\n",
- "\n",
- "## ??"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Feature selection"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "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",
- " [0.457 0.165 0.451 0.614 0.333 0.524 1. 0.009 0.596 0.479 0.217]\n",
- " [0.413 0.148 0.321 0.608 0.276 0.492 1. 0.007 0.587 0.536 0.485]\n",
- " [0.391 0.126 0.24 0.574 0.252 0.328 0. 0.002 0.56 0.574 0.214]\n",
- " [0.5 0.056 0.226 0.3 0.442 0.724 0. 0.003 0.357 0.062 0.81 ]\n",
- " [0.543 0. 0.296 0.391 0.555 0.467 0. 0.006 0.339 0.545 0.306]\n",
- " [0.391 0.074 0.4 0.482 0.376 0.48 0. 0.009 0.557 0.364 0.355]]\n"
- ]
- }
- ],
- "source": [
- "##Rescaling \n",
- "from numpy import set_printoptions\n",
- "from sklearn.preprocessing import MinMaxScaler\n",
- "\n",
- "###rescaling the data\n",
- "array = Train.values\n",
- "# separate array into input and output components\n",
- "X = array[:,0:11]\n",
- "Y = array[:,11]\n",
- "\n",
- "##Scaling the data so that it's within the range of 0 and 1\n",
- "scaler = MinMaxScaler(feature_range=(0, 1))\n",
- "rescaledX = scaler.fit_transform(X)\n",
- "# summarize transformed data\n",
- "set_printoptions(precision=3) #number of decimal points\n",
- "print(rescaledX[0:11,:])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Num Features: 4\n",
- "Selected Features: [ True True False True False False False True False False False]\n",
- "Feature Ranking: [1 1 4 1 2 3 5 1 7 8 6]\n"
- ]
- }
- ],
- "source": [
- "#feature selection using recursive feature elimination\n",
- "\n",
- "from sklearn.feature_selection import RFE\n",
- "from sklearn.linear_model import LogisticRegression\n",
- "\n",
- "# feature extraction\n",
- "model = LogisticRegression()\n",
- "rfe = RFE(model, 4)\n",
- "\n",
- "#I tried to test for the performance of the model with more features and the results hardly changed.\n",
- "#I used 11 then 7,and finally 4 features. I selected the features ranndomly.\n",
- "#As a trade off for faster performance , I decided to go with 4 features.\n",
- "\n",
- "#Accuracy at 11 =91.72482552342971\n",
- "#Accuracy at 7 =91.72482552342971\n",
- "#Accuracy at 4 =91.72482552342971\n",
- "\n",
- "#I chose RFE because it eliminates worst performing features\n",
- "fit = rfe.fit(rescaledX, Y)\n",
- "print(\"Num Features: \", fit.n_features_)\n",
- "print(\"Selected Features:\", fit.support_)\n",
- "print(\"Feature Ranking: \", fit.ranking_)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[0.457, 0. , 0.545, 0.005],\n",
- " [0.435, 0.116, 0.489, 0.011],\n",
- " [0.467, 0.116, 0.523, 0.011],\n",
- " ...,\n",
- " [0.315, 0.268, 0.573, 0.329],\n",
- " [0.391, 0.107, 0.526, 0.334],\n",
- " [0.326, 0.338, 0.397, 0.334]])"
- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "rescaledX[:,fit.support_] #extracting features of interest\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Algorithms"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy: 91.72482552342971\n"
- ]
- }
- ],
- "source": [
- "###### Used on rescaled data\n",
- "#Using Logistic Regression\n",
- "#Splitting data into Train and Test Sets\n",
- "\n",
- "from sklearn.model_selection import train_test_split\n",
- "from sklearn.linear_model import LogisticRegression \n",
- "\n",
- "test_size = 0.33 #Size of the test data\n",
- "seed = 7\n",
- "rescaledX_train, rescaledX_test, Y_train, Y_test = train_test_split(rescaledX, Y, test_size=test_size,\n",
- "random_state=seed)\n",
- "model = LogisticRegression() #Using Logistic Regression\n",
- "model.fit(rescaledX_train, Y_train)\n",
- "result = model.score(rescaledX_test, Y_test)\n",
- "print(\"Accuracy: \", (result*100.0))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy: 91.92422731804587\n"
- ]
- }
- ],
- "source": [
- "###Algorithm used on unscaled data\n",
- "#Using Logistic Regression\n",
- "#Splitting data into Train and Test Sets\n",
- "\n",
- "from sklearn.model_selection import train_test_split\n",
- "from sklearn.linear_model import LogisticRegression \n",
- "\n",
- "array = Train.values\n",
- "X = array[:,0:11]\n",
- "Y = array[:,11]\n",
- "test_size = 0.33 #Size of the test data\n",
- "seed = 7\n",
- "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\n",
- "random_state=seed)\n",
- "model = LogisticRegression() #Using Logistic Regression\n",
- "model.fit(X_train, Y_train)\n",
- "result = model.score(X_test, Y_test)\n",
- "print(\"Accuracy: \", (result*100.0))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Rescaled data gives accuracy of 91.72482552342971 and un scaled data with all the features gives 91.92422731804587. I have used unscaled data for the rest of the algorithms down"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[False True]\n",
- "2\n",
- "383\n",
- "375\n"
- ]
- }
- ],
- "source": [
- "##Use the test dataset to see how the aligorithm is performing\n",
- "out = model.predict(Test.values)\n",
- "\n",
- "out1 = pd.DataFrame(out) #Converting to data frame\n",
- "out1.columns=[\"CLASS\"] #Naming the column\n",
- "out1.index.name=\"Index\" #Creating a column index\n",
- "out1[\"CLASS\"]=out1[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n",
- "\n",
- "out1.to_csv(\"talz_csv3\") ## Writing a csv file\n",
- "print(out1['CLASS'].unique())\n",
- "print(out1['CLASS'].nunique())\n",
- "\n",
- "#printing the numbers of False and True\n",
- "print(out1.groupby('CLASS').size()[0].sum()) #\n",
- "print(out1.groupby('CLASS').size()[1].sum()) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0.880815746048289\n"
- ]
- }
- ],
- "source": [
- "###Naive Bayes\n",
- "\n",
- "from sklearn.naive_bayes import GaussianNB\n",
- "from sklearn.model_selection import KFold\n",
- "from sklearn.model_selection import cross_val_score\n",
- "array = Train.values\n",
- "X = array[:,0:11]\n",
- "Y = array[:,11]\n",
- "kfold = KFold(n_splits=10, random_state=7)\n",
- "model = GaussianNB()\n",
- "model.fit(X, Y)\n",
- "results = cross_val_score(model, X, Y, cv=kfold)\n",
- "print(results.mean())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[ True False]\n",
- "2\n",
- "370\n",
- "388\n"
- ]
- }
- ],
- "source": [
- "##Use the test dataset to see how the aligorithm is performing\n",
- "f = model.predict(Test.values)\n",
- "\n",
- "f1 = pd.DataFrame(f) #Converting to data frame\n",
- "f1.columns=[\"CLASS\"] #Naming the column\n",
- "f1.index.name=\"Index\" #Creating a column index\n",
- "f1[\"CLASS\"]=f1[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n",
- "\n",
- "f1.to_csv(\"talz_csv8\") ## Writing a csv file\n",
- "print(f1['CLASS'].unique())\n",
- "print(f1['CLASS'].nunique())\n",
- "\n",
- "#printing the numbers of False and True\n",
- "print(f1.groupby('CLASS').size()[0].sum()) #\n",
- "print(f1.groupby('CLASS').size()[1].sum()) "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Classiffication and Regression Trees"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0.7299407243355915\n"
- ]
- }
- ],
- "source": [
- "#Classiffication and Regression Trees\n",
- "from sklearn.tree import DecisionTreeClassifier\n",
- "array = Train.values\n",
- "X = array[:,0:11]\n",
- "Y = array[:,11]\n",
- "kfold = KFold(n_splits=10, random_state=7)\n",
- "model = DecisionTreeClassifier()\n",
- "model.fit(X, Y)\n",
- "results = cross_val_score(model, X, Y, cv=kfold)\n",
- "print(results.mean())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[ True False]\n",
- "2\n",
- "385\n",
- "373\n"
- ]
- }
- ],
- "source": [
- "fd = model.predict(Test.values)\n",
- "\n",
- "fd1 = pd.DataFrame(fd) #Converting to data frame\n",
- "fd1.columns=[\"CLASS\"] #Naming the column\n",
- "fd1.index.name=\"Index\" #Creating a column index\n",
- "fd1[\"CLASS\"]=fd1[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n",
- "\n",
- "fd1.to_csv(\"talz_csv9\") ## Writing a csv file\n",
- "print(fd1['CLASS'].unique())\n",
- "print(fd1['CLASS'].nunique())\n",
- "\n",
- "#printing the numbers of False and True\n",
- "print(fd1.groupby('CLASS').size()[0].sum()) #\n",
- "print(fd1.groupby('CLASS').size()[1].sum()) "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Support Vector Machines (SVM)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0.8350280093798853\n",
- "[False True]\n",
- "2\n",
- "397\n",
- "361\n"
- ]
- }
- ],
- "source": [
- "from sklearn.svm import SVC\n",
- "\n",
- "array = Train.values\n",
- "X = array[:,0:11]\n",
- "Y = array[:,11]\n",
- "kfold = KFold(n_splits=10, random_state=7)\n",
- "model = SVC()\n",
- "model.fit(X, Y)\n",
- "results = cross_val_score(model, X, Y, cv=kfold)\n",
- "print(results.mean())\n",
- "\n",
- "svm = model.predict(Test.values)\n",
- "\n",
- "svm1 = pd.DataFrame(svm) #Converting to data frame\n",
- "svm1.columns=[\"CLASS\"] #Naming the column\n",
- "svm1.index.name=\"Index\" #Creating a column index\n",
- "svm1[\"CLASS\"]=svm1[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n",
- "\n",
- "svm1.to_csv(\"talz_csv10\") ## Writing a csv file\n",
- "print(svm1['CLASS'].unique())\n",
- "print(svm1['CLASS'].nunique())\n",
- "\n",
- "#printing the numbers of False and True\n",
- "print(svm1.groupby('CLASS').size()[0].sum()) #\n",
- "print(svm1.groupby('CLASS').size()[1].sum()) "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Linear Discriminant Analysis"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0.8535044293903076\n"
- ]
- }
- ],
- "source": [
- "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
- "\n",
- "array = Train.values\n",
- "X = array[:,0:11]\n",
- "Y = array[:,11]\n",
- "num_folds = 10\n",
- "kfold = KFold(n_splits=10, random_state=7)\n",
- "model = LinearDiscriminantAnalysis()\n",
- "results = cross_val_score(model, X, Y, cv=kfold)\n",
- "print(results.mean())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## K-Nearest Neighbors"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "-0.16489729894042035\n"
- ]
- }
- ],
- "source": [
- "from sklearn.neighbors import KNeighborsRegressor\n",
- "\n",
- "array = Train.values\n",
- "X = array[:,0:11]\n",
- "Y = array[:,11]\n",
- "kfold = KFold(n_splits=10, random_state=7)\n",
- "model = KNeighborsRegressor()\n",
- "scoring = 'neg_mean_squared_error'\n",
- "results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n",
- "print(results.mean())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Ridge Regression"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "-0.11583526542366822\n"
- ]
- }
- ],
- "source": [
- "from sklearn.linear_model import Ridge\n",
- "\n",
- "array = Train.values\n",
- "X = array[:,0:11]\n",
- "Y = array[:,11]\n",
- "num_folds = 10\n",
- "kfold = KFold(n_splits=10, random_state=7)\n",
- "model = Ridge()\n",
- "scoring = 'neg_mean_squared_error'\n",
- "results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n",
- "print(results.mean())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Random Forest"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0.8087013635574085\n"
- ]
- }
- ],
- "source": [
- "# Random Forest 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.ensemble import RandomForestClassifier\n",
- "array = Train.values\n",
- "\n",
- "X = array[:,0:11]\n",
- "Y = array[:,11]\n",
- "\n",
- "num_trees = 1000\n",
- "\n",
- "max_features = 3\n",
- "\n",
- "kfold = KFold(n_splits=10, random_state=7)\n",
- "model = RandomForestClassifier(n_estimators=num_trees, max_features=max_features)\n",
- "results = cross_val_score(model, X, Y, cv=kfold)\n",
- "print(results.mean())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Stochastic Gradient Descent - SGD"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0.7882935990967518\n"
- ]
- }
- ],
- "source": [
- "\n",
- "# Stochastic 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 sklearn.ensemble import GradientBoostingClassifier\n",
- "\n",
- "array = Train.values\n",
- "\n",
- "X = array[:,0:11]\n",
- "Y = array[:,11]\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())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## XGB"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0.787307842626368\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",
- "\n",
- "array = Train.values\n",
- "\n",
- "X = array[:,0:11]\n",
- "Y = array[:,11]\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())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Comparinr the Algorithms used\n",
- "from matplotlib import pyplot\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.neighbors import KNeighborsClassifier\n",
- "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
- "from sklearn.naive_bayes import GaussianNB\n",
- "from sklearn.svm import SVC\n",
- "from sklearn.ensemble import RandomForestClassifier\n",
- "from sklearn.ensemble import GradientBoostingClassifier\n",
- "from xgboost import XGBClassifier\n",
- "# load dataset\n",
- "\n",
- "array = Train.values\n",
- "\n",
- "#split the dataset \n",
- "X = array[:,0:11]\n",
- "Y = array[:,11]\n",
- "\n",
- "# prepare models and add them to a list\n",
- "models = []\n",
- "models.append(('LR', LogisticRegression()))\n",
- "models.append(('CART', DecisionTreeClassifier()))\n",
- "models.append(('NB', GaussianNB()))\n",
- "models.append(('SVM', SVC()))\n",
- "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
- "models.append(('DTC', DecisionTreeClassifier()))\n",
- "models.append(('KNN', KNeighborsClassifier()))\n",
- "models.append(('RFC', RandomForestClassifier()))\n",
- "models.append(('GBC', GradientBoostingClassifier()))\n",
- "models.append(('XGB', XGBClassifier()))\n",
- "# evaluate each model in turn\n",
- "results = []\n",
- "names = []\n",
- "scoring = 'accuracy'\n",
- "\n",
- "for name, model in models:\n",
- " kfold = KFold(n_splits=10, random_state=7)\n",
- " cv_results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n",
- " results.append(cv_results)\n",
- " names.append(name)\n",
- " msg = (name, cv_results.mean(), cv_results.std())\n",
- " print(msg)\n",
- "\n",
- "# boxplot algorithm comparison\n",
- "fig = pyplot.figure()\n",
- "fig.suptitle('Algorithm Comparison')\n",
- "ax = fig.add_subplot(111)\n",
- "pyplot.boxplot(results)\n",
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\n",
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- "\n",
- "You can talk to your friend and see how they are doing their reports on the work they are doing.\n",
- "\n",
- "- I need to know what you are thinking at each step, a description of the concept e.t.c\n",
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diff --git a/Assignment Colab/talz_csv10 b/Assignment Colab/talz_csv10
deleted file mode 100644
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--- a/Assignment Colab/talz_csv10
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diff --git a/Assignment Colab/talz_csv3 b/Assignment Colab/talz_csv3
deleted file mode 100644
index 2907729..0000000
--- a/Assignment Colab/talz_csv3
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diff --git a/Assignment Colab/talz_csv8 b/Assignment Colab/talz_csv8
deleted file mode 100644
index d5d0b48..0000000
--- a/Assignment Colab/talz_csv8
+++ /dev/null
@@ -1,759 +0,0 @@
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diff --git a/Assignment Colab/talz_csv9 b/Assignment Colab/talz_csv9
deleted file mode 100644
index d08740d..0000000
--- a/Assignment Colab/talz_csv9
+++ /dev/null
@@ -1,759 +0,0 @@
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