From 8007710f4bed2b04a96803ea888a01301febc0ea Mon Sep 17 00:00:00 2001 From: HariomSinghalPuri Date: Thu, 4 Sep 2025 23:04:22 +0530 Subject: [PATCH] EDA analysis and Heart attack Prediction ML project added --- .../Explaination.docx | Bin 0 -> 21144 bytes .../README.md | 277 +++ .../Unit_1_Task_1/Acadmic_Stress_EDA.ipynb | 2148 +++++++++++++++++ .../Unit_1_Task_1/academic_Stress.csv | 141 ++ .../Unit_1_Task_1/temp | 1 + ...Use Case 4. Heart_Disease_Prediction.ipynb | 1557 ++++++++++++ .../Unit_1_Task_2/heart_disease_data.csv | 304 +++ .../Unit_1_Task_2/temp | 1 + .../requirements.txt | 44 + 9 files changed, 4473 insertions(+) create mode 100644 Academic_Stress_EDA_Heart_Disease_Prediction/Explaination.docx create mode 100644 Academic_Stress_EDA_Heart_Disease_Prediction/README.md create mode 100644 Academic_Stress_EDA_Heart_Disease_Prediction/Unit_1_Task_1/Acadmic_Stress_EDA.ipynb create mode 100644 Academic_Stress_EDA_Heart_Disease_Prediction/Unit_1_Task_1/academic_Stress.csv create mode 100644 Academic_Stress_EDA_Heart_Disease_Prediction/Unit_1_Task_1/temp create mode 100644 Academic_Stress_EDA_Heart_Disease_Prediction/Unit_1_Task_2/ML Use Case 4. 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b/Academic_Stress_EDA_Heart_Disease_Prediction/README.md @@ -0,0 +1,277 @@ +# Data Science & Machine Learning Projects + +Welcome to my data science and machine learning projects repository! This collection showcases comprehensive analyses and predictive modeling solutions addressing real-world problems in education and healthcare. + +## 🚀 Projects Overview + +| Project | Type | Domain | Key Technologies | Status | +|---------|------|--------|-----------------|--------| +| [Academic Stress EDA](#-Unit_1_Task_1) | Exploratory Data Analysis | Education | Python, Pandas, Seaborn | ✅ Complete | +| [Heart Disease Prediction](#-Unit_1_Taks_2) | Machine Learning | Healthcare | Python, Scikit-learn | ✅ Complete | + +--- + +## 📚 Academic Stress EDA: Insights and Recommendations + +### 📊 Project Overview + +This project presents an Exploratory Data Analysis (EDA) of academic stress among students, based on a comprehensive dataset containing 139 student responses. The analysis examines various factors contributing to academic stress, including peer pressure, academic pressure from home, study environment, coping strategies, and academic competition. + +### 🔍 What is Exploratory Data Analysis (EDA)? + +EDA is a critical first step in data analysis that helps us: + +- Understand the structure and characteristics of our dataset +- Identify patterns, relationships, and trends in the data +- Detect anomalies and outliers +- Formulate hypotheses for further investigation +- Guide data-driven decision making + +### 📈 Key Visualizations + +Our analysis employed several visualization techniques: + +- **Distribution Plots**: Bar charts showing frequency distributions of categorical variables +- **Correlation Heatmap**: Visualized relationships between numerical variables +- **Pairplot**: Grid of scatter plots showing pairwise relationships +- **Boxplots**: Compared stress index distributions across different categories +- **Pie Charts**: Displayed proportional representation of categorical responses +- **Grouped Bar Plots**: Compared average stress across multiple factors + +### 🎯 Key Findings and Recommendations + +#### 1. Promote Peaceful Study Environments +**Finding**: Students reporting a "Peaceful" study environment showed lower stress levels. + +**Actions**: +- Invest in quiet study zones on campus +- Promote noise-canceling tools for students +- Encourage better time management + +#### 2. Encourage Intellectual Coping Strategies +**Finding**: Students using analytical coping strategies reported lower stress levels. + +**Actions**: +- Integrate problem-solving workshops +- Promote mindfulness and cognitive-behavioral techniques + +#### 3. Address Peer and Home Pressure +**Finding**: Higher peer and home pressure correlated with increased stress. + +**Actions**: +- Launch parental awareness programs +- Introduce peer-mentoring programs + +### 📁 Dataset Information + +| Variable | Description | +|----------|-------------| +| Academic Stage | undergraduate, high school, post-graduate | +| Peer pressure | 1-5 rating scale | +| Academic pressure from home | 1-5 rating scale | +| Study Environment | Noisy, Peaceful, Disrupted | +| Coping strategy | Various coping mechanisms | +| Academic competition rating | 1-5 rating scale | +| Academic stress index rating | 1-5 rating scale | + +--- + +## ❤️ Heart Disease Prediction using Machine Learning + +### 🎯 Real-World Problem + +This project addresses the critical healthcare challenge of early heart disease detection. Cardiovascular diseases are the leading cause of death globally, claiming approximately 17.9 million lives each year according to the World Health Organization. Early detection can significantly improve treatment outcomes and save lives. + +### 📊 Dataset Overview + +Uses the Cleveland Heart Disease dataset from UCI Machine Learning Repository with 303 patient records and 14 clinical attributes: + +**Features**: +- **Demographic**: age, sex +- **Medical history**: chest pain type, resting blood pressure, cholesterol, fasting blood sugar +- **Exercise-induced**: maximum heart rate, exercise-induced angina +- **ECG measurements**: ST depression, slope of peak exercise ST segment +- **Cardiac indicators**: number of major vessels, thalassemia type + +**Target Variable**: 0 = no heart disease, 1 = heart disease present + +### 🔧 Machine Learning Pipeline + +#### 1. Data Preprocessing +- ✅ No missing values detected +- ✅ Stratified train-test split (80-20) +- ✅ Maintained class distribution + +#### 2. Model Performance +- **Algorithm**: Logistic Regression +- **Training Accuracy**: 85.12% +- **Test Accuracy**: 81.97% +- **Generalization**: Excellent (minimal overfitting) + +#### 3. Model Selection Rationale +- Interpretable coefficients for medical applications +- Excellent binary classification performance +- Efficient with moderate-sized datasets +- Provides probabilistic outputs + +### 🚀 Usage Example + +```python +# Making predictions on new patient data +input_data = (62, 0, 0, 140, 268, 0, 0, 160, 0, 3.6, 0, 2, 2) +prediction = model.predict(input_data) + +if prediction[0] == 0: + print('The Person does not have Heart Disease') +else: + print('The Person has Heart Disease') +``` + +### 🌍 Real-World Applications + +- **Clinical Decision Support**: Assist doctors with data-driven second opinions +- **Telemedicine**: Enable remote heart disease screening +- **Preventive Healthcare**: Identify at-risk individuals early +- **Resource Optimization**: Help prioritize patients needing cardiac care +- **Health Monitoring Apps**: Integration with wearable devices +- **Medical Research**: Pattern identification for risk factors + +--- + +## 🛠️ Technical Stack + +### Languages & Libraries +```python +# Core Data Science +import pandas as pd +import numpy as np + +# Visualization +import matplotlib.pyplot as plt +import seaborn as sns + +# Machine Learning +from sklearn.model_selection import train_test_split +from sklearn.linear_model import LogisticRegression +from sklearn.metrics import accuracy_score + +# Statistical Analysis +from scipy import stats +``` + +### Development Environment +- **Python**: 3.8+ +- **Jupyter Notebook**: For interactive analysis +- **Git**: Version control + +## 📂 Repository Structure + +``` +data-science-portfolio/ +├── Unit_1_Task_1/ +│ ├── Acadmic_Stress_EDA.ipynb +│ ├── academic_Stress.csv +| +├── Unit_1_Task_2/ +│ ├──ML Use Case 4. Heart_Disease_Prediction.ipynb +│ ├── heart_disease_data.csv +|── Explaination.docx +|── requirements.txt +|── README.md + +``` + +## 🚀 Getting Started + +### Prerequisites +```bash +pip install pandas numpy matplotlib seaborn scikit-learn jupyter +``` + +### Installation & Usage +1. **Clone the repository** + ```bash + git clone https://github.com/HariomSinghalPuri/Tasks.git + cd Unit_1_Task_1 + cd Unit_1_Task_2 + + ``` + +2. **Install dependencies** + ```bash + pip install -r requirements.txt + ``` + +3. **Launch Jupyter Notebook** + ```bash + jupyter notebook + ``` + +4. **Explore the projects** + - Navigate to individual project folders + - Open the respective `.ipynb` files + - Run cells to reproduce results + +## 📊 Key Results Summary + +| Project | Dataset Size | Key Metric | Result | Impact | +|---------|--------------|------------|---------|---------| +| Academic Stress EDA | 139 students | Insights Generated | 3 major findings | Educational policy recommendations | +| Heart Disease Prediction | 303 patients | Test Accuracy | 81.97% | Clinical decision support | + +## 🔮 Future Enhancements + +### Academic Stress EDA +- [ ] Predictive modeling for stress levels +- [ ] Longitudinal analysis implementation +- [ ] Interactive dashboard creation +- [ ] Statistical significance testing + +### Heart Disease Prediction +- [ ] Model ensemble implementation +- [ ] Hyperparameter optimization +- [ ] ROC curve and AUC analysis +- [ ] Web application deployment +- [ ] Real-time prediction API + +## ⚠️ Important Considerations + +### Academic Stress Project +- Results should inform policy but require validation across diverse student populations +- Privacy considerations for student data handling + +### Heart Disease Project +- **Medical Disclaimer**: This model is for educational purposes and should not replace professional medical diagnosis +- Requires rigorous clinical validation before real-world medical application +- Patient data privacy and security must be prioritized + +## 📚 References & Resources + +### Academic Stress EDA +- Educational psychology research on stress factors +- Statistical analysis best practices +- Data visualization principles + +### Heart Disease Prediction +- [UCI Machine Learning Repository: Heart Disease Dataset](https://archive.ics.uci.edu/ml/datasets/heart+disease) +- [WHO: Cardiovascular diseases fact sheet](https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)) +- [Scikit-learn Documentation](https://scikit-learn.org/stable/) + +## 🤝 Contributing + +Contributions are welcome! Please feel free to: +- Open issues for bugs or feature requests +- Submit pull requests for improvements +- Share feedback and suggestions + +## 📄 License + +This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. + +--- + +⭐ **If you found this repository helpful, please consider giving it a star!** + +- 📧 **Contact**: hsinghalpuri@gmail.com | +- 💼 **LinkedIn**: https://www.linkedin.com/in/hariom-singhal-puri | +- 🐱 **GitHub**: https://github.com/HariomSinghalPuri diff --git a/Academic_Stress_EDA_Heart_Disease_Prediction/Unit_1_Task_1/Acadmic_Stress_EDA.ipynb b/Academic_Stress_EDA_Heart_Disease_Prediction/Unit_1_Task_1/Acadmic_Stress_EDA.ipynb new file mode 100644 index 0000000..ad04b8d --- /dev/null +++ b/Academic_Stress_EDA_Heart_Disease_Prediction/Unit_1_Task_1/Acadmic_Stress_EDA.ipynb @@ -0,0 +1,2148 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "## Importing Dependencies" + ], + "metadata": { + "id": "17gwBx7ZYPX1" + } + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "WQ_Z-KDcXgzh" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from scipy import stats\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "# Setting plot style\n", + "plt.style.use('seaborn-v0_8')\n", + "sns.set_palette(\"husl\")" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Reaing The Dataset" + ], + "metadata": { + "id": "YZYFb1wXYelL" + } + }, + { + "cell_type": "code", + "source": [ + "acsd = pd.read_csv('/content/academic_Stress.csv')" + ], + "metadata": { + "id": "_egM8jVDYSBk" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Print the values from dataset" + ], + "metadata": { + "id": "GZODqfn3YlBR" + } + }, + { + "cell_type": "code", + "source": [ + "acsd.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 484 + }, + "id": "kIep14EuYjrO", + "outputId": "abc0e088-37e2-4a89-87e9-acb3184240cb" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Timestamp Your Academic Stage Peer pressure \\\n", + "0 24/07/2025 22:05:39 undergraduate 4 \n", + "1 24/07/2025 22:05:52 undergraduate 3 \n", + "2 24/07/2025 22:06:39 undergraduate 1 \n", + "3 24/07/2025 22:06:45 undergraduate 3 \n", + "4 24/07/2025 22:08:06 undergraduate 3 \n", + "\n", + " Academic pressure from your home Study Environment \\\n", + "0 5 Noisy \n", + "1 4 Peaceful \n", + "2 1 Peaceful \n", + "3 2 Peaceful \n", + "4 3 Peaceful \n", + "\n", + " What coping strategy you use as a student? \\\n", + "0 Analyze the situation and handle it with intel... \n", + "1 Analyze the situation and handle it with intel... \n", + "2 Social support (friends, family) \n", + "3 Analyze the situation and handle it with intel... \n", + "4 Analyze the situation and handle it with intel... \n", + "\n", + " Do you have any bad habits like smoking, drinking on a daily basis? \\\n", + "0 No \n", + "1 No \n", + "2 No \n", + "3 No \n", + "4 No \n", + "\n", + " What would you rate the academic competition in your student life \\\n", + "0 3 \n", + "1 3 \n", + "2 2 \n", + "3 4 \n", + "4 4 \n", + "\n", + " Rate your academic stress index \n", + "0 5 \n", + "1 3 \n", + "2 4 \n", + "3 3 \n", + "4 5 " + ], + "text/html": [ + "\n", + "
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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "summary": "{\n \"name\": \"acsd\",\n \"rows\": 8,\n \"fields\": [\n {\n \"column\": \"Peer pressure\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 48.19715197058795,\n \"min\": 1.0,\n \"max\": 139.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 3.0575539568345325,\n 3.0,\n 139.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Academic pressure from your home\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 48.180973600787695,\n \"min\": 1.0,\n \"max\": 139.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 3.1654676258992804,\n 3.0,\n 139.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"What would you rate the academic competition in your student life\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 48.07865610461553,\n \"min\": 1.0,\n \"max\": 139.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 139.0,\n 3.4820143884892087,\n 4.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Rate your academic stress index \",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 48.066362965358415,\n \"min\": 1.0,\n \"max\": 139.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 139.0,\n 3.7194244604316546,\n 4.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 23 + } + ] + }, + { + "cell_type": "code", + "source": [ + "acsd.columns=acsd.columns.str.strip() \\\n", + ".str.lower()\\\n", + ".str.replace(\" \",\"_\")\n", + "acsd.columns" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7mRTz8qBf5Dg", + "outputId": "f0493c83-b6ad-44ac-d414-194638b77c11" + }, + "execution_count": 38, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['timestamp', 'your_academic_stage', 'peer_pressure',\n", + " 'academic_pressure_from_your_home', 'study_environment',\n", + " 'what_coping_strategy_you_use_as_a_student?',\n", + " 'do_you_have_any_bad_habits_like_smoking,_drinking_on_a_daily_basis?',\n", + " 'what_would_you_rate_the_academic__competition_in_your_student_life',\n", + " 'rate_your_academic_stress_index'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "execution_count": 38 + } + ] + }, + { + "cell_type": "code", + "source": [ + "for col in cols:\n", + " plt.figure(figsize=(6,4))\n", + " acsd[col].value_counts().plot(kind=\"bar\")\n", + " plt.title(f\"Distribution of {col}\", fontsize=12)\n", + " plt.xlabel(col)\n", + " plt.ylabel(\"Count\")\n", + " plt.xticks(rotation=45, ha=\"right\")\n", + " plt.tight_layout()\n", + " plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "_btFlz7Gb8M_", + "outputId": "c82f3a7d-8f15-44bd-b1ff-3bbbbb5f3702" + }, + "execution_count": 45, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" 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\n" 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\n" 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\n" 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "acsd['timestamp']=pd.to_datetime(acsd['timestamp'])" + ], + "metadata": { + "id": "QSAatlc7dcV_" + }, + "execution_count": 42, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "cols=['your_academic_stage','peer_pressure','study_environment',\n", + " 'what_would_you_rate_the_academic__competition_in_your_student_life',\n", + " 'do_you_have_any_bad_habits_like_smoking,_drinking_on_a_daily_basis?']\n", + "for col in cols:\n", + " print(f\"\\nValue counts for {col} :\")\n", + " print(acsd[col].value_counts())" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5vOleh59gdyS", + "outputId": "88fdb3fd-8501-43a3-8e50-b66334e3ec0f" + }, + "execution_count": 43, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Value counts for your_academic_stage :\n", + "your_academic_stage\n", + "undergraduate 99\n", + "high school 29\n", + "post-graduate 11\n", + "Name: count, dtype: int64\n", + "\n", + "Value counts for peer_pressure :\n", + "peer_pressure\n", + "3 57\n", + "4 32\n", + "2 24\n", + "1 13\n", + "5 13\n", + "Name: count, dtype: int64\n", + "\n", + "Value counts for study_environment :\n", + "study_environment\n", + "Peaceful 69\n", + "disrupted 38\n", + "Noisy 32\n", + "Name: count, dtype: int64\n", + "\n", + "Value counts for what_would_you_rate_the_academic__competition_in_your_student_life :\n", + "what_would_you_rate_the_academic__competition_in_your_student_life\n", + "4 56\n", + "3 40\n", + "5 20\n", + "2 17\n", + "1 6\n", + "Name: count, dtype: int64\n", + "\n", + "Value counts for do_you_have_any_bad_habits_like_smoking,_drinking_on_a_daily_basis? :\n", + "do_you_have_any_bad_habits_like_smoking,_drinking_on_a_daily_basis?\n", + "No 122\n", + "Yes 10\n", + "prefer not to say 7\n", + "Name: count, dtype: int64\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "for col in cols:\n", + " plt.figure(figsize=(6,4))\n", + " acsd[col].value_counts().plot(kind=\"bar\")\n", + " plt.title(f\"Distribution of {col}\", fontsize=12)\n", + " plt.xlabel(col)\n", + " plt.ylabel(\"Count\")\n", + " plt.xticks(rotation=45, ha=\"right\")\n", + " plt.tight_layout()\n", + " plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "44W5RzlUgnug", + "outputId": "92c8334f-6972-40be-b812-e7b9af5568e7" + }, + "execution_count": 44, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" 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\n" 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\n" 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\n" 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + " # Cross-tab for counts\n", + "ct = pd.crosstab(acsd['your_academic_stage'], acsd['do_you_have_any_bad_habits_like_smoking,_drinking_on_a_daily_basis?'])\n", + "\n", + "sns.heatmap(ct, annot=True, fmt=\"d\", cmap=\"Blues\")\n", + "plt.title(\"Academic Stage vs Bad Habits\")\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 524 + }, + "id": "poU5txoGgwYY", + "outputId": "da148ac6-9905-40f0-c70d-c8f287d9c516" + }, + "execution_count": 46, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "sns.boxplot(x=\"study_environment\", y=\"rate_your_academic_stress_index\", data=acsd)\n", + "plt.title(\"Stress Index by Study Environment\")\n", + "plt.xticks(rotation=45)\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 560 + }, + "id": "n-Zxw1FWhPWS", + "outputId": "7a63d155-c5ef-46be-c619-01b00e4ad839" + }, + "execution_count": 47, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "sns.scatterplot(x=\"peer_pressure\", y=\"rate_your_academic_stress_index\", data=acsd)\n", + "plt.title(\"Peer Pressure vs Stress Index\")\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 524 + }, + "id": "kgW_dFVthUYB", + "outputId": "fff435c1-905f-4539-a985-4248fd209f59" + }, + "execution_count": 48, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + ] + }, + "metadata": {}, + "execution_count": 49 + } + ] + }, + { + "cell_type": "code", + "source": [ + "acsd.groupby(\n", + " ['your_academic_stage','do_you_have_any_bad_habits_like_smoking,_drinking_on_a_daily_basis?']\n", + ")['rate_your_academic_stress_index'].mean()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 335 + }, + "id": "lxVODx4Ahelu", + "outputId": "4f65405d-4f27-44b5-bca3-962ad743ca6f" + }, + "execution_count": 51, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "your_academic_stage do_you_have_any_bad_habits_like_smoking,_drinking_on_a_daily_basis?\n", + "high school No 3.827586\n", + "post-graduate No 3.666667\n", + " Yes 4.000000\n", + " prefer not to say 4.000000\n", + "undergraduate No 3.630952\n", + " Yes 4.333333\n", + " prefer not to say 3.500000\n", + "Name: rate_your_academic_stress_index, dtype: float64" + ], + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "execution_count": 51 + } + ] + }, + { + "cell_type": "code", + "source": [ + "import seaborn as sns\n", + "sns.boxplot(x=\"do_you_have_any_bad_habits_like_smoking,_drinking_on_a_daily_basis?\",\n", + " y=\"rate_your_academic_stress_index\", data=acsd)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 520 + }, + "id": "xgIM9hjYhkfW", + "outputId": "fb79cbf5-aa0a-469c-89d7-660c2e647c7b" + }, + "execution_count": 52, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 52 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"prefer not to say\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3535533905932738,\n \"min\": 3.5,\n \"max\": 4.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 3.5,\n 4.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 53 + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "kHMqcI82h6uA" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/Academic_Stress_EDA_Heart_Disease_Prediction/Unit_1_Task_1/academic_Stress.csv b/Academic_Stress_EDA_Heart_Disease_Prediction/Unit_1_Task_1/academic_Stress.csv new file mode 100644 index 0000000..ae3ef01 --- /dev/null +++ b/Academic_Stress_EDA_Heart_Disease_Prediction/Unit_1_Task_1/academic_Stress.csv @@ -0,0 +1,141 @@ +Timestamp,Your Academic Stage,Peer pressure,Academic pressure from your home,Study Environment,What coping strategy you use as a student?,"Do you have any bad habits like smoking, drinking on a daily basis?",What would you rate the academic competition in your student life,Rate your academic stress index +24/07/2025 22:05:39,undergraduate,4,5,Noisy,Analyze the situation and handle it with intellect,No,3,5 +24/07/2025 22:05:52,undergraduate,3,4,Peaceful,Analyze the situation and handle it with intellect,No,3,3 +24/07/2025 22:06:39,undergraduate,1,1,Peaceful,"Social support (friends, family)",No,2,4 +24/07/2025 22:06:45,undergraduate,3,2,Peaceful,Analyze the situation and handle it with intellect,No,4,3 +24/07/2025 22:08:06,undergraduate,3,3,Peaceful,Analyze the situation and handle it with intellect,No,4,5 +24/07/2025 22:08:13,undergraduate,3,3,Peaceful,Analyze the situation and handle it with intellect,No,4,4 +24/07/2025 22:09:21,undergraduate,5,5,disrupted,Emotional breakdown (crying a lot),No,4,4 +24/07/2025 22:10:06,undergraduate,3,2,Peaceful,"Social support (friends, family)",No,3,3 +24/07/2025 22:11:01,undergraduate,2,2,Peaceful,Analyze the situation and handle it with intellect,No,2,2 +24/07/2025 22:11:19,undergraduate,2,2,Peaceful,Analyze the situation and handle it with intellect,No,4,2 +24/07/2025 22:12:12,undergraduate,3,2,disrupted,Emotional breakdown (crying a lot),No,3,5 +24/07/2025 22:12:24,undergraduate,4,5,Peaceful,Analyze the situation and handle it with intellect,No,3,3 +24/07/2025 22:12:27,undergraduate,2,3,Noisy,Emotional breakdown (crying a lot),No,2,2 +24/07/2025 22:12:48,undergraduate,2,3,disrupted,Analyze the situation and handle it with intellect,No,4,2 +24/07/2025 22:14:16,undergraduate,3,4,disrupted,"Social support (friends, family)",prefer not to say,3,4 +24/07/2025 22:15:06,undergraduate,5,5,Noisy,Analyze the situation and handle it with intellect,No,4,4 +24/07/2025 22:15:39,undergraduate,3,3,Peaceful,Analyze the situation and handle it with intellect,No,5,5 +24/07/2025 22:16:10,undergraduate,3,5,disrupted,Analyze the situation and handle it with intellect,No,5,4 +24/07/2025 22:16:53,undergraduate,3,3,Peaceful,Emotional breakdown (crying a lot),No,4,3 +24/07/2025 22:17:46,undergraduate,3,4,Peaceful,"Social support (friends, family)",No,4,3 +24/07/2025 22:18:02,undergraduate,4,3,Peaceful,Analyze the situation and handle it with intellect,No,4,4 +24/07/2025 22:18:44,undergraduate,3,3,Peaceful,Analyze the situation and handle it with intellect,No,4,3 +24/07/2025 22:18:55,undergraduate,3,3,Peaceful,Analyze the situation and handle it with intellect,No,4,3 +24/07/2025 22:18:58,undergraduate,5,1,disrupted,Analyze the situation and handle it with intellect,No,5,3 +24/07/2025 22:19:06,undergraduate,4,5,disrupted,Analyze the situation and handle it with intellect,No,4,4 +24/07/2025 22:19:22,undergraduate,4,3,Peaceful,Analyze the situation and handle it with intellect,No,4,4 +24/07/2025 22:19:25,undergraduate,4,5,disrupted,Analyze the situation and handle it with intellect,Yes,4,5 +24/07/2025 22:19:33,high school,3,4,disrupted,Analyze the situation and handle it with intellect,No,3,4 +24/07/2025 22:19:51,undergraduate,5,1,disrupted,"Social support (friends, family)",No,4,5 +24/07/2025 22:20:28,undergraduate,4,3,Peaceful,Analyze the situation and handle it with intellect,No,5,4 +24/07/2025 22:21:04,undergraduate,5,5,disrupted,Emotional breakdown (crying a lot),Yes,5,5 +24/07/2025 22:23:15,undergraduate,3,1,disrupted,Emotional breakdown (crying a lot),No,4,4 +24/07/2025 22:24:13,undergraduate,3,2,Peaceful,Emotional breakdown (crying a lot),No,4,3 +24/07/2025 22:24:53,undergraduate,1,1,Peaceful,Analyze the situation and handle it with intellect,prefer not to say,1,1 +24/07/2025 22:25:17,undergraduate,1,1,Peaceful,"Social support (friends, family)",prefer not to say,4,3 +24/07/2025 22:25:27,undergraduate,3,4,Noisy,Analyze the situation and handle it with intellect,No,5,4 +24/07/2025 22:25:41,undergraduate,4,5,disrupted,Emotional breakdown (crying a lot),Yes,2,3 +24/07/2025 22:28:58,undergraduate,1,4,Peaceful,Analyze the situation and handle it with intellect,No,4,4 +24/07/2025 22:29:26,undergraduate,3,4,Peaceful,Analyze the situation and handle it with intellect,No,3,4 +24/07/2025 22:30:29,undergraduate,3,2,Peaceful,"Social support (friends, family)",No,3,4 +24/07/2025 22:32:22,undergraduate,3,4,disrupted,Analyze the situation and handle it with intellect,No,5,5 +24/07/2025 22:32:36,post-graduate,3,5,Peaceful,Analyze the situation and handle it with intellect,No,3,3 +24/07/2025 22:32:37,undergraduate,3,5,Peaceful,Analyze the situation and handle it with intellect,No,4,5 +24/07/2025 22:33:48,undergraduate,5,4,disrupted,Emotional breakdown (crying a lot),No,5,5 +24/07/2025 22:34:34,undergraduate,4,2,Noisy,Analyze the situation and handle it with intellect,No,2,2 +24/07/2025 22:34:41,undergraduate,3,2,Peaceful,Analyze the situation and handle it with intellect,No,4,3 +24/07/2025 22:34:53,undergraduate,5,5,Noisy,Emotional breakdown (crying a lot),prefer not to say,4,5 +24/07/2025 22:38:47,undergraduate,2,4,Peaceful,Analyze the situation and handle it with intellect,No,4,4 +24/07/2025 22:39:36,undergraduate,3,4,Noisy,Emotional breakdown (crying a lot),Yes,4,5 +24/07/2025 22:41:41,undergraduate,2,3,disrupted,"Social support (friends, family)",No,2,3 +24/07/2025 22:42:11,undergraduate,3,3,Peaceful,Analyze the situation and handle it with intellect,No,4,3 +24/07/2025 22:42:25,undergraduate,4,4,Noisy,Analyze the situation and handle it with intellect,No,5,4 +24/07/2025 22:42:48,undergraduate,4,5,Peaceful,Analyze the situation and handle it with intellect,No,4,4 +24/07/2025 22:44:07,undergraduate,3,3,disrupted,Emotional breakdown (crying a lot),Yes,3,3 +24/07/2025 22:44:50,undergraduate,3,1,Noisy,Analyze the situation and handle it with intellect,Yes,4,4 +24/07/2025 23:01:02,undergraduate,3,2,Peaceful,Analyze the situation and handle it with intellect,No,5,4 +24/07/2025 23:02:49,post-graduate,4,4,disrupted,Analyze the situation and handle it with intellect,prefer not to say,4,4 +24/07/2025 23:03:10,undergraduate,3,1,Noisy,Analyze the situation and handle it with intellect,No,4,3 +24/07/2025 23:04:01,undergraduate,2,3,disrupted,Analyze the situation and handle it with intellect,No,3,5 +24/07/2025 23:15:48,undergraduate,3,1,Noisy,Analyze the situation and handle it with intellect,No,5,4 +24/07/2025 23:22:17,undergraduate,2,1,Peaceful,Emotional breakdown (crying a lot),No,3,2 +24/07/2025 23:31:01,undergraduate,2,3,Peaceful,Analyze the situation and handle it with intellect,No,4,4 +24/07/2025 23:32:26,undergraduate,4,4,disrupted,Emotional breakdown (crying a lot),No,4,4 +24/07/2025 23:36:22,undergraduate,3,3,disrupted,Analyze the situation and handle it with intellect,No,3,3 +24/07/2025 23:44:35,undergraduate,3,2,Peaceful,Emotional breakdown (crying a lot),No,2,2 +25/07/2025 00:10:10,undergraduate,4,5,disrupted,Emotional breakdown (crying a lot),No,2,5 +25/07/2025 00:13:46,undergraduate,4,3,Peaceful,Analyze the situation and handle it with intellect,No,4,5 +25/07/2025 00:21:30,undergraduate,3,5,disrupted,Emotional breakdown (crying a lot),No,3,4 +25/07/2025 00:21:38,undergraduate,4,2,disrupted,Analyze the situation and handle it with intellect,No,1,1 +25/07/2025 08:13:01,undergraduate,3,3,disrupted,Analyze the situation and handle it with intellect,No,3,4 +25/07/2025 08:42:22,undergraduate,4,3,disrupted,Analyze the situation and handle it with intellect,No,4,4 +25/07/2025 10:30:12,undergraduate,3,3,disrupted,Analyze the situation and handle it with intellect,No,4,4 +25/07/2025 10:37:39,undergraduate,1,1,Noisy,Analyze the situation and handle it with intellect,No,1,1 +25/07/2025 10:43:24,undergraduate,3,3,disrupted,Analyze the situation and handle it with intellect,No,4,4 +25/07/2025 11:00:10,undergraduate,5,5,Noisy,Emotional breakdown (crying a lot),No,3,5 +25/07/2025 11:06:32,undergraduate,3,3,Peaceful,Analyze the situation and handle it with intellect,No,3,3 +25/07/2025 11:06:40,undergraduate,2,3,Noisy,"Social support (friends, family)",No,2,3 +25/07/2025 11:47:24,undergraduate,3,4,Peaceful,Analyze the situation and handle it with intellect,No,3,3 +25/07/2025 13:17:44,post-graduate,3,2,Peaceful,Analyze the situation and handle it with intellect,No,5,4 +25/07/2025 13:18:47,undergraduate,3,3,Peaceful,Analyze the situation and handle it with intellect,prefer not to say,3,5 +25/07/2025 13:38:10,undergraduate,2,2,Peaceful,Analyze the situation and handle it with intellect,No,1,1 +25/07/2025 16:00:32,undergraduate,5,3,Peaceful,Emotional breakdown (crying a lot),No,4,5 +25/07/2025 16:04:38,undergraduate,3,2,Peaceful,Analyze the situation and handle it with intellect,No,3,4 +25/07/2025 16:31:45,post-graduate,1,5,Peaceful,Analyze the situation and handle it with intellect,No,5,3 +25/07/2025 17:08:00,undergraduate,2,2,disrupted,"Social support (friends, family)",No,5,4 +25/07/2025 17:51:17,post-graduate,4,4,Noisy,Analyze the situation and handle it with intellect,No,4,3 +25/07/2025 18:18:38,undergraduate,2,3,Peaceful,"Social support (friends, family)",No,3,3 +25/07/2025 18:27:24,undergraduate,2,3,Peaceful,Analyze the situation and handle it with intellect,prefer not to say,3,3 +25/07/2025 19:34:59,undergraduate,2,5,Peaceful,"Social support (friends, family)",No,4,5 +25/07/2025 20:37:52,post-graduate,2,2,Noisy,Analyze the situation and handle it with intellect,No,4,4 +25/07/2025 23:34:18,undergraduate,3,3,Peaceful,Analyze the situation and handle it with intellect,Yes,4,4 +25/07/2025 23:39:15,undergraduate,2,2,Peaceful,Analyze the situation and handle it with intellect,No,4,3 +26/07/2025 07:25:43,high school,4,5,Peaceful,Analyze the situation and handle it with intellect,No,4,5 +26/07/2025 07:33:35,high school,3,3,Peaceful,Analyze the situation and handle it with intellect,No,4,4 +26/07/2025 07:34:28,high school,4,5,Noisy,Emotional breakdown (crying a lot),No,5,5 +26/07/2025 07:50:38,high school,3,3,Peaceful,Analyze the situation and handle it with intellect,No,3,4 +26/07/2025 08:13:18,high school,2,3,Noisy,Emotional breakdown (crying a lot),No,3,4 +26/07/2025 08:25:17,high school,1,2,Peaceful,Analyze the situation and handle it with intellect,No,5,3 +26/07/2025 08:25:18,high school,4,4,disrupted,Emotional breakdown (crying a lot),No,2,5 +26/07/2025 08:27:10,high school,4,3,Peaceful,Analyze the situation and handle it with intellect,No,4,4 +26/07/2025 08:40:35,high school,4,5,Noisy,Emotional breakdown (crying a lot),No,3,4 +26/07/2025 08:57:40,high school,1,5,Noisy,Analyze the situation and handle it with intellect,No,5,5 +26/07/2025 09:28:08,high school,5,5,Peaceful,Emotional breakdown (crying a lot),No,3,5 +26/07/2025 09:36:09,high school,1,3,Peaceful,"Social support (friends, family)",No,3,1 +26/07/2025 09:47:04,high school,5,3,Noisy,Emotional breakdown (crying a lot),No,4,5 +26/07/2025 09:56:32,high school,3,3,Noisy,Analyze the situation and handle it with intellect,No,4,3 +26/07/2025 10:01:33,high school,4,4,disrupted,Emotional breakdown (crying a lot),No,4,5 +26/07/2025 10:04:32,high school,2,3,Noisy,"Social support (friends, family)",No,3,4 +26/07/2025 10:38:24,high school,3,3,Peaceful,Analyze the situation and handle it with intellect,No,3,4 +26/07/2025 10:39:37,high school,2,4,Peaceful,Emotional breakdown (crying a lot),No,3,3 +26/07/2025 11:36:30,high school,2,4,Noisy,Analyze the situation and handle it with intellect,No,4,3 +26/07/2025 13:39:21,undergraduate,1,1,Noisy,Analyze the situation and handle it with intellect,No,3,3 +26/07/2025 14:21:53,high school,1,3,Noisy,Analyze the situation and handle it with intellect,No,5,3 +26/07/2025 14:23:45,high school,3,4,Noisy,Emotional breakdown (crying a lot),No,3,3 +26/07/2025 18:45:13,high school,1,1,Peaceful,Emotional breakdown (crying a lot),No,1,1 +26/07/2025 21:37:46,high school,3,4,Peaceful,"Social support (friends, family)",No,2,2 +26/07/2025 22:11:02,high school,3,1,Noisy,Analyze the situation and handle it with intellect,No,4,4 +27/07/2025 13:45:00,undergraduate,2,3,Noisy,Analyze the situation and handle it with intellect,No,3,5 +27/07/2025 22:56:54,high school,2,2,Peaceful,Analyze the situation and handle it with intellect,No,4,4 +30/07/2025 06:43:55,high school,4,5,Peaceful,Analyze the situation and handle it with intellect,No,3,5 +30/07/2025 16:02:16,undergraduate,4,4,disrupted,Analyze the situation and handle it with intellect,No,4,4 +11/08/2025 12:15:00,post-graduate,3,4,Peaceful,"Social support (friends, family)",No,1,4 +11/08/2025 18:07:26,undergraduate,5,1,Noisy,Analyze the situation and handle it with intellect,Yes,5,5 +11/08/2025 19:29:07,post-graduate,3,3,Peaceful,Analyze the situation and handle it with intellect,No,3,3 +12/08/2025 02:28:42,post-graduate,3,4,Peaceful,"Social support (friends, family)",No,2,4 +12/08/2025 08:11:48,post-graduate,3,1,disrupted,"Social support (friends, family)",No,4,5 +12/08/2025 08:49:45,high school,4,5,Noisy,Emotional breakdown (crying a lot),No,3,5 +12/08/2025 08:56:07,undergraduate,3,5,Peaceful,Analyze the situation and handle it with intellect,Yes,4,5 +12/08/2025 10:03:58,undergraduate,3,4,disrupted,"Social support (friends, family)",No,2,4 +12/08/2025 12:13:46,undergraduate,5,5,,Emotional breakdown (crying a lot),No,5,4 +12/08/2025 13:16:50,undergraduate,2,2,Noisy,Analyze the situation and handle it with intellect,No,4,4 +12/08/2025 15:01:20,high school,4,4,Peaceful,Analyze the situation and handle it with intellect,No,3,4 +13/08/2025 21:45:58,undergraduate,1,1,Peaceful,Analyze the situation and handle it with intellect,No,2,2 +14/08/2025 06:10:01,post-graduate,4,3,disrupted,Emotional breakdown (crying a lot),Yes,2,4 +14/08/2025 21:06:15,undergraduate,5,3,disrupted,Analyze the situation and handle it with intellect,No,2,4 +17/08/2025 13:02:04,undergraduate,3,2,Peaceful,Analyze the situation and handle it with intellect,No,3,4 +18/08/2025 14:36:00,undergraduate,4,2,disrupted,Analyze the situation and handle it with intellect,No,3,3 +18/08/2025 17:13:52,undergraduate,3,3,Peaceful,Analyze the situation and handle it with intellect,No,2,4 +18/08/2025 19:08:52,undergraduate,4,5,disrupted,"Social support (friends, family)",No,5,5 +18/08/2025 22:40:13,undergraduate,4,2,Peaceful,"Social support (friends, family)",No,4,4 \ No newline at end of file diff --git a/Academic_Stress_EDA_Heart_Disease_Prediction/Unit_1_Task_1/temp b/Academic_Stress_EDA_Heart_Disease_Prediction/Unit_1_Task_1/temp new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/Academic_Stress_EDA_Heart_Disease_Prediction/Unit_1_Task_1/temp @@ -0,0 +1 @@ + diff --git a/Academic_Stress_EDA_Heart_Disease_Prediction/Unit_1_Task_2/ML Use Case 4. Heart_Disease_Prediction.ipynb b/Academic_Stress_EDA_Heart_Disease_Prediction/Unit_1_Task_2/ML Use Case 4. Heart_Disease_Prediction.ipynb new file mode 100644 index 0000000..f268402 --- /dev/null +++ b/Academic_Stress_EDA_Heart_Disease_Prediction/Unit_1_Task_2/ML Use Case 4. Heart_Disease_Prediction.ipynb @@ -0,0 +1,1557 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "aTb-9TFFqprC" + }, + "source": [ + "Importing the Dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "3q9U3S_whh3-" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import accuracy_score" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "egMd5zeurTMR" + }, + "source": [ + "Data Collection and Processing" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "0q-3-LkQrREV" + }, + "outputs": [], + "source": [ + "# loading the csv data to a Pandas DataFrame\n", + "heart_data = pd.read_csv('heart_disease_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 198 + }, + "id": "M8dQxSTqriWD", + "outputId": "ea695a74-7589-47fe-e400-2dd925f7b5bb" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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agesexcptrestbpscholfbsrestecgthalachexangoldpeakslopecathaltarget
063131452331015002.30011
137121302500118703.50021
241011302040017201.42021
356111202360117800.82021
457001203540116310.62021
\n", + "
" + ], + "text/plain": [ + " age sex cp trestbps chol fbs restecg thalach exang oldpeak slope \\\n", + "0 63 1 3 145 233 1 0 150 0 2.3 0 \n", + "1 37 1 2 130 250 0 1 187 0 3.5 0 \n", + "2 41 0 1 130 204 0 0 172 0 1.4 2 \n", + "3 56 1 1 120 236 0 1 178 0 0.8 2 \n", + "4 57 0 0 120 354 0 1 163 1 0.6 2 \n", + "\n", + " ca thal target \n", + "0 0 1 1 \n", + "1 0 2 1 \n", + "2 0 2 1 \n", + "3 0 2 1 \n", + "4 0 2 1 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# print first 5 rows of the dataset\n", + "heart_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 198 + }, + "id": "Fx_aCZDgrqdR", + "outputId": "770eb646-bdff-45da-ac1c-06aae06f7446" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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agesexcptrestbpscholfbsrestecgthalachexangoldpeakslopecathaltarget
29857001402410112310.21030
29945131102640113201.21030
30068101441931114103.41230
30157101301310111511.21130
30257011302360017400.01120
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" + ], + "text/plain": [ + " age sex cp trestbps chol fbs restecg thalach exang oldpeak \\\n", + "298 57 0 0 140 241 0 1 123 1 0.2 \n", + "299 45 1 3 110 264 0 1 132 0 1.2 \n", + "300 68 1 0 144 193 1 1 141 0 3.4 \n", + "301 57 1 0 130 131 0 1 115 1 1.2 \n", + "302 57 0 1 130 236 0 0 174 0 0.0 \n", + "\n", + " slope ca thal target \n", + "298 1 0 3 0 \n", + "299 1 0 3 0 \n", + "300 1 2 3 0 \n", + "301 1 1 3 0 \n", + "302 1 1 2 0 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# print last 5 rows of the dataset\n", + "heart_data.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8nX1tIzbrz0u", + "outputId": "6f650e4c-22b6-4750-ca57-ba6758d1c3a2" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(303, 14)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# number of rows and columns in the dataset\n", + "heart_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7_xTcw1Sr6aJ", + "outputId": "1948e00e-0656-43ef-c4c4-740ee51a6381" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 303 entries, 0 to 302\n", + "Data columns (total 14 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 age 303 non-null int64 \n", + " 1 sex 303 non-null int64 \n", + " 2 cp 303 non-null int64 \n", + " 3 trestbps 303 non-null int64 \n", + " 4 chol 303 non-null int64 \n", + " 5 fbs 303 non-null int64 \n", + " 6 restecg 303 non-null int64 \n", + " 7 thalach 303 non-null int64 \n", + " 8 exang 303 non-null int64 \n", + " 9 oldpeak 303 non-null float64\n", + " 10 slope 303 non-null int64 \n", + " 11 ca 303 non-null int64 \n", + " 12 thal 303 non-null int64 \n", + " 13 target 303 non-null int64 \n", + "dtypes: float64(1), int64(13)\n", + "memory usage: 33.3 KB\n" + ] + } + ], + "source": [ + "# getting some info about the data\n", + "heart_data.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GjHtW31rsGlb", + "outputId": "8c1c23ce-b5b4-4872-a579-9b4185d12522" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "age 0\n", + "sex 0\n", + "cp 0\n", + "trestbps 0\n", + "chol 0\n", + "fbs 0\n", + "restecg 0\n", + "thalach 0\n", + "exang 0\n", + "oldpeak 0\n", + "slope 0\n", + "ca 0\n", + "thal 0\n", + "target 0\n", + "dtype: int64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# checking for missing values\n", + "heart_data.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 308 + }, + "id": "OHmcP7DJsSEP", + "outputId": "400a121e-dbd2-4e77-8c72-021c12af5927" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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agesexcptrestbpscholfbsrestecgthalachexangoldpeakslopecathaltarget
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50%55.0000001.0000001.000000130.000000240.0000000.0000001.000000153.0000000.0000000.8000001.0000000.0000002.0000001.000000
75%61.0000001.0000002.000000140.000000274.5000000.0000001.000000166.0000001.0000001.6000002.0000001.0000003.0000001.000000
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" + ], + "text/plain": [ + " age sex cp trestbps chol fbs \\\n", + "count 303.000000 303.000000 303.000000 303.000000 303.000000 303.000000 \n", + "mean 54.366337 0.683168 0.966997 131.623762 246.264026 0.148515 \n", + "std 9.082101 0.466011 1.032052 17.538143 51.830751 0.356198 \n", + "min 29.000000 0.000000 0.000000 94.000000 126.000000 0.000000 \n", + "25% 47.500000 0.000000 0.000000 120.000000 211.000000 0.000000 \n", + "50% 55.000000 1.000000 1.000000 130.000000 240.000000 0.000000 \n", + "75% 61.000000 1.000000 2.000000 140.000000 274.500000 0.000000 \n", + "max 77.000000 1.000000 3.000000 200.000000 564.000000 1.000000 \n", + "\n", + " restecg thalach exang oldpeak slope ca \\\n", + "count 303.000000 303.000000 303.000000 303.000000 303.000000 303.000000 \n", + "mean 0.528053 149.646865 0.326733 1.039604 1.399340 0.729373 \n", + "std 0.525860 22.905161 0.469794 1.161075 0.616226 1.022606 \n", + "min 0.000000 71.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 0.000000 133.500000 0.000000 0.000000 1.000000 0.000000 \n", + "50% 1.000000 153.000000 0.000000 0.800000 1.000000 0.000000 \n", + "75% 1.000000 166.000000 1.000000 1.600000 2.000000 1.000000 \n", + "max 2.000000 202.000000 1.000000 6.200000 2.000000 4.000000 \n", + "\n", + " thal target \n", + "count 303.000000 303.000000 \n", + "mean 2.313531 0.544554 \n", + "std 0.612277 0.498835 \n", + "min 0.000000 0.000000 \n", + "25% 2.000000 0.000000 \n", + "50% 2.000000 1.000000 \n", + "75% 3.000000 1.000000 \n", + "max 3.000000 1.000000 " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# statistical measures about the data\n", + "heart_data.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4InaOSIUsfWP", + "outputId": "6c38694f-7445-47b3-e235-cdd0157a4ec6" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "target\n", + "1 165\n", + "0 138\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# checking the distribution of Target Variable\n", + "heart_data['target'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aSOBu4qDtJy5" + }, + "source": [ + "1 --> Defective Heart\n", + "\n", + "0 --> Healthy Heart" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tW8i4igjtPRC" + }, + "source": [ + "Splitting the Features and Target" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "id": "Q6yfbswrs7m3" + }, + "outputs": [], + "source": [ + "X = heart_data.drop(columns='target', axis=1)\n", + "Y = heart_data['target']" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XJoCp4ZKtpZy", + "outputId": "549bc077-393d-4763-f64f-3d2e5faa0c7f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " age sex cp trestbps chol fbs restecg thalach exang oldpeak \\\n", + "0 63 1 3 145 233 1 0 150 0 2.3 \n", + "1 37 1 2 130 250 0 1 187 0 3.5 \n", + "2 41 0 1 130 204 0 0 172 0 1.4 \n", + "3 56 1 1 120 236 0 1 178 0 0.8 \n", + "4 57 0 0 120 354 0 1 163 1 0.6 \n", + ".. ... ... .. ... ... ... ... ... ... ... \n", + "298 57 0 0 140 241 0 1 123 1 0.2 \n", + "299 45 1 3 110 264 0 1 132 0 1.2 \n", + "300 68 1 0 144 193 1 1 141 0 3.4 \n", + "301 57 1 0 130 131 0 1 115 1 1.2 \n", + "302 57 0 1 130 236 0 0 174 0 0.0 \n", + "\n", + " slope ca thal \n", + "0 0 0 1 \n", + "1 0 0 2 \n", + "2 2 0 2 \n", + "3 2 0 2 \n", + "4 2 0 2 \n", + ".. ... .. ... \n", + "298 1 0 3 \n", + "299 1 0 3 \n", + "300 1 2 3 \n", + "301 1 1 3 \n", + "302 1 1 2 \n", + "\n", + "[303 rows x 13 columns]\n" + ] + } + ], + "source": [ + "print(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nukuj-YItq1w", + "outputId": "7c604a47-1690-4db4-fec7-bed3e9497428" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 1\n", + "1 1\n", + "2 1\n", + "3 1\n", + "4 1\n", + " ..\n", + "298 0\n", + "299 0\n", + "300 0\n", + "301 0\n", + "302 0\n", + "Name: target, Length: 303, dtype: int64\n" + ] + } + ], + "source": [ + "print(Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_EcjSE3Et18n" + }, + "source": [ + "Splitting the Data into Training data & Test Data" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "id": "a-UUfRUxtuga" + }, + "outputs": [], + "source": [ + "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "x7PrjC6zuf6X", + "outputId": "f2d66421-d671-4475-a51c-b37de3a2edac" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(303, 13) (242, 13) (61, 13)\n" + ] + } + ], + "source": [ + "print(X.shape, X_train.shape, X_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "beSkZmpVuvn9" + }, + "source": [ + "Model Training" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gi2NOWZjuxzw" + }, + "source": [ + "Logistic Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "id": "4-Md74FYuqNL" + }, + "outputs": [], + "source": [ + "model = LogisticRegression(max_iter=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kCdHYxGUu7XD", + "outputId": "ff7185b7-1dd2-418d-c22f-778005b4655b" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\wwwsi\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n" + ] + }, + { + "data": { + "text/html": [ + "
LogisticRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "LogisticRegression()" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# training the LogisticRegression model with Training data\n", + "model.fit(X_train, Y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZYIw8Gi9vXfU" + }, + "source": [ + "Model Evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wmxAekfZvZa9" + }, + "source": [ + "Accuracy Score" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "id": "g19JaUTMvPKy" + }, + "outputs": [], + "source": [ + "# accuracy on training data\n", + "X_train_prediction = model.predict(X_train)\n", + "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "uQBZvBh8v7R_", + "outputId": "e798e765-9f84-43e2-d3a0-f0fea3192ac2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy on Training data : 0.8512396694214877\n" + ] + } + ], + "source": [ + "print('Accuracy on Training data : ', training_data_accuracy)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "id": "mDONDJdlwBIO" + }, + "outputs": [], + "source": [ + "# accuracy on test data\n", + "X_test_prediction = model.predict(X_test)\n", + "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_MBS-OqdwYpf", + "outputId": "a2f5bd57-7135-47c2-c8f2-01f1823ad66a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy on Test data : 0.819672131147541\n" + ] + } + ], + "source": [ + "print('Accuracy on Test data : ', test_data_accuracy)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jIruVh3Qwq0e" + }, + "source": [ + "Building a Predictive System" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9ercruC9wb4C", + "outputId": "6a7f8964-d7c5-4a54-bb76-ef18f2add04a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0]\n", + "The Person does not have a Heart Disease\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\wwwsi\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "input_data = (62,0,0,140,268,0,0,160,0,3.6,0,2,2)\n", + "\n", + "# change the input data to a numpy array\n", + "input_data_as_numpy_array= np.asarray(input_data)\n", + "\n", + "# reshape the numpy array as we are predicting for only on instance\n", + "input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n", + "\n", + "prediction = model.predict(input_data_reshaped)\n", + "print(prediction)\n", + "\n", + "if (prediction[0]== 0):\n", + " print('The Person does not have a Heart Disease')\n", + "else:\n", + " print('The Person has Heart Disease')" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Academic_Stress_EDA_Heart_Disease_Prediction/Unit_1_Task_2/heart_disease_data.csv b/Academic_Stress_EDA_Heart_Disease_Prediction/Unit_1_Task_2/heart_disease_data.csv new file mode 100644 index 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--git a/Academic_Stress_EDA_Heart_Disease_Prediction/requirements.txt b/Academic_Stress_EDA_Heart_Disease_Prediction/requirements.txt new file mode 100644 index 0000000..3432ac2 --- /dev/null +++ b/Academic_Stress_EDA_Heart_Disease_Prediction/requirements.txt @@ -0,0 +1,44 @@ +# Core Data Science Libraries +numpy==1.24.3 +pandas==2.0.3 + +# Data Visualization +matplotlib==3.7.2 +seaborn==0.12.2 +plotly==5.15.0 + +# Machine Learning +scikit-learn==1.3.0 + +# Statistical Analysis +scipy==1.11.1 + +# Jupyter Environment +jupyter==1.0.0 +notebook==6.5.4 +ipython==8.14.0 +ipykernel==6.25.0 + +# Data Processing Utilities +openpyxl==3.1.2 +xlrd==2.0.1 + +# Optional: For advanced visualizations +plotly-express==0.4.1 + +# Optional: For model persistence +joblib==1.3.2 +pickle-mixin==1.0.2 + +# Development and Code Quality +autopep8==2.0.2 +flake8==6.0.0 + +# Optional: For enhanced notebook experience +tqdm==4.65.0 +ipywidgets==8.0.7 + +# System and Environment +python-dateutil==2.8.2 +pytz==2023.3 +six==1.16.0