AS_DAYM780201       -0.036844           -0.322378       1.000000   \n",
+       "AS_FUKS010112        0.145924            0.025580       0.045562   \n",
+       "CT_RACS820104        0.080898            0.171524      -0.256060   \n",
+       "CLASS                0.260702            0.693552      -0.437168   \n",
+       "\n",
+       "                    AS_FUKS010112  CT_RACS820104     CLASS  \n",
+       "FULL_Charge             -0.090570       0.232929  0.534602  \n",
+       "FULL_AcidicMolPerc       0.002334      -0.213543 -0.598816  \n",
+       "FULL_AURR980107          0.032958      -0.403599 -0.584111  \n",
+       "FULL_DAYM780201          0.055915      -0.326792 -0.554838  \n",
+       "FULL_GEOR030101          0.040480      -0.151935 -0.260470  \n",
+       "FULL_OOBM850104         -0.452769       0.155304 -0.453287  \n",
+       "NT_EFC195                0.145924       0.080898  0.260702  \n",
+       "AS_MeanAmphiMoment       0.025580       0.171524  0.693552  \n",
+       "AS_DAYM780201            0.045562      -0.256060 -0.437168  \n",
+       "AS_FUKS010112            1.000000      -0.445284  0.033432  \n",
+       "CT_RACS820104           -0.445284       1.000000  0.267652  \n",
+       "CLASS                    0.033432       0.267652  1.000000  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Obtaining correlation coefficient values for the different attribute pairs\n",
+    "train.corr(method='pearson')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1a11dfb490>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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hY4AfpHrvBH6crs0WtzGAnwLfLZH3AXA+8J2U9N+SZgP3AedERGHk+l/ACZIeIRtVjozMLLIR7WxgInBSRCwGkHQN2YKpzdM5HpdinUO2OOsJYO/0ueBg4G8R8V6J78PMzGqgrtdcI6JfmfRbKDPSioiBZcKN7UB9XyuRNp5s5EdEzAb2KHPsyKLPN5AWCEXE7kV55+f2S97uk+rapUze2cDZJdLLxVpAGgWXyBtLB74bM7O6aOBFSZWo94ImMzNbjvmVc92MpPvJrtXmHR0RM+rRHjMzK8Ej1+4lInasdxvMzGz51Jw/GczMrFvoitXCkoanZ7XPlXR6mTJfljQ7PQf+j5WeV9OOXM3MrBuo8b2q6fkGFwH7kD3xboqk8WlRaaHMILLnr++SHv6zTqX1unM1M7P6qf29qjsAcyPiKQBJ48iexz47V+YE4KKIeAMgIoofxLPMPC1sZmZ10wWvnOvI89o/A3xG0n2S/pl/HWlneeRqS+i32so1ibv+ziVv6a3YensXP+K5eu48dmxN4vZ58NSaxN1+7XtqEhdg9Q9q88jpXr17tl+oE2aePaYmcQF6X3JYTeKu3n+lmsRdoW9nXvzVfUgaBYzKJY1Jz0tfFr3Inoa3O9kjZ++WNCQi3uxsu9y5mplZ/VQ4LZx/8UgZHXle+3zg/ohYBDwt6XGyznZKZ9vlaWEzM6ubLnjl3BRgkKRN0pvLjiQ9lS/nZrJRK5LWIpsmfqqS8/LI1czM6qfGT2iKiI8knQzcBvQELouIWZLOAqamR+DeBuybngO/mOwNaAsqqdedq5mZNbWImABMKEr7YW4/gFPTVhXuXM3MrH78+EMzM7Mq84P7zczMqquDi5K6HXeuZmZWPzV+/GG9NOdZmZmZ1ZFHrmZmVj+1f7ZwXdRt5CppsaRpuW2gpJGSLiwqN1nSsLQ/L93gm89f6pg26pwnaUbaZkv6qaQVi8p8S9K/JK2WPq+TjlsvV+YiSaMl7S4pJB2fyxua0k5Ln6/NneM8SdNSem9JV6S2PCppdC5GydcjSbo6pc+UdJmk3ildki5I5adL+lzumImS3pT0l458R2ZmXakLni1cF/Vs2QcRMTS3zeuieveIiCFkb0rYFPhdUX4L2RM9DoGP345wDnAeQOq4dit8BmYCXy46/pHCh4g4onCOwA3AjSnrcKBPast2wInpB0bh9Uj7A4OBFkmD0zFXA1sAQ4C+QKFT35/sUV2DyJ6x+dtce84Fju7ol2Nm1qV6qLKtQTVut19jEfEu8HXgIEn9ASRtBvQDziDrJAvGAJtJ2oOs4zs5PYMS4BlgRUnrShIwHLi1uL6U92XgmkITgJUl9SLrKD8E3ib3eqSI+BAovB6JiJgQCfAA2TMySflXpqx/AqtLWj8dMwl4p5LvysysZtSjsq1B1bNlfXPTpTfVowER8TbwNNmID7JnTo4D7gE2l7RuKtcK/AfZyHNORNxdFOp6spHo54GHgIUlqtsNeDkinsgd8x7wIvAscF5EvE4HXo+UpoOPBiampI68UqksSaMkTZU09fGH/qejh5mZWRn1XND0QZoqzSv3XqvavO8qk59XaAEOjohWSTeQdZgXAkTENEkzgYtLxLgOuJZsyvYask62WAufjFohG6EuBgYAawD3SLqjg22+GLg7IqryjrH8WyWO+cGLtfyuzcyW5IdIdIkFZB1NXn/gtVpUJmkVYCDwuKQhZCPY27MZXFYgG9XmF0u1pm0JEfGSpEXAPsApFHWuaer3ELJrqwVfASam6eVXJN0HDCMbgZZ9PZKkHwFrAyfmynTklUpmZo2nSR8i0WhnNQXYpbAyN60S7sOSU55VIakf2Qjw5oh4g2xkeWZEDEzbAGCApI07GPKHwPciYnGJvL2BxyJifi7tWWDP1JaVgZ2Ax2jj9UhpVfJ+QEuaqi4YDxyTVg3vBLwVES92sN1mZvXTpNdcG2rkGhEvSzoFmKBsjfW7LN2RTJdU+HwdMB0YKemgXJmdijqyvLvS4qIewE3AT1L6kcABRWVvSuk/70Db/95G9pEsOSUM2cKoyyXNIpuavjwipgOUej1SOuYSsgVU/0ij6xsj4iyytz0cAMwF3ge+VqhE0j1k09X9JM0HjouI29o7HzMz67y6da4R0a9M+i3ALWXyBpYJN7aDdZY7nojYtETaqUWfdy/6PBmYXOK4M4s+jyxR5l2ya7ql2rLU65FSesm/r7R6+KQyebuVSjczawgNfDtNJRpq5GpmZsuZBp7arURTdq6S7ie7Vpt3dETMqEd7zMysDK8W7j4iYsd6t8HMzDrAq4XNzMysI5py5GpmZt2Ep4XNzMyqzAuazMzMqqxJr7m6c7UlHHTgujWJ+9ga/6cmcTd9+6GaxAXo8+Cp7RfqhIXbbV2TuK1Tx9QkLsCzNXrk9PY7rF2TuC99sSqP3S5p1dZS7+Wo3OaDVqpJ3FVXHVKTuFXTpNPCzfmTwczMrI48cjUzs/rxNVczM7Mqa9JpYXeuZmZWP026oKk5z8rMzKyOPHI1M7O6CU8Lm5mZVZkXNJmZmVWZO1czM7PqatZp4eb8yWBmZlZHde1cJS2WNC23DZQ0UtKFReUmSxqW9udJWqsof6lj2qizn6TfSnpS0kOSHpR0QsobKOmDojYdk/JWk3SlpLnp2CslrVbiuNkpr3euztHpuDmS9ktpK0p6QNIjkmZJ+nGu/CaS7k/HXCtphZT+b6nNH0k6rOi8jpX0RNqOzaWfLek5Se925PsxM+tS6lHZ1qDq3bIPImJobpvXBXVeCrwBDIqIzwHDgf65/CeL2nRlSv8D8FREfDoiNgOeTrGWOA4YAmwIfBlA0mDgSGDLVNfFknoCC4E9I2IbYCgwXNJOKdbPgV9FxKdTW49L6c8CI4E/5k9IUn/gR8COwA7AjyStkbL/nNLMzBqPVNnWoOrduXYpSZuRdTRnREQrQES8GhE/b+e4TwPbAT/JJZ8FDEsxPxYRi4EHgA1S0ghgXEQsjIingbnADpEpjCZ7py0kCdgTuD7lXQEclGLPi4jpQGtRE/cDbo+I1yPiDeB2so6ciPhnRLzY3ndjZlYXPXpUtjWoeresb2769aYuqG9L4JFCx1rGZkXTwrsBg4FpqeMEPu5Ep6WYH5O0ItkIcmJK2gB4LldkfkpDUk9J04BXyDrH+4E1gTcj4qPi8m0oW0dHSBolaaqkqX+7qXZvVjEzKxZSRVujqvdq4Q/SVGpeuXdbVf2dV5K+DxwOrBMRA1Lyk8VtkvSlDoTbLHWUmwB/TSPMNqUOeqik1YGbJG0FvLRMJ1EFETEGGANw4wOttXm3mJnZcqTeI9dSFgBrFKX1B16rQuzZwDZSdhU8Is5OHemqHThuaOE4gLQ/NOXBJ53yZsB2uQ75eWCjXKwNU9rHIuJN4C6yqdwFwOqSepUrX0K7dZiZNSQvaOoyU4BdJK0HkFYJ92HJac9OiYi5wFTgp2lRUWEat825hXTcw8AZueQzgIdSXr7sa8DpwOiUNB44UlIfSZsAg4AHJK2dRqxI6gvsAzwWEUHW0RZWAx8L3NLOqd0G7CtpjbSQad+UZmbW0EI9KtoaVcO1LCJeBk4BJqRp1l8DLUXXSadLmp+2X6a0kbm0+ZI2LFPF8WTXNedKmkq2+Oe7ufzia67fTOnHAZ9Jt+E8CXyGT1bxFrsZWEnSbhExC7iObIQ7ETgpTQevD9wlaTrZD4rbI+Iv6fjvAadKmpva+gcASdtLmk82lf07SbPSd/Y62WKrKWk7K6Uh6RfpmJXS93JmmTabmXW9Jl0tXNdrrhHRr0z6LZQZrUXEwDLhxnawzreBE8vkzQP6lsl7A/hqG8dtlfscwDa5z2cDZxcdMx3Ytky8pyhx+0xETCGb8i11zGXAZSXSv8uSPx7MzKzG6r2gyczMlmONPLVbiabtXCXdT3atNu/oiJhRj/aYmVkJDTy1W4mm7VwjYsd6t8HMzNrhkauZmVl1NfKDICrRnD8ZzMzM6sgjVzMzqx9PC5uZmVVXtP0Mn27LnastYdqjH7VfqBMOWOWP7RfqjJVL3ipdFduvfU9N4rZOrc3LEf4+bFRN4gJwzaM1CTvnidq8ZvjQjWa3X6iTTrlyQPuFOuEHJ7xZk7g/uaOWjwsv96yejvOtOGZmZtXWpJ1rc56VmZlZHblzNTOzuumK97lKGi5pjqS5kk4vkf91STPS8+TvlTS40vNy52pmZnVT67fipDegXQTsDwwGWkp0nn+MiCHptaG/AH5Jhdy5mplZ/dT+rTg7AHMj4qmI+BAYB4zIF0gvdClYGah4FZgXNJmZWbclaRSQXyo/JiLyS/I3YMn3gc8Hlno8rqSTgFOBFYA9K22XO1czM6ubSm/FSR1pxfe3RcRFwEWSvgKcARxbSTx3rmZmVjdd8BCJ54GNcp83TGnljAN+W2mlvuZqZmZ1U+sFTcAUYJCkTSStABwJjM8XkDQo9/GLwBOVnpdHrmZmVj81fitORHwk6WTgNqAncFlEzJJ0FjA1IsYDJ0vaG1gEvEGFU8LgztXMzJpcREwAJhSl/TC3f0q16+zyaWFJi9ONuoVtoKSRki4sKjdZ0rC0P0/SWkX5Sx3TRp2rSboy3UD8ZNpfLZe/paQ7003GT0j6gZT9nEr1vJraOkvS9ZJWSnlnSgpJn87F+lZKK7S9Jd2cPF3SxMJ5pGOfz30PB+RijE5tnSNpv1z6ZZJekTSz6Pz6S7o9tf12SWsU5W8v6SNJh3Xk+zIz6ypBj4q2RlWPln0QEUNz27wuqPMPwFMR8emI2Ax4GrgUQFJfsvn3cyJic2Ab4PPAf+aOvza1dUvgQ+CIXN4Msjn8gsOBWSl2L+C/gT0iYmtgOnByruyvct/DhHTM4BRvS2A4cHG6CRpgbEordjowKSIGAZPSZ1K8nsDPgb+1+y2ZmXWxrnhCUz00brdfJWlUuR3wk1zyWcAwSZsBXwHui4i/AUTE+2QdYKlHZPUiu8H4jVzyzaQbklO8t4DXCoekbeU0El4VeKGdJo8AxkXEwoh4GphLdhM0EXE38HqZY65I+1cAB+XyvgHcALxSrkJJoyRNlTT1ocmXttM8M7Pq6YIFTXVRj5b1zU2F3tQF9Q0GpkXE4kJC2p9GNjrcEngwf0BEPAn0k7RqSjpC0jSy5dv9gT/nir8NPCdpK7IR57W5OIuA/yAb3b6Q2vKH3LEnp+niy3JTuaVueN6gnXNcNyJeTPsvAesCSNoAOJh2lpVHxJiIGBYRwz63+/HtVGVmVj2BKtoaVb2nhQ9OaeUeNVXLFxEui2vTMyfXI+sov1OUP46sYz0I+PgHg6TeZJ3rtsAAsmnh0Sn7t8BmwFDgReD8ajQ0IoJPvrdfA9+LiNZqxDYzs45plDH1AmCNorT+fDK9WonZwFDpk/mDtD805c0mmzYml78p8G7R8yYLHdefgX8rquMvwNHAs0XHDE3HPZmOvY7sei4R8XJELE4d3+9JU78s+w3PAC9LWj+1fX0+mQIeBoyTNA84jOz67UGlQ5iZdT1PC9fWFGAXSesBpJW2fVhyerRTImIu8DDZ46wKzgAeSnlXA7ume5wKC5wuIHszQim7Ak8W1fE+8D3g7KKyzwODJa2dPu8DPJrqWT9X7mCgsAJ4PHCkpD6SNgEGAQ+0c5rj+eS+rGOBW1K7NomIgRExELge+M+IuLmdWGZmXaZZFzQ1xH2uEfGypFOACWlU+S7QUjSdOV1S4fN1ZFOsI4tGYjtFxPwSVRwH/EZSoVP8R0ojIj6QNCLlX0R2k/FVQP42nyMk7Ur2Y2Q+MLLEOYwrkfaCpB8Dd0taBDyTO/YXkoaSTeHOA05Mx8ySdB3ZiPoj4KTC9WJJ1wC7A2tJmg/8KCL+AJwDXCfpuFTHl0t8B2ZmDaeRr5tWoss714joVyb9FtKIq0TewDLhxnawzjeAr7aRP4Os0yqVN7ZcPRFxZpn03XP7lwCXlChzdBvtOZulR8FEREuZ8guAvcrFS2VGtpVvZmbV0xAjVzMzWz418nXTSjRV5yrpfrJrtXlHp5GpmZk1GE8LdwMRsdQLcM3MrHF55GpmZlZlzTpybc6fDGZmZnXkkauZmdVNs04LK3twkFlm6pw3avIfxLT5xQ/gqrQWyp4AACAASURBVI5Fi2oSFoDVV6nN/xvPvtj9/p/bquWzNYn7mcdq87Kmx95s73HcnbfZamXfgVGR9xavVJO4G8XTNYkLsN4W21Y8p/vUk09W9D/Epptt1pDzyh65mplZ3TTyU5Yq4c7VzMzqJqI5O9fmnOw2MzOrI49czcysbqJJx3juXM3MrG6a9T5Xd65mZlY3zdq5Nud43MzMrI48cjUzs7pp1pGrO1czM6sbd65mZmZV1qz3ubpzNTOzumnWkasXNJUhKSSdn/t8mqQzJX1f0rS0Lc7tf7NMnDMlPZ8rN03S6pJ2l/RWLu2O3DHHSJopaYakhyWdltIPlzRLUqukYbnyK0i6PJV/RNLuubzJkubk6lmnJl+YmZl9zCPX8hYCh0j6WUS8VkiMiLOBswEkvRsRQzsQ61cRcV4+QdnzNO+JiH8vSt8f+Bawb0S8IKkPcEzKngkcAvyuKP4JqW1DUud5q6TtI6I15R8VEVM70E4zsy7lkevy5yNgDPDtLq53NHBaRLwAEBELI+L3af/RiJhT4pjBwJ2pzCvAm8CwEuVKkjRK0lRJU2+8dmyl7Tcz67BAFW2Nyp1r2y4CjpK0WoVxvp2blr0rl75bLv37KW0r4MFljP8I8CVJvSRtAmwHbJTLvzzV8QNp6VdQRMSYiBgWEcMOOWLkMlZtZtZ5Eapoa1SeFm5DRLwt6Urgm8AHFYRaalo4WWpauJMuAz4LTAWeAf4OLE55R0XE85JWAW4AjgaurEKdZmZWhkeu7fs1cBywchfVN4ts5NlhEfFRRHw7IoZGxAhgdeDxlPd8+vMd4I/ADlVur5lZp7WiirZG5c61HRHxOnAdWQfbFX4GnCtpPfh4JfDxbR0gaSVJK6f9fYCPImJ2miZeK6X3Bv6dbFGUmVlDaNZrrp4W7pjzgZMrOP7bkr6a+3xQuYIRMUHSusAd6fpokE37Iulg4DfA2sBfJU2LiP2AdYDbJLUCz5NN/QL0Sem9gZ7AHcDvKzgPM7OqauTrppVw51pGRPTL7b8MrNRWmTbinAmcWSJrHjC5zDGXA5eXSL8JuKlE+jxg8xLp77GMU8xmZl2pkUeflfC0sJmZWZV55Fol6Vaaw4uS/5QeOmFmZiV4WtjalH9yk5mZdUyzTgu7czUzs7rxyNXMzKzKWtsv0i15QZOZmVmVeeRqS9j8nftrEveCydvUJG6JRyVXTa/ePWsSd/sd1q5J3DlPvFuTuACHPPa3msR9fIt9axL3nt9Mq0lcgFV2XaMmcQf0fbUmcXtfdVFN4gJw5qUVh/C0sJmZWZV5QZOZmVmVNevI1ddczczMqswjVzMzqxtPC5uZmVVZa9S7BbXhztXMzOrGI1czM7Mq84ImMzMz6xB3rmZmVjcRlW0dIWm4pDmS5ko6vUR+H0nXpvz7JQ2s9LzcuZqZWd20ooq29kjqCVwE7A8MBlokDS4qdhzwRkR8GvgV8PNKz8udq5mZ1U2EKto6YAdgbkQ8FREfAuOAEUVlRgBXpP3rgb1U4bNVO9S5SjpIUkjaIn3uIekCSTMlzZA0RdImbRw/T9I9RWnTJM2spPFt1NdL0quSzqly3JIPb5X0dUnHpP2xkt6XtEou/9fp+1urmu3pKEn/tx71mpm1pwumhTcAnst9np/SSpaJiI+At4A1Kzmvjo5cW4B7058ARwADgK0jYghwMPBmOzFWkbQRgKTPdqKty2If4HHg8Ep/fXRERFwSEVfmkuaSfhlJ6gHsCTxf63a0wZ2rmTUlSaMkTc1to+rdJuhA5yqpH7Ar2Zz0kSl5feDFiGgFiIj5EfFGO6GuI+uUIeukr8nV0VPSuWkEPF3SiYW6JU2S9FAaIRc6rIGSHpX0e0mzJP1NUt9cXS3AfwPPAjvn6pkn6Wdp1DxV0uck3SbpSUlfT2V2l3S3pL+mC+CXpA6yEONsSY9I+qekdVPamZJOy9U/LneuuwP3AR/lYpyaRv0zJX0rd06PpZHv45KulrS3pPskPSFph1RuZUmXSXpA0sO572SkpBslTUzlf5HSzwH6pnO+utRfTP4/zstvnNDOX6OZWfUEqmyLGBMRw3LbmKIqngc2yn3ekKUHOx+XkdQLWA1YUMl5dWTkOgKYGBGPAwskbUfWUR6Y/sE+X9K2HYhzA3BI2j8Q+HMu7zjgrYjYHtgeOCFNM/8LODgiPgfsAZyfG4kOAi6KiC3JRs2HAkhaEdg7xb+GT0bbBc9GxFDgHmAscBiwE/DjXJkdgG+QXfzeLNfulYF/RsQ2wN3ACWXO9XFgbUlrpPrHFTLS9/c1YMdU7wm57+/TwPnAFmn7CtkPm9P4ZPT5feDOiNghfSfnSlo55Q0l69SHAEdI2igiTgc+iIihEXFUqcbm/+P82iEHlDklM7Pqa43Ktg6YAgyStImkFcgGieOLyowHjk37h5H9G1vRs6M60rnmO4dxQEtEzAc2B0aTvUh+kqS92omzAHhD0pHAo8D7ubx9gWMkTQPuJ5vrHgQI+H+SpgN3kM2Lr5uOeToiCi9tfBAYmPb/HbgrIj4g69APSqvFCgpf6gzg/oh4JyJeBRZKWj3lPZAufi8m66B3TekfAn8pUWcpN5L9Je5I1pEX7ArcFBHvRcS7qdxuuXOakWYEZgGT0l/wjFxd+wKnp+9qMrAi8KmUNyki3oqIfwGzgY3baJ+ZWd3VekFTuoZ6MnAbWd9zXUTMknSWpC+lYn8A1pQ0FzgVWOp2nWXV5hOaJPUnu144RFIAPYGQ9J2IWAjcCtwq6WXgIGBSO/VdS7YkemRxVcA3IuK2ovpHAmsD20XEIknzyDoTgIW5oouBwrRwC7BrKgtZR70ncHvRca1FMVr55Pso/sVS+Lwo92tmMW1/f9eSdcBXRERrBy/9Frcn39ZCXQIOjYg5+QMl7cjS34mfwGVmy72ImABMKEr7YW7/X8Dh1ayzvZHrYcBVEbFxRAyMiI2Ap4HdJA2AjxfsbA0804H6bgJ+QfYLIu824D8k9U4xP5OmOlcDXkkd6x60MxKTtCrZKPBTqb0DgZNYemq4PTukKYQeZNOs9y7j8UTEM2RTuBcXZd1DNppeKZ3jwSw5sm3PbcA3CtPjHZySX1T4bs3MGklXPESiHtob2bSw9M20N5DdD/S6pD4p7QHgwvYqi4h3CvGKRnKXkk17PpQ6jVfJRsJXA3+WNAOYCjzWThUHk82V50dwtwC/yLW1I6aQnc+ngbvIfhQss4j4XYm0hySNJfvOAC6NiIfV8SeC/AT4NTA9df5Pk02Ft2VMKv9QueuuZmb10JEHQXRHqvCabdORtDtwWkS012E1pXemTqzJfxAn3bJNLcIW/0irql69e7ZfqBO232HtmsSd80TJ27Cr4j/3r2jhZFmPb7FvTeL+72+mtV+okw7Ytf0ynTGg76s1ibvmVWfVJC7AmmdeWvH/gH9+8KOK/s05cLteDdk7+5qcmZnVTbO+Faeqnauk+4Hi6dejI2JGNeuppYiYTLYK18zMrFOq2rlGxI7VjGdmZs2tg/eqdjueFjYzs7pp1mU/7lzNzKxuoklXC7tzNTOzumnWaWG/z9XMzKzKPHK1JYy+e+f2C3XCmH/r1HM42lfDCzYzzy5+uUZ1vPTFZXkgV8cdutHsmsQF+Oebn6tJ3HtqdD/qF74xtCZxAXo98lBN4l77wICaxJ2zqHb3uf5PFWL4mquZmVmVuXM1MzOrstYmfYiEr7mamZlVmUeuZmZWN54WNjMzqzJ3rmZmZlXWrPe5unM1M7O6ada34nhBk5mZWZV55GpmZnXja65mZmZV1qzXXLvNtLCkgySFpC3S5x6SLpA0U9IMSVMkbdLG8fNSuRmSZkv6qaQVi8p8S9K/JK2WPq+TjlsvV+YiSaMl7Z7ac3wub2hKOy19vlbStLTNkzQtpfeWdEVqy6OSRudiDJc0R9JcSafn0q9O6TMlXSapd0pX+h7mSpou6XO5YyZKelPSXzr/zZuZ1U5EZVuj6jadK9AC3Jv+BDgCGABsHRFDgIOBN9uJsUcquwOwKfC7EnVMAQ4BiIhXgHOA8wBSx7Vb4TMwE/hy0fGPFD5ExBERMTQihgI3ADemrMOBPqkt2wEnShooqSdwEbA/MBhokTQ4HXM1sAUwBOgLFDr1/YFBaRsF/DbXnnOBo9v5TszM6sadax1J6gfsChwHHJmS1wdejIhWgIiYHxFvdCReRLwLfB04SFL/VMdmQD/gDD7pwAHGAJtJ2oOs4zs5IhalvGeAFSWtK0nAcODWEu0XWSd8TaEJwMqSepF1lB8Cb5N1+nMj4qmI+BAYB4xIbZ4QCfAAsGGKNQK4MmX9E1hd0vrpmEnAOx35TszMrHq6RedK1oFMjIjHgQWStgOuAw5MU67nS9p2WQJGxNvA02QjPsg67XHAPcDmktZN5VqB/yAbec6JiLuLQl1PNhL9PPAQsLBEdbsBL0fEE7lj3gNeBJ4FzouI14ENgOdyx81PaR9L08FHAxNTUrvHtEfSKElTJU2d9Y+xy3KomVlFWqOyrVF1l861hazjI/3ZEhHzgc2B0UArMEnSXssYN3+DVQswLnWmN5B1mABExDSyKeCLS8S4LpVt4ZORaan25/N2ABaTTWtvAvyXpE072OaLgbsjomrvLYuIMRExLCKGbbnzyGqFNTNrV7NOCzf8auE0bbsnMERSAD2BkPSdiFhINg17q6SXgYOASR2MuwowEHhc0hCyEezt2QwuK5CNai/MHdKatiVExEuSFgH7AKeQjWDz9fQiu4a7XS75K2Qj8UXAK5LuA4aRjUA3ypXbEHg+F+tHwNrAibkyz7d1jJlZI2td6l/V5tAdRq6HAVdFxMYRMTAiNiLr+HaTNACylcPA1mTXQNuVruFeDNycrtO2AGem+AMjYgAwQNLGHWzjD4HvRcTiEnl7A4+lkXbBs2Q/GJC0MrAT8BjZYqpBkjaRtALZVPX4VO54YD+yUXv+P8fxwDFp1fBOwFsR8WIH221mZjXQ8CNXso7v50VpNwBXAK9L6pPSHmDJkWYpd6XFRT2Am4CfpPQjgQOKyt6U0ovrXkpE/L2N7CNZerr4IuBySbPIpqYvj4jpAJJOBm4jG6FfFhGz0jGXkP14+EcaXd8YEWcBE1Lb5wLvA18rVCLpHrIVxv0kzQeOi4jb2jsfM7Ou0shTu5Vo+M41IvYokXYBcMEyxhnYRt5S1zsj4tSiz7sXfZ4MTC5x3JlFn0eWKPMuuWu6RXkTyDrM4vSSf1dp9fBJZfJ2K5VuZtYo3LmamZlVWSOv+K1E03Wuku4H+hQlHx0RM+rRHjMzKy8qHro25lt1mq5zjYgd690GMzNbvjVd52pmZt2Hr7mamZlVWbPe5+rO1czM6sYjVzMzsyrzamFbLuyy46o1iXtPv5b2C3XCCr1KPRSrOnpfclhN4q7aWurdDpU75coBNYkLcMbxr9Qk7iq7rlGTuL0eeagmcQHe2uZz7RfqhMdOu7MmcUd9tTbfsbXNnauZmdWNp4XNzMyqLCqeF/Z9rmZmZkto1muu3eGtOGZmZt2KR65mZlY3vuZqZmZWZa1NOi/sztXMzOrGI1czM7Mqa9bO1QuazMzMqswjVzMzq5vWJh26dsuRq6SDJIWkLdLnHpIukDRT0gxJUyRt0sbx81K5aWn7vKTdJf2lqNxYSYel/cmShqX9TSQ9IWk/SStJujrFmynpXkn9UrnhkuZImivp9Fzck1NaSForl650HnMlTZf0uVzeRElvlmhjuVhHpRgzJP1d0jad/b7NzGolWivbGlV3Hbm2APemP38EHAEMALaOiFZJGwLvtRNjj4h4rfBB0u4dqTjFngj8V0TcJmk08HJEDEn5mwOLJPUELgL2AeYDUySNj4jZwH3AX4DJReH3BwalbUfgt+lPgHOBlYATi44pF+tp4AsR8Yak/YExuVhmZg0hPHJtDGlUuCtwHHBkSl4feDEi+x0TEfMj4o0aVL8+8Dfg+xExPpf2fKFARMyJiIXADsDciHgqIj4ExgEjUpmHI2JeifgjgCsj809gdUnrp2MmAe8UH1AuVkT8Pfcd/BPYsDMnbGZWS62tlW2Nqtt1rmQd0MSIeBxYIGk74DrgwDTFe76kbTsQ565U/v5lqPsK4MKIuD6XdhnwPUn/kPRTSYNS+gbAc7ly81NaWzpzTEccB9xaLlPSKElTJU2ddMuYKlRnZrZ8646dawvZKJD0Z0tEzAc2B0YDrcAkSXu1E2ePiBgaEYWp0nJzE/n0O4CvSlrp48yIacCmZNO2/cmmfz+7LCdUS5L2IOtcv1euTESMiYhhETFsrxGjuq5xZrbci4iKtkpI6i/p9rSG5nZJS72fT9LGkh5Kg7FZkr7ekdjd6pqrpP7AnsAQSQH0BELSd9JU7K3ArZJeBg4CJi1D+AVA8RfbH3gt9/kXwNHAnySNiIiPACLiXeBG4EZJrcABwN+BjXLHbkhu+riM5ztxTFmStgYuBfaPiAWdjWNmVit1fkDT6cCkiDgnLTo9naUHIi8CO0fEwnRZcmZaP/NCW4G728j1MOCqiNg4IgZGxEZkC3d2kzQAspXDwNbAM8sY+wlgQGHUKWljYBtgWlG5bwFvA39Iq3t3KfzakbQCMDjVPQUYlFYWr0B2fXg8bRsPHJPi7gS8FREvLuN5kNryKbIO/+g0hW5m1nCiNSraKjSC7HIf6c+DlmpfxIdp8AbQhw72m92tc20BbipKu4HsS/mzpJnAdOAj4MJlCZy+vK8Cl0uaBlwPHB8RbxWVC+BYsoVMvwA2A/5X0gzgYWAqcEMa1Z4M3AY8ClwXEbMAJH1T0nyykel0SZem8BOAp4C5wO+B/yzUK+ke4E/AXpLmS9qvnVg/BNYELk7TGVOX5fswM1sOrJsbwLwErFuqkKSNJE0nWxPz8/ZGrdDNpoUjYo8SaRcAFyxjnIFl0u8DdiqTt3tu/0Ng31z2lWWOmUDWYRanl2xz6rhPKhNrtzLp5WIdDxxf6hgzs0ZR6Z04kkYB+cUiYyJiTC7/DmC9Eod+f8l2RKTLjSXaGM8BW6cZ0pslXR8RL7fVrm7VuZqZWXOp9K04qSMte5tDROxdLk/Sy5LWj4gX022Pr7RT1wtphnQ3stnNsrrbtPAykXS/PnkKU2EbUu92mZlZpp6rhcnWuRyb9o8FbikuIGlDSX3T/hpkz1mY017gph655m6zMTOzBlTnRxieA1wn6TiyhahfBlD2qNuvp8trnwXOT1PGAs6LiBntBW7qztXMzKycdIviUs9EiIippDUrEXE72R0oy8Sdq5mZ1U2zvhXHnauZmdVNsz64352rmZnVTaWrhRuVO1dbwm5rz6pJ3Gtnb1mTuI/Oqt1THVfvv1L7hTph80G1ifuDE96sSVyAdz+qTZsH9H21JnGvfWBATeICPHbanTWJ+5Xz9qxJ3C2O+WNN4ma2q2Hs7s2dq5mZ1U2Tzgq7czUzs/qpwvOBG5I7VzMzqxuvFjYzM6uyZh25NvXjD83MzOrBI1czM6ubZh25unM1M7O6adK+1Z2rmZnVj0euZmZmVdasjz/0giYzM7Mq63adq6T1JI2T9KSkByXdJen99CL01yU9nfbvKHP8QEkfpDKzJV0pqXdRmV9Lel5Sj6L0/SVNTcc9LOn8lL65pMkp5qOSxqT03pKukDQjpY9O6Rulds+WNEvSKbk6+ku6XdIT6c81UvoWkv4haaGk04raNVzSHElzJZ1e4pwvkPRu575xM7PaaW2NirZG1a06V0kCbgImR8RmEbEd8C1gv4gYSvZW+e9ExNCI2LuNUE+m8kOADUkvyE119AAOBp4DvpBL3wq4EPhqRAwGhgFzU/YFwK9SvZ8FfpPSDwf6RMQQsodwnihpIPAR8F8pzk7ASZIGp2NOByZFxCBgUvoM8DrwTeC8ou+kJ3ARsD8wGGjJxSq89HeNNr4LM7O6iYiKtkbVrTpXYA9gUURcUkiIiEci4p7OBIuIxcADwAa55N2BWcBvgZZc+neBsyPiscKxEfHblLc+MD8Xt/CW+gBWltQL6At8CLwdES9GxEOp7DvAo7k2jACuSPtXAAelcq9ExBRgUdFp7ADMjYinIuJDYFyKUeh4z01tNzNrONEaFW2Nqrt1rlsBD1YrmKQVgR2BibnkFuAashHyF3NTxm3V/SvgTkm3Svq2pNVT+vXAe8CLwLPAeRHxelEbBgLbAvenpHUj4sW0/xKwbjunsQHZKLtgPp901CcD43PxSpI0Kk13T7362j+1U52ZmbWnu3Wu1bKZpGnAy8CLETEdQNIKwAHAzRHxNlmHt197wSLicuCzwJ/IRr7/lNSHbFS5GBgAbAL8l6RNC8dJ6gfcAHwr1VccN8hGv8tM0gCyaenftFc2IsZExLCIGHbUEYd3pjozs07xyLUxzKI6LxAsXHPdDNhO0pdS+n7A6sAMSfOAXflkarjNuiPihYi4LCJGkF1T3Qr4CjAxIhZFxCvAfWTXakkj4huAqyPixlyolyWtn8qsD7zSzrk8D2yU+7xhStsW+DQwN53LSpLmLn24mVn9tEZUtDWq7ta53gn0kTSqkCBpa0m7dSZYRLxGtmBodEpqAY6PiIERMZBstLmPpJXIrl3+X0mfSfX2kPT1tD+8MH0saT1gTbIO7llgz5S+MtnipcfSwqw/AI9GxC+LmjUeODbtHwvc0s5pTAEGSdokjbyPJJsK/mtErJc7l/cj4tPL+BWZmdWUR64NIE2THgzsnW7FmQX8jOzaZGfdTDaq+wIwHPhrrr73gHuBA9PU8beAayQ9CswEClO8+wIzJT0C3Ea2YvklslW8/VI7pwCXpzi7AEcDe6bbd6ZJOiDFOoesQ38C2Dt9LtyCNB84FThD0nxJq0bER2TXVm8jWxh1XUTMquD7MDPrMs26WrjbPaEpIl4gd+tMUd7IDhw/j2zKtvA5gG3Sx/4lyh+S2/8L8JcSZU4l6/SK098lu+5ZnH4voDLtWwDsVSL9JbIp31LHTAAmlMrLlenXVr6ZmVVPt+tczcyseTTygyAq0bSdq6QhwFVFyQsjYsd6tMfMzJbWyNdNK9G0nWt6kMPQerfDzMzKa+TrppVo2s7VzMwaX7S21rsJNdGtVgubmZl1Bx65mplZ3XhBk5mZWZX5mqstF9Z6uM3bZTvtlnFv1iRuj549axIXYIW+K9Yk7qqrDqlJ3J/cUbt/pH458umaxO191UU1iTtn0Vk1iQsw6qu1eYPjFsf8sSZxp279lZrEBfjiojkVx2jW1cK+5mpmZlZlHrmamVndNOvI1Z2rmZnVTWs056047lzNzKxuPHI1MzOrsmbtXL2gyczMrMo8cjUzs7rxfa5mZmZV1tqkzxZ252pmZnXTrNdc3bmamVndRJPeiuMFTXUkaT1J4yQ9KelBSRMkfUbSzDLle0l6VdI5Ren/LulhSY9Imi3pxJS+uaTJkqZJelTSmK44LzOz5Z1HrnUiScBNwBURcWRK2wZYt43D9gEeBw6XNDoiQlJvYAywQ0TMl9QHGJjKXwD8KiJuSfFr81BbM7NOatZpYY9c62cPYFFEXFJIiIhHgOfaOKYF+G/gWWDnlLYK2Y+kBSnGwogoPE17fWB+Lv6MqrXezKwKojUq2hqVO9f62Qp4sKOFJa0I7A38GbiGrKMlIl4HxgPPSLpG0lGSCn+vvwLulHSrpG9LWr1M7FGSpkqa+oc77q/glP5/e+cdLklVre/3Y0BA8khOkoMoQVExXoIgeuGSBARBVBD9iQFQFBSzgggYURQUBERgkKAYyMhVkSg5w5CjZC5Z5vv9sXfPaXq6zzldu2q6j6z3efqZrqrur/bU7lOr9tprrxUEQdAf0zyt6DWshHGdOGwCnGf7GeAkYHNJkwBs7wJsAFwMfA44Iu8/ElgVOBFYF7gwu41fgu3DbK9te+2d3/XmmfF/CYIg+I8mjOvguBZ4Qx+f3w54l6TbSSPeVwHrtw7avtr290nzslu17b/X9hG2NwP+TRoxB0EQDAXhFg7q5lxgdkm7tnZIWh1YqvODkuYF3gEsbXsZ28sAuwHbSZpb0rptH18TuCN/b+Mc8ISkRUkG+Z5m/jtBEAT942nTil7DShjXAeGU82sL0mj0VknXAvsD9wMrS7q79cqfO9f2c20SvwM2BSYBn5d0o6QrgK8DH8qf2Qi4RtKVwBnAXrbvnxn/vyAIgvHwnzpyjaU4A8T2vcA2XQ7N1mXfUR3ffQRYKG++t4f+nsCeJW0MgiBokkgiEQRBEATBuIiRaxAEQTAwpg2xa7eEGLkGQRAEA2OQAU2SJks6S9LN+d8FenxuaUln5jSy10laZiztMK5BEATBwBhwQNPewDm2VwTOydvdOBo40PaqwJuAB8cSDrdwEARBMDAGHNC0GSnBDqSg0b8AX2j/gKTXALPaPgvA9v+NRzhGrkEQBMGEpT19a37tOva3prOI7fvy+/vpXjhlJeAxSSfn6mMHtrLjjUaMXIMgCIKBUeratX0YqTJYVySdDSza5dCXOnQsqVtjZiUl8VmLVDTlBFIugV+O1q4wrkEQBMHAaDrLku139Tom6QFJi9m+T9JidJ9LvRu4wvbU/J1TgXUYw7hiO17xqvQCdp1o2hNNdyK2Oa5FXIuJ8gIOBPbO7/cGvtvlM5OAK4GF8vaRwG5jaceca1BCP3Mbw6I90XSb1J5ouk1qTzTdJrWbbPOw8R1gQ0k3k0p6fgdA0tqSfgFg+0VStbFzJF0NCDh8LOFwCwdBEAQvS2w/TCrX2bn/UmCXtu2zgNX70Y6RaxAEQRDUTBjXoISeEXpDrD3RdJvUnmi6TWpPNN0mtZts88sG5QnaIAiCIAhqIkauQRAEQVAzYVyDIAiCoGbCuAZBEARBzYRxDfpGiR0kfSVvLy3pTYNuVyeS5h90G4YZSf8z6DaMhaRjxrMvKEfSbJLWkrTwoNvyn0AY16AKPwXeAmyXt58EflIqT95MywAAIABJREFUKmm/doMoaQFJ3yqQfEjS2ZJ2npmGVtKGDel+peC7W3a8tgIOa20XtmvBju0dJP0oJ1RXiTawWof2JOANhZpdKe03Sa+TdKGkuyQd1l4bVNLF5S2c4Xw3FX7/Z5JWy+/nI2UhOhq4XNJ2o345GJMwrkEV3mx7N+BZANuPAq+oQfc9th9rbWTd9xboXQ/8AFgfuFXS7yS9X9Kche0ci9FzjlZnl7E/0pMTgI8AmwCb5n/nantfwpmtN5L2BXYELgM2BL5XRVDSPpKeBFaX9ER+PUnK/fq7wvb2orTfDgW+BrwOuAn4m6Tl87HZSoQlPdl+HfK1WL61v6LsO2xfm99/GLjJ9utIDy+fL2lvEBmagmq8kEcQBpC0EFBH9u1Jkma3/VzWnROYvUDvBdt/AP6QtTYF3g/8RNIZtrevKizp970OAa8q0O11oxRQ8lDwVlJqt0tsH5rPta7tDxdotretxZakm/ZTkn4D/LOKoO39gf0l7W97nxraCDTXb5l5bJ+e3x8k6TLgdEk7kv9WCjgSmB/Yy/YDAJJus71sgebzbe83BE4EsH1/ucMhCOMaVOFHwCnAwpK+DbwP2LcG3WNJ+TuPzNsfJhUwrsr0O4TtZ4ApwJTsAtu8QBdSCaodgM7CyQJK5p8fA97YuoG+RFi6q6qo7Uuy2/NTks4jFYSua5H7nJLWInnCJtl+Kp/zBUkvlgjb3kfSEsCrabtf2f7fipJN9VsSkeaz/TiA7fOy+/0kYHKJru1PS3oDcFyuynII5f33mKRNgHuAtwE7A0ialbIHuYAwrkEFbB+bn8o3IN2UNrd9fQ26B0i6kpRAG+Cbts8okDy2x3kep8xoA1wIPG37/M4Dkm4s0D2aZEhmMK7Abwp0sT0N+KGk3wLfL9Hq4D5G3L+PtJXwehXw7xJhSd8heRuuA1qG2kBV49pUvwEcAKyazwGA7askbQB8uVAb25dJehfwSeB8YI5CyY+RHpQXBXa3fX/evwHwx0Ltlz2RoSnoG0ndnsKftP1CgeYk4Gzb61VvWTBM5D6d3fbTBRo3Aqu3pgqCRK49upbtPw26LUF3YuQaVOGfwFLAo6SR6/zA/ZIeAD5q+7J+BW2/KGlau1utlOz+3YfkAl6YNOJpBcR8pz14quAciwBL5M17urlzK2jOB2zcrgucUdLemXQt1ib9Ll4kBcfcAFQ2rJmppGCgWo1rE/02xvkOs11Uyk3SKsBmtLU7z7tW8hpJ+ijwF9s356juI4CtgNuBnWxfXtLelzthXIMqnAX8tuWylbQR6Y/ySNIynTdX1P0/4GpJZwFPtXba/nRFvSnAucC6LZeXpEWBnfKxjSrqkucYDwXmIxk/gCUlPQZ8wnalQB5JHwS+SorAbemuB+wn6eu2j67Y5CavxX8BB5Pmi98A/B1YQNILwI62K88Vk4zzFZLOoc3AVv1NNNVvWbvXvKooi3pH0hdIS9+OB1rLepYkzcEeb/s7FWQ/A/wqv9+OVFJtWWAtkrv4HSVtfrkTbuGgbyRdnUP22/ddZXt1SVfYXrOi7k7d9tuuND8q6UbbK/d7bJzaVwAfs31Rx/51gJ/bXqOi7o2kpU6PdexfALjI9kpVdRu8FpcDG9n+l6Rlge/Z3iIHUO1lu8Rw1/2baKTfssaLwB28NHraeXsJ25WXq+U1rat1Tr1IegVwre0VK2hO/1vNkd0X2f5h3v6n7ddXbW8QI9egGvflJ+nj8/a2wAN5jq3ykhzbR+UlM0vbLg0uAbhD0ueBo9qWLywCfAgoGU0BzNV5gwawfaGkuQp0Rfco0Gm89KbdL01ei0m2/5Xf30kKyML2WZJ+UCLcwG+iqX6D5MLewPadnQdKIr0z04DFSca7ncWo/jc3Lc/dPkoKYvp227GIFi4kjGtQhe1JrstTSYbg73nfJGCbqqKSNgUOIiWkWFbSmsA3bFdN07ctsDdwvkZSuj0A/L6knZk/S/ojKbq3deNcCvggcHrPb43Nt4F/SjqzTXdp0jrEbxboNnktLpX0S5Lb+X+AvwBIeiXpN1GZBn4TTfUbpIQlC5AeMDr5bqH27qRlajfz0t/FCqTo4Sp8BbiU1Ee/byWUyG7+qWXNDcItHPRFHp0eYPtzDWhfRsqm9Bfba+V919h+bd3nqgNJ76EjwIR0kyqK4Mwu4HczY0DToyW6TSFpNuCjwGtIKfSOyAFqcwIL2+4cbfWjXftvoql+axpJs5DW4ra3+xLbldcS5zWt87T/tloPRbafLGnvy50wrkHfSLrQ9jpN6Uq6vO1GepXt1Rs414dtHzn2J//zGeZrMTN/E00iaUPbZzWkPbftzqQYVXREepDZHtjE9iLFjXsZE7mFgypcLun3knZUWzL4GnSvlbQ9KQ3iipJ+DFxQg243vl7yZUmTJH1M0jclvbXjWB3Zqrqd8+omdCm8FqMh6c+FErX+JgbRb5mm8k1DSrBRGUnrSPoRaT73d6QEHavU0bCXMzFyDfpGI+kJ27HtjxTqvhL4EiPLQs4AvmX72Yp6V/U6BKxku3LeYkm/AF5JWhaxI3C+7T3zscqRlqM8pAj4me2FKuo2eS16/V8F/MH2YgXa7b8JkX4T3yz4TTTSb/n7o+UtXt925YApSXuOov0l232nV5S0H7A1aY74OFJK00tdlq84yIRxDYYCpeT/rwZuqSOhQdZ8gDR32TlXKeAC24sXaE93TeZ5q58CC5LWC17YcmFW0H2BlLax2x/m+2zPU1G3yWvxIikdX7do5nVsD03kaVP9lvUepXfe4hNK3KySngUOpHs6yT1s911SUdKDpOo9PwBOs/2cpKm2l6vazmCEiBYO+kbSHKQk36vRlt+06shV0i7AfsCtpIjQXW33GgX0wx+AuW1f0eWcfynUnr5m0fa/gV2V6q2eC8xdoHsVcJDtazoPKOWVrUqT1+J60trRm7toFy1BUcr69EVgGV6auL/qnGtT/QbN5i3+J3Cqu2Q/y38/VViMFIW+HfADpYIOc0qaNV+boIAYuQZ9I+lE4AZS4MM3gA8A19v+TEW9a4D1chKC5YBjbb+ltgY3gKRfA7/2SImx1v5dgENtV6rfKekdwB091kqubfvSSg1uEEnvA67utg5V0ua2Ty3QvhHYC7iatvWcVSOQm+q3ppG0MvBI23ri9mOLuDB9o6TZSXV9tyNlZjrHBSUZgzCuQQVakZsayco0G/DXqhHEnXNdpXNfHdqtUmLtyxcu9svwhz8Rr4Wkv9l++6Db0Q+ayXmL60bSvMBmto8ZdFsmMuEWDqrQSsH2mKTXAveTksFXZckcrdh129XzyG5EmlO7mbY8ssAKkj5h+8yqDc76MyRSJ62XrFx+L88D7gxsQcrI09L9HfDLzvR3feg2di1ysM3jtn/ZsX9n0hrKkixNX81BSJ25hU+uKthEv2XdJvMWN154oYXtJ5TqNIdxLSBGrkHfZBfaSaRE30eS5qq+YvtnFfW65o9t4ep5ZK8H3mP79o79ywJ/sr1qFd2s0Z5I/e68e0lS7dGqidSRdBwpAf5RHbo7AZNtb1tRt8lrcRkpcKlb3ttLS9akZjfuKsC1jLiFK0emN9VvWbvJvMVnkOaFj/KMhRc2cEH+5h7nu8v2UnVqvtwI4xr8x6KUKm7VzuCMfNO/zvYKBdq1J1Jv6bpHcv7Rjo1Dt8lrcWUvw6EuRR761C4qKtBFr5F+yxo39/q+pFsKr3FjhRd6aN5pe+k6NV9uhFs46Jsc/LAVM0ZwfqOi3ml0X3rS0q2aR/YI4BJJx/PSfKzbUr6ov4lE6gCPSNoaOMn2NKCV9m5rZlxG0w/drsVSpBFb6bWYpVtQTZ57LOUCSa+xXZQooY2m+g2azVt8h2ouvKCUlKTb352AyM5USIxcg76RdDrwOHAZqTA2ALYPrqj3X6Md77a0oQ/t15CSyXfOr5VmtdkYOIQ0hzlDIvXOaNQ+dJcBDiCloWsvRn8usLft2wravCrd5xpLr8UHgU8DnyUtGYFU1/VA4JCqbv2sfT2wPHAbac5VJLdwJVdzU/3Wpt9kvum9s3Zn4YUDbD9SQXNFkhHtNM5LAffbvqV6i4MwrkHfqMFk+tk913J93lg1gGcU/QVtP1STVu2J1Dv0XwVg++E69JokG5W9gdeSRkPXkgJtitIfSnp1t/1Vl+JkzUb7baIg6Q/APrav7tj/OmA/25sOpmX/GURu4aAKF+Q/wFqRtC5pRPETUmTrTZLeWaD3Hkm3SfqbpLUkXQtcJOluSRvU0OQlgRtsn0QaxS9JDTlZJb1J0huzUV1E0p7ZeJVobtz2fj5Jv5B0laTf1OG+tf1n2/9l+1W2F8zvS/MKt4zo/MCm+TV/iWHNNNJvoyHpsBo0VpG0gTrqzrb3bZ8s0mlYAfK+ZSpqBpkwrsG4kXS1Uo7at5Nqjt6Yb9Ct/aUcDGyUb8zvJKXr+36B3v7Ae0lJCM4Gdra9PCkrzYElDZW0Nynl34U5evp04D3AFPXOAzse3a8CPwIOlbQ/yYU5F7CPpC8VNHm/tvcHk5ZPbQpcAvy8QBel2rOt9/uUaHXR/gwpHeTC+fVrSZ8q0Guk37L25B6vV5F+hyXanyYtu/kUqZjBZm2H9+v+rTEZLWXi0KSsnKiEWzgYN71cdC1KRxTqUkqs274+9KYno+hcWiDpCttrFrT1WmBtUhL424HlnDJMzQVcVNVtnoNM1gRmJxnAJfO6wzmzbh3X4iX/9xquRXs5uNoSgGS9q4C32H4qb88F/KPgOjTSb1n7RVKgVHuOZeftJWy/ousXx6d9Nek6/F+el/8tcIztH7Zf/z41jwPOtX14x/5dgA2rLvsKEhEtHPTDwsCCne6+7LJ8kBkjMPvlUqWEAb/O2x8AStL9PSbpY8C8wKOS9gCmAO9ixuTq/fKi7WckPQ88AzwMYPspqVv++nHz7zz397SkW20/kXWfkVQSzbpwHpkJmFeSPPJkXerBavIJXbQFzeX3JRe4qX4DmEpac9otdWVRjmVgFuearbZvz1Mov80PvFUbvjtwiqQPkNzjkB48XkFKYhIUEMY16IcDgA932X8dKZnE+oX6/w/YjRR5CvBX0txrVXYC9iXd/DciJQ84g/QQ8NECXUhu8d+QXLbnAEflKOr1Kauv+bykV9p+mhRxC0zP0FNiXA8HWhV1jiJVgvmXUiKCGZL598lySuXW1PZ+OgVLqSD9ri6SdEre3pyypUNN9Ruk6jILkEq4dfLdQu0HJK3pXHghj2A3IS2xqhT/kJf0vFXSeqRANIA/2j63sK0B4RYO+kDSJbbf2ONYZfftREQpTeHWJMP9W1L06fakG+tPWm7MCrqz236uy/4FgcW6BaAMGjW4lCrrv540zw8ph/XlBVqN9FvTSFqS5NW4v8uxt9n++wCaFYxCGNdg3GiULDOjHRuH7qjBUCVGOz+Vb0Vau/ciqX7l4bZvrarZNFL9CfYlTQY+CdxLGvl9EXgLqVzcfrYrJ6iQNG/Lfd3l2NLd3KTjbG9PqqzrnBmoobzFWXtp4Anbj+V517VJUc8zlCcMBk9ECwf9cLakb6ttckqJb5CSHFRlGsnwHQNsw8iyi9arEjna9oOkOpsvkOrF3kqaq9q6oL1I+qekfSUtX6LTRXcj0nKkr5EiTN8LfB24OR+ryq9JrtA3AOcBi5Lc/M8AvyrQBfhL642kczqOVS039xDJXX1pfl3W9qo8D99Uv2XtL5ByFgu4OL8EHJejlEu0e0U5n1Aa5Rw0Q4xcg3GTIyp/QRpVtebp1iDd7HZpBVxU1F6FNCe6KWnu6zfAmS4o2qy2vLbZHXi+7bcpZbv5a2Fk6G2k4gXbkKJ6jwNOsH1vVc2s20iC/VZEcH4wutv2Ep3HCtrcHi38ksjVgkjWHwDrAX8nXdu/lYzc23Qb6bes3WTe4sainINmiJFrMG5sP2V7O9I60V/l10a2399uWCWtVkH7Bttfzcs4TiPlZ92jsMnT2tyLiwOT8rlaaQVLeNT255ySm38WWJEULHOepF0LdGdlpFpLO/cAJYW8Z8kPFUsBc2e3YisLVOUlIhn3eN9te3yC9u6kJUknAjsCl0v6bn7IKKGpfoORvMWd1JG3+EXbz5AqJr0kyrlQN2iIiBYO+sb2VNKyg14cA/S11lHSEqQk8luQcuruAZwy6pfGZj/STfkmYGVSNDKSFgKuLNSeju2/An9VSm6wIakwQNWMPE0l2N8fuCG//wjwC0kGXkNyO5fQvsyn9Z68vVBV0TxSPU/S5aT//zdJLvPDR/3i+PXr7DdIS1vOUapANEPe4pK20jvKeQPKo5yDBgi3cFA7/boCJZ1PWiYyheSye0ku3ZLglTxyXQ64xTUWlJZ0vO3316XXod1Ugv1JpL/5f2c3+ZrAPbbvK9T96mjHbfdtvLO7czOSwVsIOBmYUiU4qkO3sX7L+o3kLe4S5fxm0jTKUEc5v5wJ4xrUjvrM0iPpdkbch+0/yFYFlOVqbB6SVgL2sl261nWmUrLkIi9n6Yntf452vA4k7WN7/3F+9inSKPX4/O9LblS2T66/hWVIeiXwQmvOVdLKpIC0222XemE6zzUbaW3qPbYfrFM7qIcwrkHt9GtcG2zH6sBBpHmwU0kFAQ4hPfUfbLty3uKxIjRtf6+i7iRSsM0SwOm2r8nJAr4IzFklOCjrTgOuIUXhQkeKPtulCUDG04Zx/y4k/Yre87W2/ZGKbWik37L2/5LyV98saQVStPCxJNf7xbYr512W9DPgx7avVUoo8g9ShP1k4HO2j6uqHTRDzLkGtSBp8baIy+cramxBynX6eN6eH1jXdtXlHIcDh5JuRBuTIpyPAj5g+9mKmi0Oynp/ZqTOaB38kjTHejHwI0n3kqJE9y64DgB7Au8jBcMcD5xSEt1dkXFfI9sfaqgNTfUbwAK2b87vdwKOs/2pHC18GVBS1OAdtj+e338YuMn25koZtv5MinoOhogYuQa1IOnOHIFZojHDkpCqSzm66UmaWpeLWdIapDmvjUk3zuOAc0qXi0i6Bljd9jRJc5CWiyzvmmq6SlqOFBy0GSkN5H7OKfWaps+R6w62f91rpFngGWik37L29Cxlkv4OHNh6IJJ0pe01CrTblzv9ETjR9q86jwXDQyzFCeqijhFAt99jiXdlDqU6rq/Pc47PdWxXxvaVtvfOxvuXJGN1naSSPLoAz9uels/xLDC1LsOaNaeSSpedSQq8WWn0b9RKP7+RVs3SeXq8KtFgvwFcJekgpQIRK5CuccsDU8pjkjaRtBbwNlISiVagU5SHG0LCLRzURR0ukEslfY80Nwopif9lo3x+LO4D2kc497dtm/JCA61lPWuRkqffTaoOVMIqGkkHKWD5vN0K7qpaaq19xHoXyTW8X147WYSkT9o+ZBwfPXG8mrZ/nv8tXSbUlQb6DVIxiM+QCo1v5FR8AdKc60GF2h8j1fldFNjdIzmGNwD+WKgdNEC4hYNxI+nHdDeiAnayPW+h/lzAl0kl4QDOAr41jMsMJH2EFHg0B2lpxJQ6ojbVUM3cHNB0FWnU+gQzRt+WBPI0FsCWjeBHSQZr+mCgIKCpkX7L2hvaPqvHsQNsf6GO8wQTgzCuwbiRtNNox20fNbPaUoKkDYHP296wQKMVfdsydp3GqsjNmDMRtTJdXZfduSV6X2MU70LJCLFh43oBqfTgZbTVdbV9UkW9xvotJyvZw/Yf2/bNQkoMsqjtjQu0DySt1f55x/6PAcvaLspdHNRPGNegFiQdZPtzFb/7A9u7SzqNLgag6g1P0vrAzxhZinMAqT6ogG+XrJVUQ2XWJM1Lyt+8NiP5m9ckGZed3aP6zCCR9G/g6W6HSK7syh6NbkFuJTTVb1l7WVLk7j62T8kBab8leQp2ckfO4T61LwPW7gy8ysb7Kkdu4aEj5lyDutgGqGRcSekSoXxeqpODgV1JS3Hek//de5zzg2OxDnBQaeadLvyIlM7u/a3AJkkiucsPIVX56Zt8o9+WlFryNGAv4J2kKkHftP3QKF8fi6sbjFb9g6T32v5TTXpN9Ru2b5P0LuAMSYsAO5CyM5XmyAaYvVtEc44qr3M5UVATMXINakHSXbaXGnQ72ul0V0q60fbKNWkfQirgvZtrLFQt6Wb3qJ4y2rFx6E4hld2bC1iA5Bo9jfR/WNP2JhWb3MhSEElPkrwYIrX5eVL7oWA03FS/Ze3Wb21x0nrqs4Dvto67IAuWpEuA7dvW0bb2r0haT7t2Ve2gGWLkGowb9S5gLQqW4ki6mtHnA6sWS59f0pZt27O2b5e4hW1/Mt9MD1EqE3cobZVPSm6ko1AyQnmN7dfmpRt32265R0+XVFrEYNxRwOPFduXlNmPoNtlvB7e9vwpYpG1faXT6V4A/S/oWIxH0a5MSU+xeoBs0RIxcg3GjVAuzNZroxFUTNLRFyO6W/225iXfIupWCNSQdOcphV4047TjHuqRiA+0PCHbFdIKSjmLEVeu2/V8GVrK9Y0Xd6aP4LiP6ooAkpcT9o6Uq/GZV7ay/JWm0aVId3pJMVS3Ndamx32YGkl5Lcue35levJSWquHpwrQp6EcY1GBq6uRebjEQtQdLCpFHJcsAnbNdSwi4HNP2SVLKvPaDpclJB+kqVfSQ9SFrbKtLc6/GtQ8A2thcpaPNnu+x+JbAL8Crbcxdo/5SUkKGV3m9b4Fbbu/X+1qh6jfRb1t6yY5dJuZyvsP1kXefpOOccwKa2a/ceBGWEcQ3GTZesRgYesn1Xt89X0L+CtrkwSW8Fflo1WrRL6rzWze5vtm8rbOttpBqph3cLNClF0vKk5AOQluLcWqg3U5ZRSZqHlEhhZ1IJwYNL1pFKugFYtXWNc3TstbZXrajXWL/18JRMBlYnRXqfW9N5JgHvJqVx3Ig0mn9fHdpBfcSca9APB3fZN1kpMfl2Ls9RuzNwhFLVD5EiW0tct93m7ZYBviTpa7aP73J8vLzJ9r86d0paihTpe2AVUeWcurZvlbRoe9BNH5mQZqDdeEqaO++rLXF/no/fE/gAKZjn9bYfrUH6FlLB8da61KXyvqo00m8Atj/cbX+e9phCqsZUmbyMaHtSGbuLSWkQl/VIJqhgiIiRa1CMpLWB79l+Z0168wE4V8epm2wIzq7L3ZyzCG1NGkksTqo4U3XNb5Nzo/+PFADTytv7f8ABtn9aVTPrHghsCRxGKtxdp9E+H3gjyZiQ318KPA7FSR9q67dxnKu07+4mFUY/FDjV9pOSbrO9bG2NDGolRq5BMbYvbY2GqqAeFVBay/dckJqvG7YfKV0bmN2fW5JGEisBJ5NGEUsWNk893nfbHr+otC/wVlIJv6l533LADyVNtv2tqtrAZ0nl2/YleQXa21t52UzmKwXfnYEG+220c65Muj4l/BbYnDTn/KKk31FPPu+gIcK4BsXkBfMlf+jtFVAaR9J6JJdzCQ+SRlP7kuZwrVSPthT3eN9tux92BNZwWx1b21MlbQNcCVQ2rrYbq67lnDEpB3q15xZ+pKJkU/1Gjwxjk4HFSJHvlXHKYLYHsC5ppP1dYL7cf3+q01sQ1EO4hYNxo+6J+yeTRkSfsX3azG9Vb3qsn50M3EtKR3d9gfbupCozc5EiWU8Azqq6HKlN92nSnKKA5RmZXxSwnO25en13DN0bbK/S77FBI2lX4BvAs6T1qK3RcNVlX430W9buTK1o4GHgZtvPl+p3nGs2RoKa3m17wTr1g3LCuAbjpkvEaevmcUlJRGib/lEkI/1Y3l6AFG1atQJKZ4UZAw+7xio7Ginlth2wIvBV0tzdTRX1mqqKcw6pxNw5HfvXB75se70quk0j6WbgLS5Lz9hNt9Z+y5rr2L6wpib2c9532v7fmX3eYHTCuAbjRtLStu9sUL/bOtdaU+splbXbghTd/N916Wbt15Lm8raxvUKd2qVIWo1Ubu5vvDTDz9uAzWxfO6i2jYak04Etm4yIravfOoLR/mH7LTW2cRIpf/cSwOm2r5G0CfBFYM46/0aCegjjGoybjpvHSba3qln/SlLAzaN5ezJwvu3XFeq+Avhv0g303aTMPCfX4caWND9p5ANwU2mEs6SdgcmtJSGS7iHNRQvYy/bPCrTnIF2D6aXsgGPb52GHDUlrkSoZXURbUJDtTxfq1tpvWXP6g2ADD4W/Ii1Dupi0pOde0sPR3q4hY1VQPxHQFPRDe7Rq8RxVFw4G/iHpxHyu9wH7VRWTtBEjC+3PA44G3thrPWKf2rMDPydFcN6W2/tqSacAHy+YY/s40F7380HbS2TDeAaphF4lbD8r6TxSUA+k5BRDa1gzPwfOJaUpnDbGZ8ekwX4DmCVPZczS9n7630xBEBYkQ7q6UxWcOYD7geVtP1ygGTRIGNegH0aLZC0Xt4+WdCkjCc63tH1dgeTppELbb3fOyCTph4XNbPElYDZgKefUdnmZx09I5eG+XFFXHTfME2G6YZyzamM1Uif2DaS0igLWVKoTOpR1YjOz2e7MtFVCU/0GMB/J5d4yqO1FAEzZA+nzziUI829hahjW4SbcwsG4kfQi8BTp5jEnIwWy61jP2HmuuUjrEd9fdW5U0pqkoJWtgamkfLpfsT1q0NA4ta8hZft5umP/3MCFrli8WtIt3eb9ctq/WwqiZH8F3A58wzPWiV3BdqU6sU0jaT9Su0/jpW7hSqPApvqtzzas1u8cd1sUObw0krz1t1e1clTQEGFcg6Gh4bnRt5JcxFuR1nWeYvuwAr2ret3QJF1ddZ5YKVH9I7b37dj/LWBB2x+vqNtIndimUcoF3EnJUpxG+q3PNvSdrampKPKgOcItHAycJudGW9i+ALhA0meAd5FGtIfl8/c9kgDcOafWRsnc4F7ALyTdQnoIAFiDlPJvlwLd0SjKVtUk7pLeLz+EVZZsqN/6oe/rPV7jWXeUclCdGLkGA0fSNNLc6Ifa5kan1rGwf5znrzKSuJ2RpAa+pwvxAAAJXUlEQVSdVB5ZtekvR1tUrzuq4vT7QKCG6sTOLLILe32SV2MTVyyR13S/jbMNjZVRrDtKOahOjFyDYeD1pJHk2ZJac6OTZuL5q4wklhmXcLVRMU75f6eO8pFjSNdtvHyKVCf2FqXSfjBSJ3bnfts3s5C0Dsmgbk7KrrUbUDm5ftP9NgTEaGlIiJFrMFTUPTc6znM2OZJoRLvqCEVj1IkdFqOSA5m2JlWCOQ44Bbi0m5u4ofM3+Zu40PY6DWk31u6gPxpLuB0EVbB9ge1PAUsC3wem34RylqGJRlPzmZWeim3favu0/OpWgP2YwnbVxS7AA6QSa8fkZSczcyTQd79JerVyucS8vZ6kH0ras32euCnD2jptg9pBH4RxDYYS29Nsn+mX5hWu7cYvafG2zVqTqncw0VxDw3JzXoxUqWdT4FZJxwBzSppZU1lV+m0KucJTXgZ2ImnkvQZQWjP3zHF+dKjnzl9OxJxrMJGo88Z/IbA0ND6SqA1Ji9u+N2829UAwFA8Dtl8kJQE5PWdV2oS0tvoeSefY3n6gDezOnG39swNwhO2D8xrlK0b53nhYaDwfsn1N4XmCmgjjGkwk6rzxNzZCa9AITrgHgjqw/RxpzfNJOZtSLfVXx6BKv7X/ptYH9oHkhcnRziXMJ2nLXgdtn1yoH9RMGNfg5UqTI7SmjGAjDwQzaURciTxq3QpYhhruVzkZw2OtRP2S1iNFIt8BHNLKLVyx386VNAW4D1iAlBMZSYuR6tGWMB9p9N51CREQxnXICOMaDDUlN351L+4O6QY1f2nbRjt1Q7pNPRAM84j4d8DjpJy9z43x2fEwhTTyfbxtXnR/RuZFSxJ17A5sS5ovfrvtF/L+FUjLiEq4wxXrGgeDIYxrMOyU3PgvrXislMpGcEAPBMMSxNSNJW1vPPbHxk1j86I5OcfxkErlSdqdtJzoNuAHJdrAypLeZvvv7TslvQ24v0fkdzBAwrgGw07lG7/to3qKSgdV1c3fb8oIDuKBYCiCmHpwgaTX2b66Jr3G5kUlrURao70d8BBwAimXwHolupmLgG6Vi54gGe5NazhHUCNhXINhp6kb/zYUZPqhISPY1APBAF3kpbwd+FBO4P8c5VVgmpwXvYGUxnMT27dk3T0KNVvM0+0Bw/bVkpap6RxBjYRxDQbORHSFNjkqHoWSB4JBuchLeU/Nek3Oi25JSuN5nqTTSS7iulzuC4xyrHKd36A5wrgGw0AjN35JvW6Wotl5xtJRcS+G0kXeJK1qMJIWBuaoQa+xeVHbpwKn5lrEm5EM+cKSDiWl8RxvIohuXCLpo7YPb98paRdSsFcwZERu4WCokXSQ7UqGKrsSzUyugCLpLttLVfzuaA8EV9pesnrLep7zTttL161bB5L+BzgYWBx4EHg1cL3tSqkwe8yLfs72qPVSq5LL220NbGt7gwKdRUj5lZ9nxJiuDbwC2ML2/aVtDeoljGsw1Azrjb8pIziIB4KSh4GmkXQlKfDobNtr5XWpO9iuVMmnrbzhzm3zojOtvGEp+f//2rx5re1zB9meoDfhFg6GncquUEmd1UEMPGT7rrImAWn00MsIVk7E0FTVlwG6yEt5wfbDkmaRNIvt8ySVuG+bnBdtHNvnAecNuh3B2IRxDQZOgzf+g7vsm5wrlGxnu/K6xgaNYFMPBI08DMwEHpM0N2m0eaykB4Gnqoo1PC8aBNMJt3AwcGa2K1TS2sD3bL+zQKMRIyip26hkMmlureiBYCKSjeAzpApeHyClATw2l6Cr6xy1zIsGQTthXIOXJaVFpWe2ESx9IGjYRd4oOR/wirbPlvRKYJLtJwfdriAYjXALBwNnZt/4c+Rl0VNlr6w72Qj+CKg8Ku5xvkuze7QqjbnIm0TSR4FdSQ8uywNLAD8DYoQZDDVhXINhoJEbf4/kFJOBtwKfqaI5FjUYwa6UPhDM7IeBGtkNeBMp/R+2b85rXoNgqAnjGgycBm/8nQkoDDwM7Gn7wYqao1JqBGf2A0FTDwM18pzt51tpfyXNynDnQg4CIIxrMMTUcOM/z/adtTWojQaN4Ex9IKjDRd4w50v6IjCnpA2BTwCnDbhNQTAmEdAUDC35xv8n22+o+P3pQUuSTrK9VY1t26ljV8sIXlJiBCUt3cQDwVgPA7aH0mDlUnA7AxuRosnPAH7huHEFQ04Y12DgNHXjl3S57bU639dBg0awkQeCph4GgiDoTriFg2GgKVeoe7yvg1OBJkbF7Wt961zf25iLvAkkXTXa8YKSc0EwUwjjGgwDTd3415D0BMlgzZnfw0hN0HkLtJsygk09EDT1MNAU00j//9+Q5lifGWxzgqA/wrgGw0AjN37bk+rQ6SXf430pTT0QNPUw0Ai215S0Cql6zW+A6/K/Z9r+90AbFwTjYJZBNyAImGA3/swakp6Q9CSwen7/hKQn2wxi39ieZHte2/PYnjW/b22XjLSbdJE3gu0bbH81z0GfBhwN7DHgZgXBuIiRazAMTMQbf5Oj4iZo0kXeCJKWIFWw2QJ4lGRYTxloo4JgnES0cDBwJL1IqnQiYE7g6dYhhvTGHzSLpPOBeYApwEmkALfp2H5kEO0KgvESxjUIgqFD0u2MeDHab1KtB66JMn0QvEwJ4xoEwYRF0mq2rx10O4KgkwhoCoJgInPMoBsQBN0I4xoEwURGY38kCGY+YVyDIJjIxLxWMJSEcQ2CIAiCmgnjGgTBROb5QTcgCLoR0cJBEAwdkl4NPGb78by9HrA5cAdwiO0wqsFQEyPXIAiGkSnAXACS1gROBO4E1gB+OsB2BcG4iPSHQRAMI3Pavje/3wE4wvbBuXj6FQNsVxCMixi5BkEwjLQvsVkfOAfA9jRi+U0wAYiRaxAEw8i5kqYA9wELAOcCSFoMeHaQDQuC8RDGNQiCYWR3YFtgMeDttl/I+1cAJg+sVUEwTiJaOAiCoUbSWsD2wNbAbcDJtn882FYFwejEyDUIgqFD0krAdvn1EHACaTCw3kAbFgTjJEauQRAMHZKmAX8FdrZ9S943NUrNBROFiBYOgmAY2ZIUzHSepMMlbUBECQcTiBi5BkEwtEiaC9iM5B5eHzgaOMX2mQNtWBCMQRjXIAgmBJIWIAU1bWt7g0G3JwhGI4xrEARBENRMzLkGQRAEQc2EcQ2CIAiCmgnjGgRBEAQ1E8Y1CIIgCGomjGsQBEEQ1Mz/B12U0XiTEbhzAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## Visualizing the correlation of attribute pairs using a heatmap\n",
+    "plt.figure(figsize=(6,6))\n",
+    "sns.heatmap(train.corr(method='pearson'),cmap='coolwarm')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FULL_Charge           0.534602\n",
+       "FULL_AcidicMolPerc   -0.598816\n",
+       "FULL_AURR980107      -0.584111\n",
+       "FULL_DAYM780201      -0.554838\n",
+       "FULL_GEOR030101      -0.260470\n",
+       "FULL_OOBM850104      -0.453287\n",
+       "NT_EFC195             0.260702\n",
+       "AS_MeanAmphiMoment    0.693552\n",
+       "AS_DAYM780201        -0.437168\n",
+       "AS_FUKS010112         0.033432\n",
+       "CT_RACS820104         0.267652\n",
+       "CLASS                 1.000000\n",
+       "Name: CLASS, dtype: float64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# correlation of the different attributes with the CLASS attribute\n",
+    "train.corr(method='pearson')['CLASS']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1a121f4e90>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualizing correlation of the different attributes with the CLASS attribute\n",
+    "train.corr(method='pearson')['CLASS'].plot(kind='bar', color=('skyblue'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the graph above, it can be observed that most attributes are negatively correlated with the `CLASS` attribute.\n",
+    "\n",
+    "<div class = 'alert alert-success'>\n",
+    "\n",
+    "Great job!\n",
+    "\n",
+    "What do we learn from this step? If they are negatively correlated, what does it tell us?\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Cheking for skewdness of the data\n",
+    "A skew is a deviation from the normal/Gausian ditribution such that the bell shaped curve is shifted to the right or the left.\n",
+    "* Positively skewed: If the tail is on the right.\n",
+    "* Negatively skewed: If the tail is on the left.\n",
+    "\n",
+    "To check for skewdness of the data, the `skew()` function is used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2813d207f0>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Checking if the data is normally distributed\n",
+    "train.skew().plot(kind='bar', color='orange')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the plot it can be observed that most of the attributes are positively skewed with the exception of attributes 4,6,9 and 10.\n",
+    "\n",
+    "<div class = 'alert alert-success'>\n",
+    "\n",
+    "Great job!\n",
+    "\n",
+    "How does this inform us in the context of ML\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 8. Understanding Data with Visualization\n",
+    "In order to better understand the data, different visualizations can be used.\n",
+    "This can be done using;\n",
+    "\n",
+    "#### a) Univariate plots\n",
+    "These are plots for singular variables per plot.\n",
+    "* Histograms\n",
+    "* Density plots\n",
+    "* Box plots\n",
+    "\n",
+    "#### b) Multivariate plots\n",
+    "These represent more than one variable per plot.\n",
+    "* Correlation Matrix Plot\n",
+    "* Scatter Plot Matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### a) Univariate plots\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1080x1080 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 12 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Histograms\n",
+    "plt.figure(figsize=(15,15))\n",
+    "train.hist(color='orange')\n",
+    "\n",
+    "plt.subplots_adjust(bottom=1, right=2, top=3)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the above plot, the distribution of the attributes can be observed.\n",
+    "Noteworthy is the `CLASS` attribute which is categorical and uniformly distributed and the `NT_EFC195`attribute which also appears categorical but not uniformly distributed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The NT_EFC195 attribute has 2 values: 0 and 1.\n",
+      "There are 2769 instances with 0's and 269 instances with 1's in the NT_EFC195 attribute.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1d8d6ed0>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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7wJ8mOSXJOcAf8Mu/If2p9h5/lfZfDGmhnN7RslNVP0lyJ/BO4H8WsKkXTO80L5jemadtwO8AEwy+LuPrwJEb566oqqkkLwM+B1wJ3LkI76nOeaSv5epvgGuAl4zo/fYyOFKfs6o6XFXXV9XGqtoEnAF8t7VNteefAncxuLNaWjBDX8tSVR0C7mEQ/KPwYeCvk/w6PH9lzp8fa4Ukv5rkJW35jcDhqnq4TfesavVTgTcxOBksLZjTO1rObmFhXy53fZI/G3p96Uwdq2pHktXAfW3+vRhM35DkzcDfAmPAPyXZU1UXAq8A7k3yHIPvRbqybe70Vj8VWAHcB9y+gP2Qnud370hSR5zekaSOOL2jrrVLLt96VPkf2s1c0rLj9I4kdcTpHUnqiKEvSR0x9CWpI4a+JHXE0Jekjvw//+Xa29KlOtcAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Zooming in on the NT_EFC195 attribute \n",
+    "cont = train['NT_EFC195'].unique()\n",
+    "print('The NT_EFC195 attribute has 2 values: %s and %s.' % (cont[0],cont[1]))\n",
+    "gp0 = train.groupby('NT_EFC195').size()[0].sum()\n",
+    "gp1 = train.groupby('NT_EFC195').size()[1].sum()\n",
+    "print(\"There are %s instances with 0's and %s instances with 1's in the NT_EFC195 attribute.\" % (gp0, gp1))\n",
+    "# Visualizing the distribution of the instances in the NT_EFC195 attribute \n",
+    "train.groupby('NT_EFC195').size().plot(kind='bar', color=('orange'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the above plot indicates that the variable NT_EFC195 is not uniformly distributed between the classes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function matplotlib.pyplot.show(*args, **kw)>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 12 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Density plots\n",
+    "train.plot(kind='density', subplots=True, layout=(4,3), sharex=False)\n",
+    "plt.subplots_adjust(bottom=1, right=2, top=3)\n",
+    "plt.show"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The density plots convey the same information as the histograms. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class = 'alert alert-info'>\n",
+    "\n",
+    "What do we learn from the box plots?\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 12 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Box plots\n",
+    "train.plot(kind='box', subplots=True, layout=(4,3), sharex=False,sharey=False)\n",
+    "plt.subplots_adjust(bottom=1, right=2, top=3)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### b) Multivariate plots\n",
+    "\n",
+    "<div class = 'alert alert-info'>\n",
+    "\n",
+    "Why do you choose these variables, any interest in them specifically.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x1a21e8d9d0>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 402.375x360 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualizing using scatter plot matrix\n",
+    "sns.pairplot(train, hue='CLASS',vars=['FULL_Charge','FULL_AcidicMolPerc'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 9. Preparing Data for Machine Learning\n",
+    "## Rescaling data\n",
+    "Since the data has attributes with varying scales,rescalling is done so the attributes have the same scale.\n",
+    "From `numpy`, the `set_printoptions` module is imported to determine the display of number of floating point values other Nump objects and from `scikit learn preprocessing` module, the `MinMaxScaler` option is imported to rescale the data. \n",
+    "\n",
+    "<div class = 'alert alert-success'>\n",
+    "\n",
+    "Good explanation\n",
+    "\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.457 0.    0.348 0.545 0.33  0.483 0.    0.005 0.508 0.415 0.182]\n",
+      " [0.435 0.116 0.322 0.489 0.276 0.458 1.    0.011 0.421 0.586 0.475]\n",
+      " [0.467 0.116 0.246 0.523 0.288 0.565 0.    0.011 0.442 0.273 0.666]\n",
+      " [0.457 0.089 0.275 0.399 0.403 0.647 0.    0.011 0.405 0.153 0.424]\n",
+      " [0.511 0.183 0.323 0.373 0.342 0.595 0.    0.011 0.5   0.196 0.536]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from numpy import set_printoptions\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "array = train.values\n",
+    "# seperate array into input, X and output, Y components\n",
+    "X = array[:,0:11]\n",
+    "Y = array[:,11]\n",
+    "scaler = MinMaxScaler(feature_range=(0,1))\n",
+    "# set range of the scale to between 0 and 1.\n",
+    "rescaledX = scaler.fit_transform(X)\n",
+    "# summarize transformed data\n",
+    "set_printoptions(precision=3)\n",
+    "print(rescaledX[0:5,:])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the end, no rescaling of data was done since predictions with rescaled data had a poor result."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 10. Feature selection\n",
+    "Performance of machine learning algorithms is dependent on selected features. Highly informative features improve model performance while irrelevant, partially irrelevant and redundant features negatively impact model performance.\n",
+    "\n",
+    "Feature selection reduces overfitting,improves accuracy and reduces training time. \n",
+    "\n",
+    "Feature selection can be done using:\n",
+    "* Univariate Selection\n",
+    "* Recursive Feature Elimination\n",
+    "* Principle Component Analysis\n",
+    "* Feature Importance\n",
+    "\n",
+    "For this assignment, `Recursive Feature Elimination (RFE)` is used.\n",
+    "`RFE` works by recursively removing attributes and building a model on those that remain. It uses the model accuracy to indentify the attributes, and their combinations, that contribute the most to predicting the target attribute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Num Features:  8\n",
+      "Selected Feature:  [ True False  True False  True  True  True  True False  True  True]\n",
+      "Feature Ranking:  [1 3 1 2 1 1 1 1 4 1 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.feature_selection import RFE\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "## Import the warnings module\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "model = LogisticRegression()\n",
+    "rfe = RFE(model,8)\n",
+    "fit = rfe.fit(X,Y)\n",
+    "print(\"Num Features: \", fit.n_features_)\n",
+    "print(\"Selected Feature: \", fit.support_)\n",
+    "print(\"Feature Ranking: \", fit.ranking_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Selecting features from train and test data\n",
+    "train_data = X[:,fit.support_]\n",
+    "test_d = test.values\n",
+    "test_data = test_d[:,fit.support_]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 11. Prediction by algorithms"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Algorithms can be linear or non-linear.  \n",
+    "**Linear algorithms include:**  \n",
+    "* Logistic Regression\n",
+    "* Linear Discriminant Analysis  \n",
+    "\n",
+    "**Non Linear algorithms include:**\n",
+    "* K-Nearest Neigbours\n",
+    "* Naive bayes\n",
+    "* Classification and Regression Trees\n",
+    "* Support vector machines\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Linear algorithms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:  91.40882159315338\n",
+      "MCC:  0.8342865299822478\n",
+      "[False  True]\n",
+      "False:  383\n",
+      "True:  375\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Logistic regression\n",
+    "## Logistic regression model is a binary dependent variable.\n",
+    "## Applying model with all features.\n",
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "from sklearn.metrics import plot_confusion_matrix\n",
+    "\n",
+    "# setting up the model\n",
+    "model = LogisticRegression()\n",
+    "\n",
+    "# Cross validating the model on data split using Kfold approach\n",
+    "num_folds = 7\n",
+    "seed=9\n",
+    "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n",
+    "scoring='accuracy'\n",
+    "results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n",
+    "print('Accuracy: ',results.mean()*100)\n",
+    "\n",
+    "# Training the model and using it to make a prediction\n",
+    "model.fit(X,Y)\n",
+    "opt = model.predict(test_d)\n",
+    "\n",
+    "# Applying Matthews correlation coefficient on the model\n",
+    "mcc = matthews_corrcoef(model.predict(X),Y)\n",
+    "print('MCC: ',mcc)\n",
+    "\n",
+    "# converting model prediction to a dataframe and then a csv\n",
+    "report = pd.DataFrame(opt)\n",
+    "report.columns = [\"CLASS\"]\n",
+    "report.index.name = \"Index\"\n",
+    "report.replace(0.0,'False')\n",
+    "report['CLASS']=report['CLASS'].map({0.0:False,1.0:True})\n",
+    "\n",
+    "report.to_csv(\"report_lr.csv\")\n",
+    "# Confirming number of classes in model prediction dataframe\n",
+    "print(report['CLASS'].unique())\n",
+    "# Checking for number of instances in each class of the model prediction dataframe\n",
+    "print('False: ',report.groupby('CLASS').size()[0].sum()) \n",
+    "print('True: ',report.groupby('CLASS').size()[1].sum())\n",
+    "\n",
+    "#plot_confusion_matrix(model, train_data, Y, values_format = '.1g', cmap = 'Blues')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The logistic regression algorithm is then applied on RFE selected features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:  90.28966425279788\n",
+      "MCC:  0.8054429005549071\n",
+      "[False  True]\n",
+      "False:  388\n",
+      "True:  370\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Logistic regression\n",
+    "## Logistic regression models a binary dependent variable.\n",
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "from sklearn.metrics import plot_confusion_matrix\n",
+    "\n",
+    "# setting up the model\n",
+    "model = LogisticRegression()\n",
+    "\n",
+    "# Cross validating the model on data split using Kfold approach\n",
+    "num_folds = 7\n",
+    "seed=9\n",
+    "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n",
+    "scoring='accuracy'\n",
+    "results = cross_val_score(model, train_data, Y, cv=kfold, scoring=scoring)\n",
+    "print('Accuracy: ',results.mean()*100)\n",
+    "\n",
+    "# Training the model and using it to make a prediction\n",
+    "model.fit( train_data,Y)\n",
+    "opt = model.predict(test_data)\n",
+    "\n",
+    "# Applying Matthews correlation coefficient on the model\n",
+    "mcc = matthews_corrcoef(model.predict(train_data),Y)\n",
+    "print('MCC: ',mcc)\n",
+    "\n",
+    "# converting prediction to a dataframe and then a csv\n",
+    "report = pd.DataFrame(opt)\n",
+    "report.columns = [\"CLASS\"]\n",
+    "report.index.name = \"Index\"\n",
+    "report.replace(0.0,'False')\n",
+    "report['CLASS']=report['CLASS'].map({0.0:False,1.0:True})\n",
+    "\n",
+    "report.to_csv(\"report_lr1.csv\")\n",
+    "# Confirming number of classes in model prediction dataframe\n",
+    "print(report['CLASS'].unique())\n",
+    "# Checking for number of instances in each class of the model prediction dataframe\n",
+    "print('False: ',report.groupby('CLASS').size()[0].sum())\n",
+    "print('True: ',report.groupby('CLASS').size()[1].sum())\n",
+    "\n",
+    "#plot_confusion_matrix(model, train_data, Y, values_format = '.1g', cmap = 'Blues')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From both models, the model run with all features has the higher accuracy and Matthews correlation coefficient."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:  90.42132982225148\n",
+      "MCC:  0.8130045771530646\n",
+      "[False  True]\n",
+      "False:  405\n",
+      "True:  353\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Linear Discriminant Analysis\n",
+    "## LDA is used for binary and multiclass classification\n",
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "\n",
+    "# setting up the model\n",
+    "model = LinearDiscriminantAnalysis()\n",
+    "\n",
+    "# Cross validating the model on data split using Kfold approach\n",
+    "num_folds = 7\n",
+    "seed=9\n",
+    "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n",
+    "scoring='accuracy'\n",
+    "results = cross_val_score(model, train_data, Y, cv=kfold, scoring=scoring)\n",
+    "print('Accuracy: ',results.mean()*100)\n",
+    "\n",
+    "# Training the model and using it to make a prediction\n",
+    "model.fit( train_data,Y)\n",
+    "opt = model.predict(test_data)\n",
+    "\n",
+    "# Applying Matthews correlation coefficient on the model\n",
+    "mcc = matthews_corrcoef(model.predict(train_data),Y)\n",
+    "print('MCC: ',mcc)\n",
+    "\n",
+    "# converting model prediction to a dataframe and then a csv\n",
+    "report = pd.DataFrame(opt)\n",
+    "report.columns = [\"CLASS\"]\n",
+    "report.index.name = \"Index\"\n",
+    "report.replace(0.0,'False')\n",
+    "report['CLASS']=report['CLASS'].map({0.0:False,1.0:True})\n",
+    "\n",
+    "report.to_csv(\"report_lda.csv\")\n",
+    "# Confirming number of classes in model prediction dataframe\n",
+    "print(report['CLASS'].unique())\n",
+    "# Checking for number of instances in each class of the model prediction dataframe\n",
+    "print('False: ',report.groupby('CLASS').size()[0].sum()) \n",
+    "print('True: ',report.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Non-linear algorithms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:  90.58602570783394\n",
+      "MCC:  0.8690586462107053\n",
+      "[False  True]\n",
+      "False:  402\n",
+      "True:  356\n"
+     ]
+    }
+   ],
+   "source": [
+    "# K-Nearest Neighbours\n",
+    "## This stores all available cases and classiies new cases based on a similarity measure.\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from numpy import set_printoptions\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "\n",
+    "num_folds = 10\n",
+    "seed=9\n",
+    "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n",
+    "model = KNeighborsClassifier(n_neighbors=5)\n",
+    "scoring='accuracy'\n",
+    "results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n",
+    "print('Accuracy: ',results.mean()*100)\n",
+    "\n",
+    "model.fit(X,Y)\n",
+    "opt = model.predict(test_d)\n",
+    "\n",
+    "mcc = matthews_corrcoef(model.predict(X),Y)\n",
+    "print('MCC: ',mcc)\n",
+    "\n",
+    "report = pd.DataFrame(opt)\n",
+    "report.columns = [\"CLASS\"]\n",
+    "report.index.name = \"Index\"\n",
+    "report.replace(0.0,'False')\n",
+    "report['CLASS']=report['CLASS'].map({0.0:False,1.0:True})\n",
+    "\n",
+    "report.to_csv(\"report_knn.csv\")\n",
+    "print(report['CLASS'].unique())\n",
+    "print('False: ',report.groupby('CLASS').size()[0].sum())\n",
+    "print('True: ',report.groupby('CLASS').size()[1].sum())\n",
+    "\n",
+    "set_printoptions(precision=3)\n",
+    "#plot_confusion_matrix(model, X, Y, values_format = '.1g', cmap = 'Blues')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compared to the linear algorithms, the K-nearest neighbours algorithm has a lower percentage accuracy but a higher MCC."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:  88.0815746048289\n",
+      "MCC:  0.8407203694376205\n",
+      "[ True False]\n",
+      "False:  370\n",
+      "True:  388\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Naive Bayes\n",
+    "## This calculates the probability of each class and the conditional probability of each class given each input value.\n",
+    "## Assumes independence of estimated probabilities for new data.\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "\n",
+    "kfold = KFold(n_splits=10, random_state=7)\n",
+    "model = GaussianNB()\n",
+    "results = cross_val_score(model, X, Y, cv=kfold)\n",
+    "print('Accuracy: ',results.mean()*100)\n",
+    "\n",
+    "model.fit(X,Y)\n",
+    "opt = model.predict(test_d)\n",
+    "\n",
+    "mcc = matthews_corrcoef(model.predict(X),Y)\n",
+    "print('MCC: ',mcc)\n",
+    "\n",
+    "report = pd.DataFrame(opt)\n",
+    "report.columns = [\"CLASS\"]\n",
+    "report.index.name = \"Index\"\n",
+    "report.replace(0.0,'False')\n",
+    "report['CLASS']=report['CLASS'].map({0.0:False,1.0:True})\n",
+    "\n",
+    "report.to_csv(\"report_nb.csv\")\n",
+    "print(report['CLASS'].unique())\n",
+    "print('False: ',report.groupby('CLASS').size()[0].sum())\n",
+    "print('True: ',report.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:  0.7398438857043599\n",
+      "MCC:  1.0\n",
+      "[ True False]\n",
+      "False:  387\n",
+      "True:  371\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Classiffication and regression trees (CART)\n",
+    "## CART/decision trees construct a binary tree where split points are chosen greedily by evaluating each attribute and value to minimize a cost function\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "\n",
+    "kfold = KFold(n_splits=10, random_state=7)\n",
+    "model = DecisionTreeClassifier(random_state=40)\n",
+    "results = cross_val_score(model, X, Y, cv=kfold)\n",
+    "print('Accuracy: ',results.mean())\n",
+    "\n",
+    "model.fit(X,Y)\n",
+    "opt = model.predict(test_d)\n",
+    "\n",
+    "mcc = matthews_corrcoef(model.predict(X), Y)\n",
+    "print('MCC: ',mcc)\n",
+    "\n",
+    "report = pd.DataFrame(opt)\n",
+    "report.columns = [\"CLASS\"]\n",
+    "report.index.name = \"Index\"\n",
+    "report.replace(0.0,'False')\n",
+    "report['CLASS']=report['CLASS'].map({0.0:False,1.0:True})\n",
+    "\n",
+    "report.to_csv(\"report_cart.csv\")\n",
+    "print(report['CLASS'].unique())\n",
+    "print('False: ',report.groupby('CLASS').size()[0].sum())\n",
+    "print('True: ',report.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "CART with RFE selected features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:  0.7398775403856175\n",
+      "MCC:  1.0\n",
+      "[ True False]\n",
+      "False:  370\n",
+      "True:  388\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Classiffication and regression trees (CART)\n",
+    "## CART/decision trees construct a binary tree where split points are chosen greedily by evaluating each attribute and value to minimize a cost function\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "\n",
+    "kfold = KFold(n_splits=10, random_state=7)\n",
+    "model = DecisionTreeClassifier(random_state=40)\n",
+    "results = cross_val_score(model, train_data, Y, cv=kfold)\n",
+    "print('Accuracy: ',results.mean())\n",
+    "\n",
+    "model.fit(train_data,Y)\n",
+    "opt = model.predict(test_data)\n",
+    "\n",
+    "mcc = matthews_corrcoef(model.predict(train_data), Y)\n",
+    "print('MCC: ',mcc)\n",
+    "\n",
+    "report = pd.DataFrame(opt)\n",
+    "report.columns = [\"CLASS\"]\n",
+    "report.index.name = \"Index\"\n",
+    "report.replace(0.0,'False')\n",
+    "report['CLASS']=report['CLASS'].map({0.0:False,1.0:True})\n",
+    "\n",
+    "report.to_csv(\"report_cart_rfe.csv\")\n",
+    "print(report['CLASS'].unique())\n",
+    "print('False: ',report.groupby('CLASS').size()[0].sum())\n",
+    "print('True: ',report.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Support vector machine (SVM) algorithm  \n",
+    "SVM uses the rbf kernel by default.  \n",
+    "RBF stands for Radial Basic Function and works best for non-linear problems.\n",
+    "As the problem at hand appears linear, the kernel can be changed to `linear` and the performance compared."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9061902900816398\n",
+      "MCC:  0.8187103751493263\n",
+      "[False  True]\n",
+      "False:  397\n",
+      "True:  361\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Support Vector Machines\n",
+    "## Using the default rbf kernel\n",
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "num_folds=10\n",
+    "seed=39\n",
+    "kfold = KFold(n_splits=num_folds, random_state=seed, shuffle=True)\n",
+    "model = SVC()\n",
+    "scoring='accuracy'\n",
+    "results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n",
+    "print(results.mean())\n",
+    "\n",
+    "model.fit(X,Y)\n",
+    "opt = model.predict(test_d)\n",
+    "\n",
+    "mcc = matthews_corrcoef(model.predict(X),Y)\n",
+    "print('MCC: ',mcc)\n",
+    "\n",
+    "\n",
+    "report = pd.DataFrame(opt)\n",
+    "report.columns = [\"CLASS\"]\n",
+    "report.index.name = \"Index\"\n",
+    "report.replace(0.0,'False')\n",
+    "report['CLASS']=report['CLASS'].map({0.0:False,1.0:True})\n",
+    "\n",
+    "report.to_csv(\"report_svm_rbf.csv\")\n",
+    "print(report['CLASS'].unique())\n",
+    "print('False: ',report.groupby('CLASS').size()[0].sum())\n",
+    "print('True: ',report.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9180465085982282\n",
+      "MCC:  0.8414394511707155\n",
+      "[False  True]\n",
+      "False:  385\n",
+      "True:  373\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Support Vector Machines\n",
+    "## Setting the kernel to linear\n",
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "num_folds=10\n",
+    "seed=39\n",
+    "kfold = KFold(n_splits=num_folds, random_state=seed, shuffle=True)\n",
+    "model = SVC(kernel='linear')\n",
+    "scoring='accuracy'\n",
+    "results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n",
+    "print(results.mean())\n",
+    "\n",
+    "model.fit(X,Y)\n",
+    "opt = model.predict(test_d)\n",
+    "\n",
+    "mcc = matthews_corrcoef(model.predict(X),Y)\n",
+    "print('MCC: ',mcc)\n",
+    "\n",
+    "\n",
+    "report = pd.DataFrame(opt)\n",
+    "report.columns = [\"CLASS\"]\n",
+    "report.index.name = \"Index\"\n",
+    "report.replace(0.0,'False')\n",
+    "report['CLASS']=report['CLASS'].map({0.0:False,1.0:True})\n",
+    "\n",
+    "report.to_csv(\"report_svm_linear.csv\")\n",
+    "print(report['CLASS'].unique())\n",
+    "print('False: ',report.groupby('CLASS').size()[0].sum())\n",
+    "print('True: ',report.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the results obtained, the model performs better with the linear kernel than the rbf kernel as indicated by the accuracy and MCC."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 12. Improving performance with Ensembles  \n",
+    "To boost accuracy, ensembles can be used.\n",
+    "These include;   \n",
+    "* `Bagging` (bagged decision trees, random forests and extra trees) where multiple models usually of the same type are built from different subsamples of the training dataset.\n",
+    "* `Boosting` (AdaBoost, stochastic gradient) where multiple models usually of the same type are built, each of which learns to fix the prediction errors of a prior model in the sequence of models.\n",
+    "* `Voting` which build multiple models typically of differing types and use simple statistics such as mean to combine predictions. \n",
+    "\n",
+    "For this work, `Boosting` and `voting` algorithms are ensembles."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Boosting Ensembles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7701895518499218\n",
+      "MCC:  0.8730261590913062\n",
+      "[ True False]\n",
+      "False:  386\n",
+      "True:  372\n"
+     ]
+    }
+   ],
+   "source": [
+    "# AdaBoost Classification\n",
+    "## It generally works by weighting instances in the dataset by how easy or difficult they are to classify.\n",
+    "## This allows the algorithm to pay more or less attention to these instances in the construction of subsequent models.\n",
+    "from pandas import read_csv\n",
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.ensemble import AdaBoostClassifier\n",
+    "\n",
+    "num_trees = 30\n",
+    "seed=7\n",
+    "\n",
+    "kfold = KFold(n_splits=10, random_state=seed)\n",
+    "\n",
+    "model = AdaBoostClassifier(n_estimators=num_trees, random_state=seed)\n",
+    "results = cross_val_score(model, X, Y, cv=kfold)\n",
+    "\n",
+    "print(results.mean())\n",
+    "\n",
+    "model.fit(X,Y)\n",
+    "opt = model.predict(test_d)\n",
+    "\n",
+    "mcc = matthews_corrcoef(model.predict(X),Y)\n",
+    "print('MCC: ',mcc)\n",
+    "\n",
+    "\n",
+    "report = pd.DataFrame(opt)\n",
+    "report.columns = [\"CLASS\"]\n",
+    "report.index.name = \"Index\"\n",
+    "report.replace(0.0,'False')\n",
+    "report['CLASS']=report['CLASS'].map({0.0:False,1.0:True})\n",
+    "\n",
+    "report.to_csv(\"report_AB.csv\")\n",
+    "print(report['CLASS'].unique())\n",
+    "print('False: ',report.groupby('CLASS').size()[0].sum())\n",
+    "print('True: ',report.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7882935990967518\n",
+      "MCC:  0.9355236060852458\n",
+      "[ True False]\n",
+      "False:  385\n",
+      "True:  373\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Stochastic Gradient Boosting classification\n",
+    "\n",
+    "from pandas import read_csv\n",
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.ensemble import GradientBoostingClassifier\n",
+    "\n",
+    "\n",
+    "seed = 7\n",
+    "num_trees = 100\n",
+    "\n",
+    "kfold = KFold(n_splits=10, random_state=seed)\n",
+    "model = GradientBoostingClassifier(n_estimators=num_trees, random_state=seed)\n",
+    "results = cross_val_score(model, X, Y, cv=kfold)\n",
+    "print(results.mean())\n",
+    "\n",
+    "model.fit(X,Y)\n",
+    "opt = model.predict(test_d)\n",
+    "\n",
+    "mcc = matthews_corrcoef(model.predict(X),Y)\n",
+    "print('MCC: ',mcc)\n",
+    "\n",
+    "\n",
+    "report = pd.DataFrame(opt)\n",
+    "report.columns = [\"CLASS\"]\n",
+    "report.index.name = \"Index\"\n",
+    "report.replace(0.0,'False')\n",
+    "report['CLASS']=report['CLASS'].map({0.0:False,1.0:True})\n",
+    "\n",
+    "report.to_csv(\"report_GBC.csv\")\n",
+    "print(report['CLASS'].unique())\n",
+    "print('False: ',report.groupby('CLASS').size()[0].sum())\n",
+    "print('True: ',report.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.787307842626368\n",
+      "MCC:  0.9269836637327226\n",
+      "[ True False]\n",
+      "False:  383\n",
+      "True:  375\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Stochastic X Gradient Boosting Classification\n",
+    "from pandas import read_csv\n",
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from xgboost import XGBClassifier\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "\n",
+    "seed = 7\n",
+    "num_trees = 100\n",
+    "\n",
+    "kfold = KFold(n_splits=10, random_state=seed)\n",
+    "model = XGBClassifier(n_estimators=num_trees, random_state=seed)\n",
+    "results = cross_val_score(model, X, Y, cv=kfold)\n",
+    "print(results.mean())\n",
+    "\n",
+    "model.fit(X,Y)\n",
+    "opt = model.predict(test_d)\n",
+    "\n",
+    "mcc = matthews_corrcoef(model.predict(X),Y)\n",
+    "print('MCC: ',mcc)\n",
+    "\n",
+    "\n",
+    "report = pd.DataFrame(opt)\n",
+    "report.columns = [\"CLASS\"]\n",
+    "report.index.name = \"Index\"\n",
+    "report.replace(0.0,'False')\n",
+    "report['CLASS']=report['CLASS'].map({0.0:False,1.0:True})\n",
+    "\n",
+    "report.to_csv(\"report_XGB.csv\")\n",
+    "print(report['CLASS'].unique())\n",
+    "print('False: ',report.groupby('CLASS').size()[0].sum())\n",
+    "print('True: ',report.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Voting Ensemble"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8307799200972728\n",
+      "MCC:  0.8925178806925171\n",
+      "[False  True]\n",
+      "False:  390\n",
+      "True:  368\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Voting Ensemble for Classification\n",
+    "from pandas import read_csv\n",
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from xgboost import XGBClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.ensemble import VotingClassifier\n",
+    "\n",
+    "# create the sub models\n",
+    "estimators = []\n",
+    "estimators.append(('LR', LogisticRegression()))\n",
+    "estimators.append(('LDA', LinearDiscriminantAnalysis()))\n",
+    "estimators.append(('KNN', KNeighborsClassifier()))\n",
+    "estimators.append(('NB', GaussianNB()))\n",
+    "estimators.append(('CART', DecisionTreeClassifier()))\n",
+    "estimators.append(('SVM_rbf', SVC()))\n",
+    "estimators.append(('SVM_linear', SVC(kernel='linear')))\n",
+    "estimators.append(('AB', AdaBoostClassifier())) \n",
+    "estimators.append(('GBC', GradientBoostingClassifier()))\n",
+    "estimators.append(('XGB', XGBClassifier()))\n",
+    "\n",
+    "# create the ensemble model\n",
+    "ensemble = VotingClassifier(estimators)\n",
+    "results = cross_val_score(ensemble, X, Y, cv=kfold)\n",
+    "print(results.mean())\n",
+    "\n",
+    "ensemble.fit(X,Y)\n",
+    "opt = ensemble.predict(test_d)\n",
+    "\n",
+    "mcc = matthews_corrcoef(ensemble.predict(X),Y)\n",
+    "print('MCC: ',mcc)\n",
+    "\n",
+    "\n",
+    "report = pd.DataFrame(opt)\n",
+    "report.columns = [\"CLASS\"]\n",
+    "report.index.name = \"Index\"\n",
+    "report.replace(0.0,'False')\n",
+    "report['CLASS']=report['CLASS'].map({0.0:False,1.0:True})\n",
+    "\n",
+    "report.to_csv(\"report_vote.csv\")\n",
+    "print(report['CLASS'].unique())\n",
+    "print('False: ',report.groupby('CLASS').size()[0].sum())\n",
+    "print('True: ',report.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 13. Comparing Machine Learning Algorithms used"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('LR', 0.9140839412888656, 'MCC: ', 0.8342865299822478, 'score: ', 0.8712572258609209)\n",
+      "('LDA', 0.9186935469862776, 'MCC: ', 0.8377162908048379, 'score: ', 0.8632353313098651)\n",
+      "('KNN', 0.9065116380059057, 'MCC: ', 0.8690586462107053, 'score: ', 0.8184834462987218)\n",
+      "('NB', 0.9193546986277574, 'MCC: ', 0.8407203694376205, 'score: ', 1.0)\n",
+      "('CART', 0.9009206183776272, 'MCC: ', 1.0, 'score: ', 0.7660594085245541)\n",
+      "('SVM_rbf', 0.9088131839499741, 'MCC: ', 0.8187103751493263, 'score: ', 0.8413643707604765)\n",
+      "('SVM_linear', 0.9190192374500608, 'MCC: ', 0.8414394511707155, 'score: ', 0.8820016475190336)\n",
+      "('AB', 0.9285663105784264, 'MCC: ', 0.8848771929251248, 'score: ', 0.8526978735336366)\n",
+      "('GBC', 0.9394291731804758, 'MCC: ', 0.9355236060852458, 'score: ', 0.8714432576134836)\n",
+      "('XGB', 0.9391056539864515, 'MCC: ', 0.9269836637327226, 'score: ', 0.87653605323293)\n",
+      "('VC', 0.9328447976376584, 'MCC: ', 0.8925178806925171, 'score: ', 0.8904974309653444)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compare perfomance of the different algorithms used\n",
+    "from matplotlib import pyplot\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "\n",
+    "models = []\n",
+    "models.append(('LR', LogisticRegression()))\n",
+    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
+    "models.append(('KNN', KNeighborsClassifier()))\n",
+    "models.append(('NB', GaussianNB()))\n",
+    "models.append(('CART', DecisionTreeClassifier(random_state=40)))\n",
+    "models.append(('SVM_rbf', SVC()))\n",
+    "models.append(('SVM_linear', SVC(kernel='linear')))\n",
+    "models.append(('AB', AdaBoostClassifier())) \n",
+    "models.append(('GBC', GradientBoostingClassifier()))\n",
+    "models.append(('XGB', XGBClassifier()))\n",
+    "models.append(('VC',VotingClassifier(estimators)))\n",
+    "\n",
+    "results = []\n",
+    "names = []\n",
+    "scoring = 'accuracy'\n",
+    "\n",
+    "\n",
+    "\n",
+    "num_folds=10\n",
+    "seed=7\n",
+    "for name, model in models:\n",
+    "    kfold = KFold(n_splits=num_folds, random_state=seed, shuffle=True)\n",
+    "    cv_results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n",
+    "    nb = GaussianNB()\n",
+    "    nb.fit(X,Y)\n",
+    "    f = nb.predict(test_d)\n",
+    "    model.fit(X,Y)\n",
+    "    results.append(cv_results)\n",
+    "    names.append(name)\n",
+    "    msg = (name, cv_results.mean(),'MCC: ',matthews_corrcoef(model.predict(X),Y),'score: ',matthews_corrcoef(model.predict(test_d),f))\n",
+    "    print(msg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# A plot for comparing the different algorithms\n",
+    "fig = pyplot.figure()\n",
+    "fig.suptitle('Algorithm Comparison')\n",
+    "ax = fig.add_subplot(111)\n",
+    "pyplot.boxplot(results)\n",
+    "ax.set_xticklabels(names)\n",
+    "plt.xticks(rotation=45)\n",
+    "pyplot.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the above graph, it can be observed that models implementing gradient boost classification have higher percentage accuracies.  \n",
+    "However, other metrics have to be put in place to measure performance of a model such as Matthews correlation coefficient, area under the ROC curve or logarithmic loss since a single parameter is not enough to measure model performance and thus assist in model choice.\n",
+    "\n",
+    "\n",
+    "<div class = 'alert alert-success'>\n",
+    "\n",
+    "- Great presentation.\n",
+    "- Well organized code, easy to read.\n",
+    "- This is going to make a good report.\n",
+    "- Fo you: please read on these areas: GridSearch, Parameter optimizing and run samples on a gradient boosting algorithm of your choice.\n",
+    "\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
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+     "delete_cmd_prefix": "rm(",
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+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
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+   "types_to_exclude": [
+    "module",
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+ "nbformat_minor": 4
+}
diff --git a/Assignment Colab/Kiberu_Davis.ipynb b/Assignment Colab/Kiberu_Davis.ipynb
new file mode 100644
index 0000000..c28e811
--- /dev/null
+++ b/Assignment Colab/Kiberu_Davis.ipynb	
@@ -0,0 +1,3304 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ACE CLASS ASSIGNMENT\n",
+    "by `Davis Kiberu`  \n",
+    "2019/HD07/24872U\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Outline:\n",
+    "  1. Import required packages\n",
+    "  2. Reading in Datasets\n",
+    "  3. Data exploration\n",
+    "  4. Descriptive statistics for the data\n",
+    "  5. Class distribution of the data \n",
+    "  6. Checking for correlation between the different attributes\n",
+    "  7. Cheking for skewdness of the data\n",
+    "  8. Understanding Data with visualization\n",
+    "  9. Preparing Data for Machine Learning\n",
+    "  10. Feature selection\n",
+    "  11. Predictions by algorithm\n",
+    "  12. Improving performance with Ensembles\n",
+    "  13. Comparing Machine Learning Algorithms used"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Import required packages\n",
+    "For this assignment, the required packages and modules are;\n",
+    "* numpy;for computing scientific/mathematical data.\n",
+    "* pandas; for data wrangling and manipulation\n",
+    "* seaborn; for statistical data visualization\n",
+    "* matplotlib.pyplot;  for plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
+   },
+   "outputs": [],
+   "source": [
+    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
+    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
+    "# For example, here's several helpful packages to load in \n",
+    "\n",
+    "import numpy as np # linear algebra\n",
+    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Input data files are available in the \"../input/\" directory.\n",
+    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
+    "\n",
+    "#import os\n",
+    "#for dirname, _, filenames in os.walk('/kaggle/input'):\n",
+    "#    for filename in filenames:\n",
+    "#        print(os.path.join(dirname, filename))\n",
+    "\n",
+    "# Remove unnecessary warnings\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "# Any results you write to the current directory are saved as output."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Reading in Datasets\n",
+    "Comma Seperated Variables(CSV) data files are read into DataFrame objects using Pandas' read_csv() method.\n",
+    "Two datasets are read in: \n",
+    "* The train set assigned to train\n",
+    "* The test set assigned to test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
+    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
+   },
+   "outputs": [],
+   "source": [
+    "# Reading in Datasets\n",
+    "train = pd.read_csv(\"../AMP Data Sets/AMP_TrainSet.csv\")\n",
+    "test = pd.read_csv(\"../AMP Data Sets/Test.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Data exploration\n",
+    "To get a feel of the data, we visualize a few of the rows(instances) and columns(attributes) of the data using the `head()` function which shows the first five rows.\n",
+    "\n",
+    "a) We the check for the dimensions of the data using the `shape` property of the data which returns a tuple indicating number of rows and columns.\n",
+    "\n",
+    "b) We can then explore the names of the various attributes using the columns property and then check for the data type of each attribute using the `dtypes` property.\n",
+    "\n",
+    "c) We can also check for missing values (NAs and NaNs) using the `isnull()` function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>FULL_Charge</th>\n",
+       "      <th>FULL_AcidicMolPerc</th>\n",
+       "      <th>FULL_AURR980107</th>\n",
+       "      <th>FULL_DAYM780201</th>\n",
+       "      <th>FULL_GEOR030101</th>\n",
+       "      <th>FULL_OOBM850104</th>\n",
+       "      <th>NT_EFC195</th>\n",
+       "      <th>AS_MeanAmphiMoment</th>\n",
+       "      <th>AS_DAYM780201</th>\n",
+       "      <th>AS_FUKS010112</th>\n",
+       "      <th>CT_RACS820104</th>\n",
+       "      <th>CLASS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.951</td>\n",
+       "      <td>74.842</td>\n",
+       "      <td>0.975</td>\n",
+       "      <td>-3.663</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.282</td>\n",
+       "      <td>73.444</td>\n",
+       "      <td>5.661</td>\n",
+       "      <td>1.041</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5.405</td>\n",
+       "      <td>0.931</td>\n",
+       "      <td>71.595</td>\n",
+       "      <td>0.957</td>\n",
+       "      <td>-4.011</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.600</td>\n",
+       "      <td>68.222</td>\n",
+       "      <td>6.537</td>\n",
+       "      <td>1.453</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5.5</td>\n",
+       "      <td>5.405</td>\n",
+       "      <td>0.873</td>\n",
+       "      <td>73.595</td>\n",
+       "      <td>0.961</td>\n",
+       "      <td>-2.512</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.593</td>\n",
+       "      <td>69.444</td>\n",
+       "      <td>4.934</td>\n",
+       "      <td>1.722</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4.167</td>\n",
+       "      <td>0.895</td>\n",
+       "      <td>66.250</td>\n",
+       "      <td>0.999</td>\n",
+       "      <td>-1.362</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.614</td>\n",
+       "      <td>67.222</td>\n",
+       "      <td>4.316</td>\n",
+       "      <td>1.382</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.5</td>\n",
+       "      <td>8.537</td>\n",
+       "      <td>0.932</td>\n",
+       "      <td>64.720</td>\n",
+       "      <td>0.979</td>\n",
+       "      <td>-2.091</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.616</td>\n",
+       "      <td>72.944</td>\n",
+       "      <td>4.540</td>\n",
+       "      <td>1.539</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   FULL_Charge  FULL_AcidicMolPerc  FULL_AURR980107  FULL_DAYM780201  \\\n",
+       "0          5.0               0.000            0.951           74.842   \n",
+       "1          4.0               5.405            0.931           71.595   \n",
+       "2          5.5               5.405            0.873           73.595   \n",
+       "3          5.0               4.167            0.895           66.250   \n",
+       "4          7.5               8.537            0.932           64.720   \n",
+       "\n",
+       "   FULL_GEOR030101  FULL_OOBM850104  NT_EFC195  AS_MeanAmphiMoment  \\\n",
+       "0            0.975           -3.663          0               0.282   \n",
+       "1            0.957           -4.011          1               0.600   \n",
+       "2            0.961           -2.512          0               0.593   \n",
+       "3            0.999           -1.362          0               0.614   \n",
+       "4            0.979           -2.091          0               0.616   \n",
+       "\n",
+       "   AS_DAYM780201  AS_FUKS010112  CT_RACS820104  CLASS  \n",
+       "0         73.444          5.661          1.041      1  \n",
+       "1         68.222          6.537          1.453      1  \n",
+       "2         69.444          4.934          1.722      1  \n",
+       "3         67.222          4.316          1.382      1  \n",
+       "4         72.944          4.540          1.539      1  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Viewing the first five rows of the data\n",
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>FULL_Charge</th>\n",
+       "      <th>FULL_AcidicMolPerc</th>\n",
+       "      <th>FULL_AURR980107</th>\n",
+       "      <th>FULL_DAYM780201</th>\n",
+       "      <th>FULL_GEOR030101</th>\n",
+       "      <th>FULL_OOBM850104</th>\n",
+       "      <th>NT_EFC195</th>\n",
+       "      <th>AS_MeanAmphiMoment</th>\n",
+       "      <th>AS_DAYM780201</th>\n",
+       "      <th>AS_FUKS010112</th>\n",
+       "      <th>CT_RACS820104</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>3.704</td>\n",
+       "      <td>0.873</td>\n",
+       "      <td>73.519</td>\n",
+       "      <td>0.987</td>\n",
+       "      <td>-4.833</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.382</td>\n",
+       "      <td>74.556</td>\n",
+       "      <td>7.225</td>\n",
+       "      <td>1.234</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.444</td>\n",
+       "      <td>0.892</td>\n",
+       "      <td>62.444</td>\n",
+       "      <td>0.931</td>\n",
+       "      <td>-0.584</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.320</td>\n",
+       "      <td>56.056</td>\n",
+       "      <td>4.942</td>\n",
+       "      <td>1.853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.901</td>\n",
+       "      <td>47.000</td>\n",
+       "      <td>1.039</td>\n",
+       "      <td>-5.664</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.164</td>\n",
+       "      <td>47.000</td>\n",
+       "      <td>5.969</td>\n",
+       "      <td>1.174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.5</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.869</td>\n",
+       "      <td>69.222</td>\n",
+       "      <td>0.982</td>\n",
+       "      <td>-5.423</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.010</td>\n",
+       "      <td>69.222</td>\n",
+       "      <td>5.462</td>\n",
+       "      <td>1.138</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-4.0</td>\n",
+       "      <td>21.591</td>\n",
+       "      <td>1.061</td>\n",
+       "      <td>71.682</td>\n",
+       "      <td>0.976</td>\n",
+       "      <td>-2.002</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.758</td>\n",
+       "      <td>66.000</td>\n",
+       "      <td>5.582</td>\n",
+       "      <td>1.453</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   FULL_Charge  FULL_AcidicMolPerc  FULL_AURR980107  FULL_DAYM780201  \\\n",
+       "0          4.0               3.704            0.873           73.519   \n",
+       "1          4.0               4.444            0.892           62.444   \n",
+       "2          2.0               0.000            0.901           47.000   \n",
+       "3          4.5               0.000            0.869           69.222   \n",
+       "4         -4.0              21.591            1.061           71.682   \n",
+       "\n",
+       "   FULL_GEOR030101  FULL_OOBM850104  NT_EFC195  AS_MeanAmphiMoment  \\\n",
+       "0            0.987           -4.833          0               0.382   \n",
+       "1            0.931           -0.584          0               0.320   \n",
+       "2            1.039           -5.664          0               0.164   \n",
+       "3            0.982           -5.423          0               2.010   \n",
+       "4            0.976           -2.002          0               2.758   \n",
+       "\n",
+       "   AS_DAYM780201  AS_FUKS010112  CT_RACS820104  \n",
+       "0         74.556          7.225          1.234  \n",
+       "1         56.056          4.942          1.853  \n",
+       "2         47.000          5.969          1.174  \n",
+       "3         69.222          5.462          1.138  \n",
+       "4         66.000          5.582          1.453  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### a) Data dimensions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((3038, 12), (758, 11))"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Checking for number of rows and columns in the datasets\n",
+    "train.shape, test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### b) Data type for each attribute"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['FULL_Charge', 'FULL_AcidicMolPerc', 'FULL_AURR980107',\n",
+       "       'FULL_DAYM780201', 'FULL_GEOR030101', 'FULL_OOBM850104', 'NT_EFC195',\n",
+       "       'AS_MeanAmphiMoment', 'AS_DAYM780201', 'AS_FUKS010112', 'CT_RACS820104',\n",
+       "       'CLASS'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# First lets take a look at the column names.\n",
+    "train.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['FULL_Charge', 'FULL_AcidicMolPerc', 'FULL_AURR980107',\n",
+       "       'FULL_DAYM780201', 'FULL_GEOR030101', 'FULL_OOBM850104', 'NT_EFC195',\n",
+       "       'AS_MeanAmphiMoment', 'AS_DAYM780201', 'AS_FUKS010112',\n",
+       "       'CT_RACS820104'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FULL_Charge           float64\n",
+       "FULL_AcidicMolPerc    float64\n",
+       "FULL_AURR980107       float64\n",
+       "FULL_DAYM780201       float64\n",
+       "FULL_GEOR030101       float64\n",
+       "FULL_OOBM850104       float64\n",
+       "NT_EFC195               int64\n",
+       "AS_MeanAmphiMoment    float64\n",
+       "AS_DAYM780201         float64\n",
+       "AS_FUKS010112         float64\n",
+       "CT_RACS820104         float64\n",
+       "CLASS                   int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Next, determine the data type for each column\n",
+    "train.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FULL_Charge           float64\n",
+       "FULL_AcidicMolPerc    float64\n",
+       "FULL_AURR980107       float64\n",
+       "FULL_DAYM780201       float64\n",
+       "FULL_GEOR030101       float64\n",
+       "FULL_OOBM850104       float64\n",
+       "NT_EFC195               int64\n",
+       "AS_MeanAmphiMoment    float64\n",
+       "AS_DAYM780201         float64\n",
+       "AS_FUKS010112         float64\n",
+       "CT_RACS820104         float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### c) Missing values\n",
+    "Missing values include the standard `NaN` and `Na` "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FULL_Charge           0\n",
+       "FULL_AcidicMolPerc    0\n",
+       "FULL_AURR980107       0\n",
+       "FULL_DAYM780201       0\n",
+       "FULL_GEOR030101       0\n",
+       "FULL_OOBM850104       0\n",
+       "NT_EFC195             0\n",
+       "AS_MeanAmphiMoment    0\n",
+       "AS_DAYM780201         0\n",
+       "AS_FUKS010112         0\n",
+       "CT_RACS820104         0\n",
+       "CLASS                 0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check for missing values.\n",
+    "train.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.isnull().sum().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 758 entries, 0 to 757\n",
+      "Data columns (total 11 columns):\n",
+      "FULL_Charge           758 non-null float64\n",
+      "FULL_AcidicMolPerc    758 non-null float64\n",
+      "FULL_AURR980107       758 non-null float64\n",
+      "FULL_DAYM780201       758 non-null float64\n",
+      "FULL_GEOR030101       758 non-null float64\n",
+      "FULL_OOBM850104       758 non-null float64\n",
+      "NT_EFC195             758 non-null int64\n",
+      "AS_MeanAmphiMoment    758 non-null float64\n",
+      "AS_DAYM780201         758 non-null float64\n",
+      "AS_FUKS010112         758 non-null float64\n",
+      "CT_RACS820104         758 non-null float64\n",
+      "dtypes: float64(10), int64(1)\n",
+      "memory usage: 65.2 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Another way to check for null values and datatypes is using the .info() function. \n",
+    "test.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1c466690>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1728x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualization to check for missing data\n",
+    "import missingno as msno\n",
+    "msno.bar(train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above plot indicates that there are no missing values in the `train` dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In summary, the train data has 3028 instances and 12 attributes while the test dataset has 758 instances and 11 attributes. \n",
+    "The data types for each instance are numerical i.e. integers(int64) and floating point numbers(float64).\n",
+    "There are no missing values in the datasets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Descriptive statistics for the data\n",
+    "For descriptive statistics, a summary is obtained using the `describe()` function.\n",
+    "The statistics summary show the counts for each attribtute, the mean, standard deviation, the minimum value for numerical atrributes, the 25th, 50th and 75th percentile for each numeric attribute andthe maximum value. \n",
+    "\n",
+    "This offers insight into the spread of the data and represents the data in a form that is readily understandable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>FULL_Charge</th>\n",
+       "      <th>FULL_AcidicMolPerc</th>\n",
+       "      <th>FULL_AURR980107</th>\n",
+       "      <th>FULL_DAYM780201</th>\n",
+       "      <th>FULL_GEOR030101</th>\n",
+       "      <th>FULL_OOBM850104</th>\n",
+       "      <th>NT_EFC195</th>\n",
+       "      <th>AS_MeanAmphiMoment</th>\n",
+       "      <th>AS_DAYM780201</th>\n",
+       "      <th>AS_FUKS010112</th>\n",
+       "      <th>CT_RACS820104</th>\n",
+       "      <th>CLASS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2.060237</td>\n",
+       "      <td>8.521520</td>\n",
+       "      <td>0.971410</td>\n",
+       "      <td>73.668760</td>\n",
+       "      <td>0.994007</td>\n",
+       "      <td>-2.432927</td>\n",
+       "      <td>0.088545</td>\n",
+       "      <td>15.683233</td>\n",
+       "      <td>73.650828</td>\n",
+       "      <td>5.911361</td>\n",
+       "      <td>1.235255</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3.819929</td>\n",
+       "      <td>7.586652</td>\n",
+       "      <td>0.107413</td>\n",
+       "      <td>8.527489</td>\n",
+       "      <td>0.031333</td>\n",
+       "      <td>1.707223</td>\n",
+       "      <td>0.284133</td>\n",
+       "      <td>11.575665</td>\n",
+       "      <td>9.166092</td>\n",
+       "      <td>0.693689</td>\n",
+       "      <td>0.210012</td>\n",
+       "      <td>0.500082</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-16.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.684000</td>\n",
+       "      <td>42.750000</td>\n",
+       "      <td>0.866000</td>\n",
+       "      <td>-10.432000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.041000</td>\n",
+       "      <td>42.778000</td>\n",
+       "      <td>3.533000</td>\n",
+       "      <td>0.785000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.516000</td>\n",
+       "      <td>0.895000</td>\n",
+       "      <td>68.294000</td>\n",
+       "      <td>0.974000</td>\n",
+       "      <td>-3.606000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.587500</td>\n",
+       "      <td>67.556000</td>\n",
+       "      <td>5.459250</td>\n",
+       "      <td>1.082000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>7.143000</td>\n",
+       "      <td>0.963000</td>\n",
+       "      <td>74.059500</td>\n",
+       "      <td>0.994000</td>\n",
+       "      <td>-2.296500</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.988500</td>\n",
+       "      <td>73.697000</td>\n",
+       "      <td>5.925500</td>\n",
+       "      <td>1.184000</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>13.158000</td>\n",
+       "      <td>1.041000</td>\n",
+       "      <td>79.343750</td>\n",
+       "      <td>1.011000</td>\n",
+       "      <td>-1.283250</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>26.807750</td>\n",
+       "      <td>79.778000</td>\n",
+       "      <td>6.382000</td>\n",
+       "      <td>1.351000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>46.667000</td>\n",
+       "      <td>1.451000</td>\n",
+       "      <td>101.682000</td>\n",
+       "      <td>1.196000</td>\n",
+       "      <td>3.576000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>51.280000</td>\n",
+       "      <td>103.167000</td>\n",
+       "      <td>8.662000</td>\n",
+       "      <td>2.192000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       FULL_Charge  FULL_AcidicMolPerc  FULL_AURR980107  FULL_DAYM780201  \\\n",
+       "count  3038.000000         3038.000000      3038.000000      3038.000000   \n",
+       "mean      2.060237            8.521520         0.971410        73.668760   \n",
+       "std       3.819929            7.586652         0.107413         8.527489   \n",
+       "min     -16.000000            0.000000         0.684000        42.750000   \n",
+       "25%       0.000000            2.516000         0.895000        68.294000   \n",
+       "50%       2.000000            7.143000         0.963000        74.059500   \n",
+       "75%       4.000000           13.158000         1.041000        79.343750   \n",
+       "max      30.000000           46.667000         1.451000       101.682000   \n",
+       "\n",
+       "       FULL_GEOR030101  FULL_OOBM850104    NT_EFC195  AS_MeanAmphiMoment  \\\n",
+       "count      3038.000000      3038.000000  3038.000000         3038.000000   \n",
+       "mean          0.994007        -2.432927     0.088545           15.683233   \n",
+       "std           0.031333         1.707223     0.284133           11.575665   \n",
+       "min           0.866000       -10.432000     0.000000            0.041000   \n",
+       "25%           0.974000        -3.606000     0.000000            5.587500   \n",
+       "50%           0.994000        -2.296500     0.000000           14.988500   \n",
+       "75%           1.011000        -1.283250     0.000000           26.807750   \n",
+       "max           1.196000         3.576000     1.000000           51.280000   \n",
+       "\n",
+       "       AS_DAYM780201  AS_FUKS010112  CT_RACS820104        CLASS  \n",
+       "count    3038.000000    3038.000000    3038.000000  3038.000000  \n",
+       "mean       73.650828       5.911361       1.235255     0.500000  \n",
+       "std         9.166092       0.693689       0.210012     0.500082  \n",
+       "min        42.778000       3.533000       0.785000     0.000000  \n",
+       "25%        67.556000       5.459250       1.082000     0.000000  \n",
+       "50%        73.697000       5.925500       1.184000     0.500000  \n",
+       "75%        79.778000       6.382000       1.351000     1.000000  \n",
+       "max       103.167000       8.662000       2.192000     1.000000  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Obtain summary statistics\n",
+    "train.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>FULL_Charge</th>\n",
+       "      <th>FULL_AcidicMolPerc</th>\n",
+       "      <th>FULL_AURR980107</th>\n",
+       "      <th>FULL_DAYM780201</th>\n",
+       "      <th>FULL_GEOR030101</th>\n",
+       "      <th>FULL_OOBM850104</th>\n",
+       "      <th>NT_EFC195</th>\n",
+       "      <th>AS_MeanAmphiMoment</th>\n",
+       "      <th>AS_DAYM780201</th>\n",
+       "      <th>AS_FUKS010112</th>\n",
+       "      <th>CT_RACS820104</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>758.000000</td>\n",
+       "      <td>758.000000</td>\n",
+       "      <td>758.000000</td>\n",
+       "      <td>758.000000</td>\n",
+       "      <td>758.000000</td>\n",
+       "      <td>758.000000</td>\n",
+       "      <td>758.000000</td>\n",
+       "      <td>758.000000</td>\n",
+       "      <td>758.000000</td>\n",
+       "      <td>758.000000</td>\n",
+       "      <td>758.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2.073879</td>\n",
+       "      <td>8.945091</td>\n",
+       "      <td>0.973046</td>\n",
+       "      <td>73.821891</td>\n",
+       "      <td>0.994212</td>\n",
+       "      <td>-2.397922</td>\n",
+       "      <td>0.084433</td>\n",
+       "      <td>15.570067</td>\n",
+       "      <td>73.844204</td>\n",
+       "      <td>5.904492</td>\n",
+       "      <td>1.250189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>4.230615</td>\n",
+       "      <td>7.814449</td>\n",
+       "      <td>0.110676</td>\n",
+       "      <td>8.029524</td>\n",
+       "      <td>0.032370</td>\n",
+       "      <td>1.597138</td>\n",
+       "      <td>0.278219</td>\n",
+       "      <td>11.362589</td>\n",
+       "      <td>8.915193</td>\n",
+       "      <td>0.656911</td>\n",
+       "      <td>0.218102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-13.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.699000</td>\n",
+       "      <td>47.000000</td>\n",
+       "      <td>0.889000</td>\n",
+       "      <td>-7.844000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.060000</td>\n",
+       "      <td>47.000000</td>\n",
+       "      <td>3.843000</td>\n",
+       "      <td>0.841000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>-0.500000</td>\n",
+       "      <td>2.721750</td>\n",
+       "      <td>0.894000</td>\n",
+       "      <td>68.740250</td>\n",
+       "      <td>0.973000</td>\n",
+       "      <td>-3.457250</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.709000</td>\n",
+       "      <td>68.346000</td>\n",
+       "      <td>5.471250</td>\n",
+       "      <td>1.096000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>7.500000</td>\n",
+       "      <td>0.965000</td>\n",
+       "      <td>74.069500</td>\n",
+       "      <td>0.994000</td>\n",
+       "      <td>-2.238000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>15.057000</td>\n",
+       "      <td>73.646000</td>\n",
+       "      <td>5.935500</td>\n",
+       "      <td>1.188000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>14.230250</td>\n",
+       "      <td>1.053500</td>\n",
+       "      <td>79.284750</td>\n",
+       "      <td>1.013000</td>\n",
+       "      <td>-1.306250</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>25.290250</td>\n",
+       "      <td>80.069250</td>\n",
+       "      <td>6.375250</td>\n",
+       "      <td>1.378500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>44.118000</td>\n",
+       "      <td>1.431000</td>\n",
+       "      <td>102.929000</td>\n",
+       "      <td>1.182000</td>\n",
+       "      <td>2.017000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>50.098000</td>\n",
+       "      <td>102.929000</td>\n",
+       "      <td>7.588000</td>\n",
+       "      <td>2.283000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       FULL_Charge  FULL_AcidicMolPerc  FULL_AURR980107  FULL_DAYM780201  \\\n",
+       "count   758.000000          758.000000       758.000000       758.000000   \n",
+       "mean      2.073879            8.945091         0.973046        73.821891   \n",
+       "std       4.230615            7.814449         0.110676         8.029524   \n",
+       "min     -13.000000            0.000000         0.699000        47.000000   \n",
+       "25%      -0.500000            2.721750         0.894000        68.740250   \n",
+       "50%       2.000000            7.500000         0.965000        74.069500   \n",
+       "75%       4.000000           14.230250         1.053500        79.284750   \n",
+       "max      30.000000           44.118000         1.431000       102.929000   \n",
+       "\n",
+       "       FULL_GEOR030101  FULL_OOBM850104   NT_EFC195  AS_MeanAmphiMoment  \\\n",
+       "count       758.000000       758.000000  758.000000          758.000000   \n",
+       "mean          0.994212        -2.397922    0.084433           15.570067   \n",
+       "std           0.032370         1.597138    0.278219           11.362589   \n",
+       "min           0.889000        -7.844000    0.000000            0.060000   \n",
+       "25%           0.973000        -3.457250    0.000000            5.709000   \n",
+       "50%           0.994000        -2.238000    0.000000           15.057000   \n",
+       "75%           1.013000        -1.306250    0.000000           25.290250   \n",
+       "max           1.182000         2.017000    1.000000           50.098000   \n",
+       "\n",
+       "       AS_DAYM780201  AS_FUKS010112  CT_RACS820104  \n",
+       "count     758.000000     758.000000     758.000000  \n",
+       "mean       73.844204       5.904492       1.250189  \n",
+       "std         8.915193       0.656911       0.218102  \n",
+       "min        47.000000       3.843000       0.841000  \n",
+       "25%        68.346000       5.471250       1.096000  \n",
+       "50%        73.646000       5.935500       1.188000  \n",
+       "75%        80.069250       6.375250       1.378500  \n",
+       "max       102.929000       7.588000       2.283000  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Class distribution of the data\n",
+    "The data being used has a `CLASS` attribute.\n",
+    "We therefore have to determine the number of clases, their labels/values and distribution of observations within these classes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Checking for the number of classes in the data\n",
+    "train['CLASS'].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 0])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Checking for the class values\n",
+    "train['CLASS'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are 1519 instances in class 1 and 1519 instances in class 0.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1c78b4d0>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# There are two classes i.e 1 and 0.\n",
+    "# checking for number of instances in each class \n",
+    "class1 = train.groupby('CLASS').size()[0].sum()\n",
+    "class0 = train.groupby('CLASS').size()[1].sum()\n",
+    "print(\"There are %s instances in class 1 and %s instances in class 0.\" % (class1, class0))\n",
+    "# Visualizing the distribution of the data in the different classes\n",
+    "train.groupby('CLASS').size().plot(kind='bar', color=('skyblue', 'pink'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Classes 1 and 0 have equal instances and as such our observations are balanced.  \n",
+    "Due to this uniform distribution, chances of algorithm bias are reduced."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Checking for correlation between the different attributes\n",
+    "Correlation helps us determine how the different attributes relate with each other. It also helps us when deciding which attributes to select especially in the presence of highly correlated attributes in which case one of the two attributes would be sufficient.\n",
+    "\n",
+    "The `corr` function is used with Pearson's correlation method since most of the attributes are continous with the exception of `NT_EFC195` and `CLASS`.\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>FULL_Charge</th>\n",
+       "      <th>FULL_AcidicMolPerc</th>\n",
+       "      <th>FULL_AURR980107</th>\n",
+       "      <th>FULL_DAYM780201</th>\n",
+       "      <th>FULL_GEOR030101</th>\n",
+       "      <th>FULL_OOBM850104</th>\n",
+       "      <th>NT_EFC195</th>\n",
+       "      <th>AS_MeanAmphiMoment</th>\n",
+       "      <th>AS_DAYM780201</th>\n",
+       "      <th>AS_FUKS010112</th>\n",
+       "      <th>CT_RACS820104</th>\n",
+       "      <th>CLASS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>FULL_Charge</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.612996</td>\n",
+       "      <td>-0.490977</td>\n",
+       "      <td>-0.434603</td>\n",
+       "      <td>-0.058725</td>\n",
+       "      <td>-0.283758</td>\n",
+       "      <td>0.088068</td>\n",
+       "      <td>0.355477</td>\n",
+       "      <td>-0.365374</td>\n",
+       "      <td>-0.090570</td>\n",
+       "      <td>0.232929</td>\n",
+       "      <td>0.534602</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FULL_AcidicMolPerc</th>\n",
+       "      <td>-0.612996</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.794796</td>\n",
+       "      <td>0.541481</td>\n",
+       "      <td>0.115201</td>\n",
+       "      <td>0.513344</td>\n",
+       "      <td>-0.143168</td>\n",
+       "      <td>-0.431590</td>\n",
+       "      <td>0.449621</td>\n",
+       "      <td>0.002334</td>\n",
+       "      <td>-0.213543</td>\n",
+       "      <td>-0.598816</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FULL_AURR980107</th>\n",
+       "      <td>-0.490977</td>\n",
+       "      <td>0.794796</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.548253</td>\n",
+       "      <td>0.346139</td>\n",
+       "      <td>0.462712</td>\n",
+       "      <td>-0.169540</td>\n",
+       "      <td>-0.426097</td>\n",
+       "      <td>0.456260</td>\n",
+       "      <td>0.032958</td>\n",
+       "      <td>-0.403599</td>\n",
+       "      <td>-0.584111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FULL_DAYM780201</th>\n",
+       "      <td>-0.434603</td>\n",
+       "      <td>0.541481</td>\n",
+       "      <td>0.548253</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.010118</td>\n",
+       "      <td>0.334778</td>\n",
+       "      <td>-0.090058</td>\n",
+       "      <td>-0.408793</td>\n",
+       "      <td>0.894191</td>\n",
+       "      <td>0.055915</td>\n",
+       "      <td>-0.326792</td>\n",
+       "      <td>-0.554838</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FULL_GEOR030101</th>\n",
+       "      <td>-0.058725</td>\n",
+       "      <td>0.115201</td>\n",
+       "      <td>0.346139</td>\n",
+       "      <td>0.010118</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.319157</td>\n",
+       "      <td>-0.230417</td>\n",
+       "      <td>-0.160269</td>\n",
+       "      <td>-0.029085</td>\n",
+       "      <td>0.040480</td>\n",
+       "      <td>-0.151935</td>\n",
+       "      <td>-0.260470</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FULL_OOBM850104</th>\n",
+       "      <td>-0.283758</td>\n",
+       "      <td>0.513344</td>\n",
+       "      <td>0.462712</td>\n",
+       "      <td>0.334778</td>\n",
+       "      <td>0.319157</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.230561</td>\n",
+       "      <td>-0.336297</td>\n",
+       "      <td>0.275640</td>\n",
+       "      <td>-0.452769</td>\n",
+       "      <td>0.155304</td>\n",
+       "      <td>-0.453287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NT_EFC195</th>\n",
+       "      <td>0.088068</td>\n",
+       "      <td>-0.143168</td>\n",
+       "      <td>-0.169540</td>\n",
+       "      <td>-0.090058</td>\n",
+       "      <td>-0.230417</td>\n",
+       "      <td>-0.230561</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.178683</td>\n",
+       "      <td>-0.036844</td>\n",
+       "      <td>0.145924</td>\n",
+       "      <td>0.080898</td>\n",
+       "      <td>0.260702</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AS_MeanAmphiMoment</th>\n",
+       "      <td>0.355477</td>\n",
+       "      <td>-0.431590</td>\n",
+       "      <td>-0.426097</td>\n",
+       "      <td>-0.408793</td>\n",
+       "      <td>-0.160269</td>\n",
+       "      <td>-0.336297</td>\n",
+       "      <td>0.178683</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.322378</td>\n",
+       "      <td>0.025580</td>\n",
+       "      <td>0.171524</td>\n",
+       "      <td>0.693552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AS_DAYM780201</th>\n",
+       "      <td>-0.365374</td>\n",
+       "      <td>0.449621</td>\n",
+       "      <td>0.456260</td>\n",
+       "      <td>0.894191</td>\n",
+       "      <td>-0.029085</td>\n",
+       "      <td>0.275640</td>\n",
+       "      <td>-0.036844</td>\n",
+       "      <td>-0.322378</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.045562</td>\n",
+       "      <td>-0.256060</td>\n",
+       "      <td>-0.437168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AS_FUKS010112</th>\n",
+       "      <td>-0.090570</td>\n",
+       "      <td>0.002334</td>\n",
+       "      <td>0.032958</td>\n",
+       "      <td>0.055915</td>\n",
+       "      <td>0.040480</td>\n",
+       "      <td>-0.452769</td>\n",
+       "      <td>0.145924</td>\n",
+       "      <td>0.025580</td>\n",
+       "      <td>0.045562</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.445284</td>\n",
+       "      <td>0.033432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CT_RACS820104</th>\n",
+       "      <td>0.232929</td>\n",
+       "      <td>-0.213543</td>\n",
+       "      <td>-0.403599</td>\n",
+       "      <td>-0.326792</td>\n",
+       "      <td>-0.151935</td>\n",
+       "      <td>0.155304</td>\n",
+       "      <td>0.080898</td>\n",
+       "      <td>0.171524</td>\n",
+       "      <td>-0.256060</td>\n",
+       "      <td>-0.445284</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.267652</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CLASS</th>\n",
+       "      <td>0.534602</td>\n",
+       "      <td>-0.598816</td>\n",
+       "      <td>-0.584111</td>\n",
+       "      <td>-0.554838</td>\n",
+       "      <td>-0.260470</td>\n",
+       "      <td>-0.453287</td>\n",
+       "      <td>0.260702</td>\n",
+       "      <td>0.693552</td>\n",
+       "      <td>-0.437168</td>\n",
+       "      <td>0.033432</td>\n",
+       "      <td>0.267652</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    FULL_Charge  FULL_AcidicMolPerc  FULL_AURR980107  \\\n",
+       "FULL_Charge            1.000000           -0.612996        -0.490977   \n",
+       "FULL_AcidicMolPerc    -0.612996            1.000000         0.794796   \n",
+       "FULL_AURR980107       -0.490977            0.794796         1.000000   \n",
+       "FULL_DAYM780201       -0.434603            0.541481         0.548253   \n",
+       "FULL_GEOR030101       -0.058725            0.115201         0.346139   \n",
+       "FULL_OOBM850104       -0.283758            0.513344         0.462712   \n",
+       "NT_EFC195              0.088068           -0.143168        -0.169540   \n",
+       "AS_MeanAmphiMoment     0.355477           -0.431590        -0.426097   \n",
+       "AS_DAYM780201         -0.365374            0.449621         0.456260   \n",
+       "AS_FUKS010112         -0.090570            0.002334         0.032958   \n",
+       "CT_RACS820104          0.232929           -0.213543        -0.403599   \n",
+       "CLASS                  0.534602           -0.598816        -0.584111   \n",
+       "\n",
+       "                    FULL_DAYM780201  FULL_GEOR030101  FULL_OOBM850104  \\\n",
+       "FULL_Charge               -0.434603        -0.058725        -0.283758   \n",
+       "FULL_AcidicMolPerc         0.541481         0.115201         0.513344   \n",
+       "FULL_AURR980107            0.548253         0.346139         0.462712   \n",
+       "FULL_DAYM780201            1.000000         0.010118         0.334778   \n",
+       "FULL_GEOR030101            0.010118         1.000000         0.319157   \n",
+       "FULL_OOBM850104            0.334778         0.319157         1.000000   \n",
+       "NT_EFC195                 -0.090058        -0.230417        -0.230561   \n",
+       "AS_MeanAmphiMoment        -0.408793        -0.160269        -0.336297   \n",
+       "AS_DAYM780201              0.894191        -0.029085         0.275640   \n",
+       "AS_FUKS010112              0.055915         0.040480        -0.452769   \n",
+       "CT_RACS820104             -0.326792        -0.151935         0.155304   \n",
+       "CLASS                     -0.554838        -0.260470        -0.453287   \n",
+       "\n",
+       "                    NT_EFC195  AS_MeanAmphiMoment  AS_DAYM780201  \\\n",
+       "FULL_Charge          0.088068            0.355477      -0.365374   \n",
+       "FULL_AcidicMolPerc  -0.143168           -0.431590       0.449621   \n",
+       "FULL_AURR980107     -0.169540           -0.426097       0.456260   \n",
+       "FULL_DAYM780201     -0.090058           -0.408793       0.894191   \n",
+       "FULL_GEOR030101     -0.230417           -0.160269      -0.029085   \n",
+       "FULL_OOBM850104     -0.230561           -0.336297       0.275640   \n",
+       "NT_EFC195            1.000000            0.178683      -0.036844   \n",
+       "AS_MeanAmphiMoment   0.178683            1.000000      -0.322378   \n",
+       "AS_DAYM780201       -0.036844           -0.322378       1.000000   \n",
+       "AS_FUKS010112        0.145924            0.025580       0.045562   \n",
+       "CT_RACS820104        0.080898            0.171524      -0.256060   \n",
+       "CLASS                0.260702            0.693552      -0.437168   \n",
+       "\n",
+       "                    AS_FUKS010112  CT_RACS820104     CLASS  \n",
+       "FULL_Charge             -0.090570       0.232929  0.534602  \n",
+       "FULL_AcidicMolPerc       0.002334      -0.213543 -0.598816  \n",
+       "FULL_AURR980107          0.032958      -0.403599 -0.584111  \n",
+       "FULL_DAYM780201          0.055915      -0.326792 -0.554838  \n",
+       "FULL_GEOR030101          0.040480      -0.151935 -0.260470  \n",
+       "FULL_OOBM850104         -0.452769       0.155304 -0.453287  \n",
+       "NT_EFC195                0.145924       0.080898  0.260702  \n",
+       "AS_MeanAmphiMoment       0.025580       0.171524  0.693552  \n",
+       "AS_DAYM780201            0.045562      -0.256060 -0.437168  \n",
+       "AS_FUKS010112            1.000000      -0.445284  0.033432  \n",
+       "CT_RACS820104           -0.445284       1.000000  0.267652  \n",
+       "CLASS                    0.033432       0.267652  1.000000  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Obtaining correlation coefficient values for the different attribute pairs\n",
+    "train.corr(method='pearson')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1c768810>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## Visualizing the correlation of attribute pairs using a heatmap\n",
+    "plt.figure(figsize=(6,6))\n",
+    "sns.heatmap(train.corr(method='pearson'),cmap='coolwarm')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FULL_Charge           0.534602\n",
+       "FULL_AcidicMolPerc   -0.598816\n",
+       "FULL_AURR980107      -0.584111\n",
+       "FULL_DAYM780201      -0.554838\n",
+       "FULL_GEOR030101      -0.260470\n",
+       "FULL_OOBM850104      -0.453287\n",
+       "NT_EFC195             0.260702\n",
+       "AS_MeanAmphiMoment    0.693552\n",
+       "AS_DAYM780201        -0.437168\n",
+       "AS_FUKS010112         0.033432\n",
+       "CT_RACS820104         0.267652\n",
+       "CLASS                 1.000000\n",
+       "Name: CLASS, dtype: float64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# correlation of the different attributes with the CLASS attribute\n",
+    "train.corr(method='pearson')['CLASS']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1c761510>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualizing correlation of the different attributes with the CLASS attribute\n",
+    "train.corr(method='pearson')['CLASS'].plot(kind='bar', color=('skyblue'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the graph above, it can be observed that most attributes are negatively correlated with the `CLASS` attribute.\n",
+    "\n",
+    "Correlation does not imply causation but highly correlated features to the outcome or class could be indicative of the outcome's cause. \n",
+    "Correlation can be useful in feature selection since attributes that have a very low correlation with the class can be considered non-informative and as such discarded."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Cheking for skewdness of the data\n",
+    "A skew is a deviation from the normal/Gausian ditribution such that the bell shaped curve is shifted to the right or the left.\n",
+    "* Positively skewed: If the tail is on the right.\n",
+    "* Negatively skewed: If the tail is on the left.\n",
+    "\n",
+    "To check for skewdness of the data, the `skew()` function is used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1c9e1850>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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d9qtaiAXLEwHAi4Gn2v69pM8DZ9UMZPt9wPskva928p5Bp8cKcB/bJzaPPyDpTOBESa9geeKt6dPARsCbbf8GQNLltrdpIRaUL96BZwFfBLD965oXc9OU0D8MfAXYTNI/U8683t5ivM8B35L06eb5q4DDK8dY9pdszsiPBo5uLsl2qxwLytnPLba/vdKOSD9pId71wBMGH5iReFfUDmb7jKZ99+8knQq8hXaSwcD6kranNF2ubfv3zX7cLunONgLaPkDSFsCDGPr82v5O5VBdHytI2tD2DQC2T22azL4EbFI7lu3XSXo8cKSkY4GDafdYuV7S84GrgCcD+wJIWoeKJzdT0+QCIOkRwM6URPgt2xe1HG8X4JnN05Ntn1R5+39v+wM1t7mQNPccjrO9UhuopANtv6XF2FsA/wYssf3glmKcOrJob9u/knQ/4KQ2Cj1J+hfKVdyFwOBLw7an+uavpL2By2yfNrJ8MfCPtl/TUty1gL8FdgceYvuBLcV5GOWk9AHAh2x/pln+HODZtt9UJc60JHRJ476lb7J9ewux1ga+afsZtbe9EEi6P7BF8/SqcWfQsfqa42c927fM+eJV3/ZPgEcPmgLbdnc5ViRtDmxv+/hJ78uamKYml7OArYDfUc7QNwJ+Lek3wGtsn1krkO07Jd01fAnYhqZp5QBK88pmlEu+q4GvAv9i+/rK8bYHPg5sSLn0A9hS0vXA39iu2u7bxNyQcsN3WVKgnL1W/d2GYnX2fg7FXUI5Nu8Efmr7xyy/cVnbZcC6QKsJfRLHyiz7cqjt/VrY7iOAXRk6Npt29OpX/pJeA/y37YubHlCHAS8BfgbsY/vsGnGmKaGfDBwzaPaQ9GzKG/JpSpfGP6sc72bgfEknA78fLLT9uooxjgZOodzx/jVAc9d7n2bdsyvGgvJe/ZXt04cXStqxWVe1x5CkvwTeCXyD5UnhGcB7Jf2T7c/WjEfH76ekpwEHUe4VPB74PrBx09vmFbar3yegfFGcI+lbDCX1yscldH+szNROLuB5NWM18d5C6V12FDBoEtyS0qZ+lO1/qRzy9cBnmsd7AY8GtgG2pzTFPLVGkGlqcjnf9p+OLDvP9qMlnWP7sZXj7TNuue1qN0Yl/cT2w1d13RrEu9j2tjOsu8T2QyvH+wnwZ6Nnxk0f49NtP6x2vI7fz7Mp7Z/XSNoG+KDtFzU3Zt9su/YXcifHZROn62PlTsoMZsNdPtw838L2PSrH+ymw3WiTraR7ABfM9LuvQbxlOarpBXW67X9vnlfrDj1NZ+i/ar5Vj2qe7wH8pmmvrN590fbhTVfCxbZbuasP/FzS/wUOH+o6dX/glUAbZ3cnSPovyoCGwfa3Av4SOHHG/7X6xPieA3ex4ge3lq7fz7VtX9M8/gWl5wm2T5b0oRbidXVcQvfHymXAzrZ/MbqijR5RlGPwgaw8DebmtNMd+q6mnf53lI4d/zy0rlovl2lK6HtTLt+PpSSJ7zfL1gZeVjuYpBcAHwDuAWwj6bHAuyr3JtgDeCvwbS0fAvwb4Dha+J2arlrPZaTdEPhoSzeD/hk4S9I3WJ4UFlP64b67hXidvp/AUkmfojTzvBD4bwBJ96Icl9V1dFxO4lj5ELAx5Ytx1L+2EG9/Srfki1nx2HwopddLbe8AllKOi+NsXwDLmu0uqxVkKppcmrPwA23/fYcxzwR2otzIGIxM/ZHtP+lqH/qgaV55DivfFP3d5PaqDknrAq8BHkUZyn1Yc0N9fWAz26szCfpcMXNcVtJ0WdyBFY/NM2y3Moag6XN+n+Fjf/Dlb/umGjGm4gy9+ZA8peOwt9u+QSuO4mpzZOoKJL3K9qfnfuUqbXNtyrDqLSnDjX8wtO7ttmvXqqE5eI+a84Uta+P9bNpfPzZm+R9Y+VK+lk6Oy0kcK7Psy7Nsn1x7u7bvogygGo13b9ujI2RrxLuD0uRC09NlJ0orw/OB+9eIMU3Fuc6WdJykV2ioAFOL8S5QGeywtqRtJX0E+MFc/6mif2phm/8BPA34LfARSR8cWtfme7kSlTo2XWrj/ZyRpBNa2nRXx+WCOVbouHAcZdBWKyTtKOnDlC/8rwLfAR5RbfvT0OQCoOVD8IfZlQsuDcW7F/APLO/qdhLwHtt/rBjjvJlWAQ+zXbV2zKBXUPN4HcrZ5aaUblSnuX4BpJk++AIOsb2ocryu38+ZeiYI+LrtzWvGa2IOH5eiHJfvrnlcNnG6Pla6Lgb2xlni/YPtquUGJL2XMhr1F5QCfF8Blrpy7ZipSehdUin89SDgkrYGozRxfkNpXx5tTxbwg9rDkCX92PYjRpa9o9mHzVroqnU7pSbOuIPspbbvUzle1+/nncC3Gd9jZ0fbbRQg68QEjpXfMXMxsC/YrtIkMRTvj8D7gTvGrH6D7arlrCVdDfyUcvP3a7ZvlXSZK5elmIo2dABJ96QUtNkOuOdgee0zdEmvBt4LXErpRbCf7ZnOHtbU14F72z5nzH78dwvxlkraxcur2mH7XZJ+SRkVWNt5wAds/2h0haRnjnn9mur6/byIMvjm4jHx2uhqNxiV+jZga1YszvXoyqG6Pla6LgZ2FnCsx4wwb3JAbZtTenftBXxIpQ7Q+pLWadrWq5iaM3RJXwR+TLmJ8C7gL4CLbL++cpwfAc9oBos8GPic7SfWjHF3IempwM9n6Fu8xPbSCexWNZJeCpw/rj+4pN1sH9tCzJ8AbwbOZ+hmaBs9avpM0sOB64bGEQyvu79brFkjaT3KjdC9KCNEv+VKpbKnKaGfbXt7LR8dui7wXds7Vo6zwqit0ee1NXe7R7tO/dAt/WE0pn4FpV9sq5Uru9L1+9k1Sd+z3UmPr0kcK7qbFAMbkHRfYFfbVaYRnKaE/kPbO0j6DvA3wK8pH9SqbVBNW9dwN7s9h5+7Ys0MlXo0HwMuZqgAEmVww9/Y/katWE284foVVw7F2xOoXr+iuZm2L/Aiyqg8KL/nV4FPjQ67rhCv6/fzjcANtj81snxfSn/j6qNFJe1M+RuO1nL5cuU4XR8rY4uBUerkVC8GpgkVcpthX6rN3jVNCf3VlGL3j6YUB7o38A7bh1SOM7ZWxoDr1nK5CHiu7Z+NLN8GON72I2vFarbbdf2KIykfyMNZMSnsA2xie4/K8bp+P8+k3Pwc934ubaFdG0n/SenmdgHLm1yq9/aawLFyDjMXA/sPV55qUtJJlBG+h3vlQm47u4U6PLPsyxW2t6qxram5KWr7k83DbwOtTFjQxKk9K9Fs1mF5oht2FaVEam1d1694vFcuwHUlcJramZC36/dznXFXGbZva5p+2vAEVy4yNoOuj5UNRpM5gO3TJLUx1+3Wtg8cifVr4EBJrXSFnkW1s+qpSejNjYSXsPLd/XdVjvM1ZnmDXbdmxmHAGZKOYsV6EnvQzmCKrutXXCdpd+BLzai8wXDr3Vm5a2EN497PrSjNBG28n2uNu4HWtAO35QeSHmW7tcEvja6Pla6Lgf1cHRZyUxlINy6viEqjRGG6mlxOBG4AzmT51FvYPqhynKfNtn5ct6o1jPcoSmGn0RtPrXxg1WH9CklbAwdShjgPT0xyCvBW25e3EPORjL+RV/39VKn3/jrgTSyfFPrxlP7NB7dxtdc0Kz0EuJzShi5Kk0sbzTtd1zoZVwzsOLdQDEylxtBbm3ijhdwOtH1d5XjbUhL36JfFVsCvbV9SJc4UJfTOCxA17YWDJoOf1L6JN0PMTW1f2+L2FwM32r6+SbhLKN0/L2grZhP3fgC2f9tmnK41SeitwJ9QzsAuoNxUa2Xov6QHjVveRrfFSR0rfSTp68ABts8fWf6nwHttv6BGnGmq5fKD5pfvhKSnU3pLfJTSc+Knkv68coznSrpc0vckbS/pAuB0SVc2vRmqkvRWyj2I05qbzCcCzwWO1sxDodc05g6SntAk8vtLemOTBNuItcvQ4w0lfVLSeZI+31YziO0TbD/N9v1sb9o8bquOyyBxbwS8oPnZqKVk3vmxMsu+HNrSdh8haefRNvrh46ii+48mc4Bm2dbVothe0D+UARTnUQrm3A78pHl+PnBei3HPBB4+9PxhwJmVY5wDPBJ4IqUI0o7N8kcCZ7XwO11AKaZ/P+AmYFGzfAPgRy3EeydlBOBS4H2UppZ/pBQk+ocW4p019PiTwHsoJRzeQBkVWDveN4YeH9DGcTgm5uuBH1EG172r+Rz8XQ+OlU1m+LkfcGUL8V7X5JJjKfN67jruOKoY7+JZ1l1SK8403BR9/oTiruuhEYC2f9oMZqrpLjeDNCTdYvu0JtZFTftlbXfa/oOk24A/UL5EsP37ljplvBR4LLAeZdzAlrZvlPQB4HRWnLWltiVePi3hv83VHXU1DRcX253ypdW2fSnT+v0eQNKBwP8AH6kcp+tj5RpmnoJus7H/Y828htIL6+amOekYSVu7TAvXxi+4VNJrbH9ieGFz9VNtgvtpSOibAZt65DK2uWy/mvbqTi+V9EngP5vnf0E506zpekl/BdwX+J2kN1AmM34mKxcpquEslfkMN6AMTDm8udm8E+2UDL3D5QbaLZIutX0jlHrhktro+rZZ0xwg4L6S5OYUiHaaFydxA0oMdQpoHreRgLo+Vrqegm4tNzXPbf+saWI9prlH0cb7uT/wFUl/wfIEvoQy89SLagWZhoR+IPCqMcsvpAww2qmluH8NvJZyaQbwXcZMZrCG9gHeTkkMz6aMzDuJ8iX1msqxoExYsHsT7xhKD4a9KZeeH20h3m2S7mX7FkrvD2DZKL02EvongEEFx8Mp5V6vaQaMrFSwq4IHq5R91dDjZVx5WrjGpyn3Wb7SPN+Ndrpkdn2sdD0F3W8kPdZNIbfmTP35lK6v1e/VuXSNfJKkZ1BuoAP8l+1TasZZ8L1cJJ1h+wkzrFtWszkWHknr2b51zPJNgc095ibRNOm6i+tQ3McBg3ou37V9dhtx+kzSlpQryF+PWfdk29+fwG6tsWlI6JfYfuiqrluDeDNNkgDUL1PafGO/hNIf9U5KzeRP2L60Zpwm1lnAl4Ej29j+DDE7K5YlaRPKoJdfUs5a30a54XwRpWtY1cFMku47aEYas27xuOaDNYg164QLrt9vehLHSqfFwGbolvljjyn3PC2modviNyX9s4buxKh4F6XXRG13URLrEZSZ4l8w8lONpPdRRsKdRunBc2nzc4zKCMvaNqZ0eTtV0g8lvUFS1UkfhqkUy7oY+H/A85qffwIubtbV9p+UNt/HA6cCD6A02f0B+EwL8f578EDSt0bW1S6dey2l2Whp83Pm0E8bZYi7PlbeQikEJuCHzY+AI5sulLXjzdQt8wtdd8usqnb3nBa6+2xAmbLpUkpxri8Bl1D++PduKeYjKInnLEqSeB6lbkftOOcPPV4H+H7zeGPa6Ro23K3vqZR7Ar+mJL/9Woh3EaVmxujybSgDVGrHO6f5V5TSqyutqxzv7HGPxz2vEOtDwLnN3+ypNFfXbf1M4Fj5KaVn2ejyezBLl781iNdpt8yufhb8Gbrt39veizLbx2ean2fb3tNDM3NL2q5izB/bfqdLHfSvUepLvKHW9ofcNXQp/UBg7Sb+YJh8a2x/1/bfUC5vD6Q0TdTWdbGstVSGdG8F3Lu5jB6MUr1HC/E8w+Nxz9cskL0/pQvoF4FXUCZN/1eVSpKt6uhYGRQDG9VWMbA7bf+BUg10hW6ZLcTqzDT0cgHA9mWUrk0zOQKoMhGFpC0oBZ1eRKlB8gbKpK61vZfywfwp8HBKzxpU5jQ9t4V4K1U4dOlWeCLtFEDquljW+yizWgH8b+CTkgw8inLFVdtwN8nNhi7VxYp91KtwOYU8VdLZlPfw3ZQmrU/M+h9XT9fHStfFwGbqlrkz7XTL7MSCvyk6X2pmNKqwnW9Tur4dTWneWaH2iOvffNqEUg641QmpJ0UdFstq4q1NOa7vUJlg47GU5pdftRDrnbOtt13tS6QZnr4rpRLnIsoNy6Nd8cbrpKnbwnHrsGK3zD+jdBv+BfDRaT1T71NCrzJVnKSfsfxyefjNGVS1a60W+9A+PAx4s+2qfdHnutlj+4M1482xL9W7hjXd+WbkyrPezJekA2yv0ShSSb+nnI0f1fy7wgfX9Wcs6m7OVQIAABWOSURBVPRYkXQv4HY3BfBU5vx8HvAz221cHY/GX5fSP/wq21e3Ha8tSegTJOnRwAcobYfHUgZsHEw5WzjI9r9VjncXpafECSwvvbpMzTPKJt7alJ5CWwAn2v5RM3jjbcD6Na6oRuLdRalzMqhWucIwctttDUKbVY1jU9JnmLld3q4/Y1HXx8p3gH1tXyzpoZReLp+jNJf90PYBleMdAnzE9gXNQLf/ofRu2wT4e9tH1ozXlalO6JIeaPuXzePTXHHCaEkvAk6xfUPzfCPg6a44k7uk0ynzKP4PsAsl0R1OmVrvj7XiDMV7DOWychdKd7cjKTOOtzUh9WcobeY/pHxJ/ZLS1/etNd/HoXj7U+rH3EA5k/3K8I3zSanVHNilCRwr59v+0+bxuylTFL5WpYT1mYN1FeNdYHu75vH+lM/2bs2o4hOm7e81MO0JvdrkqmO2fY6XF3caLKv6wRyNIemyLpp0mlhPonxgnwm8xfZxc/yX1YnxI+DRtu+SdE9Kt7eHuOWa6JIeTLlpuCuljMJ73QzxnoRKZ+gvt/2fMzWFtNlc1tGxsmzUt6TvA+8ffOlLOtf15xRd9llWmSnpi7Y/M7pu2kxNL5cZtNm1b1yXztrv1z1VZjsf/B63Dj9vq8236UWzPaVmxZWUImdtuM3N1HO2/9h8YbU+wYXtyyR9ldLP+BWU0scTS+jUOU4HNbvvM+urKuvwWDlPpQrnVZSeLd9o4m/UUrzrm+a/q4AnU6pYDm6Wrt9SzNblDH3mbR9G6aM6KET0Wspl4Csrxjh1ltXV23xVJr99GXBPyp39o9u8ASTpFsogMChJ7SHN81amTRs5M7+C0uzyX01/4+ok/a3tg+fxurfZfm8b+9CWCRwr61NqvW8OHGb73Gb5kyhXdUdUjvcw4MOU0cQfGjo7fw5lnMubasbryoJP6JI+wsyTq+5j+74txd2AMhnDM5tFJwPvmdbuTLDCTcNByeHRnhJVqwNqhunShuJVLX3c/H7nAV8FbmTl3692z4zOb8Q3Z8yvYeXJ0tu4KdrlsfIs2yfPsO5A22+pGa+vpqHJZbY6FW3UsACWjRirXkNiPiQ9C/i/tp9VedPPqLy9WQ0SdjOacTCS98JmkFgb3sXyxHPvlmJM2lcppZy/yYp10Wvr9FgBPirpDbb/a7Cg6Zd+GOUsuipJ76eM/fiPkeV/BWxjeyKf/TW14M/QZyPpA7b/vvI2P2R7f0lfY8yVQc0zE0k7AYewvNvigZR61wL+uYW+xW8BPtDGQI0Z4t2XMhXcEpa3YT+W0mtiX89QqXBaSLoDuGXcKkqTUvWrx3E369swgWNlG0oXyQNsf6W5iX4M5UprH1eeoF3SmZRZrTyyfC3K1JadTkhfyzScoc/mZUDVhE4pIQClf3jbDgL2o3RbfG7z71vn0y67mrYCzpT02tqDembwYcow6j0HN0clidKUdTCl0mQ1TRLYg1Ku4WvAm4E/pxR2e7fta2f576vj/An0hvi6pOfZPr7lOJ0eK7Yvl/RM4CSVCb1fThkl2kYNJYD1xnXBbHpktVpHqU3TfoZ+he2tJr0fq2u0DVbST2w/vOWYj6Mk04sofeCXFT6q3atG0sW2t13VdWsQ72hKGeINaCpWUhL7U4DH2q46P22X3dsk3cTyOTY3AG6j/K7Q3tVAl8fK4HPwQMpYjJMZmqmohXhnAHvbvnhk+baUGvBLasbryoI/Q9fMhf1FC90WJZ3PLJXyKvfM2EjSi4eerzP8vHaTS7PNsyS9jVKn5iGsWOagy5GUbZwFPcr2nzRdz660PZhR6ERJbRQ7+2IL2xzLdqfdFZuYXR4rBw09Pg+4/9CyNuK9AzhB0ntYcY7PAyiFwqbSgk/olDd7cGYy6rYW4g3O4l7b/Dtognk59ScF/jYrTprxnaHnphRgqkbSZpQPyYOBnQZdw1r0A0nvoDR3LHvvJP0jpXmpttsAXApz/XJkXRttwes2v984tv3uFmLSfOk/hXKMfLelUbedHiu2u75hf4Kk3SjNcn/XLL4AeImneGrEqW5yadO4y+lJdFOrSdLllBKznxjXfthCvPtSyuQ+jhVvip4NvNqVq0tKuprls97s0Tymef4y2/evHG9cX+V7USZYvp/t6j1tJH2MMvBmUGtkD+BS26+d+X+tVpyuj5UXjywyzSxNtm9qO/7QftwTeIHtzq6+alrwCV0rV9AzcK3tK8a9vmLcc4BlN4SaAQ4fq9nDYMww7sFB/D3bl9eKMxRvke1rxizfinLj8v21YzbbfwilyBKUboutzFEpaZ/Z1ts+vI24Tez7UAbG7EspvXxQGwNxJP0YeOQgyTa9Mi6w/cjKcTo9ViR9esziTYBHU3pEtTHd5CD22sBzKOUNnk256nlpW/HaNA1NLgeNWbaJStGevdxejY59gcNUKrGJ0nOi6uANxg/j3hr4B0n/z/ZRY9avtuEPaDNAZXfKQfxAWpjAQ039EduXSnrAcG+J+Y6yXBXDCVvSvZtlrRbnau7xvBH4C8rNvMe58mTUIy6hTPwwGPCzFctH41bT9bFi+1XjljeD046mFHerStLTgL0pZXp/SCkBsI3tcV1Rp8KCP0OfiaQlwAdt/3nLcTYEcFN1sQtNkvhm7ead5izyxZSD+GGUNvo9bG9ZM85QvGVNVGN69LTSfCXpryk3tga1T24GDrT9sRZivZ/yfh5KmRSh9cqOKhOwPIGSgGgeL6VUmKw2TqLrY2WOfal+rEi6kjKZxceBY23fJOly261P6demaThDH8v20sFZWE2aoardoGuqO5gEwvZ1LfWFvZqSCN5OadaxSpngtmiGx+Oer3kw6e3AkyilUC9rlj0Y+HdJm9h+T+WQb6LUCn875apq2a7QUldCSu+MLnR9rIylMtHFrS1s+hhgN8o9iDtVirlN59ntkKlN6M3ggzb+ABOpajdM0jMoTTy1HUApXvUx4EhJX2ghxrDOJlFuvAJ4jIdqybtUXnwZZY7WqgnddueTrNv+Niy74Txcy6Xq1Ih0fKzMMDJ7E0qxrpfXjucyGvwNwNMpTUn/CmzYHCvHd3G11YYF3+Si8cW5NqGcib3e9te636s6ZujzvgllIoh9bF/UUtxBVcK9gG2Bd1Img1hpYuA1jDOotjhcaZHm+YNtbzDT/13NeD+2/YhVXTdNJO1HqVnzR8pAn1anRuzwWHnayCJT5vO92HYb3ZNH46/L8hujz7G9adsx2zANCX2058LgD31GG70IhuIeTvnCuL55vjGl50K1G6NauRqhgd+6w4qOkv6E0k76MtsPrbztrqstfosymcW3RpbvBPxj132d2yDpYuCJrl/GYD6x2zxWdrR9Ws1tri5Jf277O5Pej9UxDQl9sScws/kM/dBbH+qtUrb3RZQePP+rpRgbUc62AH7a5Q3fNknajlKN8HusOPrvycCuti+Y1L7VIulE4MVd9cTo6lgZuYH+P7af2EacoXidznfblWloQz+WMjAFSV+y/ZKO4q4laeNBF7Sm50kr71fTBfN/Uc5+nkMZan1IC3HWA/6DcjPocsrl+oMkfQX4P7UvbSXtS5kU5P3N86so9yYEvNl21d/RZcLfwVnkoFzvd4C/cgtztE7IAZQRuKczdLPQ9utqBun6WGHFm+T3rLztcT7F8vluP9yMLG5tvtuuTENCH/5DdzLfZuMg4H8kfbHZh5cCVWedkfRslg9mOBX4LPCEmfrkVvAPwLrAVoPRd033tI9SKiD+Y+V4/4cyyfDA1ba3aEbjnUQLX1ouU92dyvKp0i7sUTKHkmRPAc5nqFhWC7o+VtZqmjXXGnq87LPfwk3fJUxgvtu2TUOTy4x9mTuI/SiWFwU6xfaFlbd/F2WyglcORoaqxYmiVSZt3mH0cr3p/nmaK9eAlrTUQ1XrNDQVm6Qf2t6hcrxB/fXHU0oNiB7VX4dumv2aOF0fKz9j+U3eUdVv+nY1LqJr03CG/hhJN1L+0Os3j6Hdvr5QNn4hcGHTrv1iSe+v3K79OEoPgm9KuoxSe2Ttitsfdde4tlfbN0tq45t9hQl+h5L5WkAbvQg6rb8+ISc0PV2+xopNLrXPYDs9VmxvPZ/XSdqu0r2QR0g6b7BZ4CHN81bmu+3Kgj9Dn5QZ2rW/3FY3SZVaMXsBL6H0mf6K7UMrxziX0u923FnQqbYfUznex4DrbL99ZPl7gE1t/5/K8Tqtvz4JKkWzRrVxBtvpsTJftc6ku+6B1ZUk9BFj2rW/AHxkvmcQFeKvRZmYes9BF8laZyUTuKzdgNIE8gTKlxTAYyhD1V9de/DGHAn9ktpd7RYKSfdo4Yb2z+jwWJmvrpqchuK13uOmpiT0EV23a89zn7q+d1DrsnawvQez4iTRl46sr/WFdTjLp5sbrb/+MNuvWNMYC0XTlLQT5Qry+a5cGngV9qPqsTKPeF1/Fjr9AllT09CG3rWu27Xno+s5Do+g6Spag0tdlcs6iPd3lO5ol6iUP4bl9df3rbD9iZO0IyWJ70YZVfxa6s+ruyqqHisL0FSd8Sahj3Apx3sO8Nahdu11JZ1AC+3a892tjuN1/QVSJV7Ti2V3zVF/veuzyhokvZdSwvYXlMkt/glY6hZrvM9T18dK62UAplnnxYWmie0f2P47YEvg34AdB+uaUYl91fUXSNV4ti+1/bXmZ9xkGkeMWbbQvRr4DaXc6xFNf+mFcPZYZR8kPUhNqerm+TMk/bukNzYdFEowe8fxW2hN119YayQJfR5s32X7G16xjkurSUHSA4ee5qykrqn6kDY2p1SLfAFwqaQjKN14+3KVfTRNpVNJj6VMwP0Lyk30NmrZf2OeL52q+y59ORgmoe2kcBplZppOzkokPdD2YGLlLqrbdRpvxEI4s10ltu8ETgRObIblPx9YH7hK0rds7z2hXav1t1t/6Hh4OXCY7YOaXl9tzEq2aD4vsv2jFmK3Jgl99bWdFLo+i+z0C2QC8XrD9q2UcRFfaobjV594oumnfb2bYlwqNfp3o0x9d/Cgm2TFv93w8b4TpWYNzdD8Nj4LG2rliamXsf3lFmK2Lgl94cqN0JrBJntFUE1zdv4SytyzbX5+j6Z8Udww1ATyPpY3gby6crxTJB0N/ArYmFKvBkmbU2q/17Yh5SpnbD97ypR7UycJfRXUTgoaP3kHlINsozHL2zTVN0LnoS9XBF+lzB96Ju1MzTbQdRPI/pTp4DYHnmL79mb5QyndM2v7uSvObbBQJKGvmtpJYelqrlstXX+BLLAvrGm8ETrOlrZ3mftla6zTJpBmINhRAJK2l7Q/pZvm5cCHascDHi7pyba/P7xQ0pOBX8/QO2rBS0JfNVUP5Nn6EEv6QM1YjU6/QCYQbzZTdyN0Bj+Q9Ke2z285TqdNIJIeRhnzsRdwLaXkhtzeLFOnA+Oqb95I+QJ5QUtxW5WEvmq6TAovo/IIwK6/QLqOt8CuCNryFOCVTZGuW2mvOmDXTSA/ppTceL7tSwBUJnFuy33GfSnaPl/S1i3GbVUS+ogFlBS6biKo/gUygXgL6YqgLc/tIsgEmkBeTCm5carKNHtH0e5nYONZ1q3fYtxWJaGvrLOkoDKt3dhV9LzXSRvxJtCE1blBWVdJm9HiVG1dN4G4TPt2bFOhc1fKFcJmkj5OKbkx34FA83WGpNfY/sTwQkmvZvl8tFMn1RZXgaQP2K52VtlcNpvuytnO9gVyru0tpzneHPvyC9uLu4rXFkkvpEyP+EDKNHsPAi6yXbUUxVDV0X2HmkA6rTqqMg3d7sAetneuvO37A1+h9FYbnlD8HsCLbP+6ZryuJKGvgmlPChP4Auk03hz7coXtrbqK15Zm4omdgG/a3r4Z8PNy21WrSUrajdIE8mTKCNWjgE/a3qZmnElr3r/BdHoX2D5lkvuzppLQV0HtpCBptOyogWttX1Erxt3JQroiaIuaeVqbxL59043w3LZmEBpqAtmL8kXyWdppAokK0oY+ouN27YPGLNukqS63V1PKt5quv0Am8IV1JjNfEUzt6NAR16tM1Pxd4HOSrgZ+31Yw278HPg98fqgJ5C1AEvoClDP0EQuhmUDSEuCDtv+88nZPHbN4E0q7YRtfIJ3Guztozpj/QKmU+heUIeyfa8rpxt1cEvoC1eVUW219gXQd7+7ShNUUztrW9jcl3QtY2/ZNk96vmLw0uYxYCEmhuQPf2Tet7aXNZfy0x+u0CWsSJL0G2I9ypfMQYAvgEKBqL5CYTknoK+ssKcwwiGkT4EnA62vFmcd+dPoF0la8mfpIN1cEHwY6uQJp2WuBHShD17F9cdMnPSIJfVTHSWF0oJKB3wJvtH11xThA918gC+ULq+srkJbdavu2QX2sZsaitJsGkIQ+by0lhVNt/6LyNmfT6RfIBOKN1fUVSMu+LeltlOnnngX8DfC1Ce9TLBC5KTpPTVI43vbjK25z2Y1PSV+y/ZJa254h3uIuv0AmEG/WKwLbU5/4mnrk+wLPpvTEOoky4Ccf5EhCH9VlUpB0tu3tRx+3ZQJfIF3H22dk0eCK4IwurwgiJiVNLivrspnAMzxuy3Df+i6G3Xcdr+smrM5IOm+29S2Uz40plIS+si6TwmMk3UhJfOs3j2F5jev7Vo7X9RdI1/GOBTq7IujYXZT38POUNvM/THZ3YiFKk8uIrpsJuiTpTsowcVFqPt8yWEULXyATiNdpE1bXJD2CUlPlBcCFlOT+Ddt3THTHYsHIGfrKum4m6Izttfscj+6vCDpl+8fAO4F3StqDUijrQOD9E92xWDCS0FfW66TQc103YXVK0haUkrYvAn4HvIFS0zsCSJPLSrpuJoiYD0nfBu4DHA18iXKjfhnb101iv2JhSUKPmAKSfsbyK8bhD+3gRKNXzYOxepLQI3pE0na2L5j0fsRkrDXpHYiIqo6Y9A7E5CShR/RL7Vm1YookoUf0S9pQ78aS0CMieiIJPaJf+jIZdqyG9HKJmALNPKLX276hef4MYDfg58DBtpPII2foEVPiaGADAEmPBb4I/AJ4DPCxCe5XLCAZ+h8xHda3/cvm8cuBw2wf1Ex4MfWTX0cdOUOPmA7D3RF3Ar4FYPsu0lUxGjlDj5gOp0g6GvgVsDFwCoCkzYE/TnLHYuFIQo+YDvsDewCbA0+xfXuz/KGUKRIj0sslYtpI2h7YG9gduBz4su2PTHavYiHIGXrEFJD0MMpsRXsB1wJfoJyQPWOiOxYLSs7QI6aApLuA7wL72r6kWXZZyubGsPRyiZgOL6bcED1V0ick7Ux6t8SInKFHTBFJGwC7UppedqLMK/oV29+Y6I7FgpCEHjGlJG1MuTG6h+2dJ70/MXlJ6BERPZE29IiInkhCj4joiST0iIieSEKPiOiJJPSIiJ74/4qGILZq7EihAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Checking if the data is normally distributed\n",
+    "train.skew().plot(kind='bar', color='orange')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the plot it can be observed that most of the attributes are positively skewed with the exception of attributes 4,6,9 and 10. \n",
+    "\n",
+    "Skewed data can affect the performance of a machine learning algorithm and as such skewed data should be normalized using different transformation methods such as square transformation for negatively skewed data and log transfomations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 8. Understanding Data with Visualization\n",
+    "In order to better understand the data, different visualizations can be used.\n",
+    "This can be done using;\n",
+    "\n",
+    "#### a) Univariate plots\n",
+    "These are plots for singular variables per plot.\n",
+    "* Histograms\n",
+    "* Density plots\n",
+    "* Box plots\n",
+    "\n",
+    "#### b) Multivariate plots\n",
+    "These represent more than one variable per plot.\n",
+    "* Correlation Matrix Plot\n",
+    "* Scatter Plot Matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### a) Univariate plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1080x1080 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 12 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Histograms\n",
+    "plt.figure(figsize=(15,15))\n",
+    "train.hist(color='orange')\n",
+    "\n",
+    "plt.subplots_adjust(bottom=1, right=2, top=3)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the above plot, the distribution of the attributes can be observed.\n",
+    "Noteworthy is the `CLASS` attribute which is categorical and uniformly distributed and the `NT_EFC195`attribute which also appears categorical but not uniformly distributed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The NT_EFC195 attribute has 2 values: 0 and 1.\n",
+      "There are 2769 instances with 0's and 269 instances with 1's in the NT_EFC195 attribute.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2553f5d0>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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7wJ8mOSXJOcAf8Mu/If2p9h5/lfZfDGmhnN7RslNVP0lyJ/BO4H8WsKkXTO80L5jemadtwO8AEwy+LuPrwJEb566oqqkkLwM+B1wJ3LkI76nOeaSv5epvgGuAl4zo/fYyOFKfs6o6XFXXV9XGqtoEnAF8t7VNteefAncxuLNaWjBDX8tSVR0C7mEQ/KPwYeCvk/w6PH9lzp8fa4Ukv5rkJW35jcDhqnq4TfesavVTgTcxOBksLZjTO1rObmFhXy53fZI/G3p96Uwdq2pHktXAfW3+vRhM35DkzcDfAmPAPyXZU1UXAq8A7k3yHIPvRbqybe70Vj8VWAHcB9y+gP2Qnud370hSR5zekaSOOL2jrrVLLt96VPkf2s1c0rLj9I4kdcTpHUnqiKEvSR0x9CWpI4a+JHXE0Jekjvw//+Xa29KlOtcAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Zooming in on the NT_EFC195 attribute \n",
+    "cont = train['NT_EFC195'].unique()\n",
+    "print('The NT_EFC195 attribute has 2 values: %s and %s.' % (cont[0],cont[1]))\n",
+    "gp0 = train.groupby('NT_EFC195').size()[0].sum()\n",
+    "gp1 = train.groupby('NT_EFC195').size()[1].sum()\n",
+    "print(\"There are %s instances with 0's and %s instances with 1's in the NT_EFC195 attribute.\" % (gp0, gp1))\n",
+    "# Visualizing the distribution of the instances in the NT_EFC195 attribute \n",
+    "train.groupby('NT_EFC195').size().plot(kind='bar', color=('orange'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the above plot indicates that the variable NT_EFC195 is not uniformly distributed between the classes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function matplotlib.pyplot.show(*args, **kw)>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 12 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Density plots\n",
+    "train.plot(kind='density', subplots=True, layout=(4,3), sharex=False)\n",
+    "plt.subplots_adjust(bottom=1, right=2, top=3)\n",
+    "plt.show"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The density plots convey the same information as the histograms. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 12 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Box plots\n",
+    "train.plot(kind='box', subplots=True, layout=(4,3), sharex=False,sharey=False)\n",
+    "plt.subplots_adjust(bottom=1, right=2, top=3)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Box and whisker plots are used to review the distribution of each attribute while indicating the median, 25th and 75th percentiles and the outliers for each attribute."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### b) Multivariate plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x1a25632510>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 402.375x360 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualizing using scatter plot matrix\n",
+    "sns.pairplot(train, hue='CLASS',vars=['FULL_Charge','FULL_AcidicMolPerc'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above graphs show the distribution of the 2 classes within the features FULL_Charge and FULL_AcidicMolPerc.\n",
+    "This plot can be used in selection of features that clearly distinguish between the 2 classes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 9. Preparing Data for Machine Learning\n",
+    "## Rescaling data\n",
+    "Since the data has attributes with varying scales,rescalling is done so the attributes have the same scale.\n",
+    "From `numpy`, the `set_printoptions` module is imported to determine the display of number of floating point values other Nump objects and from `scikit learn preprocessing` module, the `MinMaxScaler` option is imported to rescale the data. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.457 0.    0.348 0.545 0.33  0.483 0.    0.005 0.508 0.415 0.182]\n",
+      " [0.435 0.116 0.322 0.489 0.276 0.458 1.    0.011 0.421 0.586 0.475]\n",
+      " [0.467 0.116 0.246 0.523 0.288 0.565 0.    0.011 0.442 0.273 0.666]\n",
+      " [0.457 0.089 0.275 0.399 0.403 0.647 0.    0.011 0.405 0.153 0.424]\n",
+      " [0.511 0.183 0.323 0.373 0.342 0.595 0.    0.011 0.5   0.196 0.536]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from numpy import set_printoptions\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "array = train.values\n",
+    "# seperate array into input, X and output, Y components\n",
+    "X = array[:,0:11]\n",
+    "Y = array[:,11]\n",
+    "scaler = MinMaxScaler(feature_range=(0,1))\n",
+    "# set range of the scale to between 0 and 1.\n",
+    "rescaledX = scaler.fit_transform(X)\n",
+    "# summarize transformed data\n",
+    "set_printoptions(precision=3)\n",
+    "print(rescaledX[0:5,:])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the end, no rescaling of data was done since predictions with rescaled data had a poor result."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### PCA plot\n",
+    "Principal component analysis is a dimensionality reduction technique. It uses linear algebra to transform the dataset into a compressed form."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1a2525b610>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "import seaborn as sns\n",
+    "\n",
+    "X = array[:,0:11]\n",
+    "Y = array[:,11]\n",
+    "\n",
+    "high_dim = train.drop(columns='CLASS')\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "\n",
+    "scaler.fit(high_dim)\n",
+    "scaled_data = scaler.transform(high_dim)\n",
+    "\n",
+    "from sklearn.decomposition import PCA\n",
+    "pca = PCA(n_components=2)\n",
+    "pca.fit(scaled_data)\n",
+    "\n",
+    "x_pca = pca.transform(scaled_data)\n",
+    "\n",
+    "plt.figure(figsize=(8,6))\n",
+    "plt.scatter(x_pca[:,0], x_pca[:,1], c=train['CLASS'], cmap='Spectral')\n",
+    "plt.xlabel('First Principal Component')\n",
+    "plt.ylabel('First Principal Component')\n",
+    "labels = np.unique(Y)\n",
+    "plt.legend(labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the pca plot, 2 distinct classes are obseved. Noteworthy is the class distribution and region of overlap between the two classes.  \n",
+    "The overlap problem is a critical problem in which instances appear as valid for more than one class which may be responsible for the presence of noise in datasets and thus affect the performance of the learning algorithm (Gupta and Gupta, 2018)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 10. Feature selection\n",
+    "Performance of machine learning algorithms is dependent on selected features. Highly informative features improve model performance while irrelevant, partially irrelevant and redundant features negatively impact model performance.\n",
+    "\n",
+    "Feature selection reduces overfitting,improves accuracy and reduces training time. \n",
+    "\n",
+    "Feature selection can be done using:\n",
+    "* Univariate Selection\n",
+    "* Recursive Feature Elimination\n",
+    "* Principle Component Analysis\n",
+    "* Feature Importance\n",
+    "\n",
+    "For this assignment, `Recursive Feature Elimination (RFE)` is used.\n",
+    "`RFE` works by recursively removing attributes and building a model on those that remain. It uses the model accuracy to indentify the attributes, and their combinations, that contribute the most to predicting the target attribute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Num Features:  8\n",
+      "Selected Feature:  [ True False  True False  True  True  True  True False  True  True]\n",
+      "Feature Ranking:  [1 3 1 2 1 1 1 1 4 1 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.feature_selection import RFE\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "model = LogisticRegression()\n",
+    "rfe = RFE(model,8)\n",
+    "fit = rfe.fit(X,Y)\n",
+    "print(\"Num Features: \", fit.n_features_)\n",
+    "print(\"Selected Feature: \", fit.support_)\n",
+    "print(\"Feature Ranking: \", fit.ranking_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Selecting features from train and test data\n",
+    "train_data = X[:,fit.support_]\n",
+    "test_d = test.values\n",
+    "test_data = test_d[:,fit.support_]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 11. Prediction by algorithms"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Algorithms can be linear or non-linear.  \n",
+    "**Linear algorithms include:**  \n",
+    "* Logistic Regression\n",
+    "* Linear Discriminant Analysis  \n",
+    "\n",
+    "**Non Linear algorithms include:**\n",
+    "* K-Nearest Neigbours\n",
+    "* Naive bayes\n",
+    "* Classification and Regression Trees\n",
+    "* Support vector machines\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Linear algorithms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:  91.54051589369463\n",
+      "MCC:  0.8342865299822478\n",
+      "[False  True]\n",
+      "False:  383\n",
+      "True:  375\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Logistic regression\n",
+    "## Logistic regression model is a binary dependent variable.\n",
+    "## Applying model with all features.\n",
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "from sklearn.metrics import plot_confusion_matrix\n",
+    "\n",
+    "# setting up the model\n",
+    "model = LogisticRegression()\n",
+    "\n",
+    "# Cross validating the model on data split using Kfold approach\n",
+    "num_folds = 10\n",
+    "seed=42\n",
+    "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n",
+    "scoring='accuracy'\n",
+    "results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n",
+    "print('Accuracy: ',results.mean()*100)\n",
+    "\n",
+    "# Training the model and using it to make a prediction\n",
+    "model.fit(X,Y)\n",
+    "opt = model.predict(test_d)\n",
+    "\n",
+    "# Applying Matthews correlation coefficient on the model\n",
+    "mcc = matthews_corrcoef(model.predict(X),Y)\n",
+    "print('MCC: ',mcc)\n",
+    "\n",
+    "# converting model prediction to a dataframe and then a csv\n",
+    "report = pd.DataFrame(opt)\n",
+    "report.columns = [\"CLASS\"]\n",
+    "report.index.name = \"Index\"\n",
+    "report.replace(0.0,'False')\n",
+    "report['CLASS']=report['CLASS'].map({0.0:False,1.0:True})\n",
+    "\n",
+    "report.to_csv(\"report_lr.csv\")\n",
+    "# Confirming number of classes in model prediction dataframe\n",
+    "print(report['CLASS'].unique())\n",
+    "# Checking for number of instances in each class of the model prediction dataframe\n",
+    "print('False: ',report.groupby('CLASS').size()[0].sum()) \n",
+    "print('True: ',report.groupby('CLASS').size()[1].sum())\n",
+    "\n",
+    "#plot_confusion_matrix(model, train_data, Y, values_format = '.1g', cmap = 'Blues')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The logistic regression algorithm is then applied on RFE selected features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:  90.22440072954664\n",
+      "MCC:  0.8054429005549071\n",
+      "[False  True]\n",
+      "False:  388\n",
+      "True:  370\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Logistic regression\n",
+    "## Logistic regression models a binary dependent variable.\n",
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "from sklearn.metrics import plot_confusion_matrix\n",
+    "\n",
+    "# setting up the model\n",
+    "model = LogisticRegression()\n",
+    "\n",
+    "# Cross validating the model on data split using Kfold approach\n",
+    "num_folds = 10\n",
+    "seed=42\n",
+    "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n",
+    "scoring='accuracy'\n",
+    "results = cross_val_score(model, train_data, Y, cv=kfold, scoring=scoring)\n",
+    "print('Accuracy: ',results.mean()*100)\n",
+    "\n",
+    "# Training the model and using it to make a prediction\n",
+    "model.fit( train_data,Y)\n",
+    "opt = model.predict(test_data)\n",
+    "\n",
+    "# Applying Matthews correlation coefficient on the model\n",
+    "mcc = matthews_corrcoef(model.predict(train_data),Y)\n",
+    "print('MCC: ',mcc)\n",
+    "\n",
+    "# converting prediction to a dataframe and then a csv\n",
+    "report = pd.DataFrame(opt)\n",
+    "report.columns = [\"CLASS\"]\n",
+    "report.index.name = \"Index\"\n",
+    "report.replace(0.0,'False')\n",
+    "report['CLASS']=report['CLASS'].map({0.0:False,1.0:True})\n",
+    "\n",
+    "report.to_csv(\"report_lr1.csv\")\n",
+    "# Confirming number of classes in model prediction dataframe\n",
+    "print(report['CLASS'].unique())\n",
+    "# Checking for number of instances in each class of the model prediction dataframe\n",
+    "print('False: ',report.groupby('CLASS').size()[0].sum())\n",
+    "print('True: ',report.groupby('CLASS').size()[1].sum())\n",
+    "\n",
+    "#plot_confusion_matrix(model, train_data, Y, values_format = '.1g', cmap = 'Blues')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From both models, the model run with all features has the higher accuracy and Matthews correlation coefficient."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:  90.42209484106306\n",
+      "MCC:  0.8130045771530646\n",
+      "[False  True]\n",
+      "False:  405\n",
+      "True:  353\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Linear Discriminant Analysis\n",
+    "## LDA is used for binary and multiclass classification\n",
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "\n",
+    "# setting up the model\n",
+    "model = LinearDiscriminantAnalysis()\n",
+    "\n",
+    "# Cross validating the model on data split using Kfold approach\n",
+    "num_folds = 10\n",
+    "seed=42\n",
+    "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n",
+    "scoring='accuracy'\n",
+    "results = cross_val_score(model, train_data, Y, cv=kfold, scoring=scoring)\n",
+    "print('Accuracy: ',results.mean()*100)\n",
+    "\n",
+    "# Training the model and using it to make a prediction\n",
+    "model.fit( train_data,Y)\n",
+    "opt = model.predict(test_data)\n",
+    "\n",
+    "# Applying Matthews correlation coefficient on the model\n",
+    "mcc = matthews_corrcoef(model.predict(train_data),Y)\n",
+    "print('MCC: ',mcc)\n",
+    "\n",
+    "# converting model prediction to a dataframe and then a csv\n",
+    "report = pd.DataFrame(opt)\n",
+    "report.columns = [\"CLASS\"]\n",
+    "report.index.name = \"Index\"\n",
+    "report.replace(0.0,'False')\n",
+    "report['CLASS']=report['CLASS'].map({0.0:False,1.0:True})\n",
+    "\n",
+    "report.to_csv(\"report_lda.csv\")\n",
+    "# Confirming number of classes in model prediction dataframe\n",
+    "print(report['CLASS'].unique())\n",
+    "# Checking for number of instances in each class of the model prediction dataframe\n",
+    "print('False: ',report.groupby('CLASS').size()[0].sum()) \n",
+    "print('True: ',report.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Non-linear algorithms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:  90.7838283828383\n",
+      "MCC:  0.8690586462107053\n",
+      "[False  True]\n",
+      "False:  402\n",
+      "True:  356\n"
+     ]
+    }
+   ],
+   "source": [
+    "# K-Nearest Neighbours\n",
+    "## This stores all available cases and classiies new cases based on a similarity measure.\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from numpy import set_printoptions\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "\n",
+    "num_folds = 10\n",
+    "seed=42\n",
+    "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n",
+    "model = KNeighborsClassifier(n_neighbors=5)\n",
+    "scoring='accuracy'\n",
+    "results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n",
+    "print('Accuracy: ',results.mean()*100)\n",
+    "\n",
+    "model.fit(X,Y)\n",
+    "opt1 = model.predict(test_d)\n",
+    "\n",
+    "mcc = matthews_corrcoef(model.predict(X),Y)\n",
+    "print('MCC: ',mcc)\n",
+    "\n",
+    "report = pd.DataFrame(opt1)\n",
+    "report.columns = [\"CLASS\"]\n",
+    "report.index.name = \"Index\"\n",
+    "report.replace(0.0,'False')\n",
+    "report['CLASS']=report['CLASS'].map({0.0:False,1.0:True})\n",
+    "\n",
+    "report.to_csv(\"report_knn.csv\")\n",
+    "print(report['CLASS'].unique())\n",
+    "print('False: ',report.groupby('CLASS').size()[0].sum())\n",
+    "print('True: ',report.groupby('CLASS').size()[1].sum())\n",
+    "\n",
+    "set_printoptions(precision=3)\n",
+    "#plot_confusion_matrix(model, X, Y, values_format = '.1g', cmap = 'Blues')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compared to the linear algorithms, the K-nearest neighbours algorithm has a lower percentage accuracy but a higher MCC."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:  88.0815746048289\n",
+      "MCC:  0.8407203694376205\n",
+      "[ True False]\n",
+      "False:  370\n",
+      "True:  388\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Naive Bayes\n",
+    "## This calculates the probability of each class and the conditional probability of each class given each input value.\n",
+    "## Assumes independence of estimated probabilities for new data.\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "\n",
+    "num_folds=10\n",
+    "seed=42\n",
+    "kfold = KFold(n_splits=num_folds, random_state=seed)\n",
+    "model = GaussianNB()\n",
+    "results = cross_val_score(model, X, Y, cv=kfold)\n",
+    "print('Accuracy: ',results.mean()*100)\n",
+    "\n",
+    "model.fit(X,Y)\n",
+    "opt = model.predict(test_d)\n",
+    "\n",
+    "mcc = matthews_corrcoef(model.predict(X),Y)\n",
+    "print('MCC: ',mcc)\n",
+    "\n",
+    "report = pd.DataFrame(opt)\n",
+    "report.columns = [\"CLASS\"]\n",
+    "report.index.name = \"Index\"\n",
+    "report.replace(0.0,'False')\n",
+    "report['CLASS']=report['CLASS'].map({0.0:False,1.0:True})\n",
+    "\n",
+    "report.to_csv(\"report_nb.csv\")\n",
+    "print(report['CLASS'].unique())\n",
+    "print('False: ',report.groupby('CLASS').size()[0].sum())\n",
+    "print('True: ',report.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:  0.7286347055758207\n",
+      "MCC:  1.0\n",
+      "[ True False]\n",
+      "False:  386\n",
+      "True:  372\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Classiffication and regression trees (CART)\n",
+    "## CART/decision trees construct a binary tree where split points are chosen greedily by evaluating each attribute and value to minimize a cost function\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "\n",
+    "num_folds=10\n",
+    "seed=42\n",
+    "kfold = KFold(n_splits=num_folds, random_state=seed)\n",
+    "model = DecisionTreeClassifier(random_state=seed)\n",
+    "results = cross_val_score(model, X, Y, cv=kfold)\n",
+    "print('Accuracy: ',results.mean())\n",
+    "\n",
+    "model.fit(X,Y)\n",
+    "opt = model.predict(test_d)\n",
+    "\n",
+    "mcc = matthews_corrcoef(model.predict(X), Y)\n",
+    "print('MCC: ',mcc)\n",
+    "\n",
+    "report = pd.DataFrame(opt)\n",
+    "report.columns = [\"CLASS\"]\n",
+    "report.index.name = \"Index\"\n",
+    "report.replace(0.0,'False')\n",
+    "report['CLASS']=report['CLASS'].map({0.0:False,1.0:True})\n",
+    "\n",
+    "report.to_csv(\"report_cart.csv\")\n",
+    "print(report['CLASS'].unique())\n",
+    "print('False: ',report.groupby('CLASS').size()[0].sum())\n",
+    "print('True: ',report.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "CART with RFE selected features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:  0.7398775403856175\n",
+      "MCC:  1.0\n",
+      "[ True False]\n",
+      "False:  370\n",
+      "True:  388\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Classiffication and regression trees (CART)\n",
+    "## CART/decision trees construct a binary tree where split points are chosen greedily by evaluating each attribute and value to minimize a cost function\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "\n",
+    "num_folds=10\n",
+    "seed=42\n",
+    "kfold = KFold(n_splits=num_folds, random_state=seed)\n",
+    "model = DecisionTreeClassifier(random_state=40)\n",
+    "results = cross_val_score(model, train_data, Y, cv=kfold)\n",
+    "print('Accuracy: ',results.mean())\n",
+    "\n",
+    "model.fit(train_data,Y)\n",
+    "opt = model.predict(test_data)\n",
+    "\n",
+    "mcc = matthews_corrcoef(model.predict(train_data), Y)\n",
+    "print('MCC: ',mcc)\n",
+    "\n",
+    "report = pd.DataFrame(opt)\n",
+    "report.columns = [\"CLASS\"]\n",
+    "report.index.name = \"Index\"\n",
+    "report.replace(0.0,'False')\n",
+    "report['CLASS']=report['CLASS'].map({0.0:False,1.0:True})\n",
+    "\n",
+    "report.to_csv(\"report_cart_rfe.csv\")\n",
+    "print(report['CLASS'].unique())\n",
+    "print('False: ',report.groupby('CLASS').size()[0].sum())\n",
+    "print('True: ',report.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Support vector machine (SVM) algorithm  \n",
+    "SVM uses the rbf kernel by default.  \n",
+    "RBF stands for Radial Basic Function and works best for non-linear problems.\n",
+    "As the problem at hand appears linear, the kernel can be changed to `linear` and the performance compared."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9081618030224075\n",
+      "MCC:  0.8187103751493263\n",
+      "[False  True]\n",
+      "False:  397\n",
+      "True:  361\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Support Vector Machines\n",
+    "## Using the default rbf kernel\n",
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "num_folds=10\n",
+    "seed=42\n",
+    "kfold = KFold(n_splits=num_folds, random_state=seed, shuffle=True)\n",
+    "model = SVC()\n",
+    "scoring='accuracy'\n",
+    "results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n",
+    "print(results.mean())\n",
+    "\n",
+    "model.fit(X,Y)\n",
+    "opt = model.predict(test_d)\n",
+    "\n",
+    "mcc = matthews_corrcoef(model.predict(X),Y)\n",
+    "print('MCC: ',mcc)\n",
+    "\n",
+    "\n",
+    "report = pd.DataFrame(opt)\n",
+    "report.columns = [\"CLASS\"]\n",
+    "report.index.name = \"Index\"\n",
+    "report.replace(0.0,'False')\n",
+    "report['CLASS']=report['CLASS'].map({0.0:False,1.0:True})\n",
+    "\n",
+    "report.to_csv(\"report_svm_rbf.csv\")\n",
+    "print(report['CLASS'].unique())\n",
+    "print('False: ',report.groupby('CLASS').size()[0].sum())\n",
+    "print('True: ',report.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9193579555323954\n",
+      "MCC:  0.8414394511707155\n",
+      "[False  True]\n",
+      "False:  385\n",
+      "True:  373\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Support Vector Machines\n",
+    "## Setting the kernel to linear\n",
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "num_folds=10\n",
+    "seed=42\n",
+    "kfold = KFold(n_splits=num_folds, random_state=seed, shuffle=True)\n",
+    "model = SVC(kernel='linear')\n",
+    "scoring='accuracy'\n",
+    "results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n",
+    "print(results.mean())\n",
+    "\n",
+    "model.fit(X,Y)\n",
+    "opt = model.predict(test_d)\n",
+    "\n",
+    "mcc = matthews_corrcoef(model.predict(X),Y)\n",
+    "print('MCC: ',mcc)\n",
+    "\n",
+    "\n",
+    "report = pd.DataFrame(opt)\n",
+    "report.columns = [\"CLASS\"]\n",
+    "report.index.name = \"Index\"\n",
+    "#report.replace(0.0,'False')\n",
+    "report['CLASS']=report['CLASS'].map({0.0:False,1.0:True})\n",
+    "\n",
+    "report.to_csv(\"report_svm_linear.csv\")\n",
+    "\n",
+    "print(report['CLASS'].unique())\n",
+    "print('False: ',report.groupby('CLASS').size()[0].sum())\n",
+    "print('True: ',report.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the results obtained, the model performs better with the linear kernel than the rbf kernel as indicated by the accuracy and MCC."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Learning curves for radial basic function kernel and linear kernel of Support vector machines."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x1080 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## comparing svm rbf and linear kernel\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.datasets import load_digits\n",
+    "from sklearn.model_selection import learning_curve\n",
+    "from sklearn.model_selection import ShuffleSplit\n",
+    "\n",
+    "\n",
+    "def plot_learning_curve(estimator, title, X, y, axes=None, ylim=None, cv=None,\n",
+    "                        n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):\n",
+    "   \n",
+    "    if axes is None:\n",
+    "        _, axes = plt.subplots(1, 3, figsize=(20, 5))\n",
+    "\n",
+    "    axes[0].set_title(title)\n",
+    "    if ylim is not None:\n",
+    "        axes[0].set_ylim(*ylim)\n",
+    "    axes[0].set_xlabel(\"Training examples\")\n",
+    "    axes[0].set_ylabel(\"Score\")\n",
+    "\n",
+    "    train_sizes, train_scores, test_scores, fit_times, _ = \\\n",
+    "        learning_curve(estimator, X, y, cv=cv, n_jobs=n_jobs,\n",
+    "                       train_sizes=train_sizes,\n",
+    "                       return_times=True)\n",
+    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
+    "    train_scores_std = np.std(train_scores, axis=1)\n",
+    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
+    "    test_scores_std = np.std(test_scores, axis=1)\n",
+    "    fit_times_mean = np.mean(fit_times, axis=1)\n",
+    "    fit_times_std = np.std(fit_times, axis=1)\n",
+    "\n",
+    "    # Plot learning curve\n",
+    "    axes[0].grid()\n",
+    "    axes[0].fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
+    "                         train_scores_mean + train_scores_std, alpha=0.1,\n",
+    "                         color=\"r\")\n",
+    "    axes[0].fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
+    "                         test_scores_mean + test_scores_std, alpha=0.1,\n",
+    "                         color=\"g\")\n",
+    "    axes[0].plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
+    "                 label=\"Training score\")\n",
+    "    axes[0].plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
+    "                 label=\"Cross-validation score\")\n",
+    "    axes[0].legend(loc=\"best\")\n",
+    "\n",
+    "    # Plot n_samples vs fit_times\n",
+    "    axes[1].grid()\n",
+    "    axes[1].plot(train_sizes, fit_times_mean, 'o-')\n",
+    "    axes[1].fill_between(train_sizes, fit_times_mean - fit_times_std,\n",
+    "                         fit_times_mean + fit_times_std, alpha=0.1)\n",
+    "    axes[1].set_xlabel(\"Training examples\")\n",
+    "    axes[1].set_ylabel(\"fit_times\")\n",
+    "    axes[1].set_title(\"Scalability of the model\")\n",
+    "\n",
+    "    # Plot fit_time vs score\n",
+    "    axes[2].grid()\n",
+    "    axes[2].plot(fit_times_mean, test_scores_mean, 'o-')\n",
+    "    axes[2].fill_between(fit_times_mean, test_scores_mean - test_scores_std,\n",
+    "                         test_scores_mean + test_scores_std, alpha=0.1)\n",
+    "    axes[2].set_xlabel(\"fit_times\")\n",
+    "    axes[2].set_ylabel(\"Score\")\n",
+    "    axes[2].set_title(\"Performance of the model\")\n",
+    "\n",
+    "    return plt\n",
+    "\n",
+    "\n",
+    "fig, axes = plt.subplots(3, 2, figsize=(10, 15))\n",
+    "\n",
+    "X, y = load_digits(return_X_y=True)\n",
+    "\n",
+    "title = \"Learning Curves (SVM, rbf kernel)\"\n",
+    "# Cross validation with 100 iterations to get smoother mean test and train\n",
+    "# score curves, each time with 20% data randomly selected as a validation set.\n",
+    "cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)\n",
+    "\n",
+    "estimator = SVC(kernel='rbf')\n",
+    "plot_learning_curve(estimator, title, X, y, axes=axes[:, 0], ylim=(0.7, 1.01),\n",
+    "                    cv=cv, n_jobs=4)\n",
+    "\n",
+    "title = r\"Learning Curves (SVM, linear kernel)\"\n",
+    "# SVC is more expensive so we do a lower number of CV iterations:\n",
+    "cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)\n",
+    "estimator = SVC(kernel='linear')\n",
+    "plot_learning_curve(estimator, title, X, y, axes=axes[:, 1], ylim=(0.7, 1.01),\n",
+    "                    cv=cv, n_jobs=4)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "3 plots are generated for each model: \n",
+    "* the test and training learning curve  \n",
+    "This shows the training and cross-valdation scores with increasing training examples.\n",
+    "\n",
+    "* the training samples vs fit times curve   \n",
+    "This shows the times required by the models to train with various sizes of training dataset.\n",
+    "* the fit times vs score curve  \n",
+    "This shows how much time was required to train the models.\n",
+    "\n",
+    "Reviewing learning curves of models during training can be used to diagnose problems with learning, such as an underfit or overfit model, as well as whether the training and validation datasets are suitably representative."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 12. Improving performance with Ensembles  \n",
+    "To boost accuracy, ensembles can be used.\n",
+    "These include;   \n",
+    "* `Bagging` (bagged decision trees, random forests and extra trees) where multiple models usually of the same type are built from different subsamples of the training dataset.\n",
+    "* `Boosting` (AdaBoost, stochastic gradient) where multiple models usually of the same type are built, each of which learns to fix the prediction errors of a prior model in the sequence of models.\n",
+    "* `Voting` which build multiple models typically of differing types and use simple statistics such as mean to combine predictions. \n",
+    "\n",
+    "For this work, `Boosting` and `voting` algorithms are ensembles."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Boosting Ensembles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7701895518499218\n",
+      "MCC:  0.8730261590913062\n",
+      "[ True False]\n",
+      "False:  386\n",
+      "True:  372\n"
+     ]
+    }
+   ],
+   "source": [
+    "# AdaBoost Classification\n",
+    "## It generally works by weighting instances in the dataset by how easy or difficult they are to classify.\n",
+    "## This allows the algorithm to pay more or less attention to these instances in the construction of subsequent models.\n",
+    "from pandas import read_csv\n",
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.ensemble import AdaBoostClassifier\n",
+    "\n",
+    "array = train.values\n",
+    "# seperate array into input, X and output, Y components\n",
+    "X = array[:,0:11]\n",
+    "Y = array[:,11]\n",
+    "\n",
+    "num_trees = 30\n",
+    "seed=42\n",
+    "\n",
+    "kfold = KFold(n_splits=10, random_state=seed)\n",
+    "\n",
+    "model = AdaBoostClassifier(n_estimators=num_trees, random_state=seed)\n",
+    "results = cross_val_score(model, X, Y, cv=kfold)\n",
+    "\n",
+    "print(results.mean())\n",
+    "\n",
+    "model.fit(X,Y)\n",
+    "opt = model.predict(test_d)\n",
+    "\n",
+    "mcc = matthews_corrcoef(model.predict(X),Y)\n",
+    "print('MCC: ',mcc)\n",
+    "\n",
+    "\n",
+    "report = pd.DataFrame(opt)\n",
+    "report.columns = [\"CLASS\"]\n",
+    "report.index.name = \"Index\"\n",
+    "report.replace(0.0,'False')\n",
+    "report['CLASS']=report['CLASS'].map({0.0:False,1.0:True})\n",
+    "\n",
+    "report.to_csv(\"report_AB.csv\")\n",
+    "print(report['CLASS'].unique())\n",
+    "print('False: ',report.groupby('CLASS').size()[0].sum())\n",
+    "print('True: ',report.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.788623632100052\n",
+      "MCC:  0.9355236060852458\n",
+      "[ True False]\n",
+      "False:  385\n",
+      "True:  373\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Stochastic Gradient Boosting classification\n",
+    "\n",
+    "from pandas import read_csv\n",
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.ensemble import GradientBoostingClassifier\n",
+    "\n",
+    "\n",
+    "seed = 42\n",
+    "num_trees = 100\n",
+    "\n",
+    "kfold = KFold(n_splits=10, random_state=seed)\n",
+    "model = GradientBoostingClassifier(n_estimators=num_trees, random_state=seed)\n",
+    "results = cross_val_score(model, X, Y, cv=kfold)\n",
+    "print(results.mean())\n",
+    "\n",
+    "model.fit(X,Y)\n",
+    "opt = model.predict(test_d)\n",
+    "\n",
+    "mcc = matthews_corrcoef(model.predict(X),Y)\n",
+    "print('MCC: ',mcc)\n",
+    "\n",
+    "\n",
+    "report = pd.DataFrame(opt)\n",
+    "report.columns = [\"CLASS\"]\n",
+    "report.index.name = \"Index\"\n",
+    "report.replace(0.0,'False')\n",
+    "report['CLASS']=report['CLASS'].map({0.0:False,1.0:True})\n",
+    "\n",
+    "report.to_csv(\"report_GBC.csv\")\n",
+    "print(report['CLASS'].unique())\n",
+    "print('False: ',report.groupby('CLASS').size()[0].sum())\n",
+    "print('True: ',report.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.787307842626368\n",
+      "MCC:  0.9269836637327226\n",
+      "[ True False]\n",
+      "False:  383\n",
+      "True:  375\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Stochastic X Gradient Boosting Classification\n",
+    "from pandas import read_csv\n",
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from xgboost import XGBClassifier\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "\n",
+    "seed = 42\n",
+    "num_trees = 100\n",
+    "\n",
+    "kfold = KFold(n_splits=10, random_state=seed)\n",
+    "model = XGBClassifier(n_estimators=num_trees, random_state=seed)\n",
+    "results = cross_val_score(model, X, Y, cv=kfold)\n",
+    "print(results.mean())\n",
+    "\n",
+    "model.fit(X,Y)\n",
+    "opt = model.predict(test_d)\n",
+    "\n",
+    "mcc = matthews_corrcoef(model.predict(X),Y)\n",
+    "print('MCC: ',mcc)\n",
+    "\n",
+    "\n",
+    "report = pd.DataFrame(opt)\n",
+    "report.columns = [\"CLASS\"]\n",
+    "report.index.name = \"Index\"\n",
+    "report['CLASS']=report['CLASS'].map({0.0:False,1.0:True})\n",
+    "report.to_csv(\"report_XGB.csv\")\n",
+    "\n",
+    "print(report['CLASS'].unique())\n",
+    "print('False: ',report.groupby('CLASS').size()[0].sum())\n",
+    "print('True: ',report.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Voting Ensemble"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8307799200972728\n",
+      "MCC:  0.8925178806925171\n",
+      "[False  True]\n",
+      "False:  390\n",
+      "True:  368\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Voting Ensemble for Classification\n",
+    "from pandas import read_csv\n",
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.ensemble import GradientBoostingClassifier\n",
+    "from xgboost import XGBClassifier\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.ensemble import VotingClassifier\n",
+    "\n",
+    "# create the sub models\n",
+    "estimators = []\n",
+    "estimators.append(('LR', LogisticRegression()))\n",
+    "estimators.append(('LDA', LinearDiscriminantAnalysis()))\n",
+    "estimators.append(('KNN', KNeighborsClassifier()))\n",
+    "estimators.append(('NB', GaussianNB()))\n",
+    "estimators.append(('CART', DecisionTreeClassifier()))\n",
+    "estimators.append(('SVM_rbf', SVC()))\n",
+    "estimators.append(('SVM_linear', SVC(kernel='linear')))\n",
+    "estimators.append(('AB', AdaBoostClassifier())) \n",
+    "estimators.append(('GBC', GradientBoostingClassifier()))\n",
+    "estimators.append(('XGB', XGBClassifier()))\n",
+    "\n",
+    "# create the ensemble model\n",
+    "ensemble = VotingClassifier(estimators)\n",
+    "results = cross_val_score(ensemble, X, Y, cv=kfold)\n",
+    "print(results.mean())\n",
+    "\n",
+    "ensemble.fit(X,Y)\n",
+    "opt = ensemble.predict(test_d)\n",
+    "\n",
+    "mcc = matthews_corrcoef(ensemble.predict(X),Y)\n",
+    "print('MCC: ',mcc)\n",
+    "\n",
+    "\n",
+    "report = pd.DataFrame(opt)\n",
+    "report.columns = [\"CLASS\"]\n",
+    "report.index.name = \"Index\"\n",
+    "report['CLASS']=report['CLASS'].map({0.0:False,1.0:True})\n",
+    "report.to_csv(\"report_vote.csv\")\n",
+    "\n",
+    "print(report['CLASS'].unique())\n",
+    "print('False: ',report.groupby('CLASS').size()[0].sum())\n",
+    "print('True: ',report.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 13. Comparing Machine Learning Algorithms used"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('LR', 0.9154051589369463, 'MCC: ', 0.8342865299822478)\n",
+      "('LDA', 0.9170574952232066, 'MCC: ', 0.8377162908048379)\n",
+      "('KNN', 0.9078382838283829, 'MCC: ', 0.8690586462107053)\n",
+      "('NB', 0.9193622980719125, 'MCC: ', 0.8407203694376205)\n",
+      "('CART', 0.898943677262463, 'MCC: ', 1.0)\n",
+      "('SVM_rbf', 0.9081618030224075, 'MCC: ', 0.8187103751493263)\n",
+      "('SVM_linear', 0.9193579555323954, 'MCC: ', 0.8414394511707155)\n",
+      "('AB', 0.9292394042035783, 'MCC: ', 0.8848771929251248)\n",
+      "('GBC', 0.9384445023449712, 'MCC: ', 0.9355236060852458)\n",
+      "('XGB', 0.9381188118811881, 'MCC: ', 0.9269836637327226)\n",
+      "('VC', 0.9292361472989403, 'MCC: ', 0.8925178806925171)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compare perfomance of the different algorithms used\n",
+    "from matplotlib import pyplot\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "\n",
+    "models = []\n",
+    "models.append(('LR', LogisticRegression()))\n",
+    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
+    "models.append(('KNN', KNeighborsClassifier()))\n",
+    "models.append(('NB', GaussianNB()))\n",
+    "models.append(('CART', DecisionTreeClassifier(random_state=40)))\n",
+    "models.append(('SVM_rbf', SVC()))\n",
+    "models.append(('SVM_linear', SVC(kernel='linear')))\n",
+    "models.append(('AB', AdaBoostClassifier())) \n",
+    "models.append(('GBC', GradientBoostingClassifier()))\n",
+    "models.append(('XGB', XGBClassifier()))\n",
+    "models.append(('VC',VotingClassifier(estimators)))\n",
+    "\n",
+    "results = []\n",
+    "names = []\n",
+    "scoring = 'accuracy'\n",
+    "\n",
+    "num_folds=10\n",
+    "seed=42\n",
+    "for name, model in models:\n",
+    "    kfold = KFold(n_splits=num_folds, random_state=seed, shuffle=True)\n",
+    "    cv_results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n",
+    "    model.fit(X,Y)\n",
+    "    results.append(cv_results)\n",
+    "    names.append(name)\n",
+    "    msg = (name, cv_results.mean(),'MCC: ',matthews_corrcoef(model.predict(X),Y))\n",
+    "    print(msg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# A plot for comparing the different algorithms\n",
+    "fig = pyplot.figure()\n",
+    "fig.suptitle('Algorithm Comparison')\n",
+    "ax = fig.add_subplot(111)\n",
+    "pyplot.boxplot(results)\n",
+    "ax.set_xticklabels(names)\n",
+    "plt.xticks(rotation=45)\n",
+    "pyplot.show()"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "From the above graph, it can be observed that models implementing gradient boost classification have higher percentage accuracies.  \n",
+    "However, other metrics have to be put in place to measure performance of a model such as Matthews correlation coefficient, area under the ROC curve or logarithmic loss since a single parameter is not enough to measure model performance and thus assist in model choice."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# *Grid Search*  \n",
+    "Grid Search is used to optimize estimators by hyper-parameter tuning. Hyper-parameters are parameters are parameters that are not directly learnt with estimators but are instead passed as arguments to the constructor of the estimator classes. \n",
+    "\n",
+    "The grid search provided by `GridSearchCV` exhaustively considers all parameter combinations from a grid of parameter values specified with the `param_grid` parameter.\n",
+    "The parameter combination with the best score is retained."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Grid search\n",
+    "\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.ensemble import GradientBoostingClassifier\n",
+    "\n",
+    "model = GradientBoostingClassifier()\n",
+    "\n",
+    "# establishing parameters to grid-search.\n",
+    "## These change depending on the model.\n",
+    "criterion = ['friedman_mse', 'mse','mae']\n",
+    "loss = ['deviance','exponential']\n",
+    "n_estimators = [50,100,150]\n",
+    "\n",
+    "grid = GridSearchCV(estimator=model, cv=3, param_grid=dict(criterion=criterion,loss=loss, n_estimators=n_estimators))\n",
+    "grid.fit(X,Y)\n",
+    "\n",
+    "print('Best score:', grid.best_score_)\n",
+    "print('Best parameters:', grid.best_params_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Using parameters selected by the Grid search\n",
+    "model = GradientBoostingClassifier(criterion='friedman_mse', loss='deviance',n_estimators=50)\n",
+    "model.fit(X,Y)\n",
+    "\n",
+    "opt = model.predict(test_d)\n",
+    "\n",
+    "mcc = matthews_corrcoef(model.predict(X),Y)\n",
+    "print('MCC: ',mcc)\n",
+    "\n",
+    "\n",
+    "report = pd.DataFrame(opt)\n",
+    "report.columns = [\"CLASS\"]\n",
+    "report.index.name = \"Index\"\n",
+    "report.replace(0.0,'False')\n",
+    "report['CLASS']=report['CLASS'].map({0.0:False,1.0:True})\n",
+    "\n",
+    "report.to_csv(\"report_GBC_GS.csv\")\n",
+    "print(report['CLASS'].unique())\n",
+    "print('False: ',report.groupby('CLASS').size()[0].sum())\n",
+    "print('True: ',report.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# *References*\n",
+    "1. Gupta, S., & Gupta, A. (2018). Handling class overlapping to detect noisy instances in classification. The Knowledge Engineering Review, 33, E8. doi:10.1017/S0269888918000115\n",
+    "2. https://github.com/atwine/ace-class-notes/blob/master/Thur%2020%20Feb/ACE_ClassML%20Pipeline_edit.ipynb\n",
+    "3. https://machinelearningmastery.com/naive-bayes-for-machine-learning/\n",
+    "4. https://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html\n",
+    "5. https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html\n",
+    "6. https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier\n",
+    "7. https://towardsdatascience.com/introduction-to-logistic-regression-66248243c148htmlhtmlhtmlhtmlhtmlhtmlhtmlhtml\n",
+    "8. https://www.analyticsvidhya.com/blog/2017/09/understaing-support-vector-machine-example-code/"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Assignment Colab/report_AB.csv b/Assignment Colab/report_AB.csv
new file mode 100644
index 0000000..c672511
--- /dev/null
+++ b/Assignment Colab/report_AB.csv	
@@ -0,0 +1,759 @@
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diff --git a/Assignment Colab/report_GBC.csv b/Assignment Colab/report_GBC.csv
new file mode 100644
index 0000000..08b9e00
--- /dev/null
+++ b/Assignment Colab/report_GBC.csv	
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diff --git a/Assignment Colab/report_XGB.csv b/Assignment Colab/report_XGB.csv
new file mode 100644
index 0000000..4ffceb1
--- /dev/null
+++ b/Assignment Colab/report_XGB.csv	
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diff --git a/Assignment Colab/report_cart.csv b/Assignment Colab/report_cart.csv
new file mode 100644
index 0000000..01b8b55
--- /dev/null
+++ b/Assignment Colab/report_cart.csv	
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diff --git a/Assignment Colab/report_cart_rfe.csv b/Assignment Colab/report_cart_rfe.csv
new file mode 100644
index 0000000..0e1d7e8
--- /dev/null
+++ b/Assignment Colab/report_cart_rfe.csv	
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diff --git a/Assignment Colab/report_knn.csv b/Assignment Colab/report_knn.csv
new file mode 100644
index 0000000..147b18d
--- /dev/null
+++ b/Assignment Colab/report_knn.csv	
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diff --git a/Assignment Colab/report_lda.csv b/Assignment Colab/report_lda.csv
new file mode 100644
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--- /dev/null
+++ b/Assignment Colab/report_lda.csv	
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diff --git a/Assignment Colab/report_lr.csv b/Assignment Colab/report_lr.csv
new file mode 100644
index 0000000..04dda38
--- /dev/null
+++ b/Assignment Colab/report_lr.csv	
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diff --git a/Assignment Colab/report_lr1.csv b/Assignment Colab/report_lr1.csv
new file mode 100644
index 0000000..b609016
--- /dev/null
+++ b/Assignment Colab/report_lr1.csv	
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diff --git a/Assignment Colab/report_nb.csv b/Assignment Colab/report_nb.csv
new file mode 100644
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--- /dev/null
+++ b/Assignment Colab/report_nb.csv	
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diff --git a/Assignment Colab/report_svm_linear.csv b/Assignment Colab/report_svm_linear.csv
new file mode 100644
index 0000000..ebc629d
--- /dev/null
+++ b/Assignment Colab/report_svm_linear.csv	
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diff --git a/Assignment Colab/report_svm_rbf.csv b/Assignment Colab/report_svm_rbf.csv
new file mode 100644
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+++ b/Assignment Colab/report_svm_rbf.csv	
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diff --git a/Assignment Colab/report_vote.csv b/Assignment Colab/report_vote.csv
new file mode 100644
index 0000000..cfef6bc
--- /dev/null
+++ b/Assignment Colab/report_vote.csv	
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diff --git a/Thur 20 Feb/ACE_ClassML Pipeline.ipynb b/Thur 20 Feb/ACE_ClassML Pipeline.ipynb
index e55fe6d..c6ffee2 100644
--- a/Thur 20 Feb/ACE_ClassML Pipeline.ipynb	
+++ b/Thur 20 Feb/ACE_ClassML Pipeline.ipynb	
@@ -3865,7 +3865,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.3"
   },
   "varInspector": {
    "cols": {
diff --git a/pandas_exercises b/pandas_exercises
new file mode 160000
index 0000000..eb62966
--- /dev/null
+++ b/pandas_exercises
@@ -0,0 +1 @@
+Subproject commit eb629669319f161fbe4b82e8387771ae8b64789d