├── requirements.txt ├── saved_models ├── diabetes_model.sav ├── parkinsons_model.sav ├── breast_cancer_model.sav └── heart_disease_model.sav ├── .gitignore ├── .devcontainer └── devcontainer.json ├── LICENSE ├── README.md ├── dataset ├── heart.csv ├── diabetes.csv └── parkinsons.csv ├── app.py └── colab_files_to_train_models ├── Multiple disease prediction system - heart.ipynb └── Multiple disease prediction system - Parkinsons.ipynb /requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | streamlit-option-menu 3 | huggingface_hub 4 | pandas 5 | numpy 6 | scikit-learn -------------------------------------------------------------------------------- /saved_models/diabetes_model.sav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rohanmistry231/Multi-Disease-Prediction-and-Health-Recommendation-System/HEAD/saved_models/diabetes_model.sav -------------------------------------------------------------------------------- /saved_models/parkinsons_model.sav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rohanmistry231/Multi-Disease-Prediction-and-Health-Recommendation-System/HEAD/saved_models/parkinsons_model.sav -------------------------------------------------------------------------------- /saved_models/breast_cancer_model.sav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rohanmistry231/Multi-Disease-Prediction-and-Health-Recommendation-System/HEAD/saved_models/breast_cancer_model.sav -------------------------------------------------------------------------------- /saved_models/heart_disease_model.sav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rohanmistry231/Multi-Disease-Prediction-and-Health-Recommendation-System/HEAD/saved_models/heart_disease_model.sav -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Streamlit configuration 2 | .streamlit/ 3 | 4 | # Folders with large or generated files 5 | colab_files_to_train_models/*.ipynb_checkpoints/ 6 | colab_files_to_train_models/*.log 7 | 8 | # Python-specific exclusions 9 | __pycache__/ 10 | *.pyc 11 | *.pyo 12 | *.pyd 13 | .Python 14 | pip-log.txt 15 | pip-delete-this-directory.txt 16 | 17 | # Virtual environments 18 | venv/ 19 | env/ 20 | .venv/ 21 | 22 | # Common temporary files 23 | *.log 24 | .DS_Store 25 | *.swp 26 | *~ 27 | 28 | # IDE-specific files 29 | .vscode/ 30 | .idea/ -------------------------------------------------------------------------------- /.devcontainer/devcontainer.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "Python 3", 3 | // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile 4 | "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye", 5 | "customizations": { 6 | "codespaces": { 7 | "openFiles": [ 8 | "README.md", 9 | "app.py" 10 | ] 11 | }, 12 | "vscode": { 13 | "settings": {}, 14 | "extensions": [ 15 | "ms-python.python", 16 | "ms-python.vscode-pylance" 17 | ] 18 | } 19 | }, 20 | "updateContentCommand": "[ -f packages.txt ] && sudo apt update && sudo apt upgrade -y && sudo xargs apt install -y **⚠️ Disclaimer**: This application is for educational purposes only. Predictions and reports are not definitive diagnoses. Always consult a medical professional for accurate diagnosis and treatment. 9 | 10 | --- 11 | 12 | ## 🌟 Features 13 | 14 | - **Multi-Disease Prediction**: 15 | - Predicts Diabetes, Heart Disease, Parkinson's Disease, and Breast Cancer. 16 | - Uses pre-trained machine learning models (Logistic Regression, SVM) for accurate predictions. 17 | - **Personalized Health Reports**: 18 | - Generates detailed health reports using Hugging Face's Mixtral-8x7B model. 19 | - Provides actionable steps, focus areas, and precautions for disease management, or healthy living tips if no disease is predicted. 20 | - **Interactive Interface**: 21 | - Built with Streamlit for a seamless user experience. 22 | - Sidebar navigation for easy access to different prediction pages. 23 | - **Robust Error Handling**: 24 | - Validates user inputs and handles API errors gracefully. 25 | - **Timestamped Reports**: 26 | - Health reports include a timestamp for reference (e.g., "Generated on 04:22 PM IST, Tuesday, May 13, 2025"). 27 | 28 | --- 29 | 30 | ## 🚀 Getting Started 31 | 32 | ### Prerequisites 33 | - Python 3.8 or higher 34 | - A Hugging Face API token (for generating health reports) 35 | 36 | ### Installation 37 | 38 | 1. **Clone the Repository**: 39 | ```bash 40 | git clone https://github.com/rohanmistry231/Multi-Disease-Prediction-and-Health-Recommendation-System.git 41 | cd Multi-Disease-Prediction-and-Health-Recommendation-System 42 | ``` 43 | 44 | 2. **Install Dependencies**: 45 | Ensure you have the required packages installed. Use the provided `requirements.txt`: 46 | ```bash 47 | pip install -r requirements.txt 48 | ``` 49 | The `requirements.txt` includes: 50 | - `streamlit` 51 | - `streamlit-option-menu` 52 | - `huggingface_hub` 53 | - `pandas` 54 | - `numpy` 55 | - `scikit-learn` 56 | 57 | 3. **Set Up Hugging Face API Token**: 58 | - Create a `.streamlit/secrets.toml` file in the project root. 59 | - Add your Hugging Face API token: 60 | ```toml 61 | HF_API_TOKEN = "your_huggingface_api_token" 62 | ``` 63 | - Get your token from [Hugging Face](https://huggingface.co/settings/tokens). 64 | 65 | 4. **Download Pre-trained Models**: 66 | - The `saved_models/` directory should contain the following pre-trained models: 67 | - `diabetes_model.sav` 68 | - `heart_disease_model.sav` 69 | - `parkinsons_model.sav` 70 | - `breast_cancer_model.sav` 71 | - If these models are missing, you can train them using the notebooks in `colab_files_to_train_models/`. See the *Training Models* section for details. 72 | 73 | ### Running the Application 74 | 75 | 1. **Start the Streamlit App**: 76 | ```bash 77 | streamlit run app.py 78 | ``` 79 | This will launch the app in your default web browser (typically at `http://localhost:8501`). 80 | 81 | 2. **Navigate the App**: 82 | - Use the sidebar to select a prediction page (e.g., "Diabetes Prediction"). 83 | - Enter the required medical parameters in the input fields. 84 | - Click the "Test Result" button to view the prediction and health report. 85 | 86 | --- 87 | 88 | ## 📖 Usage 89 | 90 | 1. **Home Page**: 91 | - Overview of the application and instructions on how to use it. 92 | - Includes a disclaimer about the educational nature of the tool. 93 | 94 | 2. **Prediction Pages**: 95 | - **Diabetes Prediction**: Input parameters like Pregnancies, Glucose, BMI, etc. 96 | - **Heart Disease Prediction**: Input parameters like Age, Sex, Cholesterol, etc. 97 | - **Parkinson's Prediction**: Input vocal and motor-related metrics (e.g., MDVP:Fo(Hz), Jitter). 98 | - **Breast Cancer Prediction**: Input tumor metrics (e.g., Mean Radius, Worst Texture). 99 | - Each page provides a prediction (e.g., "The person is diabetic") and a generated health report. 100 | 101 | 3. **Health Reports**: 102 | - If a disease is predicted, the report includes: 103 | - A summary of the diagnosis. 104 | - **Actionable Steps**: Lifestyle changes or recommendations. 105 | - **Focus Areas**: Key health aspects to monitor. 106 | - **Precautions**: Steps to prevent worsening the condition. 107 | - If no disease is predicted, the report includes: 108 | - A confirmation of good health. 109 | - **Healthy Living Tips**: General wellness advice. 110 | - **Wellness Recommendations**: Practices for long-term health. 111 | 112 | --- 113 | 114 | ## 📂 Project Structure 115 | 116 | ``` 117 | multi-disease-prediction/ 118 | │ 119 | ├── .streamlit/ # Streamlit configuration (e.g., secrets.toml) 120 | ├── colab_files_to_train_models/ # Jupyter notebooks for training models 121 | ├── dataset/ # Datasets used for training 122 | ├── saved_models/ # Pre-trained model files 123 | ├── app.py # Main Streamlit application script 124 | ├── README.md # Project documentation 125 | ├── requirements.txt # Python dependencies 126 | └── .gitignore # Git ignore file 127 | ``` 128 | 129 | --- 130 | 131 | ## 🛠️ Training Models 132 | 133 | The pre-trained models in `saved_models/` were trained using scikit-learn (version 1.0.2). To retrain or create new models: 134 | 135 | 1. **Access the Notebooks**: 136 | - Navigate to `colab_files_to_train_models/`. 137 | - Open the relevant notebook (e.g., `diabetes_model_training.ipynb`). 138 | 139 | 2. **Prepare the Data**: 140 | - Datasets are stored in `dataset/`. 141 | - Ensure you have the required datasets (e.g., `diabetes.csv`, `heart.csv`). 142 | 143 | 3. **Train the Models**: 144 | - The notebooks use scikit-learn models like `LogisticRegression` and `SVM`. 145 | - Example training process: 146 | ```python 147 | from sklearn.svm import SVC 148 | import pandas as pd 149 | import pickle 150 | 151 | # Load dataset 152 | data = pd.read_csv('dataset/diabetes.csv') 153 | X = data.drop('Outcome', axis=1) 154 | y = data['Outcome'] 155 | 156 | # Train model 157 | model = SVC() 158 | model.fit(X, y) 159 | 160 | # Save model 161 | with open('saved_models/diabetes_model.sav', 'wb') as f: 162 | pickle.dump(model, f) 163 | ``` 164 | - Repeat for each disease model. 165 | 166 | 4. **Update the App**: 167 | - Ensure the new models are placed in `saved_models/` and match the expected filenames. 168 | 169 | > **Note**: The app currently uses scikit-learn 1.5.1, which may cause version mismatch warnings with models trained on 1.0.2. Retraining with 1.5.1 is recommended for compatibility. 170 | 171 | --- 172 | 173 | ## 📦 Dependencies 174 | 175 | See `requirements.txt` for the full list of dependencies. Key packages include: 176 | - `streamlit`: For the web interface. 177 | - `streamlit-option-menu`: For sidebar navigation. 178 | - `huggingface_hub`: For generating health reports via Hugging Face API. 179 | - `pandas` & `numpy`: For data manipulation. 180 | - `scikit-learn`: For machine learning models. 181 | 182 | Install them using: 183 | ```bash 184 | pip install -r requirements.txt 185 | ``` 186 | 187 | --- 188 | 189 | ## 🤝 Contributing 190 | 191 | Contributions are welcome! To contribute: 192 | 193 | 1. Fork the repository. 194 | 2. Create a new branch (`git checkout -b feature/YourFeature`). 195 | 3. Make your changes and commit (`git commit -m "Add your feature"`). 196 | 4. Push to your branch (`git push origin feature/YourFeature`). 197 | 5. Open a Pull Request. -------------------------------------------------------------------------------- /dataset/heart.csv: -------------------------------------------------------------------------------- 1 | age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal,target 2 | 63,1,3,145,233,1,0,150,0,2.3,0,0,1,1 3 | 37,1,2,130,250,0,1,187,0,3.5,0,0,2,1 4 | 41,0,1,130,204,0,0,172,0,1.4,2,0,2,1 5 | 56,1,1,120,236,0,1,178,0,0.8,2,0,2,1 6 | 57,0,0,120,354,0,1,163,1,0.6,2,0,2,1 7 | 57,1,0,140,192,0,1,148,0,0.4,1,0,1,1 8 | 56,0,1,140,294,0,0,153,0,1.3,1,0,2,1 9 | 44,1,1,120,263,0,1,173,0,0,2,0,3,1 10 | 52,1,2,172,199,1,1,162,0,0.5,2,0,3,1 11 | 57,1,2,150,168,0,1,174,0,1.6,2,0,2,1 12 | 54,1,0,140,239,0,1,160,0,1.2,2,0,2,1 13 | 48,0,2,130,275,0,1,139,0,0.2,2,0,2,1 14 | 49,1,1,130,266,0,1,171,0,0.6,2,0,2,1 15 | 64,1,3,110,211,0,0,144,1,1.8,1,0,2,1 16 | 58,0,3,150,283,1,0,162,0,1,2,0,2,1 17 | 50,0,2,120,219,0,1,158,0,1.6,1,0,2,1 18 | 58,0,2,120,340,0,1,172,0,0,2,0,2,1 19 | 66,0,3,150,226,0,1,114,0,2.6,0,0,2,1 20 | 43,1,0,150,247,0,1,171,0,1.5,2,0,2,1 21 | 69,0,3,140,239,0,1,151,0,1.8,2,2,2,1 22 | 59,1,0,135,234,0,1,161,0,0.5,1,0,3,1 23 | 44,1,2,130,233,0,1,179,1,0.4,2,0,2,1 24 | 42,1,0,140,226,0,1,178,0,0,2,0,2,1 25 | 61,1,2,150,243,1,1,137,1,1,1,0,2,1 26 | 40,1,3,140,199,0,1,178,1,1.4,2,0,3,1 27 | 71,0,1,160,302,0,1,162,0,0.4,2,2,2,1 28 | 59,1,2,150,212,1,1,157,0,1.6,2,0,2,1 29 | 51,1,2,110,175,0,1,123,0,0.6,2,0,2,1 30 | 65,0,2,140,417,1,0,157,0,0.8,2,1,2,1 31 | 53,1,2,130,197,1,0,152,0,1.2,0,0,2,1 32 | 41,0,1,105,198,0,1,168,0,0,2,1,2,1 33 | 65,1,0,120,177,0,1,140,0,0.4,2,0,3,1 34 | 44,1,1,130,219,0,0,188,0,0,2,0,2,1 35 | 54,1,2,125,273,0,0,152,0,0.5,0,1,2,1 36 | 51,1,3,125,213,0,0,125,1,1.4,2,1,2,1 37 | 46,0,2,142,177,0,0,160,1,1.4,0,0,2,1 38 | 54,0,2,135,304,1,1,170,0,0,2,0,2,1 39 | 54,1,2,150,232,0,0,165,0,1.6,2,0,3,1 40 | 65,0,2,155,269,0,1,148,0,0.8,2,0,2,1 41 | 65,0,2,160,360,0,0,151,0,0.8,2,0,2,1 42 | 51,0,2,140,308,0,0,142,0,1.5,2,1,2,1 43 | 48,1,1,130,245,0,0,180,0,0.2,1,0,2,1 44 | 45,1,0,104,208,0,0,148,1,3,1,0,2,1 45 | 53,0,0,130,264,0,0,143,0,0.4,1,0,2,1 46 | 39,1,2,140,321,0,0,182,0,0,2,0,2,1 47 | 52,1,1,120,325,0,1,172,0,0.2,2,0,2,1 48 | 44,1,2,140,235,0,0,180,0,0,2,0,2,1 49 | 47,1,2,138,257,0,0,156,0,0,2,0,2,1 50 | 53,0,2,128,216,0,0,115,0,0,2,0,0,1 51 | 53,0,0,138,234,0,0,160,0,0,2,0,2,1 52 | 51,0,2,130,256,0,0,149,0,0.5,2,0,2,1 53 | 66,1,0,120,302,0,0,151,0,0.4,1,0,2,1 54 | 62,1,2,130,231,0,1,146,0,1.8,1,3,3,1 55 | 44,0,2,108,141,0,1,175,0,0.6,1,0,2,1 56 | 63,0,2,135,252,0,0,172,0,0,2,0,2,1 57 | 52,1,1,134,201,0,1,158,0,0.8,2,1,2,1 58 | 48,1,0,122,222,0,0,186,0,0,2,0,2,1 59 | 45,1,0,115,260,0,0,185,0,0,2,0,2,1 60 | 34,1,3,118,182,0,0,174,0,0,2,0,2,1 61 | 57,0,0,128,303,0,0,159,0,0,2,1,2,1 62 | 71,0,2,110,265,1,0,130,0,0,2,1,2,1 63 | 54,1,1,108,309,0,1,156,0,0,2,0,3,1 64 | 52,1,3,118,186,0,0,190,0,0,1,0,1,1 65 | 41,1,1,135,203,0,1,132,0,0,1,0,1,1 66 | 58,1,2,140,211,1,0,165,0,0,2,0,2,1 67 | 35,0,0,138,183,0,1,182,0,1.4,2,0,2,1 68 | 51,1,2,100,222,0,1,143,1,1.2,1,0,2,1 69 | 45,0,1,130,234,0,0,175,0,0.6,1,0,2,1 70 | 44,1,1,120,220,0,1,170,0,0,2,0,2,1 71 | 62,0,0,124,209,0,1,163,0,0,2,0,2,1 72 | 54,1,2,120,258,0,0,147,0,0.4,1,0,3,1 73 | 51,1,2,94,227,0,1,154,1,0,2,1,3,1 74 | 29,1,1,130,204,0,0,202,0,0,2,0,2,1 75 | 51,1,0,140,261,0,0,186,1,0,2,0,2,1 76 | 43,0,2,122,213,0,1,165,0,0.2,1,0,2,1 77 | 55,0,1,135,250,0,0,161,0,1.4,1,0,2,1 78 | 51,1,2,125,245,1,0,166,0,2.4,1,0,2,1 79 | 59,1,1,140,221,0,1,164,1,0,2,0,2,1 80 | 52,1,1,128,205,1,1,184,0,0,2,0,2,1 81 | 58,1,2,105,240,0,0,154,1,0.6,1,0,3,1 82 | 41,1,2,112,250,0,1,179,0,0,2,0,2,1 83 | 45,1,1,128,308,0,0,170,0,0,2,0,2,1 84 | 60,0,2,102,318,0,1,160,0,0,2,1,2,1 85 | 52,1,3,152,298,1,1,178,0,1.2,1,0,3,1 86 | 42,0,0,102,265,0,0,122,0,0.6,1,0,2,1 87 | 67,0,2,115,564,0,0,160,0,1.6,1,0,3,1 88 | 68,1,2,118,277,0,1,151,0,1,2,1,3,1 89 | 46,1,1,101,197,1,1,156,0,0,2,0,3,1 90 | 54,0,2,110,214,0,1,158,0,1.6,1,0,2,1 91 | 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43,1,0,110,211,0,1,161,0,0,2,0,3,1 116 | 55,1,1,130,262,0,1,155,0,0,2,0,2,1 117 | 37,0,2,120,215,0,1,170,0,0,2,0,2,1 118 | 41,1,2,130,214,0,0,168,0,2,1,0,2,1 119 | 56,1,3,120,193,0,0,162,0,1.9,1,0,3,1 120 | 46,0,1,105,204,0,1,172,0,0,2,0,2,1 121 | 46,0,0,138,243,0,0,152,1,0,1,0,2,1 122 | 64,0,0,130,303,0,1,122,0,2,1,2,2,1 123 | 59,1,0,138,271,0,0,182,0,0,2,0,2,1 124 | 41,0,2,112,268,0,0,172,1,0,2,0,2,1 125 | 54,0,2,108,267,0,0,167,0,0,2,0,2,1 126 | 39,0,2,94,199,0,1,179,0,0,2,0,2,1 127 | 34,0,1,118,210,0,1,192,0,0.7,2,0,2,1 128 | 47,1,0,112,204,0,1,143,0,0.1,2,0,2,1 129 | 67,0,2,152,277,0,1,172,0,0,2,1,2,1 130 | 52,0,2,136,196,0,0,169,0,0.1,1,0,2,1 131 | 74,0,1,120,269,0,0,121,1,0.2,2,1,2,1 132 | 54,0,2,160,201,0,1,163,0,0,2,1,2,1 133 | 49,0,1,134,271,0,1,162,0,0,1,0,2,1 134 | 42,1,1,120,295,0,1,162,0,0,2,0,2,1 135 | 41,1,1,110,235,0,1,153,0,0,2,0,2,1 136 | 41,0,1,126,306,0,1,163,0,0,2,0,2,1 137 | 49,0,0,130,269,0,1,163,0,0,2,0,2,1 138 | 60,0,2,120,178,1,1,96,0,0,2,0,2,1 139 | 62,1,1,128,208,1,0,140,0,0,2,0,2,1 140 | 57,1,0,110,201,0,1,126,1,1.5,1,0,1,1 141 | 64,1,0,128,263,0,1,105,1,0.2,1,1,3,1 142 | 51,0,2,120,295,0,0,157,0,0.6,2,0,2,1 143 | 43,1,0,115,303,0,1,181,0,1.2,1,0,2,1 144 | 42,0,2,120,209,0,1,173,0,0,1,0,2,1 145 | 67,0,0,106,223,0,1,142,0,0.3,2,2,2,1 146 | 76,0,2,140,197,0,2,116,0,1.1,1,0,2,1 147 | 70,1,1,156,245,0,0,143,0,0,2,0,2,1 148 | 44,0,2,118,242,0,1,149,0,0.3,1,1,2,1 149 | 60,0,3,150,240,0,1,171,0,0.9,2,0,2,1 150 | 44,1,2,120,226,0,1,169,0,0,2,0,2,1 151 | 42,1,2,130,180,0,1,150,0,0,2,0,2,1 152 | 66,1,0,160,228,0,0,138,0,2.3,2,0,1,1 153 | 71,0,0,112,149,0,1,125,0,1.6,1,0,2,1 154 | 64,1,3,170,227,0,0,155,0,0.6,1,0,3,1 155 | 66,0,2,146,278,0,0,152,0,0,1,1,2,1 156 | 39,0,2,138,220,0,1,152,0,0,1,0,2,1 157 | 58,0,0,130,197,0,1,131,0,0.6,1,0,2,1 158 | 47,1,2,130,253,0,1,179,0,0,2,0,2,1 159 | 35,1,1,122,192,0,1,174,0,0,2,0,2,1 160 | 58,1,1,125,220,0,1,144,0,0.4,1,4,3,1 161 | 56,1,1,130,221,0,0,163,0,0,2,0,3,1 162 | 56,1,1,120,240,0,1,169,0,0,0,0,2,1 163 | 55,0,1,132,342,0,1,166,0,1.2,2,0,2,1 164 | 41,1,1,120,157,0,1,182,0,0,2,0,2,1 165 | 38,1,2,138,175,0,1,173,0,0,2,4,2,1 166 | 38,1,2,138,175,0,1,173,0,0,2,4,2,1 167 | 67,1,0,160,286,0,0,108,1,1.5,1,3,2,0 168 | 67,1,0,120,229,0,0,129,1,2.6,1,2,3,0 169 | 62,0,0,140,268,0,0,160,0,3.6,0,2,2,0 170 | 63,1,0,130,254,0,0,147,0,1.4,1,1,3,0 171 | 53,1,0,140,203,1,0,155,1,3.1,0,0,3,0 172 | 56,1,2,130,256,1,0,142,1,0.6,1,1,1,0 173 | 48,1,1,110,229,0,1,168,0,1,0,0,3,0 174 | 58,1,1,120,284,0,0,160,0,1.8,1,0,2,0 175 | 58,1,2,132,224,0,0,173,0,3.2,2,2,3,0 176 | 60,1,0,130,206,0,0,132,1,2.4,1,2,3,0 177 | 40,1,0,110,167,0,0,114,1,2,1,0,3,0 178 | 60,1,0,117,230,1,1,160,1,1.4,2,2,3,0 179 | 64,1,2,140,335,0,1,158,0,0,2,0,2,0 180 | 43,1,0,120,177,0,0,120,1,2.5,1,0,3,0 181 | 57,1,0,150,276,0,0,112,1,0.6,1,1,1,0 182 | 55,1,0,132,353,0,1,132,1,1.2,1,1,3,0 183 | 65,0,0,150,225,0,0,114,0,1,1,3,3,0 184 | 61,0,0,130,330,0,0,169,0,0,2,0,2,0 185 | 58,1,2,112,230,0,0,165,0,2.5,1,1,3,0 186 | 50,1,0,150,243,0,0,128,0,2.6,1,0,3,0 187 | 44,1,0,112,290,0,0,153,0,0,2,1,2,0 188 | 60,1,0,130,253,0,1,144,1,1.4,2,1,3,0 189 | 54,1,0,124,266,0,0,109,1,2.2,1,1,3,0 190 | 50,1,2,140,233,0,1,163,0,0.6,1,1,3,0 191 | 41,1,0,110,172,0,0,158,0,0,2,0,3,0 192 | 51,0,0,130,305,0,1,142,1,1.2,1,0,3,0 193 | 58,1,0,128,216,0,0,131,1,2.2,1,3,3,0 194 | 54,1,0,120,188,0,1,113,0,1.4,1,1,3,0 195 | 60,1,0,145,282,0,0,142,1,2.8,1,2,3,0 196 | 60,1,2,140,185,0,0,155,0,3,1,0,2,0 197 | 59,1,0,170,326,0,0,140,1,3.4,0,0,3,0 198 | 46,1,2,150,231,0,1,147,0,3.6,1,0,2,0 199 | 67,1,0,125,254,1,1,163,0,0.2,1,2,3,0 200 | 62,1,0,120,267,0,1,99,1,1.8,1,2,3,0 201 | 65,1,0,110,248,0,0,158,0,0.6,2,2,1,0 202 | 44,1,0,110,197,0,0,177,0,0,2,1,2,0 203 | 60,1,0,125,258,0,0,141,1,2.8,1,1,3,0 204 | 58,1,0,150,270,0,0,111,1,0.8,2,0,3,0 205 | 68,1,2,180,274,1,0,150,1,1.6,1,0,3,0 206 | 62,0,0,160,164,0,0,145,0,6.2,0,3,3,0 207 | 52,1,0,128,255,0,1,161,1,0,2,1,3,0 208 | 59,1,0,110,239,0,0,142,1,1.2,1,1,3,0 209 | 60,0,0,150,258,0,0,157,0,2.6,1,2,3,0 210 | 49,1,2,120,188,0,1,139,0,2,1,3,3,0 211 | 59,1,0,140,177,0,1,162,1,0,2,1,3,0 212 | 57,1,2,128,229,0,0,150,0,0.4,1,1,3,0 213 | 61,1,0,120,260,0,1,140,1,3.6,1,1,3,0 214 | 39,1,0,118,219,0,1,140,0,1.2,1,0,3,0 215 | 61,0,0,145,307,0,0,146,1,1,1,0,3,0 216 | 56,1,0,125,249,1,0,144,1,1.2,1,1,2,0 217 | 43,0,0,132,341,1,0,136,1,3,1,0,3,0 218 | 62,0,2,130,263,0,1,97,0,1.2,1,1,3,0 219 | 63,1,0,130,330,1,0,132,1,1.8,2,3,3,0 220 | 65,1,0,135,254,0,0,127,0,2.8,1,1,3,0 221 | 48,1,0,130,256,1,0,150,1,0,2,2,3,0 222 | 63,0,0,150,407,0,0,154,0,4,1,3,3,0 223 | 55,1,0,140,217,0,1,111,1,5.6,0,0,3,0 224 | 65,1,3,138,282,1,0,174,0,1.4,1,1,2,0 225 | 56,0,0,200,288,1,0,133,1,4,0,2,3,0 226 | 54,1,0,110,239,0,1,126,1,2.8,1,1,3,0 227 | 70,1,0,145,174,0,1,125,1,2.6,0,0,3,0 228 | 62,1,1,120,281,0,0,103,0,1.4,1,1,3,0 229 | 35,1,0,120,198,0,1,130,1,1.6,1,0,3,0 230 | 59,1,3,170,288,0,0,159,0,0.2,1,0,3,0 231 | 64,1,2,125,309,0,1,131,1,1.8,1,0,3,0 232 | 47,1,2,108,243,0,1,152,0,0,2,0,2,0 233 | 57,1,0,165,289,1,0,124,0,1,1,3,3,0 234 | 55,1,0,160,289,0,0,145,1,0.8,1,1,3,0 235 | 64,1,0,120,246,0,0,96,1,2.2,0,1,2,0 236 | 70,1,0,130,322,0,0,109,0,2.4,1,3,2,0 237 | 51,1,0,140,299,0,1,173,1,1.6,2,0,3,0 238 | 58,1,0,125,300,0,0,171,0,0,2,2,3,0 239 | 60,1,0,140,293,0,0,170,0,1.2,1,2,3,0 240 | 77,1,0,125,304,0,0,162,1,0,2,3,2,0 241 | 35,1,0,126,282,0,0,156,1,0,2,0,3,0 242 | 70,1,2,160,269,0,1,112,1,2.9,1,1,3,0 243 | 59,0,0,174,249,0,1,143,1,0,1,0,2,0 244 | 64,1,0,145,212,0,0,132,0,2,1,2,1,0 245 | 57,1,0,152,274,0,1,88,1,1.2,1,1,3,0 246 | 56,1,0,132,184,0,0,105,1,2.1,1,1,1,0 247 | 48,1,0,124,274,0,0,166,0,0.5,1,0,3,0 248 | 56,0,0,134,409,0,0,150,1,1.9,1,2,3,0 249 | 66,1,1,160,246,0,1,120,1,0,1,3,1,0 250 | 54,1,1,192,283,0,0,195,0,0,2,1,3,0 251 | 69,1,2,140,254,0,0,146,0,2,1,3,3,0 252 | 51,1,0,140,298,0,1,122,1,4.2,1,3,3,0 253 | 43,1,0,132,247,1,0,143,1,0.1,1,4,3,0 254 | 62,0,0,138,294,1,1,106,0,1.9,1,3,2,0 255 | 67,1,0,100,299,0,0,125,1,0.9,1,2,2,0 256 | 59,1,3,160,273,0,0,125,0,0,2,0,2,0 257 | 45,1,0,142,309,0,0,147,1,0,1,3,3,0 258 | 58,1,0,128,259,0,0,130,1,3,1,2,3,0 259 | 50,1,0,144,200,0,0,126,1,0.9,1,0,3,0 260 | 62,0,0,150,244,0,1,154,1,1.4,1,0,2,0 261 | 38,1,3,120,231,0,1,182,1,3.8,1,0,3,0 262 | 66,0,0,178,228,1,1,165,1,1,1,2,3,0 263 | 52,1,0,112,230,0,1,160,0,0,2,1,2,0 264 | 53,1,0,123,282,0,1,95,1,2,1,2,3,0 265 | 63,0,0,108,269,0,1,169,1,1.8,1,2,2,0 266 | 54,1,0,110,206,0,0,108,1,0,1,1,2,0 267 | 66,1,0,112,212,0,0,132,1,0.1,2,1,2,0 268 | 55,0,0,180,327,0,2,117,1,3.4,1,0,2,0 269 | 49,1,2,118,149,0,0,126,0,0.8,2,3,2,0 270 | 54,1,0,122,286,0,0,116,1,3.2,1,2,2,0 271 | 56,1,0,130,283,1,0,103,1,1.6,0,0,3,0 272 | 46,1,0,120,249,0,0,144,0,0.8,2,0,3,0 273 | 61,1,3,134,234,0,1,145,0,2.6,1,2,2,0 274 | 67,1,0,120,237,0,1,71,0,1,1,0,2,0 275 | 58,1,0,100,234,0,1,156,0,0.1,2,1,3,0 276 | 47,1,0,110,275,0,0,118,1,1,1,1,2,0 277 | 52,1,0,125,212,0,1,168,0,1,2,2,3,0 278 | 58,1,0,146,218,0,1,105,0,2,1,1,3,0 279 | 57,1,1,124,261,0,1,141,0,0.3,2,0,3,0 280 | 58,0,1,136,319,1,0,152,0,0,2,2,2,0 281 | 61,1,0,138,166,0,0,125,1,3.6,1,1,2,0 282 | 42,1,0,136,315,0,1,125,1,1.8,1,0,1,0 283 | 52,1,0,128,204,1,1,156,1,1,1,0,0,0 284 | 59,1,2,126,218,1,1,134,0,2.2,1,1,1,0 285 | 40,1,0,152,223,0,1,181,0,0,2,0,3,0 286 | 61,1,0,140,207,0,0,138,1,1.9,2,1,3,0 287 | 46,1,0,140,311,0,1,120,1,1.8,1,2,3,0 288 | 59,1,3,134,204,0,1,162,0,0.8,2,2,2,0 289 | 57,1,1,154,232,0,0,164,0,0,2,1,2,0 290 | 57,1,0,110,335,0,1,143,1,3,1,1,3,0 291 | 55,0,0,128,205,0,2,130,1,2,1,1,3,0 292 | 61,1,0,148,203,0,1,161,0,0,2,1,3,0 293 | 58,1,0,114,318,0,2,140,0,4.4,0,3,1,0 294 | 58,0,0,170,225,1,0,146,1,2.8,1,2,1,0 295 | 67,1,2,152,212,0,0,150,0,0.8,1,0,3,0 296 | 44,1,0,120,169,0,1,144,1,2.8,0,0,1,0 297 | 63,1,0,140,187,0,0,144,1,4,2,2,3,0 298 | 63,0,0,124,197,0,1,136,1,0,1,0,2,0 299 | 59,1,0,164,176,1,0,90,0,1,1,2,1,0 300 | 57,0,0,140,241,0,1,123,1,0.2,1,0,3,0 301 | 45,1,3,110,264,0,1,132,0,1.2,1,0,3,0 302 | 68,1,0,144,193,1,1,141,0,3.4,1,2,3,0 303 | 57,1,0,130,131,0,1,115,1,1.2,1,1,3,0 304 | 57,0,1,130,236,0,0,174,0,0,1,1,2,0 305 | -------------------------------------------------------------------------------- /app.py: -------------------------------------------------------------------------------- 1 | import os 2 | import pickle 3 | import streamlit as st 4 | from streamlit_option_menu import option_menu 5 | from huggingface_hub import InferenceClient 6 | from datetime import datetime 7 | import pandas as pd 8 | import warnings 9 | 10 | # Suppress scikit-learn warnings 11 | warnings.filterwarnings("ignore", category=UserWarning) 12 | warnings.filterwarnings("ignore", category=FutureWarning) 13 | warnings.filterwarnings("ignore", category=DeprecationWarning) 14 | 15 | # Set page configuration 16 | st.set_page_config(page_title="Health Assistant", 17 | layout="wide", 18 | page_icon="🧑‍⚕️") 19 | 20 | # Getting the working directory 21 | working_dir = os.path.dirname(os.path.abspath(__file__)) 22 | 23 | # Loading the saved models 24 | diabetes_model = pickle.load(open(f'{working_dir}/saved_models/diabetes_model.sav', 'rb')) 25 | heart_disease_model = pickle.load(open(f'{working_dir}/saved_models/heart_disease_model.sav', 'rb')) 26 | parkinsons_model = pickle.load(open(f'{working_dir}/saved_models/parkinsons_model.sav', 'rb')) 27 | breast_cancer_model = pickle.load(open(f'{working_dir}/saved_models/breast_cancer_model.sav', 'rb')) 28 | 29 | # Define feature names for each model 30 | diabetes_features = ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age'] 31 | heart_disease_features = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak', 'slope', 'ca', 'thal'] 32 | parkinsons_features = ['MDVP:Fo(Hz)', 'MDVP:Fhi(Hz)', 'MDVP:Flo(Hz)', 'MDVP:Jitter(%)', 'MDVP:Jitter(Abs)', 'MDVP:RAP', 'MDVP:PPQ', 'Jitter:DDP', 33 | 'MDVP:Shimmer', 'MDVP:Shimmer(dB)', 'Shimmer:APQ3', 'Shimmer:APQ5', 'MDVP:APQ', 'Shimmer:DDA', 'NHR', 'HNR', 'RPDE', 'DFA', 34 | 'spread1', 'spread2', 'D2', 'PPE'] 35 | breast_cancer_features = ['mean_radius', 'mean_texture', 'mean_perimeter', 'mean_area', 'mean_smoothness', 'mean_compactness', 'mean_concavity', 36 | 'mean_concave_points', 'mean_symmetry', 'mean_fractal_dimension', 'radius_error', 'texture_error', 'perimeter_error', 37 | 'area_error', 'smoothness_error', 'compactness_error', 'concavity_error', 'concave_points_error', 'symmetry_error', 38 | 'fractal_dimension_error', 'worst_radius', 'worst_texture', 'worst_perimeter', 'worst_area', 'worst_smoothness', 39 | 'worst_compactness', 'worst_concavity', 'worst_concave_points', 'worst_symmetry', 'worst_fractal_dimension'] 40 | 41 | # Function to call Hugging Face Inference API using InferenceClient 42 | def generate_report_with_hf(patient_data, predicted_disease, api_token): 43 | generated_report = "Unable to generate report due to an unexpected error. Please try again later." 44 | try: 45 | client = InferenceClient(model="mistralai/Mixtral-8x7B-Instruct-v0.1", token=api_token) 46 | has_disease = "not" not in predicted_disease.lower() 47 | 48 | if has_disease: 49 | prompt = f""" 50 | Based on the following patient data: {patient_data}, the predicted condition is: {predicted_disease}. 51 | Generate a concise health report (100-150 words) in a professional tone. Structure the report as follows: 52 | - A brief introduction summarizing the diagnosis (1-2 sentences). 53 | - A section titled 'Actionable Steps' with 3-4 bullet points listing specific lifestyle changes or recommendations. 54 | - A section titled 'Focus Areas' with 2-3 bullet points highlighting key areas to monitor or prioritize. 55 | - A section titled 'Precautions' with 3-4 bullet points listing specific preventive measures to avoid worsening the condition. 56 | Ensure each section is separated by an empty line for clear readability. Use markdown formatting: section titles with **bold** (e.g., **Actionable Steps**), and bullet points with `-` (e.g., - Item). Start each bullet point with an action verb (e.g., Monitor, Avoid, Engage) for clarity. Ensure all sentences are complete, advice is actionable, and the tone is supportive. 57 | """ 58 | else: 59 | prompt = f""" 60 | Based on the following patient data: {patient_data}, the predicted condition is: {predicted_disease}. 61 | Generate a concise health report (100-150 words) in a professional tone to help the person maintain and improve their overall health. Structure the report as follows: 62 | - A brief introduction confirming the absence of the condition and emphasizing the importance of maintaining good health (1-2 sentences). 63 | - A section titled 'Healthy Living Tips' with 3-4 bullet points listing general lifestyle recommendations to enhance overall health. 64 | - A section titled 'Wellness Recommendations' with 3-4 bullet points suggesting practices to promote long-term well-being. 65 | Ensure each section is separated by an empty line for clear readability. Use markdown formatting: section titles with **bold** (e.g., **Healthy Living Tips**), and bullet points with `-` (e.g., - Item). Start each bullet point with an action verb (e.g., Maintain, Incorporate, Ensure) for clarity. Ensure all sentences are complete, advice is practical, and the tone is supportive. 66 | """ 67 | 68 | response = client.text_generation(prompt, max_new_tokens=250, temperature=0.7) 69 | generated_report = response.split(prompt)[-1].strip() if prompt in response else response.strip() 70 | 71 | words = generated_report.split() 72 | if len(words) > 150: 73 | generated_report = " ".join(words[:150]) + "..." 74 | 75 | if generated_report and generated_report[-1] not in ".!?": 76 | last_punctuation = max(generated_report.rfind("."), generated_report.rfind("!"), generated_report.rfind("?")) 77 | if last_punctuation != -1: 78 | generated_report = generated_report[:last_punctuation + 1] 79 | 80 | return generated_report 81 | 82 | except Exception as e: 83 | error_message = "Unable to generate report. " 84 | if "rate limit" in str(e).lower(): 85 | error_message += "Hugging Face API rate limit exceeded. Please wait and try again." 86 | elif "token" in str(e).lower() or "authentication" in str(e).lower(): 87 | error_message += "Invalid or expired API token. Please check your HF_API_TOKEN in secrets.toml." 88 | else: 89 | error_message += f"An error occurred: {str(e)}. Please try again later." 90 | return error_message 91 | 92 | # Function to parse and display the report 93 | def display_health_report(report): 94 | lines = [line.strip() for line in report.split("\n") if line.strip()] 95 | current_section = None 96 | section_items = [] 97 | introduction = "" 98 | 99 | for line in lines: 100 | if line.startswith("**") and line.endswith("**"): 101 | if current_section and section_items: 102 | st.subheader(current_section) 103 | for item in section_items: 104 | st.write(f"• {item[2:]}") 105 | current_section = line.replace("**", "").strip() 106 | section_items = [] 107 | elif line.startswith("- "): 108 | section_items.append(line) 109 | else: 110 | if not current_section: 111 | introduction = line 112 | 113 | if introduction: 114 | st.text(introduction) 115 | 116 | if current_section and section_items: 117 | st.subheader(current_section) 118 | for item in section_items: 119 | st.write(f"• {item[2:]}") 120 | 121 | # Sidebar for navigation 122 | with st.sidebar: 123 | selected = option_menu('Multi-Disease Prediction and Health Recommendation System', 124 | ['Home', 'Diabetes Prediction', 'Heart Disease Prediction', 'Parkinsons Prediction', 'Breast Cancer Prediction'], 125 | menu_icon='hospital-fill', 126 | icons=['house', 'activity', 'heart', 'person', 'bi-virus2'], 127 | default_index=0) 128 | 129 | # Home Page 130 | if selected == 'Home': 131 | st.title('Welcome to the Multi-Disease Prediction and Health Recommendation System 🧑‍⚕️') 132 | st.markdown(""" 133 | ### About This Application 134 | This application uses machine learning models to predict the likelihood of four medical conditions: 135 | - **Diabetes** 136 | - **Heart Disease** 137 | - **Parkinson's Disease** 138 | - **Breast Cancer** 139 | 140 | The predictions are based on trained models that analyze input data provided by the user. Each prediction page allows you to input relevant medical parameters, and the system will provide a model-based prediction and a generated report with actionable advice. 141 | 142 | ### How to Use This Application 143 | 1. **Navigate to a Prediction Page**: Use the sidebar to select a disease prediction option. 144 | 2. **Enter Required Data**: Input numerical medical parameters in the provided fields. 145 | 3. **Get Prediction and Report**: Click the "Test Result" button to see the prediction and a detailed report. 146 | 4. **Interpret Results**: The prediction indicates the presence or absence of the condition, and the report provides management tips. 147 | 148 | ### Important Disclaimer 149 | **This application is for educational purposes only.** Predictions and reports are not definitive diagnoses. Consult a medical professional for accurate diagnosis and treatment. The developers are not responsible for decisions based on this app. 150 | 151 | ### Get Started 152 | Select a prediction page from the sidebar! 153 | """) 154 | 155 | # Diabetes Prediction Page 156 | if selected == 'Diabetes Prediction': 157 | st.title('Diabetes Prediction using ML') 158 | col1, col2, col3 = st.columns(3) 159 | 160 | with col1: 161 | Pregnancies = st.text_input('Number of Pregnancies') 162 | with col2: 163 | Glucose = st.text_input('Glucose Level') 164 | with col3: 165 | BloodPressure = st.text_input('Blood Pressure value') 166 | with col1: 167 | SkinThickness = st.text_input('Skin Thickness value') 168 | with col2: 169 | Insulin = st.text_input('Insulin Level') 170 | with col3: 171 | BMI = st.text_input('BMI value') 172 | with col1: 173 | DiabetesPedigreeFunction = st.text_input('Diabetes Pedigree Function value') 174 | with col2: 175 | Age = st.text_input('Age of the Person') 176 | 177 | diab_diagnosis = '' 178 | if st.button('Diabetes Test Result'): 179 | try: 180 | user_input = [Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age] 181 | user_input = [float(x) for x in user_input] 182 | input_df = pd.DataFrame([user_input], columns=diabetes_features) 183 | diab_prediction = diabetes_model.predict(input_df) 184 | diab_diagnosis = 'The person is diabetic' if diab_prediction[0] == 1 else 'The person is not diabetic' 185 | st.success(diab_diagnosis) 186 | 187 | patient_data = f"Pregnancies: {Pregnancies}, Glucose: {Glucose}, Blood Pressure: {BloodPressure}, " \ 188 | f"Skin Thickness: {SkinThickness}, Insulin: {Insulin}, BMI: {BMI}, " \ 189 | f"Diabetes Pedigree: {DiabetesPedigreeFunction}, Age: {Age}" 190 | 191 | api_token = st.secrets.get("HF_API_TOKEN", "") 192 | if api_token: 193 | with st.spinner("Generating report..."): 194 | report = generate_report_with_hf(patient_data, diab_diagnosis, api_token) 195 | st.subheader("Health Report") 196 | current_time = datetime.now().strftime("%I:%M %p IST, %A, %B %d, %Y") 197 | st.caption(f"Generated on {current_time}") 198 | st.divider() 199 | display_health_report(report) 200 | else: 201 | st.error("Hugging Face API token not found. Add it to secrets.toml.") 202 | except ValueError: 203 | st.error("Please enter valid numerical values for all fields.") 204 | 205 | # Heart Disease Prediction Page 206 | if selected == 'Heart Disease Prediction': 207 | st.title('Heart Disease Prediction using ML') 208 | col1, col2, col3 = st.columns(3) 209 | 210 | with col1: 211 | age = st.text_input('Age') 212 | with col2: 213 | sex = st.text_input('Sex') 214 | with col3: 215 | cp = st.text_input('Chest Pain types') 216 | with col1: 217 | trestbps = st.text_input('Resting Blood Pressure') 218 | with col2: 219 | chol = st.text_input('Serum Cholestoral in mg/dl') 220 | with col3: 221 | fbs = st.text_input('Fasting Blood Sugar > 120 mg/dl') 222 | with col1: 223 | restecg = st.text_input('Resting Electrocardiographic results') 224 | with col2: 225 | thalach = st.text_input('Maximum Heart Rate achieved') 226 | with col3: 227 | exang = st.text_input('Exercise Induced Angina') 228 | with col1: 229 | oldpeak = st.text_input('ST depression induced by exercise') 230 | with col2: 231 | slope = st.text_input('Slope of the peak exercise ST segment') 232 | with col3: 233 | ca = st.text_input('Major vessels colored by flourosopy') 234 | with col1: 235 | thal = st.text_input('thal: 0 = normal; 1 = fixed defect; 2 = reversable defect') 236 | 237 | heart_diagnosis = '' 238 | if st.button('Heart Disease Test Result'): 239 | try: 240 | user_input = [age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal] 241 | user_input = [float(x) for x in user_input] 242 | input_df = pd.DataFrame([user_input], columns=heart_disease_features) 243 | heart_prediction = heart_disease_model.predict(input_df) 244 | heart_diagnosis = 'The person is having heart disease' if heart_prediction[0] == 1 else 'The person does not have any heart disease' 245 | st.success(heart_diagnosis) 246 | 247 | patient_data = f"Age: {age}, Sex: {sex}, Chest Pain: {cp}, Resting BP: {trestbps}, Cholesterol: {chol}, " \ 248 | f"Fasting Blood Sugar: {fbs}, Resting ECG: {restecg}, Max Heart Rate: {thalach}, " \ 249 | f"Exercise Angina: {exang}, Oldpeak: {oldpeak}, Slope: {slope}, Major Vessels: {ca}, Thal: {thal}" 250 | 251 | api_token = st.secrets.get("HF_API_TOKEN", "") 252 | if api_token: 253 | with st.spinner("Generating report..."): 254 | report = generate_report_with_hf(patient_data, heart_diagnosis, api_token) 255 | st.subheader("Health Report") 256 | current_time = datetime.now().strftime("%I:%M %p IST, %A, %B %d, %Y") 257 | st.caption(f"Generated on {current_time}") 258 | st.divider() 259 | display_health_report(report) 260 | else: 261 | st.error("Hugging Face API token not found. Add it to secrets.toml.") 262 | except ValueError: 263 | st.error("Please enter valid numerical values for all fields.") 264 | 265 | # Parkinson's Prediction Page 266 | if selected == "Parkinsons Prediction": 267 | st.title("Parkinson's Disease Prediction using ML") 268 | col1, col2, col3, col4, col5 = st.columns(5) 269 | 270 | with col1: 271 | fo = st.text_input('MDVP:Fo(Hz)') 272 | with col2: 273 | fhi = st.text_input('MDVP:Fhi(Hz)') 274 | with col3: 275 | flo = st.text_input('MDVP:Flo(Hz)') 276 | with col4: 277 | Jitter_percent = st.text_input('MDVP:Jitter(%)') 278 | with col5: 279 | Jitter_Abs = st.text_input('MDVP:Jitter(Abs)') 280 | with col1: 281 | RAP = st.text_input('MDVP:RAP') 282 | with col2: 283 | PPQ = st.text_input('MDVP:PPQ') 284 | with col3: 285 | DDP = st.text_input('Jitter:DDP') 286 | with col4: 287 | Shimmer = st.text_input('MDVP:Shimmer') 288 | with col5: 289 | Shimmer_dB = st.text_input('MDVP:Shimmer(dB)') 290 | with col1: 291 | APQ3 = st.text_input('Shimmer:APQ3') 292 | with col2: 293 | APQ5 = st.text_input('Shimmer:APQ5') 294 | with col3: 295 | APQ = st.text_input('MDVP:APQ') 296 | with col4: 297 | DDA = st.text_input('Shimmer:DDA') 298 | with col5: 299 | NHR = st.text_input('NHR') 300 | with col1: 301 | HNR = st.text_input('HNR') 302 | with col2: 303 | RPDE = st.text_input('RPDE') 304 | with col3: 305 | DFA = st.text_input('DFA') 306 | with col4: 307 | spread1 = st.text_input('spread1') 308 | with col5: 309 | spread2 = st.text_input('spread2') 310 | with col1: 311 | D2 = st.text_input('D2') 312 | with col2: 313 | PPE = st.text_input('PPE') 314 | 315 | parkinsons_diagnosis = '' 316 | if st.button("Parkinson's Test Result"): 317 | try: 318 | user_input = [fo, fhi, flo, Jitter_percent, Jitter_Abs, RAP, PPQ, DDP, Shimmer, Shimmer_dB, APQ3, APQ5, 319 | APQ, DDA, NHR, HNR, RPDE, DFA, spread1, spread2, D2, PPE] 320 | user_input = [float(x) for x in user_input] 321 | input_df = pd.DataFrame([user_input], columns=parkinsons_features) 322 | parkinsons_prediction = parkinsons_model.predict(input_df) 323 | parkinsons_diagnosis = "The person has Parkinson's disease" if parkinsons_prediction[0] == 1 else "The person does not have Parkinson's disease" 324 | st.success(parkinsons_diagnosis) 325 | 326 | patient_data = f"MDVP:Fo(Hz): {fo}, Fhi(Hz): {fhi}, Flo(Hz): {flo}, Jitter(%): {Jitter_percent}, " \ 327 | f"Jitter(Abs): {Jitter_Abs}, RAP: {RAP}, PPQ: {PPQ}, DDP: {DDP}, Shimmer: {Shimmer}, " \ 328 | f"Shimmer(dB): {Shimmer_dB}, APQ3: {APQ3}, APQ5: {APQ5}, APQ: {APQ}, DDA: {DDA}, " \ 329 | f"NHR: {NHR}, HNR: {HNR}, RPDE: {RPDE}, DFA: {DFA}, spread1: {spread1}, " \ 330 | f"spread2: {spread2}, D2: {D2}, PPE: {PPE}" 331 | 332 | api_token = st.secrets.get("HF_API_TOKEN", "") 333 | if api_token: 334 | with st.spinner("Generating report..."): 335 | report = generate_report_with_hf(patient_data, parkinsons_diagnosis, api_token) 336 | st.subheader("Health Report") 337 | current_time = datetime.now().strftime("%I:%M %p IST, %A, %B %d, %Y") 338 | st.caption(f"Generated on {current_time}") 339 | st.divider() 340 | display_health_report(report) 341 | else: 342 | st.error("Hugging Face API token not found. Add it to secrets.toml.") 343 | except ValueError: 344 | st.error("Please enter valid numerical values for all fields.") 345 | 346 | # Breast Cancer Prediction Page 347 | if selected == 'Breast Cancer Prediction': 348 | st.title('Breast Cancer Prediction using ML') 349 | col1, col2, col3 = st.columns(3) 350 | 351 | with col1: 352 | mean_radius = st.text_input('Mean Radius') 353 | with col2: 354 | mean_texture = st.text_input('Mean Texture') 355 | with col3: 356 | mean_perimeter = st.text_input('Mean Perimeter') 357 | with col1: 358 | mean_area = st.text_input('Mean Area') 359 | with col2: 360 | mean_smoothness = st.text_input('Mean Smoothness') 361 | with col3: 362 | mean_compactness = st.text_input('Mean Compactness') 363 | with col1: 364 | mean_concavity = st.text_input('Mean Concavity') 365 | with col2: 366 | mean_concave_points = st.text_input('Mean Concave Points') 367 | with col3: 368 | mean_symmetry = st.text_input('Mean Symmetry') 369 | with col1: 370 | mean_fractal_dimension = st.text_input('Mean Fractal Dimension') 371 | with col2: 372 | radius_error = st.text_input('Radius Error') 373 | with col3: 374 | texture_error = st.text_input('Texture Error') 375 | with col1: 376 | perimeter_error = st.text_input('Perimeter Error') 377 | with col2: 378 | area_error = st.text_input('Area Error') 379 | with col3: 380 | smoothness_error = st.text_input('Smoothness Error') 381 | with col1: 382 | compactness_error = st.text_input('Compactness Error') 383 | with col2: 384 | concavity_error = st.text_input('Concavity Error') 385 | with col3: 386 | concave_points_error = st.text_input('Concave Points Error') 387 | with col1: 388 | symmetry_error = st.text_input('Symmetry Error') 389 | with col2: 390 | fractal_dimension_error = st.text_input('Fractal Dimension Error') 391 | with col3: 392 | worst_radius = st.text_input('Worst Radius') 393 | with col1: 394 | worst_texture = st.text_input('Worst Texture') 395 | with col2: 396 | worst_perimeter = st.text_input('Worst Perimeter') 397 | with col3: 398 | worst_area = st.text_input('Worst Area') 399 | with col1: 400 | worst_smoothness = st.text_input('Worst Smoothness') 401 | with col2: 402 | worst_compactness = st.text_input('Worst Compactness') 403 | with col3: 404 | worst_concavity = st.text_input('Worst Concavity') 405 | with col1: 406 | worst_concave_points = st.text_input('Worst Concave Points') 407 | with col2: 408 | worst_symmetry = st.text_input('Worst Symmetry') 409 | with col3: 410 | worst_fractal_dimension = st.text_input('Worst Fractal Dimension') 411 | 412 | cancer_diagnosis = '' 413 | if st.button('Breast Cancer Test Result'): 414 | try: 415 | user_input = [ 416 | mean_radius, mean_texture, mean_perimeter, mean_area, mean_smoothness, 417 | mean_compactness, mean_concavity, mean_concave_points, mean_symmetry, 418 | mean_fractal_dimension, radius_error, texture_error, perimeter_error, 419 | area_error, smoothness_error, compactness_error, concavity_error, 420 | concave_points_error, symmetry_error, fractal_dimension_error, 421 | worst_radius, worst_texture, worst_perimeter, worst_area, 422 | worst_smoothness, worst_compactness, worst_concavity, 423 | worst_concave_points, worst_symmetry, worst_fractal_dimension 424 | ] 425 | user_input = [float(x) for x in user_input] 426 | input_df = pd.DataFrame([user_input], columns=breast_cancer_features) 427 | prediction = breast_cancer_model.predict(input_df) 428 | cancer_diagnosis = 'The Person has Breast Cancer' if prediction[0] == 1 else 'The Person does not have Breast Cancer' 429 | st.success(cancer_diagnosis) 430 | 431 | patient_data = f"Mean Radius: {mean_radius}, Mean Texture: {mean_texture}, Mean Perimeter: {mean_perimeter}, " \ 432 | f"Mean Area: {mean_area}, Mean Smoothness: {mean_smoothness}, Mean Compactness: {mean_compactness}, " \ 433 | f"Mean Concavity: {mean_concavity}, Mean Concave Points: {mean_concave_points}, " \ 434 | f"Mean Symmetry: {mean_symmetry}, Mean Fractal Dimension: {mean_fractal_dimension}, " \ 435 | f"Radius Error: {radius_error}, Texture Error: {texture_error}, Perimeter Error: {perimeter_error}, " \ 436 | f"Area Error: {area_error}, Smoothness Error: {smoothness_error}, Compactness Error: {compactness_error}, " \ 437 | f"Concavity Error: {concavity_error}, Concave Points Error: {concave_points_error}, " \ 438 | f"Symmetry Error: {symmetry_error}, Fractal Dimension Error: {fractal_dimension_error}, " \ 439 | f"Worst Radius: {worst_radius}, Worst Texture: {worst_texture}, Worst Perimeter: {worst_perimeter}, " \ 440 | f"Worst Area: {worst_area}, Worst Smoothness: {worst_smoothness}, Worst Compactness: {worst_compactness}, " \ 441 | f"Worst Concavity: {worst_concavity}, Worst Concave Points: {worst_concave_points}, " \ 442 | f"Worst Symmetry: {worst_symmetry}, Worst Fractal Dimension: {worst_fractal_dimension}" 443 | 444 | api_token = st.secrets.get("HF_API_TOKEN", "") 445 | if api_token: 446 | with st.spinner("Generating report..."): 447 | report = generate_report_with_hf(patient_data, cancer_diagnosis, api_token) 448 | st.subheader("Health Report") 449 | current_time = datetime.now().strftime("%I:%M %p IST, %A, %B %d, %Y") 450 | st.caption(f"Generated on {current_time}") 451 | st.divider() 452 | display_health_report(report) 453 | else: 454 | st.error("Hugging Face API token not found. Add it to secrets.toml.") 455 | except ValueError: 456 | st.error("Please enter valid numerical values for all fields.") -------------------------------------------------------------------------------- /dataset/diabetes.csv: -------------------------------------------------------------------------------- 1 | Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome 2 | 6,148,72,35,0,33.6,0.627,50,1 3 | 1,85,66,29,0,26.6,0.351,31,0 4 | 8,183,64,0,0,23.3,0.672,32,1 5 | 1,89,66,23,94,28.1,0.167,21,0 6 | 0,137,40,35,168,43.1,2.288,33,1 7 | 5,116,74,0,0,25.6,0.201,30,0 8 | 3,78,50,32,88,31,0.248,26,1 9 | 10,115,0,0,0,35.3,0.134,29,0 10 | 2,197,70,45,543,30.5,0.158,53,1 11 | 8,125,96,0,0,0,0.232,54,1 12 | 4,110,92,0,0,37.6,0.191,30,0 13 | 10,168,74,0,0,38,0.537,34,1 14 | 10,139,80,0,0,27.1,1.441,57,0 15 | 1,189,60,23,846,30.1,0.398,59,1 16 | 5,166,72,19,175,25.8,0.587,51,1 17 | 7,100,0,0,0,30,0.484,32,1 18 | 0,118,84,47,230,45.8,0.551,31,1 19 | 7,107,74,0,0,29.6,0.254,31,1 20 | 1,103,30,38,83,43.3,0.183,33,0 21 | 1,115,70,30,96,34.6,0.529,32,1 22 | 3,126,88,41,235,39.3,0.704,27,0 23 | 8,99,84,0,0,35.4,0.388,50,0 24 | 7,196,90,0,0,39.8,0.451,41,1 25 | 9,119,80,35,0,29,0.263,29,1 26 | 11,143,94,33,146,36.6,0.254,51,1 27 | 10,125,70,26,115,31.1,0.205,41,1 28 | 7,147,76,0,0,39.4,0.257,43,1 29 | 1,97,66,15,140,23.2,0.487,22,0 30 | 13,145,82,19,110,22.2,0.245,57,0 31 | 5,117,92,0,0,34.1,0.337,38,0 32 | 5,109,75,26,0,36,0.546,60,0 33 | 3,158,76,36,245,31.6,0.851,28,1 34 | 3,88,58,11,54,24.8,0.267,22,0 35 | 6,92,92,0,0,19.9,0.188,28,0 36 | 10,122,78,31,0,27.6,0.512,45,0 37 | 4,103,60,33,192,24,0.966,33,0 38 | 11,138,76,0,0,33.2,0.42,35,0 39 | 9,102,76,37,0,32.9,0.665,46,1 40 | 2,90,68,42,0,38.2,0.503,27,1 41 | 4,111,72,47,207,37.1,1.39,56,1 42 | 3,180,64,25,70,34,0.271,26,0 43 | 7,133,84,0,0,40.2,0.696,37,0 44 | 7,106,92,18,0,22.7,0.235,48,0 45 | 9,171,110,24,240,45.4,0.721,54,1 46 | 7,159,64,0,0,27.4,0.294,40,0 47 | 0,180,66,39,0,42,1.893,25,1 48 | 1,146,56,0,0,29.7,0.564,29,0 49 | 2,71,70,27,0,28,0.586,22,0 50 | 7,103,66,32,0,39.1,0.344,31,1 51 | 7,105,0,0,0,0,0.305,24,0 52 | 1,103,80,11,82,19.4,0.491,22,0 53 | 1,101,50,15,36,24.2,0.526,26,0 54 | 5,88,66,21,23,24.4,0.342,30,0 55 | 8,176,90,34,300,33.7,0.467,58,1 56 | 7,150,66,42,342,34.7,0.718,42,0 57 | 1,73,50,10,0,23,0.248,21,0 58 | 7,187,68,39,304,37.7,0.254,41,1 59 | 0,100,88,60,110,46.8,0.962,31,0 60 | 0,146,82,0,0,40.5,1.781,44,0 61 | 0,105,64,41,142,41.5,0.173,22,0 62 | 2,84,0,0,0,0,0.304,21,0 63 | 8,133,72,0,0,32.9,0.27,39,1 64 | 5,44,62,0,0,25,0.587,36,0 65 | 2,141,58,34,128,25.4,0.699,24,0 66 | 7,114,66,0,0,32.8,0.258,42,1 67 | 5,99,74,27,0,29,0.203,32,0 68 | 0,109,88,30,0,32.5,0.855,38,1 69 | 2,109,92,0,0,42.7,0.845,54,0 70 | 1,95,66,13,38,19.6,0.334,25,0 71 | 4,146,85,27,100,28.9,0.189,27,0 72 | 2,100,66,20,90,32.9,0.867,28,1 73 | 5,139,64,35,140,28.6,0.411,26,0 74 | 13,126,90,0,0,43.4,0.583,42,1 75 | 4,129,86,20,270,35.1,0.231,23,0 76 | 1,79,75,30,0,32,0.396,22,0 77 | 1,0,48,20,0,24.7,0.14,22,0 78 | 7,62,78,0,0,32.6,0.391,41,0 79 | 5,95,72,33,0,37.7,0.37,27,0 80 | 0,131,0,0,0,43.2,0.27,26,1 81 | 2,112,66,22,0,25,0.307,24,0 82 | 3,113,44,13,0,22.4,0.14,22,0 83 | 2,74,0,0,0,0,0.102,22,0 84 | 7,83,78,26,71,29.3,0.767,36,0 85 | 0,101,65,28,0,24.6,0.237,22,0 86 | 5,137,108,0,0,48.8,0.227,37,1 87 | 2,110,74,29,125,32.4,0.698,27,0 88 | 13,106,72,54,0,36.6,0.178,45,0 89 | 2,100,68,25,71,38.5,0.324,26,0 90 | 15,136,70,32,110,37.1,0.153,43,1 91 | 1,107,68,19,0,26.5,0.165,24,0 92 | 1,80,55,0,0,19.1,0.258,21,0 93 | 4,123,80,15,176,32,0.443,34,0 94 | 7,81,78,40,48,46.7,0.261,42,0 95 | 4,134,72,0,0,23.8,0.277,60,1 96 | 2,142,82,18,64,24.7,0.761,21,0 97 | 6,144,72,27,228,33.9,0.255,40,0 98 | 2,92,62,28,0,31.6,0.13,24,0 99 | 1,71,48,18,76,20.4,0.323,22,0 100 | 6,93,50,30,64,28.7,0.356,23,0 101 | 1,122,90,51,220,49.7,0.325,31,1 102 | 1,163,72,0,0,39,1.222,33,1 103 | 1,151,60,0,0,26.1,0.179,22,0 104 | 0,125,96,0,0,22.5,0.262,21,0 105 | 1,81,72,18,40,26.6,0.283,24,0 106 | 2,85,65,0,0,39.6,0.93,27,0 107 | 1,126,56,29,152,28.7,0.801,21,0 108 | 1,96,122,0,0,22.4,0.207,27,0 109 | 4,144,58,28,140,29.5,0.287,37,0 110 | 3,83,58,31,18,34.3,0.336,25,0 111 | 0,95,85,25,36,37.4,0.247,24,1 112 | 3,171,72,33,135,33.3,0.199,24,1 113 | 8,155,62,26,495,34,0.543,46,1 114 | 1,89,76,34,37,31.2,0.192,23,0 115 | 4,76,62,0,0,34,0.391,25,0 116 | 7,160,54,32,175,30.5,0.588,39,1 117 | 4,146,92,0,0,31.2,0.539,61,1 118 | 5,124,74,0,0,34,0.22,38,1 119 | 5,78,48,0,0,33.7,0.654,25,0 120 | 4,97,60,23,0,28.2,0.443,22,0 121 | 4,99,76,15,51,23.2,0.223,21,0 122 | 0,162,76,56,100,53.2,0.759,25,1 123 | 6,111,64,39,0,34.2,0.26,24,0 124 | 2,107,74,30,100,33.6,0.404,23,0 125 | 5,132,80,0,0,26.8,0.186,69,0 126 | 0,113,76,0,0,33.3,0.278,23,1 127 | 1,88,30,42,99,55,0.496,26,1 128 | 3,120,70,30,135,42.9,0.452,30,0 129 | 1,118,58,36,94,33.3,0.261,23,0 130 | 1,117,88,24,145,34.5,0.403,40,1 131 | 0,105,84,0,0,27.9,0.741,62,1 132 | 4,173,70,14,168,29.7,0.361,33,1 133 | 9,122,56,0,0,33.3,1.114,33,1 134 | 3,170,64,37,225,34.5,0.356,30,1 135 | 8,84,74,31,0,38.3,0.457,39,0 136 | 2,96,68,13,49,21.1,0.647,26,0 137 | 2,125,60,20,140,33.8,0.088,31,0 138 | 0,100,70,26,50,30.8,0.597,21,0 139 | 0,93,60,25,92,28.7,0.532,22,0 140 | 0,129,80,0,0,31.2,0.703,29,0 141 | 5,105,72,29,325,36.9,0.159,28,0 142 | 3,128,78,0,0,21.1,0.268,55,0 143 | 5,106,82,30,0,39.5,0.286,38,0 144 | 2,108,52,26,63,32.5,0.318,22,0 145 | 10,108,66,0,0,32.4,0.272,42,1 146 | 4,154,62,31,284,32.8,0.237,23,0 147 | 0,102,75,23,0,0,0.572,21,0 148 | 9,57,80,37,0,32.8,0.096,41,0 149 | 2,106,64,35,119,30.5,1.4,34,0 150 | 5,147,78,0,0,33.7,0.218,65,0 151 | 2,90,70,17,0,27.3,0.085,22,0 152 | 1,136,74,50,204,37.4,0.399,24,0 153 | 4,114,65,0,0,21.9,0.432,37,0 154 | 9,156,86,28,155,34.3,1.189,42,1 155 | 1,153,82,42,485,40.6,0.687,23,0 156 | 8,188,78,0,0,47.9,0.137,43,1 157 | 7,152,88,44,0,50,0.337,36,1 158 | 2,99,52,15,94,24.6,0.637,21,0 159 | 1,109,56,21,135,25.2,0.833,23,0 160 | 2,88,74,19,53,29,0.229,22,0 161 | 17,163,72,41,114,40.9,0.817,47,1 162 | 4,151,90,38,0,29.7,0.294,36,0 163 | 7,102,74,40,105,37.2,0.204,45,0 164 | 0,114,80,34,285,44.2,0.167,27,0 165 | 2,100,64,23,0,29.7,0.368,21,0 166 | 0,131,88,0,0,31.6,0.743,32,1 167 | 6,104,74,18,156,29.9,0.722,41,1 168 | 3,148,66,25,0,32.5,0.256,22,0 169 | 4,120,68,0,0,29.6,0.709,34,0 170 | 4,110,66,0,0,31.9,0.471,29,0 171 | 3,111,90,12,78,28.4,0.495,29,0 172 | 6,102,82,0,0,30.8,0.18,36,1 173 | 6,134,70,23,130,35.4,0.542,29,1 174 | 2,87,0,23,0,28.9,0.773,25,0 175 | 1,79,60,42,48,43.5,0.678,23,0 176 | 2,75,64,24,55,29.7,0.37,33,0 177 | 8,179,72,42,130,32.7,0.719,36,1 178 | 6,85,78,0,0,31.2,0.382,42,0 179 | 0,129,110,46,130,67.1,0.319,26,1 180 | 5,143,78,0,0,45,0.19,47,0 181 | 5,130,82,0,0,39.1,0.956,37,1 182 | 6,87,80,0,0,23.2,0.084,32,0 183 | 0,119,64,18,92,34.9,0.725,23,0 184 | 1,0,74,20,23,27.7,0.299,21,0 185 | 5,73,60,0,0,26.8,0.268,27,0 186 | 4,141,74,0,0,27.6,0.244,40,0 187 | 7,194,68,28,0,35.9,0.745,41,1 188 | 8,181,68,36,495,30.1,0.615,60,1 189 | 1,128,98,41,58,32,1.321,33,1 190 | 8,109,76,39,114,27.9,0.64,31,1 191 | 5,139,80,35,160,31.6,0.361,25,1 192 | 3,111,62,0,0,22.6,0.142,21,0 193 | 9,123,70,44,94,33.1,0.374,40,0 194 | 7,159,66,0,0,30.4,0.383,36,1 195 | 11,135,0,0,0,52.3,0.578,40,1 196 | 8,85,55,20,0,24.4,0.136,42,0 197 | 5,158,84,41,210,39.4,0.395,29,1 198 | 1,105,58,0,0,24.3,0.187,21,0 199 | 3,107,62,13,48,22.9,0.678,23,1 200 | 4,109,64,44,99,34.8,0.905,26,1 201 | 4,148,60,27,318,30.9,0.15,29,1 202 | 0,113,80,16,0,31,0.874,21,0 203 | 1,138,82,0,0,40.1,0.236,28,0 204 | 0,108,68,20,0,27.3,0.787,32,0 205 | 2,99,70,16,44,20.4,0.235,27,0 206 | 6,103,72,32,190,37.7,0.324,55,0 207 | 5,111,72,28,0,23.9,0.407,27,0 208 | 8,196,76,29,280,37.5,0.605,57,1 209 | 5,162,104,0,0,37.7,0.151,52,1 210 | 1,96,64,27,87,33.2,0.289,21,0 211 | 7,184,84,33,0,35.5,0.355,41,1 212 | 2,81,60,22,0,27.7,0.29,25,0 213 | 0,147,85,54,0,42.8,0.375,24,0 214 | 7,179,95,31,0,34.2,0.164,60,0 215 | 0,140,65,26,130,42.6,0.431,24,1 216 | 9,112,82,32,175,34.2,0.26,36,1 217 | 12,151,70,40,271,41.8,0.742,38,1 218 | 5,109,62,41,129,35.8,0.514,25,1 219 | 6,125,68,30,120,30,0.464,32,0 220 | 5,85,74,22,0,29,1.224,32,1 221 | 5,112,66,0,0,37.8,0.261,41,1 222 | 0,177,60,29,478,34.6,1.072,21,1 223 | 2,158,90,0,0,31.6,0.805,66,1 224 | 7,119,0,0,0,25.2,0.209,37,0 225 | 7,142,60,33,190,28.8,0.687,61,0 226 | 1,100,66,15,56,23.6,0.666,26,0 227 | 1,87,78,27,32,34.6,0.101,22,0 228 | 0,101,76,0,0,35.7,0.198,26,0 229 | 3,162,52,38,0,37.2,0.652,24,1 230 | 4,197,70,39,744,36.7,2.329,31,0 231 | 0,117,80,31,53,45.2,0.089,24,0 232 | 4,142,86,0,0,44,0.645,22,1 233 | 6,134,80,37,370,46.2,0.238,46,1 234 | 1,79,80,25,37,25.4,0.583,22,0 235 | 4,122,68,0,0,35,0.394,29,0 236 | 3,74,68,28,45,29.7,0.293,23,0 237 | 4,171,72,0,0,43.6,0.479,26,1 238 | 7,181,84,21,192,35.9,0.586,51,1 239 | 0,179,90,27,0,44.1,0.686,23,1 240 | 9,164,84,21,0,30.8,0.831,32,1 241 | 0,104,76,0,0,18.4,0.582,27,0 242 | 1,91,64,24,0,29.2,0.192,21,0 243 | 4,91,70,32,88,33.1,0.446,22,0 244 | 3,139,54,0,0,25.6,0.402,22,1 245 | 6,119,50,22,176,27.1,1.318,33,1 246 | 2,146,76,35,194,38.2,0.329,29,0 247 | 9,184,85,15,0,30,1.213,49,1 248 | 10,122,68,0,0,31.2,0.258,41,0 249 | 0,165,90,33,680,52.3,0.427,23,0 250 | 9,124,70,33,402,35.4,0.282,34,0 251 | 1,111,86,19,0,30.1,0.143,23,0 252 | 9,106,52,0,0,31.2,0.38,42,0 253 | 2,129,84,0,0,28,0.284,27,0 254 | 2,90,80,14,55,24.4,0.249,24,0 255 | 0,86,68,32,0,35.8,0.238,25,0 256 | 12,92,62,7,258,27.6,0.926,44,1 257 | 1,113,64,35,0,33.6,0.543,21,1 258 | 3,111,56,39,0,30.1,0.557,30,0 259 | 2,114,68,22,0,28.7,0.092,25,0 260 | 1,193,50,16,375,25.9,0.655,24,0 261 | 11,155,76,28,150,33.3,1.353,51,1 262 | 3,191,68,15,130,30.9,0.299,34,0 263 | 3,141,0,0,0,30,0.761,27,1 264 | 4,95,70,32,0,32.1,0.612,24,0 265 | 3,142,80,15,0,32.4,0.2,63,0 266 | 4,123,62,0,0,32,0.226,35,1 267 | 5,96,74,18,67,33.6,0.997,43,0 268 | 0,138,0,0,0,36.3,0.933,25,1 269 | 2,128,64,42,0,40,1.101,24,0 270 | 0,102,52,0,0,25.1,0.078,21,0 271 | 2,146,0,0,0,27.5,0.24,28,1 272 | 10,101,86,37,0,45.6,1.136,38,1 273 | 2,108,62,32,56,25.2,0.128,21,0 274 | 3,122,78,0,0,23,0.254,40,0 275 | 1,71,78,50,45,33.2,0.422,21,0 276 | 13,106,70,0,0,34.2,0.251,52,0 277 | 2,100,70,52,57,40.5,0.677,25,0 278 | 7,106,60,24,0,26.5,0.296,29,1 279 | 0,104,64,23,116,27.8,0.454,23,0 280 | 5,114,74,0,0,24.9,0.744,57,0 281 | 2,108,62,10,278,25.3,0.881,22,0 282 | 0,146,70,0,0,37.9,0.334,28,1 283 | 10,129,76,28,122,35.9,0.28,39,0 284 | 7,133,88,15,155,32.4,0.262,37,0 285 | 7,161,86,0,0,30.4,0.165,47,1 286 | 2,108,80,0,0,27,0.259,52,1 287 | 7,136,74,26,135,26,0.647,51,0 288 | 5,155,84,44,545,38.7,0.619,34,0 289 | 1,119,86,39,220,45.6,0.808,29,1 290 | 4,96,56,17,49,20.8,0.34,26,0 291 | 5,108,72,43,75,36.1,0.263,33,0 292 | 0,78,88,29,40,36.9,0.434,21,0 293 | 0,107,62,30,74,36.6,0.757,25,1 294 | 2,128,78,37,182,43.3,1.224,31,1 295 | 1,128,48,45,194,40.5,0.613,24,1 296 | 0,161,50,0,0,21.9,0.254,65,0 297 | 6,151,62,31,120,35.5,0.692,28,0 298 | 2,146,70,38,360,28,0.337,29,1 299 | 0,126,84,29,215,30.7,0.52,24,0 300 | 14,100,78,25,184,36.6,0.412,46,1 301 | 8,112,72,0,0,23.6,0.84,58,0 302 | 0,167,0,0,0,32.3,0.839,30,1 303 | 2,144,58,33,135,31.6,0.422,25,1 304 | 5,77,82,41,42,35.8,0.156,35,0 305 | 5,115,98,0,0,52.9,0.209,28,1 306 | 3,150,76,0,0,21,0.207,37,0 307 | 2,120,76,37,105,39.7,0.215,29,0 308 | 10,161,68,23,132,25.5,0.326,47,1 309 | 0,137,68,14,148,24.8,0.143,21,0 310 | 0,128,68,19,180,30.5,1.391,25,1 311 | 2,124,68,28,205,32.9,0.875,30,1 312 | 6,80,66,30,0,26.2,0.313,41,0 313 | 0,106,70,37,148,39.4,0.605,22,0 314 | 2,155,74,17,96,26.6,0.433,27,1 315 | 3,113,50,10,85,29.5,0.626,25,0 316 | 7,109,80,31,0,35.9,1.127,43,1 317 | 2,112,68,22,94,34.1,0.315,26,0 318 | 3,99,80,11,64,19.3,0.284,30,0 319 | 3,182,74,0,0,30.5,0.345,29,1 320 | 3,115,66,39,140,38.1,0.15,28,0 321 | 6,194,78,0,0,23.5,0.129,59,1 322 | 4,129,60,12,231,27.5,0.527,31,0 323 | 3,112,74,30,0,31.6,0.197,25,1 324 | 0,124,70,20,0,27.4,0.254,36,1 325 | 13,152,90,33,29,26.8,0.731,43,1 326 | 2,112,75,32,0,35.7,0.148,21,0 327 | 1,157,72,21,168,25.6,0.123,24,0 328 | 1,122,64,32,156,35.1,0.692,30,1 329 | 10,179,70,0,0,35.1,0.2,37,0 330 | 2,102,86,36,120,45.5,0.127,23,1 331 | 6,105,70,32,68,30.8,0.122,37,0 332 | 8,118,72,19,0,23.1,1.476,46,0 333 | 2,87,58,16,52,32.7,0.166,25,0 334 | 1,180,0,0,0,43.3,0.282,41,1 335 | 12,106,80,0,0,23.6,0.137,44,0 336 | 1,95,60,18,58,23.9,0.26,22,0 337 | 0,165,76,43,255,47.9,0.259,26,0 338 | 0,117,0,0,0,33.8,0.932,44,0 339 | 5,115,76,0,0,31.2,0.343,44,1 340 | 9,152,78,34,171,34.2,0.893,33,1 341 | 7,178,84,0,0,39.9,0.331,41,1 342 | 1,130,70,13,105,25.9,0.472,22,0 343 | 1,95,74,21,73,25.9,0.673,36,0 344 | 1,0,68,35,0,32,0.389,22,0 345 | 5,122,86,0,0,34.7,0.29,33,0 346 | 8,95,72,0,0,36.8,0.485,57,0 347 | 8,126,88,36,108,38.5,0.349,49,0 348 | 1,139,46,19,83,28.7,0.654,22,0 349 | 3,116,0,0,0,23.5,0.187,23,0 350 | 3,99,62,19,74,21.8,0.279,26,0 351 | 5,0,80,32,0,41,0.346,37,1 352 | 4,92,80,0,0,42.2,0.237,29,0 353 | 4,137,84,0,0,31.2,0.252,30,0 354 | 3,61,82,28,0,34.4,0.243,46,0 355 | 1,90,62,12,43,27.2,0.58,24,0 356 | 3,90,78,0,0,42.7,0.559,21,0 357 | 9,165,88,0,0,30.4,0.302,49,1 358 | 1,125,50,40,167,33.3,0.962,28,1 359 | 13,129,0,30,0,39.9,0.569,44,1 360 | 12,88,74,40,54,35.3,0.378,48,0 361 | 1,196,76,36,249,36.5,0.875,29,1 362 | 5,189,64,33,325,31.2,0.583,29,1 363 | 5,158,70,0,0,29.8,0.207,63,0 364 | 5,103,108,37,0,39.2,0.305,65,0 365 | 4,146,78,0,0,38.5,0.52,67,1 366 | 4,147,74,25,293,34.9,0.385,30,0 367 | 5,99,54,28,83,34,0.499,30,0 368 | 6,124,72,0,0,27.6,0.368,29,1 369 | 0,101,64,17,0,21,0.252,21,0 370 | 3,81,86,16,66,27.5,0.306,22,0 371 | 1,133,102,28,140,32.8,0.234,45,1 372 | 3,173,82,48,465,38.4,2.137,25,1 373 | 0,118,64,23,89,0,1.731,21,0 374 | 0,84,64,22,66,35.8,0.545,21,0 375 | 2,105,58,40,94,34.9,0.225,25,0 376 | 2,122,52,43,158,36.2,0.816,28,0 377 | 12,140,82,43,325,39.2,0.528,58,1 378 | 0,98,82,15,84,25.2,0.299,22,0 379 | 1,87,60,37,75,37.2,0.509,22,0 380 | 4,156,75,0,0,48.3,0.238,32,1 381 | 0,93,100,39,72,43.4,1.021,35,0 382 | 1,107,72,30,82,30.8,0.821,24,0 383 | 0,105,68,22,0,20,0.236,22,0 384 | 1,109,60,8,182,25.4,0.947,21,0 385 | 1,90,62,18,59,25.1,1.268,25,0 386 | 1,125,70,24,110,24.3,0.221,25,0 387 | 1,119,54,13,50,22.3,0.205,24,0 388 | 5,116,74,29,0,32.3,0.66,35,1 389 | 8,105,100,36,0,43.3,0.239,45,1 390 | 5,144,82,26,285,32,0.452,58,1 391 | 3,100,68,23,81,31.6,0.949,28,0 392 | 1,100,66,29,196,32,0.444,42,0 393 | 5,166,76,0,0,45.7,0.34,27,1 394 | 1,131,64,14,415,23.7,0.389,21,0 395 | 4,116,72,12,87,22.1,0.463,37,0 396 | 4,158,78,0,0,32.9,0.803,31,1 397 | 2,127,58,24,275,27.7,1.6,25,0 398 | 3,96,56,34,115,24.7,0.944,39,0 399 | 0,131,66,40,0,34.3,0.196,22,1 400 | 3,82,70,0,0,21.1,0.389,25,0 401 | 3,193,70,31,0,34.9,0.241,25,1 402 | 4,95,64,0,0,32,0.161,31,1 403 | 6,137,61,0,0,24.2,0.151,55,0 404 | 5,136,84,41,88,35,0.286,35,1 405 | 9,72,78,25,0,31.6,0.28,38,0 406 | 5,168,64,0,0,32.9,0.135,41,1 407 | 2,123,48,32,165,42.1,0.52,26,0 408 | 4,115,72,0,0,28.9,0.376,46,1 409 | 0,101,62,0,0,21.9,0.336,25,0 410 | 8,197,74,0,0,25.9,1.191,39,1 411 | 1,172,68,49,579,42.4,0.702,28,1 412 | 6,102,90,39,0,35.7,0.674,28,0 413 | 1,112,72,30,176,34.4,0.528,25,0 414 | 1,143,84,23,310,42.4,1.076,22,0 415 | 1,143,74,22,61,26.2,0.256,21,0 416 | 0,138,60,35,167,34.6,0.534,21,1 417 | 3,173,84,33,474,35.7,0.258,22,1 418 | 1,97,68,21,0,27.2,1.095,22,0 419 | 4,144,82,32,0,38.5,0.554,37,1 420 | 1,83,68,0,0,18.2,0.624,27,0 421 | 3,129,64,29,115,26.4,0.219,28,1 422 | 1,119,88,41,170,45.3,0.507,26,0 423 | 2,94,68,18,76,26,0.561,21,0 424 | 0,102,64,46,78,40.6,0.496,21,0 425 | 2,115,64,22,0,30.8,0.421,21,0 426 | 8,151,78,32,210,42.9,0.516,36,1 427 | 4,184,78,39,277,37,0.264,31,1 428 | 0,94,0,0,0,0,0.256,25,0 429 | 1,181,64,30,180,34.1,0.328,38,1 430 | 0,135,94,46,145,40.6,0.284,26,0 431 | 1,95,82,25,180,35,0.233,43,1 432 | 2,99,0,0,0,22.2,0.108,23,0 433 | 3,89,74,16,85,30.4,0.551,38,0 434 | 1,80,74,11,60,30,0.527,22,0 435 | 2,139,75,0,0,25.6,0.167,29,0 436 | 1,90,68,8,0,24.5,1.138,36,0 437 | 0,141,0,0,0,42.4,0.205,29,1 438 | 12,140,85,33,0,37.4,0.244,41,0 439 | 5,147,75,0,0,29.9,0.434,28,0 440 | 1,97,70,15,0,18.2,0.147,21,0 441 | 6,107,88,0,0,36.8,0.727,31,0 442 | 0,189,104,25,0,34.3,0.435,41,1 443 | 2,83,66,23,50,32.2,0.497,22,0 444 | 4,117,64,27,120,33.2,0.23,24,0 445 | 8,108,70,0,0,30.5,0.955,33,1 446 | 4,117,62,12,0,29.7,0.38,30,1 447 | 0,180,78,63,14,59.4,2.42,25,1 448 | 1,100,72,12,70,25.3,0.658,28,0 449 | 0,95,80,45,92,36.5,0.33,26,0 450 | 0,104,64,37,64,33.6,0.51,22,1 451 | 0,120,74,18,63,30.5,0.285,26,0 452 | 1,82,64,13,95,21.2,0.415,23,0 453 | 2,134,70,0,0,28.9,0.542,23,1 454 | 0,91,68,32,210,39.9,0.381,25,0 455 | 2,119,0,0,0,19.6,0.832,72,0 456 | 2,100,54,28,105,37.8,0.498,24,0 457 | 14,175,62,30,0,33.6,0.212,38,1 458 | 1,135,54,0,0,26.7,0.687,62,0 459 | 5,86,68,28,71,30.2,0.364,24,0 460 | 10,148,84,48,237,37.6,1.001,51,1 461 | 9,134,74,33,60,25.9,0.46,81,0 462 | 9,120,72,22,56,20.8,0.733,48,0 463 | 1,71,62,0,0,21.8,0.416,26,0 464 | 8,74,70,40,49,35.3,0.705,39,0 465 | 5,88,78,30,0,27.6,0.258,37,0 466 | 10,115,98,0,0,24,1.022,34,0 467 | 0,124,56,13,105,21.8,0.452,21,0 468 | 0,74,52,10,36,27.8,0.269,22,0 469 | 0,97,64,36,100,36.8,0.6,25,0 470 | 8,120,0,0,0,30,0.183,38,1 471 | 6,154,78,41,140,46.1,0.571,27,0 472 | 1,144,82,40,0,41.3,0.607,28,0 473 | 0,137,70,38,0,33.2,0.17,22,0 474 | 0,119,66,27,0,38.8,0.259,22,0 475 | 7,136,90,0,0,29.9,0.21,50,0 476 | 4,114,64,0,0,28.9,0.126,24,0 477 | 0,137,84,27,0,27.3,0.231,59,0 478 | 2,105,80,45,191,33.7,0.711,29,1 479 | 7,114,76,17,110,23.8,0.466,31,0 480 | 8,126,74,38,75,25.9,0.162,39,0 481 | 4,132,86,31,0,28,0.419,63,0 482 | 3,158,70,30,328,35.5,0.344,35,1 483 | 0,123,88,37,0,35.2,0.197,29,0 484 | 4,85,58,22,49,27.8,0.306,28,0 485 | 0,84,82,31,125,38.2,0.233,23,0 486 | 0,145,0,0,0,44.2,0.63,31,1 487 | 0,135,68,42,250,42.3,0.365,24,1 488 | 1,139,62,41,480,40.7,0.536,21,0 489 | 0,173,78,32,265,46.5,1.159,58,0 490 | 4,99,72,17,0,25.6,0.294,28,0 491 | 8,194,80,0,0,26.1,0.551,67,0 492 | 2,83,65,28,66,36.8,0.629,24,0 493 | 2,89,90,30,0,33.5,0.292,42,0 494 | 4,99,68,38,0,32.8,0.145,33,0 495 | 4,125,70,18,122,28.9,1.144,45,1 496 | 3,80,0,0,0,0,0.174,22,0 497 | 6,166,74,0,0,26.6,0.304,66,0 498 | 5,110,68,0,0,26,0.292,30,0 499 | 2,81,72,15,76,30.1,0.547,25,0 500 | 7,195,70,33,145,25.1,0.163,55,1 501 | 6,154,74,32,193,29.3,0.839,39,0 502 | 2,117,90,19,71,25.2,0.313,21,0 503 | 3,84,72,32,0,37.2,0.267,28,0 504 | 6,0,68,41,0,39,0.727,41,1 505 | 7,94,64,25,79,33.3,0.738,41,0 506 | 3,96,78,39,0,37.3,0.238,40,0 507 | 10,75,82,0,0,33.3,0.263,38,0 508 | 0,180,90,26,90,36.5,0.314,35,1 509 | 1,130,60,23,170,28.6,0.692,21,0 510 | 2,84,50,23,76,30.4,0.968,21,0 511 | 8,120,78,0,0,25,0.409,64,0 512 | 12,84,72,31,0,29.7,0.297,46,1 513 | 0,139,62,17,210,22.1,0.207,21,0 514 | 9,91,68,0,0,24.2,0.2,58,0 515 | 2,91,62,0,0,27.3,0.525,22,0 516 | 3,99,54,19,86,25.6,0.154,24,0 517 | 3,163,70,18,105,31.6,0.268,28,1 518 | 9,145,88,34,165,30.3,0.771,53,1 519 | 7,125,86,0,0,37.6,0.304,51,0 520 | 13,76,60,0,0,32.8,0.18,41,0 521 | 6,129,90,7,326,19.6,0.582,60,0 522 | 2,68,70,32,66,25,0.187,25,0 523 | 3,124,80,33,130,33.2,0.305,26,0 524 | 6,114,0,0,0,0,0.189,26,0 525 | 9,130,70,0,0,34.2,0.652,45,1 526 | 3,125,58,0,0,31.6,0.151,24,0 527 | 3,87,60,18,0,21.8,0.444,21,0 528 | 1,97,64,19,82,18.2,0.299,21,0 529 | 3,116,74,15,105,26.3,0.107,24,0 530 | 0,117,66,31,188,30.8,0.493,22,0 531 | 0,111,65,0,0,24.6,0.66,31,0 532 | 2,122,60,18,106,29.8,0.717,22,0 533 | 0,107,76,0,0,45.3,0.686,24,0 534 | 1,86,66,52,65,41.3,0.917,29,0 535 | 6,91,0,0,0,29.8,0.501,31,0 536 | 1,77,56,30,56,33.3,1.251,24,0 537 | 4,132,0,0,0,32.9,0.302,23,1 538 | 0,105,90,0,0,29.6,0.197,46,0 539 | 0,57,60,0,0,21.7,0.735,67,0 540 | 0,127,80,37,210,36.3,0.804,23,0 541 | 3,129,92,49,155,36.4,0.968,32,1 542 | 8,100,74,40,215,39.4,0.661,43,1 543 | 3,128,72,25,190,32.4,0.549,27,1 544 | 10,90,85,32,0,34.9,0.825,56,1 545 | 4,84,90,23,56,39.5,0.159,25,0 546 | 1,88,78,29,76,32,0.365,29,0 547 | 8,186,90,35,225,34.5,0.423,37,1 548 | 5,187,76,27,207,43.6,1.034,53,1 549 | 4,131,68,21,166,33.1,0.16,28,0 550 | 1,164,82,43,67,32.8,0.341,50,0 551 | 4,189,110,31,0,28.5,0.68,37,0 552 | 1,116,70,28,0,27.4,0.204,21,0 553 | 3,84,68,30,106,31.9,0.591,25,0 554 | 6,114,88,0,0,27.8,0.247,66,0 555 | 1,88,62,24,44,29.9,0.422,23,0 556 | 1,84,64,23,115,36.9,0.471,28,0 557 | 7,124,70,33,215,25.5,0.161,37,0 558 | 1,97,70,40,0,38.1,0.218,30,0 559 | 8,110,76,0,0,27.8,0.237,58,0 560 | 11,103,68,40,0,46.2,0.126,42,0 561 | 11,85,74,0,0,30.1,0.3,35,0 562 | 6,125,76,0,0,33.8,0.121,54,1 563 | 0,198,66,32,274,41.3,0.502,28,1 564 | 1,87,68,34,77,37.6,0.401,24,0 565 | 6,99,60,19,54,26.9,0.497,32,0 566 | 0,91,80,0,0,32.4,0.601,27,0 567 | 2,95,54,14,88,26.1,0.748,22,0 568 | 1,99,72,30,18,38.6,0.412,21,0 569 | 6,92,62,32,126,32,0.085,46,0 570 | 4,154,72,29,126,31.3,0.338,37,0 571 | 0,121,66,30,165,34.3,0.203,33,1 572 | 3,78,70,0,0,32.5,0.27,39,0 573 | 2,130,96,0,0,22.6,0.268,21,0 574 | 3,111,58,31,44,29.5,0.43,22,0 575 | 2,98,60,17,120,34.7,0.198,22,0 576 | 1,143,86,30,330,30.1,0.892,23,0 577 | 1,119,44,47,63,35.5,0.28,25,0 578 | 6,108,44,20,130,24,0.813,35,0 579 | 2,118,80,0,0,42.9,0.693,21,1 580 | 10,133,68,0,0,27,0.245,36,0 581 | 2,197,70,99,0,34.7,0.575,62,1 582 | 0,151,90,46,0,42.1,0.371,21,1 583 | 6,109,60,27,0,25,0.206,27,0 584 | 12,121,78,17,0,26.5,0.259,62,0 585 | 8,100,76,0,0,38.7,0.19,42,0 586 | 8,124,76,24,600,28.7,0.687,52,1 587 | 1,93,56,11,0,22.5,0.417,22,0 588 | 8,143,66,0,0,34.9,0.129,41,1 589 | 6,103,66,0,0,24.3,0.249,29,0 590 | 3,176,86,27,156,33.3,1.154,52,1 591 | 0,73,0,0,0,21.1,0.342,25,0 592 | 11,111,84,40,0,46.8,0.925,45,1 593 | 2,112,78,50,140,39.4,0.175,24,0 594 | 3,132,80,0,0,34.4,0.402,44,1 595 | 2,82,52,22,115,28.5,1.699,25,0 596 | 6,123,72,45,230,33.6,0.733,34,0 597 | 0,188,82,14,185,32,0.682,22,1 598 | 0,67,76,0,0,45.3,0.194,46,0 599 | 1,89,24,19,25,27.8,0.559,21,0 600 | 1,173,74,0,0,36.8,0.088,38,1 601 | 1,109,38,18,120,23.1,0.407,26,0 602 | 1,108,88,19,0,27.1,0.4,24,0 603 | 6,96,0,0,0,23.7,0.19,28,0 604 | 1,124,74,36,0,27.8,0.1,30,0 605 | 7,150,78,29,126,35.2,0.692,54,1 606 | 4,183,0,0,0,28.4,0.212,36,1 607 | 1,124,60,32,0,35.8,0.514,21,0 608 | 1,181,78,42,293,40,1.258,22,1 609 | 1,92,62,25,41,19.5,0.482,25,0 610 | 0,152,82,39,272,41.5,0.27,27,0 611 | 1,111,62,13,182,24,0.138,23,0 612 | 3,106,54,21,158,30.9,0.292,24,0 613 | 3,174,58,22,194,32.9,0.593,36,1 614 | 7,168,88,42,321,38.2,0.787,40,1 615 | 6,105,80,28,0,32.5,0.878,26,0 616 | 11,138,74,26,144,36.1,0.557,50,1 617 | 3,106,72,0,0,25.8,0.207,27,0 618 | 6,117,96,0,0,28.7,0.157,30,0 619 | 2,68,62,13,15,20.1,0.257,23,0 620 | 9,112,82,24,0,28.2,1.282,50,1 621 | 0,119,0,0,0,32.4,0.141,24,1 622 | 2,112,86,42,160,38.4,0.246,28,0 623 | 2,92,76,20,0,24.2,1.698,28,0 624 | 6,183,94,0,0,40.8,1.461,45,0 625 | 0,94,70,27,115,43.5,0.347,21,0 626 | 2,108,64,0,0,30.8,0.158,21,0 627 | 4,90,88,47,54,37.7,0.362,29,0 628 | 0,125,68,0,0,24.7,0.206,21,0 629 | 0,132,78,0,0,32.4,0.393,21,0 630 | 5,128,80,0,0,34.6,0.144,45,0 631 | 4,94,65,22,0,24.7,0.148,21,0 632 | 7,114,64,0,0,27.4,0.732,34,1 633 | 0,102,78,40,90,34.5,0.238,24,0 634 | 2,111,60,0,0,26.2,0.343,23,0 635 | 1,128,82,17,183,27.5,0.115,22,0 636 | 10,92,62,0,0,25.9,0.167,31,0 637 | 13,104,72,0,0,31.2,0.465,38,1 638 | 5,104,74,0,0,28.8,0.153,48,0 639 | 2,94,76,18,66,31.6,0.649,23,0 640 | 7,97,76,32,91,40.9,0.871,32,1 641 | 1,100,74,12,46,19.5,0.149,28,0 642 | 0,102,86,17,105,29.3,0.695,27,0 643 | 4,128,70,0,0,34.3,0.303,24,0 644 | 6,147,80,0,0,29.5,0.178,50,1 645 | 4,90,0,0,0,28,0.61,31,0 646 | 3,103,72,30,152,27.6,0.73,27,0 647 | 2,157,74,35,440,39.4,0.134,30,0 648 | 1,167,74,17,144,23.4,0.447,33,1 649 | 0,179,50,36,159,37.8,0.455,22,1 650 | 11,136,84,35,130,28.3,0.26,42,1 651 | 0,107,60,25,0,26.4,0.133,23,0 652 | 1,91,54,25,100,25.2,0.234,23,0 653 | 1,117,60,23,106,33.8,0.466,27,0 654 | 5,123,74,40,77,34.1,0.269,28,0 655 | 2,120,54,0,0,26.8,0.455,27,0 656 | 1,106,70,28,135,34.2,0.142,22,0 657 | 2,155,52,27,540,38.7,0.24,25,1 658 | 2,101,58,35,90,21.8,0.155,22,0 659 | 1,120,80,48,200,38.9,1.162,41,0 660 | 11,127,106,0,0,39,0.19,51,0 661 | 3,80,82,31,70,34.2,1.292,27,1 662 | 10,162,84,0,0,27.7,0.182,54,0 663 | 1,199,76,43,0,42.9,1.394,22,1 664 | 8,167,106,46,231,37.6,0.165,43,1 665 | 9,145,80,46,130,37.9,0.637,40,1 666 | 6,115,60,39,0,33.7,0.245,40,1 667 | 1,112,80,45,132,34.8,0.217,24,0 668 | 4,145,82,18,0,32.5,0.235,70,1 669 | 10,111,70,27,0,27.5,0.141,40,1 670 | 6,98,58,33,190,34,0.43,43,0 671 | 9,154,78,30,100,30.9,0.164,45,0 672 | 6,165,68,26,168,33.6,0.631,49,0 673 | 1,99,58,10,0,25.4,0.551,21,0 674 | 10,68,106,23,49,35.5,0.285,47,0 675 | 3,123,100,35,240,57.3,0.88,22,0 676 | 8,91,82,0,0,35.6,0.587,68,0 677 | 6,195,70,0,0,30.9,0.328,31,1 678 | 9,156,86,0,0,24.8,0.23,53,1 679 | 0,93,60,0,0,35.3,0.263,25,0 680 | 3,121,52,0,0,36,0.127,25,1 681 | 2,101,58,17,265,24.2,0.614,23,0 682 | 2,56,56,28,45,24.2,0.332,22,0 683 | 0,162,76,36,0,49.6,0.364,26,1 684 | 0,95,64,39,105,44.6,0.366,22,0 685 | 4,125,80,0,0,32.3,0.536,27,1 686 | 5,136,82,0,0,0,0.64,69,0 687 | 2,129,74,26,205,33.2,0.591,25,0 688 | 3,130,64,0,0,23.1,0.314,22,0 689 | 1,107,50,19,0,28.3,0.181,29,0 690 | 1,140,74,26,180,24.1,0.828,23,0 691 | 1,144,82,46,180,46.1,0.335,46,1 692 | 8,107,80,0,0,24.6,0.856,34,0 693 | 13,158,114,0,0,42.3,0.257,44,1 694 | 2,121,70,32,95,39.1,0.886,23,0 695 | 7,129,68,49,125,38.5,0.439,43,1 696 | 2,90,60,0,0,23.5,0.191,25,0 697 | 7,142,90,24,480,30.4,0.128,43,1 698 | 3,169,74,19,125,29.9,0.268,31,1 699 | 0,99,0,0,0,25,0.253,22,0 700 | 4,127,88,11,155,34.5,0.598,28,0 701 | 4,118,70,0,0,44.5,0.904,26,0 702 | 2,122,76,27,200,35.9,0.483,26,0 703 | 6,125,78,31,0,27.6,0.565,49,1 704 | 1,168,88,29,0,35,0.905,52,1 705 | 2,129,0,0,0,38.5,0.304,41,0 706 | 4,110,76,20,100,28.4,0.118,27,0 707 | 6,80,80,36,0,39.8,0.177,28,0 708 | 10,115,0,0,0,0,0.261,30,1 709 | 2,127,46,21,335,34.4,0.176,22,0 710 | 9,164,78,0,0,32.8,0.148,45,1 711 | 2,93,64,32,160,38,0.674,23,1 712 | 3,158,64,13,387,31.2,0.295,24,0 713 | 5,126,78,27,22,29.6,0.439,40,0 714 | 10,129,62,36,0,41.2,0.441,38,1 715 | 0,134,58,20,291,26.4,0.352,21,0 716 | 3,102,74,0,0,29.5,0.121,32,0 717 | 7,187,50,33,392,33.9,0.826,34,1 718 | 3,173,78,39,185,33.8,0.97,31,1 719 | 10,94,72,18,0,23.1,0.595,56,0 720 | 1,108,60,46,178,35.5,0.415,24,0 721 | 5,97,76,27,0,35.6,0.378,52,1 722 | 4,83,86,19,0,29.3,0.317,34,0 723 | 1,114,66,36,200,38.1,0.289,21,0 724 | 1,149,68,29,127,29.3,0.349,42,1 725 | 5,117,86,30,105,39.1,0.251,42,0 726 | 1,111,94,0,0,32.8,0.265,45,0 727 | 4,112,78,40,0,39.4,0.236,38,0 728 | 1,116,78,29,180,36.1,0.496,25,0 729 | 0,141,84,26,0,32.4,0.433,22,0 730 | 2,175,88,0,0,22.9,0.326,22,0 731 | 2,92,52,0,0,30.1,0.141,22,0 732 | 3,130,78,23,79,28.4,0.323,34,1 733 | 8,120,86,0,0,28.4,0.259,22,1 734 | 2,174,88,37,120,44.5,0.646,24,1 735 | 2,106,56,27,165,29,0.426,22,0 736 | 2,105,75,0,0,23.3,0.56,53,0 737 | 4,95,60,32,0,35.4,0.284,28,0 738 | 0,126,86,27,120,27.4,0.515,21,0 739 | 8,65,72,23,0,32,0.6,42,0 740 | 2,99,60,17,160,36.6,0.453,21,0 741 | 1,102,74,0,0,39.5,0.293,42,1 742 | 11,120,80,37,150,42.3,0.785,48,1 743 | 3,102,44,20,94,30.8,0.4,26,0 744 | 1,109,58,18,116,28.5,0.219,22,0 745 | 9,140,94,0,0,32.7,0.734,45,1 746 | 13,153,88,37,140,40.6,1.174,39,0 747 | 12,100,84,33,105,30,0.488,46,0 748 | 1,147,94,41,0,49.3,0.358,27,1 749 | 1,81,74,41,57,46.3,1.096,32,0 750 | 3,187,70,22,200,36.4,0.408,36,1 751 | 6,162,62,0,0,24.3,0.178,50,1 752 | 4,136,70,0,0,31.2,1.182,22,1 753 | 1,121,78,39,74,39,0.261,28,0 754 | 3,108,62,24,0,26,0.223,25,0 755 | 0,181,88,44,510,43.3,0.222,26,1 756 | 8,154,78,32,0,32.4,0.443,45,1 757 | 1,128,88,39,110,36.5,1.057,37,1 758 | 7,137,90,41,0,32,0.391,39,0 759 | 0,123,72,0,0,36.3,0.258,52,1 760 | 1,106,76,0,0,37.5,0.197,26,0 761 | 6,190,92,0,0,35.5,0.278,66,1 762 | 2,88,58,26,16,28.4,0.766,22,0 763 | 9,170,74,31,0,44,0.403,43,1 764 | 9,89,62,0,0,22.5,0.142,33,0 765 | 10,101,76,48,180,32.9,0.171,63,0 766 | 2,122,70,27,0,36.8,0.34,27,0 767 | 5,121,72,23,112,26.2,0.245,30,0 768 | 1,126,60,0,0,30.1,0.349,47,1 769 | 1,93,70,31,0,30.4,0.315,23,0 -------------------------------------------------------------------------------- /colab_files_to_train_models/Multiple disease prediction system - heart.ipynb: -------------------------------------------------------------------------------- 1 | {"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Multiple disease prediction system - heart.ipynb","provenance":[],"authorship_tag":"ABX9TyNkEJ4hIKtyX260ZMlxNVm4"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"aTb-9TFFqprC"},"source":["Importing the Dependencies"]},{"cell_type":"code","metadata":{"id":"3q9U3S_whh3-","executionInfo":{"status":"ok","timestamp":1653200233515,"user_tz":-330,"elapsed":1659,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"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"],"execution_count":1,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"egMd5zeurTMR"},"source":["Data Collection and Processing"]},{"cell_type":"code","metadata":{"id":"0q-3-LkQrREV","executionInfo":{"status":"ok","timestamp":1653200233516,"user_tz":-330,"elapsed":13,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["# loading the csv data to a Pandas DataFrame\n","heart_data = pd.read_csv('/content/heart.csv')"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"M8dQxSTqriWD","executionInfo":{"status":"ok","timestamp":1653200233516,"user_tz":-330,"elapsed":12,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"0ab48855-8403-4103-c7c8-e307c07da055"},"source":["# print first 5 rows of the dataset\n","heart_data.head()"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"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 "],"text/html":["\n","
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agesexcptrestbpscholfbsrestecgthalachexangoldpeakslopecathaltarget
063131452331015002.30011
137121302500118703.50021
241011302040017201.42021
356111202360117800.82021
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agesexcptrestbpscholfbsrestecgthalachexangoldpeakslopecathaltarget
29857001402410112310.21030
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30068101441931114103.41230
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agesexcptrestbpscholfbsrestecgthalachexangoldpeakslopecathaltarget
count303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000
mean54.3663370.6831680.966997131.623762246.2640260.1485150.528053149.6468650.3267331.0396041.3993400.7293732.3135310.544554
std9.0821010.4660111.03205217.53814351.8307510.3561980.52586022.9051610.4697941.1610750.6162261.0226060.6122770.498835
min29.0000000.0000000.00000094.000000126.0000000.0000000.00000071.0000000.0000000.0000000.0000000.0000000.0000000.000000
25%47.5000000.0000000.000000120.000000211.0000000.0000000.000000133.5000000.0000000.0000001.0000000.0000002.0000000.000000
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
max77.0000001.0000003.000000200.000000564.0000001.0000002.000000202.0000001.0000006.2000002.0000004.0000003.0000001.000000
\n","
\n"," \n"," \n"," \n","\n"," \n","
\n","
\n"," "]},"metadata":{},"execution_count":8}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4InaOSIUsfWP","executionInfo":{"status":"ok","timestamp":1653200234318,"user_tz":-330,"elapsed":24,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"fb25a289-d5e9-438f-d21d-f09474cacbc6"},"source":["# checking the distribution of Target Variable\n","heart_data['target'].value_counts()"],"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1 165\n","0 138\n","Name: target, dtype: int64"]},"metadata":{},"execution_count":9}]},{"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","metadata":{"id":"Q6yfbswrs7m3","executionInfo":{"status":"ok","timestamp":1653200234318,"user_tz":-330,"elapsed":22,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["X = heart_data.drop(columns='target', axis=1)\n","Y = heart_data['target']"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XJoCp4ZKtpZy","executionInfo":{"status":"ok","timestamp":1653200234319,"user_tz":-330,"elapsed":23,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"14301a08-f0ce-41ef-ea17-b6a315aebef5"},"source":["print(X)"],"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","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"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nukuj-YItq1w","executionInfo":{"status":"ok","timestamp":1653200234319,"user_tz":-330,"elapsed":21,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"ee5e6451-0b29-4a02-aebf-24367d73874b"},"source":["print(Y)"],"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","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"]}]},{"cell_type":"markdown","metadata":{"id":"_EcjSE3Et18n"},"source":["Splitting the Data into Training data & Test Data"]},{"cell_type":"code","metadata":{"id":"a-UUfRUxtuga","executionInfo":{"status":"ok","timestamp":1653200234319,"user_tz":-330,"elapsed":20,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2)"],"execution_count":13,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"x7PrjC6zuf6X","executionInfo":{"status":"ok","timestamp":1653200234320,"user_tz":-330,"elapsed":21,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"d78b7e79-4659-4101-c9d6-61df1d0d5981"},"source":["print(X.shape, X_train.shape, X_test.shape)"],"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["(303, 13) (242, 13) (61, 13)\n"]}]},{"cell_type":"markdown","metadata":{"id":"beSkZmpVuvn9"},"source":["Model Training"]},{"cell_type":"markdown","metadata":{"id":"gi2NOWZjuxzw"},"source":["Logistic Regression"]},{"cell_type":"code","metadata":{"id":"4-Md74FYuqNL","executionInfo":{"status":"ok","timestamp":1653200234320,"user_tz":-330,"elapsed":20,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["model = LogisticRegression()"],"execution_count":15,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kCdHYxGUu7XD","executionInfo":{"status":"ok","timestamp":1653200234321,"user_tz":-330,"elapsed":20,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"e24ea90f-2542-44ca-abbd-9d0c9cce231d"},"source":["# training the LogisticRegression model with Training data\n","model.fit(X_train, Y_train)"],"execution_count":16,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: 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"," extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"]},{"output_type":"execute_result","data":{"text/plain":["LogisticRegression()"]},"metadata":{},"execution_count":16}]},{"cell_type":"markdown","metadata":{"id":"ZYIw8Gi9vXfU"},"source":["Model Evaluation"]},{"cell_type":"markdown","metadata":{"id":"wmxAekfZvZa9"},"source":["Accuracy Score"]},{"cell_type":"code","metadata":{"id":"g19JaUTMvPKy","executionInfo":{"status":"ok","timestamp":1653200234321,"user_tz":-330,"elapsed":18,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["# accuracy on training data\n","X_train_prediction = model.predict(X_train)\n","training_data_accuracy = accuracy_score(X_train_prediction, Y_train)"],"execution_count":17,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uQBZvBh8v7R_","executionInfo":{"status":"ok","timestamp":1653200234908,"user_tz":-330,"elapsed":605,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"69f64655-7f79-4bdf-a0f1-6288620f032b"},"source":["print('Accuracy on Training data : ', training_data_accuracy)"],"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy on Training data : 0.8512396694214877\n"]}]},{"cell_type":"code","metadata":{"id":"mDONDJdlwBIO","executionInfo":{"status":"ok","timestamp":1653200234908,"user_tz":-330,"elapsed":12,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["# accuracy on test data\n","X_test_prediction = model.predict(X_test)\n","test_data_accuracy = accuracy_score(X_test_prediction, Y_test)"],"execution_count":19,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_MBS-OqdwYpf","executionInfo":{"status":"ok","timestamp":1653200234909,"user_tz":-330,"elapsed":13,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"bbe13e78-0dab-4925-e541-fb39be7d847f"},"source":["print('Accuracy on Test data : ', test_data_accuracy)"],"execution_count":20,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy on Test data : 0.819672131147541\n"]}]},{"cell_type":"markdown","metadata":{"id":"jIruVh3Qwq0e"},"source":["Building a Predictive System"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9ercruC9wb4C","executionInfo":{"status":"ok","timestamp":1653200234909,"user_tz":-330,"elapsed":12,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"e4cddd3b-acdb-482c-a25f-b720f02111c9"},"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')"],"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["[0]\n","The Person does not have a Heart Disease\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n"," \"X does not have valid feature names, but\"\n"]}]},{"cell_type":"markdown","metadata":{"id":"FCHCMHpshHU4"},"source":["Saving the trained model"]},{"cell_type":"code","metadata":{"id":"cdmTOR4MhHCB","executionInfo":{"status":"ok","timestamp":1653200234910,"user_tz":-330,"elapsed":11,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["import pickle"],"execution_count":22,"outputs":[]},{"cell_type":"code","metadata":{"id":"4gN09lokhKuZ","executionInfo":{"status":"ok","timestamp":1653200234910,"user_tz":-330,"elapsed":10,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["filename = 'heart_disease_model.sav'\n","pickle.dump(model, open(filename, 'wb'))"],"execution_count":23,"outputs":[]},{"cell_type":"code","metadata":{"id":"IKW4D5CqhP5X","executionInfo":{"status":"ok","timestamp":1653200234911,"user_tz":-330,"elapsed":11,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["# loading the saved model\n","loaded_model = pickle.load(open('heart_disease_model.sav', 'rb'))"],"execution_count":24,"outputs":[]},{"cell_type":"code","source":["for column in X.columns:\n"," print(column)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1SiD-oDpPSxY","executionInfo":{"status":"ok","timestamp":1653200234911,"user_tz":-330,"elapsed":11,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"13c5618c-e337-4637-a35a-452e607e5ef4"},"execution_count":25,"outputs":[{"output_type":"stream","name":"stdout","text":["age\n","sex\n","cp\n","trestbps\n","chol\n","fbs\n","restecg\n","thalach\n","exang\n","oldpeak\n","slope\n","ca\n","thal\n"]}]},{"cell_type":"code","source":[""],"metadata":{"id":"A23YZI2tPaFk","executionInfo":{"status":"ok","timestamp":1653200234911,"user_tz":-330,"elapsed":8,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"execution_count":25,"outputs":[]}]} -------------------------------------------------------------------------------- /dataset/parkinsons.csv: -------------------------------------------------------------------------------- 1 | name,MDVP:Fo(Hz),MDVP:Fhi(Hz),MDVP:Flo(Hz),MDVP:Jitter(%),MDVP:Jitter(Abs),MDVP:RAP,MDVP:PPQ,Jitter:DDP,MDVP:Shimmer,MDVP:Shimmer(dB),Shimmer:APQ3,Shimmer:APQ5,MDVP:APQ,Shimmer:DDA,NHR,HNR,status,RPDE,DFA,spread1,spread2,D2,PPE 2 | phon_R01_S01_1,119.99200,157.30200,74.99700,0.00784,0.00007,0.00370,0.00554,0.01109,0.04374,0.42600,0.02182,0.03130,0.02971,0.06545,0.02211,21.03300,1,0.414783,0.815285,-4.813031,0.266482,2.301442,0.284654 3 | phon_R01_S01_2,122.40000,148.65000,113.81900,0.00968,0.00008,0.00465,0.00696,0.01394,0.06134,0.62600,0.03134,0.04518,0.04368,0.09403,0.01929,19.08500,1,0.458359,0.819521,-4.075192,0.335590,2.486855,0.368674 4 | phon_R01_S01_3,116.68200,131.11100,111.55500,0.01050,0.00009,0.00544,0.00781,0.01633,0.05233,0.48200,0.02757,0.03858,0.03590,0.08270,0.01309,20.65100,1,0.429895,0.825288,-4.443179,0.311173,2.342259,0.332634 5 | phon_R01_S01_4,116.67600,137.87100,111.36600,0.00997,0.00009,0.00502,0.00698,0.01505,0.05492,0.51700,0.02924,0.04005,0.03772,0.08771,0.01353,20.64400,1,0.434969,0.819235,-4.117501,0.334147,2.405554,0.368975 6 | phon_R01_S01_5,116.01400,141.78100,110.65500,0.01284,0.00011,0.00655,0.00908,0.01966,0.06425,0.58400,0.03490,0.04825,0.04465,0.10470,0.01767,19.64900,1,0.417356,0.823484,-3.747787,0.234513,2.332180,0.410335 7 | phon_R01_S01_6,120.55200,131.16200,113.78700,0.00968,0.00008,0.00463,0.00750,0.01388,0.04701,0.45600,0.02328,0.03526,0.03243,0.06985,0.01222,21.37800,1,0.415564,0.825069,-4.242867,0.299111,2.187560,0.357775 8 | phon_R01_S02_1,120.26700,137.24400,114.82000,0.00333,0.00003,0.00155,0.00202,0.00466,0.01608,0.14000,0.00779,0.00937,0.01351,0.02337,0.00607,24.88600,1,0.596040,0.764112,-5.634322,0.257682,1.854785,0.211756 9 | phon_R01_S02_2,107.33200,113.84000,104.31500,0.00290,0.00003,0.00144,0.00182,0.00431,0.01567,0.13400,0.00829,0.00946,0.01256,0.02487,0.00344,26.89200,1,0.637420,0.763262,-6.167603,0.183721,2.064693,0.163755 10 | phon_R01_S02_3,95.73000,132.06800,91.75400,0.00551,0.00006,0.00293,0.00332,0.00880,0.02093,0.19100,0.01073,0.01277,0.01717,0.03218,0.01070,21.81200,1,0.615551,0.773587,-5.498678,0.327769,2.322511,0.231571 11 | phon_R01_S02_4,95.05600,120.10300,91.22600,0.00532,0.00006,0.00268,0.00332,0.00803,0.02838,0.25500,0.01441,0.01725,0.02444,0.04324,0.01022,21.86200,1,0.547037,0.798463,-5.011879,0.325996,2.432792,0.271362 12 | phon_R01_S02_5,88.33300,112.24000,84.07200,0.00505,0.00006,0.00254,0.00330,0.00763,0.02143,0.19700,0.01079,0.01342,0.01892,0.03237,0.01166,21.11800,1,0.611137,0.776156,-5.249770,0.391002,2.407313,0.249740 13 | phon_R01_S02_6,91.90400,115.87100,86.29200,0.00540,0.00006,0.00281,0.00336,0.00844,0.02752,0.24900,0.01424,0.01641,0.02214,0.04272,0.01141,21.41400,1,0.583390,0.792520,-4.960234,0.363566,2.642476,0.275931 14 | phon_R01_S04_1,136.92600,159.86600,131.27600,0.00293,0.00002,0.00118,0.00153,0.00355,0.01259,0.11200,0.00656,0.00717,0.01140,0.01968,0.00581,25.70300,1,0.460600,0.646846,-6.547148,0.152813,2.041277,0.138512 15 | phon_R01_S04_2,139.17300,179.13900,76.55600,0.00390,0.00003,0.00165,0.00208,0.00496,0.01642,0.15400,0.00728,0.00932,0.01797,0.02184,0.01041,24.88900,1,0.430166,0.665833,-5.660217,0.254989,2.519422,0.199889 16 | phon_R01_S04_3,152.84500,163.30500,75.83600,0.00294,0.00002,0.00121,0.00149,0.00364,0.01828,0.15800,0.01064,0.00972,0.01246,0.03191,0.00609,24.92200,1,0.474791,0.654027,-6.105098,0.203653,2.125618,0.170100 17 | phon_R01_S04_4,142.16700,217.45500,83.15900,0.00369,0.00003,0.00157,0.00203,0.00471,0.01503,0.12600,0.00772,0.00888,0.01359,0.02316,0.00839,25.17500,1,0.565924,0.658245,-5.340115,0.210185,2.205546,0.234589 18 | phon_R01_S04_5,144.18800,349.25900,82.76400,0.00544,0.00004,0.00211,0.00292,0.00632,0.02047,0.19200,0.00969,0.01200,0.02074,0.02908,0.01859,22.33300,1,0.567380,0.644692,-5.440040,0.239764,2.264501,0.218164 19 | phon_R01_S04_6,168.77800,232.18100,75.60300,0.00718,0.00004,0.00284,0.00387,0.00853,0.03327,0.34800,0.01441,0.01893,0.03430,0.04322,0.02919,20.37600,1,0.631099,0.605417,-2.931070,0.434326,3.007463,0.430788 20 | phon_R01_S05_1,153.04600,175.82900,68.62300,0.00742,0.00005,0.00364,0.00432,0.01092,0.05517,0.54200,0.02471,0.03572,0.05767,0.07413,0.03160,17.28000,1,0.665318,0.719467,-3.949079,0.357870,3.109010,0.377429 21 | phon_R01_S05_2,156.40500,189.39800,142.82200,0.00768,0.00005,0.00372,0.00399,0.01116,0.03995,0.34800,0.01721,0.02374,0.04310,0.05164,0.03365,17.15300,1,0.649554,0.686080,-4.554466,0.340176,2.856676,0.322111 22 | phon_R01_S05_3,153.84800,165.73800,65.78200,0.00840,0.00005,0.00428,0.00450,0.01285,0.03810,0.32800,0.01667,0.02383,0.04055,0.05000,0.03871,17.53600,1,0.660125,0.704087,-4.095442,0.262564,2.739710,0.365391 23 | phon_R01_S05_4,153.88000,172.86000,78.12800,0.00480,0.00003,0.00232,0.00267,0.00696,0.04137,0.37000,0.02021,0.02591,0.04525,0.06062,0.01849,19.49300,1,0.629017,0.698951,-5.186960,0.237622,2.557536,0.259765 24 | phon_R01_S05_5,167.93000,193.22100,79.06800,0.00442,0.00003,0.00220,0.00247,0.00661,0.04351,0.37700,0.02228,0.02540,0.04246,0.06685,0.01280,22.46800,1,0.619060,0.679834,-4.330956,0.262384,2.916777,0.285695 25 | phon_R01_S05_6,173.91700,192.73500,86.18000,0.00476,0.00003,0.00221,0.00258,0.00663,0.04192,0.36400,0.02187,0.02470,0.03772,0.06562,0.01840,20.42200,1,0.537264,0.686894,-5.248776,0.210279,2.547508,0.253556 26 | phon_R01_S06_1,163.65600,200.84100,76.77900,0.00742,0.00005,0.00380,0.00390,0.01140,0.01659,0.16400,0.00738,0.00948,0.01497,0.02214,0.01778,23.83100,1,0.397937,0.732479,-5.557447,0.220890,2.692176,0.215961 27 | phon_R01_S06_2,104.40000,206.00200,77.96800,0.00633,0.00006,0.00316,0.00375,0.00948,0.03767,0.38100,0.01732,0.02245,0.03780,0.05197,0.02887,22.06600,1,0.522746,0.737948,-5.571843,0.236853,2.846369,0.219514 28 | phon_R01_S06_3,171.04100,208.31300,75.50100,0.00455,0.00003,0.00250,0.00234,0.00750,0.01966,0.18600,0.00889,0.01169,0.01872,0.02666,0.01095,25.90800,1,0.418622,0.720916,-6.183590,0.226278,2.589702,0.147403 29 | phon_R01_S06_4,146.84500,208.70100,81.73700,0.00496,0.00003,0.00250,0.00275,0.00749,0.01919,0.19800,0.00883,0.01144,0.01826,0.02650,0.01328,25.11900,1,0.358773,0.726652,-6.271690,0.196102,2.314209,0.162999 30 | phon_R01_S06_5,155.35800,227.38300,80.05500,0.00310,0.00002,0.00159,0.00176,0.00476,0.01718,0.16100,0.00769,0.01012,0.01661,0.02307,0.00677,25.97000,1,0.470478,0.676258,-7.120925,0.279789,2.241742,0.108514 31 | phon_R01_S06_6,162.56800,198.34600,77.63000,0.00502,0.00003,0.00280,0.00253,0.00841,0.01791,0.16800,0.00793,0.01057,0.01799,0.02380,0.01170,25.67800,1,0.427785,0.723797,-6.635729,0.209866,1.957961,0.135242 32 | phon_R01_S07_1,197.07600,206.89600,192.05500,0.00289,0.00001,0.00166,0.00168,0.00498,0.01098,0.09700,0.00563,0.00680,0.00802,0.01689,0.00339,26.77500,0,0.422229,0.741367,-7.348300,0.177551,1.743867,0.085569 33 | phon_R01_S07_2,199.22800,209.51200,192.09100,0.00241,0.00001,0.00134,0.00138,0.00402,0.01015,0.08900,0.00504,0.00641,0.00762,0.01513,0.00167,30.94000,0,0.432439,0.742055,-7.682587,0.173319,2.103106,0.068501 34 | phon_R01_S07_3,198.38300,215.20300,193.10400,0.00212,0.00001,0.00113,0.00135,0.00339,0.01263,0.11100,0.00640,0.00825,0.00951,0.01919,0.00119,30.77500,0,0.465946,0.738703,-7.067931,0.175181,1.512275,0.096320 35 | phon_R01_S07_4,202.26600,211.60400,197.07900,0.00180,0.000009,0.00093,0.00107,0.00278,0.00954,0.08500,0.00469,0.00606,0.00719,0.01407,0.00072,32.68400,0,0.368535,0.742133,-7.695734,0.178540,1.544609,0.056141 36 | phon_R01_S07_5,203.18400,211.52600,196.16000,0.00178,0.000009,0.00094,0.00106,0.00283,0.00958,0.08500,0.00468,0.00610,0.00726,0.01403,0.00065,33.04700,0,0.340068,0.741899,-7.964984,0.163519,1.423287,0.044539 37 | phon_R01_S07_6,201.46400,210.56500,195.70800,0.00198,0.000010,0.00105,0.00115,0.00314,0.01194,0.10700,0.00586,0.00760,0.00957,0.01758,0.00135,31.73200,0,0.344252,0.742737,-7.777685,0.170183,2.447064,0.057610 38 | phon_R01_S08_1,177.87600,192.92100,168.01300,0.00411,0.00002,0.00233,0.00241,0.00700,0.02126,0.18900,0.01154,0.01347,0.01612,0.03463,0.00586,23.21600,1,0.360148,0.778834,-6.149653,0.218037,2.477082,0.165827 39 | phon_R01_S08_2,176.17000,185.60400,163.56400,0.00369,0.00002,0.00205,0.00218,0.00616,0.01851,0.16800,0.00938,0.01160,0.01491,0.02814,0.00340,24.95100,1,0.341435,0.783626,-6.006414,0.196371,2.536527,0.173218 40 | phon_R01_S08_3,180.19800,201.24900,175.45600,0.00284,0.00002,0.00153,0.00166,0.00459,0.01444,0.13100,0.00726,0.00885,0.01190,0.02177,0.00231,26.73800,1,0.403884,0.766209,-6.452058,0.212294,2.269398,0.141929 41 | phon_R01_S08_4,187.73300,202.32400,173.01500,0.00316,0.00002,0.00168,0.00182,0.00504,0.01663,0.15100,0.00829,0.01003,0.01366,0.02488,0.00265,26.31000,1,0.396793,0.758324,-6.006647,0.266892,2.382544,0.160691 42 | phon_R01_S08_5,186.16300,197.72400,177.58400,0.00298,0.00002,0.00165,0.00175,0.00496,0.01495,0.13500,0.00774,0.00941,0.01233,0.02321,0.00231,26.82200,1,0.326480,0.765623,-6.647379,0.201095,2.374073,0.130554 43 | phon_R01_S08_6,184.05500,196.53700,166.97700,0.00258,0.00001,0.00134,0.00147,0.00403,0.01463,0.13200,0.00742,0.00901,0.01234,0.02226,0.00257,26.45300,1,0.306443,0.759203,-7.044105,0.063412,2.361532,0.115730 44 | phon_R01_S10_1,237.22600,247.32600,225.22700,0.00298,0.00001,0.00169,0.00182,0.00507,0.01752,0.16400,0.01035,0.01024,0.01133,0.03104,0.00740,22.73600,0,0.305062,0.654172,-7.310550,0.098648,2.416838,0.095032 45 | phon_R01_S10_2,241.40400,248.83400,232.48300,0.00281,0.00001,0.00157,0.00173,0.00470,0.01760,0.15400,0.01006,0.01038,0.01251,0.03017,0.00675,23.14500,0,0.457702,0.634267,-6.793547,0.158266,2.256699,0.117399 46 | phon_R01_S10_3,243.43900,250.91200,232.43500,0.00210,0.000009,0.00109,0.00137,0.00327,0.01419,0.12600,0.00777,0.00898,0.01033,0.02330,0.00454,25.36800,0,0.438296,0.635285,-7.057869,0.091608,2.330716,0.091470 47 | phon_R01_S10_4,242.85200,255.03400,227.91100,0.00225,0.000009,0.00117,0.00139,0.00350,0.01494,0.13400,0.00847,0.00879,0.01014,0.02542,0.00476,25.03200,0,0.431285,0.638928,-6.995820,0.102083,2.365800,0.102706 48 | phon_R01_S10_5,245.51000,262.09000,231.84800,0.00235,0.000010,0.00127,0.00148,0.00380,0.01608,0.14100,0.00906,0.00977,0.01149,0.02719,0.00476,24.60200,0,0.467489,0.631653,-7.156076,0.127642,2.392122,0.097336 49 | phon_R01_S10_6,252.45500,261.48700,182.78600,0.00185,0.000007,0.00092,0.00113,0.00276,0.01152,0.10300,0.00614,0.00730,0.00860,0.01841,0.00432,26.80500,0,0.610367,0.635204,-7.319510,0.200873,2.028612,0.086398 50 | phon_R01_S13_1,122.18800,128.61100,115.76500,0.00524,0.00004,0.00169,0.00203,0.00507,0.01613,0.14300,0.00855,0.00776,0.01433,0.02566,0.00839,23.16200,0,0.579597,0.733659,-6.439398,0.266392,2.079922,0.133867 51 | phon_R01_S13_2,122.96400,130.04900,114.67600,0.00428,0.00003,0.00124,0.00155,0.00373,0.01681,0.15400,0.00930,0.00802,0.01400,0.02789,0.00462,24.97100,0,0.538688,0.754073,-6.482096,0.264967,2.054419,0.128872 52 | phon_R01_S13_3,124.44500,135.06900,117.49500,0.00431,0.00003,0.00141,0.00167,0.00422,0.02184,0.19700,0.01241,0.01024,0.01685,0.03724,0.00479,25.13500,0,0.553134,0.775933,-6.650471,0.254498,1.840198,0.103561 53 | phon_R01_S13_4,126.34400,134.23100,112.77300,0.00448,0.00004,0.00131,0.00169,0.00393,0.02033,0.18500,0.01143,0.00959,0.01614,0.03429,0.00474,25.03000,0,0.507504,0.760361,-6.689151,0.291954,2.431854,0.105993 54 | phon_R01_S13_5,128.00100,138.05200,122.08000,0.00436,0.00003,0.00137,0.00166,0.00411,0.02297,0.21000,0.01323,0.01072,0.01677,0.03969,0.00481,24.69200,0,0.459766,0.766204,-7.072419,0.220434,1.972297,0.119308 55 | phon_R01_S13_6,129.33600,139.86700,118.60400,0.00490,0.00004,0.00165,0.00183,0.00495,0.02498,0.22800,0.01396,0.01219,0.01947,0.04188,0.00484,25.42900,0,0.420383,0.785714,-6.836811,0.269866,2.223719,0.147491 56 | phon_R01_S16_1,108.80700,134.65600,102.87400,0.00761,0.00007,0.00349,0.00486,0.01046,0.02719,0.25500,0.01483,0.01609,0.02067,0.04450,0.01036,21.02800,1,0.536009,0.819032,-4.649573,0.205558,1.986899,0.316700 57 | phon_R01_S16_2,109.86000,126.35800,104.43700,0.00874,0.00008,0.00398,0.00539,0.01193,0.03209,0.30700,0.01789,0.01992,0.02454,0.05368,0.01180,20.76700,1,0.558586,0.811843,-4.333543,0.221727,2.014606,0.344834 58 | phon_R01_S16_3,110.41700,131.06700,103.37000,0.00784,0.00007,0.00352,0.00514,0.01056,0.03715,0.33400,0.02032,0.02302,0.02802,0.06097,0.00969,21.42200,1,0.541781,0.821364,-4.438453,0.238298,1.922940,0.335041 59 | phon_R01_S16_4,117.27400,129.91600,110.40200,0.00752,0.00006,0.00299,0.00469,0.00898,0.02293,0.22100,0.01189,0.01459,0.01948,0.03568,0.00681,22.81700,1,0.530529,0.817756,-4.608260,0.290024,2.021591,0.314464 60 | phon_R01_S16_5,116.87900,131.89700,108.15300,0.00788,0.00007,0.00334,0.00493,0.01003,0.02645,0.26500,0.01394,0.01625,0.02137,0.04183,0.00786,22.60300,1,0.540049,0.813432,-4.476755,0.262633,1.827012,0.326197 61 | phon_R01_S16_6,114.84700,271.31400,104.68000,0.00867,0.00008,0.00373,0.00520,0.01120,0.03225,0.35000,0.01805,0.01974,0.02519,0.05414,0.01143,21.66000,1,0.547975,0.817396,-4.609161,0.221711,1.831691,0.316395 62 | phon_R01_S17_1,209.14400,237.49400,109.37900,0.00282,0.00001,0.00147,0.00152,0.00442,0.01861,0.17000,0.00975,0.01258,0.01382,0.02925,0.00871,25.55400,0,0.341788,0.678874,-7.040508,0.066994,2.460791,0.101516 63 | phon_R01_S17_2,223.36500,238.98700,98.66400,0.00264,0.00001,0.00154,0.00151,0.00461,0.01906,0.16500,0.01013,0.01296,0.01340,0.03039,0.00301,26.13800,0,0.447979,0.686264,-7.293801,0.086372,2.321560,0.098555 64 | phon_R01_S17_3,222.23600,231.34500,205.49500,0.00266,0.00001,0.00152,0.00144,0.00457,0.01643,0.14500,0.00867,0.01108,0.01200,0.02602,0.00340,25.85600,0,0.364867,0.694399,-6.966321,0.095882,2.278687,0.103224 65 | phon_R01_S17_4,228.83200,234.61900,223.63400,0.00296,0.00001,0.00175,0.00155,0.00526,0.01644,0.14500,0.00882,0.01075,0.01179,0.02647,0.00351,25.96400,0,0.256570,0.683296,-7.245620,0.018689,2.498224,0.093534 66 | phon_R01_S17_5,229.40100,252.22100,221.15600,0.00205,0.000009,0.00114,0.00113,0.00342,0.01457,0.12900,0.00769,0.00957,0.01016,0.02308,0.00300,26.41500,0,0.276850,0.673636,-7.496264,0.056844,2.003032,0.073581 67 | phon_R01_S17_6,228.96900,239.54100,113.20100,0.00238,0.00001,0.00136,0.00140,0.00408,0.01745,0.15400,0.00942,0.01160,0.01234,0.02827,0.00420,24.54700,0,0.305429,0.681811,-7.314237,0.006274,2.118596,0.091546 68 | phon_R01_S18_1,140.34100,159.77400,67.02100,0.00817,0.00006,0.00430,0.00440,0.01289,0.03198,0.31300,0.01830,0.01810,0.02428,0.05490,0.02183,19.56000,1,0.460139,0.720908,-5.409423,0.226850,2.359973,0.226156 69 | phon_R01_S18_2,136.96900,166.60700,66.00400,0.00923,0.00007,0.00507,0.00463,0.01520,0.03111,0.30800,0.01638,0.01759,0.02603,0.04914,0.02659,19.97900,1,0.498133,0.729067,-5.324574,0.205660,2.291558,0.226247 70 | phon_R01_S18_3,143.53300,162.21500,65.80900,0.01101,0.00008,0.00647,0.00467,0.01941,0.05384,0.47800,0.03152,0.02422,0.03392,0.09455,0.04882,20.33800,1,0.513237,0.731444,-5.869750,0.151814,2.118496,0.185580 71 | phon_R01_S18_4,148.09000,162.82400,67.34300,0.00762,0.00005,0.00467,0.00354,0.01400,0.05428,0.49700,0.03357,0.02494,0.03635,0.10070,0.02431,21.71800,1,0.487407,0.727313,-6.261141,0.120956,2.137075,0.141958 72 | phon_R01_S18_5,142.72900,162.40800,65.47600,0.00831,0.00006,0.00469,0.00419,0.01407,0.03485,0.36500,0.01868,0.01906,0.02949,0.05605,0.02599,20.26400,1,0.489345,0.730387,-5.720868,0.158830,2.277927,0.180828 73 | phon_R01_S18_6,136.35800,176.59500,65.75000,0.00971,0.00007,0.00534,0.00478,0.01601,0.04978,0.48300,0.02749,0.02466,0.03736,0.08247,0.03361,18.57000,1,0.543299,0.733232,-5.207985,0.224852,2.642276,0.242981 74 | phon_R01_S19_1,120.08000,139.71000,111.20800,0.00405,0.00003,0.00180,0.00220,0.00540,0.01706,0.15200,0.00974,0.00925,0.01345,0.02921,0.00442,25.74200,1,0.495954,0.762959,-5.791820,0.329066,2.205024,0.188180 75 | phon_R01_S19_2,112.01400,588.51800,107.02400,0.00533,0.00005,0.00268,0.00329,0.00805,0.02448,0.22600,0.01373,0.01375,0.01956,0.04120,0.00623,24.17800,1,0.509127,0.789532,-5.389129,0.306636,1.928708,0.225461 76 | phon_R01_S19_3,110.79300,128.10100,107.31600,0.00494,0.00004,0.00260,0.00283,0.00780,0.02442,0.21600,0.01432,0.01325,0.01831,0.04295,0.00479,25.43800,1,0.437031,0.815908,-5.313360,0.201861,2.225815,0.244512 77 | phon_R01_S19_4,110.70700,122.61100,105.00700,0.00516,0.00005,0.00277,0.00289,0.00831,0.02215,0.20600,0.01284,0.01219,0.01715,0.03851,0.00472,25.19700,1,0.463514,0.807217,-5.477592,0.315074,1.862092,0.228624 78 | phon_R01_S19_5,112.87600,148.82600,106.98100,0.00500,0.00004,0.00270,0.00289,0.00810,0.03999,0.35000,0.02413,0.02231,0.02704,0.07238,0.00905,23.37000,1,0.489538,0.789977,-5.775966,0.341169,2.007923,0.193918 79 | phon_R01_S19_6,110.56800,125.39400,106.82100,0.00462,0.00004,0.00226,0.00280,0.00677,0.02199,0.19700,0.01284,0.01199,0.01636,0.03852,0.00420,25.82000,1,0.429484,0.816340,-5.391029,0.250572,1.777901,0.232744 80 | phon_R01_S20_1,95.38500,102.14500,90.26400,0.00608,0.00006,0.00331,0.00332,0.00994,0.03202,0.26300,0.01803,0.01886,0.02455,0.05408,0.01062,21.87500,1,0.644954,0.779612,-5.115212,0.249494,2.017753,0.260015 81 | phon_R01_S20_2,100.77000,115.69700,85.54500,0.01038,0.00010,0.00622,0.00576,0.01865,0.03121,0.36100,0.01773,0.01783,0.02139,0.05320,0.02220,19.20000,1,0.594387,0.790117,-4.913885,0.265699,2.398422,0.277948 82 | 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phon_R01_S21_1,176.85800,205.56000,75.34400,0.00747,0.00004,0.00418,0.00420,0.01254,0.04795,0.43500,0.02679,0.03022,0.03510,0.08037,0.05470,13.89300,1,0.556494,0.729586,-5.288912,0.343073,3.099301,0.242119 87 | phon_R01_S21_2,180.97800,200.12500,155.49500,0.00406,0.00002,0.00220,0.00244,0.00659,0.03852,0.33100,0.02107,0.02493,0.02877,0.06321,0.02782,16.17600,1,0.583574,0.727747,-5.657899,0.315903,3.098256,0.200423 88 | phon_R01_S21_3,178.22200,202.45000,141.04700,0.00321,0.00002,0.00163,0.00194,0.00488,0.03759,0.32700,0.02073,0.02415,0.02784,0.06219,0.03151,15.92400,1,0.598714,0.712199,-6.366916,0.335753,2.654271,0.144614 89 | phon_R01_S21_4,176.28100,227.38100,125.61000,0.00520,0.00003,0.00287,0.00312,0.00862,0.06511,0.58000,0.03671,0.04159,0.04683,0.11012,0.04824,13.92200,1,0.602874,0.740837,-5.515071,0.299549,3.136550,0.220968 90 | phon_R01_S21_5,173.89800,211.35000,74.67700,0.00448,0.00003,0.00237,0.00254,0.00710,0.06727,0.65000,0.03788,0.04254,0.04802,0.11363,0.04214,14.73900,1,0.599371,0.743937,-5.783272,0.299793,3.007096,0.194052 91 | phon_R01_S21_6,179.71100,225.93000,144.87800,0.00709,0.00004,0.00391,0.00419,0.01172,0.04313,0.44200,0.02297,0.02768,0.03455,0.06892,0.07223,11.86600,1,0.590951,0.745526,-4.379411,0.375531,3.671155,0.332086 92 | phon_R01_S21_7,166.60500,206.00800,78.03200,0.00742,0.00004,0.00387,0.00453,0.01161,0.06640,0.63400,0.03650,0.04282,0.05114,0.10949,0.08725,11.74400,1,0.653410,0.733165,-4.508984,0.389232,3.317586,0.301952 93 | phon_R01_S22_1,151.95500,163.33500,147.22600,0.00419,0.00003,0.00224,0.00227,0.00672,0.07959,0.77200,0.04421,0.04962,0.05690,0.13262,0.01658,19.66400,1,0.501037,0.714360,-6.411497,0.207156,2.344876,0.134120 94 | phon_R01_S22_2,148.27200,164.98900,142.29900,0.00459,0.00003,0.00250,0.00256,0.00750,0.04190,0.38300,0.02383,0.02521,0.03051,0.07150,0.01914,18.78000,1,0.454444,0.734504,-5.952058,0.087840,2.344336,0.186489 95 | phon_R01_S22_3,152.12500,161.46900,76.59600,0.00382,0.00003,0.00191,0.00226,0.00574,0.05925,0.63700,0.03341,0.03794,0.04398,0.10024,0.01211,20.96900,1,0.447456,0.697790,-6.152551,0.173520,2.080121,0.160809 96 | phon_R01_S22_4,157.82100,172.97500,68.40100,0.00358,0.00002,0.00196,0.00196,0.00587,0.03716,0.30700,0.02062,0.02321,0.02764,0.06185,0.00850,22.21900,1,0.502380,0.712170,-6.251425,0.188056,2.143851,0.160812 97 | phon_R01_S22_5,157.44700,163.26700,149.60500,0.00369,0.00002,0.00201,0.00197,0.00602,0.03272,0.28300,0.01813,0.01909,0.02571,0.05439,0.01018,21.69300,1,0.447285,0.705658,-6.247076,0.180528,2.344348,0.164916 98 | phon_R01_S22_6,159.11600,168.91300,144.81100,0.00342,0.00002,0.00178,0.00184,0.00535,0.03381,0.30700,0.01806,0.02024,0.02809,0.05417,0.00852,22.66300,1,0.366329,0.693429,-6.417440,0.194627,2.473239,0.151709 99 | phon_R01_S24_1,125.03600,143.94600,116.18700,0.01280,0.00010,0.00743,0.00623,0.02228,0.03886,0.34200,0.02135,0.02174,0.03088,0.06406,0.08151,15.33800,1,0.629574,0.714485,-4.020042,0.265315,2.671825,0.340623 100 | phon_R01_S24_2,125.79100,140.55700,96.20600,0.01378,0.00011,0.00826,0.00655,0.02478,0.04689,0.42200,0.02542,0.02630,0.03908,0.07625,0.10323,15.43300,1,0.571010,0.690892,-5.159169,0.202146,2.441612,0.260375 101 | phon_R01_S24_3,126.51200,141.75600,99.77000,0.01936,0.00015,0.01159,0.00990,0.03476,0.06734,0.65900,0.03611,0.03963,0.05783,0.10833,0.16744,12.43500,1,0.638545,0.674953,-3.760348,0.242861,2.634633,0.378483 102 | phon_R01_S24_4,125.64100,141.06800,116.34600,0.03316,0.00026,0.02144,0.01522,0.06433,0.09178,0.89100,0.05358,0.04791,0.06196,0.16074,0.31482,8.86700,1,0.671299,0.656846,-3.700544,0.260481,2.991063,0.370961 103 | phon_R01_S24_5,128.45100,150.44900,75.63200,0.01551,0.00012,0.00905,0.00909,0.02716,0.06170,0.58400,0.03223,0.03672,0.05174,0.09669,0.11843,15.06000,1,0.639808,0.643327,-4.202730,0.310163,2.638279,0.356881 104 | phon_R01_S24_6,139.22400,586.56700,66.15700,0.03011,0.00022,0.01854,0.01628,0.05563,0.09419,0.93000,0.05551,0.05005,0.06023,0.16654,0.25930,10.48900,1,0.596362,0.641418,-3.269487,0.270641,2.690917,0.444774 105 | phon_R01_S25_1,150.25800,154.60900,75.34900,0.00248,0.00002,0.00105,0.00136,0.00315,0.01131,0.10700,0.00522,0.00659,0.01009,0.01567,0.00495,26.75900,1,0.296888,0.722356,-6.878393,0.089267,2.004055,0.113942 106 | phon_R01_S25_2,154.00300,160.26700,128.62100,0.00183,0.00001,0.00076,0.00100,0.00229,0.01030,0.09400,0.00469,0.00582,0.00871,0.01406,0.00243,28.40900,1,0.263654,0.691483,-7.111576,0.144780,2.065477,0.093193 107 | phon_R01_S25_3,149.68900,160.36800,133.60800,0.00257,0.00002,0.00116,0.00134,0.00349,0.01346,0.12600,0.00660,0.00818,0.01059,0.01979,0.00578,27.42100,1,0.365488,0.719974,-6.997403,0.210279,1.994387,0.112878 108 | phon_R01_S25_4,155.07800,163.73600,144.14800,0.00168,0.00001,0.00068,0.00092,0.00204,0.01064,0.09700,0.00522,0.00632,0.00928,0.01567,0.00233,29.74600,1,0.334171,0.677930,-6.981201,0.184550,2.129924,0.106802 109 | phon_R01_S25_5,151.88400,157.76500,133.75100,0.00258,0.00002,0.00115,0.00122,0.00346,0.01450,0.13700,0.00633,0.00788,0.01267,0.01898,0.00659,26.83300,1,0.393563,0.700246,-6.600023,0.249172,2.499148,0.105306 110 | phon_R01_S25_6,151.98900,157.33900,132.85700,0.00174,0.00001,0.00075,0.00096,0.00225,0.01024,0.09300,0.00455,0.00576,0.00993,0.01364,0.00238,29.92800,1,0.311369,0.676066,-6.739151,0.160686,2.296873,0.115130 111 | phon_R01_S26_1,193.03000,208.90000,80.29700,0.00766,0.00004,0.00450,0.00389,0.01351,0.03044,0.27500,0.01771,0.01815,0.02084,0.05312,0.00947,21.93400,1,0.497554,0.740539,-5.845099,0.278679,2.608749,0.185668 112 | phon_R01_S26_2,200.71400,223.98200,89.68600,0.00621,0.00003,0.00371,0.00337,0.01112,0.02286,0.20700,0.01192,0.01439,0.01852,0.03576,0.00704,23.23900,1,0.436084,0.727863,-5.258320,0.256454,2.550961,0.232520 113 | phon_R01_S26_3,208.51900,220.31500,199.02000,0.00609,0.00003,0.00368,0.00339,0.01105,0.01761,0.15500,0.00952,0.01058,0.01307,0.02855,0.00830,22.40700,1,0.338097,0.712466,-6.471427,0.184378,2.502336,0.136390 114 | phon_R01_S26_4,204.66400,221.30000,189.62100,0.00841,0.00004,0.00502,0.00485,0.01506,0.02378,0.21000,0.01277,0.01483,0.01767,0.03831,0.01316,21.30500,1,0.498877,0.722085,-4.876336,0.212054,2.376749,0.268144 115 | phon_R01_S26_5,210.14100,232.70600,185.25800,0.00534,0.00003,0.00321,0.00280,0.00964,0.01680,0.14900,0.00861,0.01017,0.01301,0.02583,0.00620,23.67100,1,0.441097,0.722254,-5.963040,0.250283,2.489191,0.177807 116 | phon_R01_S26_6,206.32700,226.35500,92.02000,0.00495,0.00002,0.00302,0.00246,0.00905,0.02105,0.20900,0.01107,0.01284,0.01604,0.03320,0.01048,21.86400,1,0.331508,0.715121,-6.729713,0.181701,2.938114,0.115515 117 | phon_R01_S27_1,151.87200,492.89200,69.08500,0.00856,0.00006,0.00404,0.00385,0.01211,0.01843,0.23500,0.00796,0.00832,0.01271,0.02389,0.06051,23.69300,1,0.407701,0.662668,-4.673241,0.261549,2.702355,0.274407 118 | phon_R01_S27_2,158.21900,442.55700,71.94800,0.00476,0.00003,0.00214,0.00207,0.00642,0.01458,0.14800,0.00606,0.00747,0.01312,0.01818,0.01554,26.35600,1,0.450798,0.653823,-6.051233,0.273280,2.640798,0.170106 119 | phon_R01_S27_3,170.75600,450.24700,79.03200,0.00555,0.00003,0.00244,0.00261,0.00731,0.01725,0.17500,0.00757,0.00971,0.01652,0.02270,0.01802,25.69000,1,0.486738,0.676023,-4.597834,0.372114,2.975889,0.282780 120 | phon_R01_S27_4,178.28500,442.82400,82.06300,0.00462,0.00003,0.00157,0.00194,0.00472,0.01279,0.12900,0.00617,0.00744,0.01151,0.01851,0.00856,25.02000,1,0.470422,0.655239,-4.913137,0.393056,2.816781,0.251972 121 | phon_R01_S27_5,217.11600,233.48100,93.97800,0.00404,0.00002,0.00127,0.00128,0.00381,0.01299,0.12400,0.00679,0.00631,0.01075,0.02038,0.00681,24.58100,1,0.462516,0.582710,-5.517173,0.389295,2.925862,0.220657 122 | phon_R01_S27_6,128.94000,479.69700,88.25100,0.00581,0.00005,0.00241,0.00314,0.00723,0.02008,0.22100,0.00849,0.01117,0.01734,0.02548,0.02350,24.74300,1,0.487756,0.684130,-6.186128,0.279933,2.686240,0.152428 123 | phon_R01_S27_7,176.82400,215.29300,83.96100,0.00460,0.00003,0.00209,0.00221,0.00628,0.01169,0.11700,0.00534,0.00630,0.01104,0.01603,0.01161,27.16600,1,0.400088,0.656182,-4.711007,0.281618,2.655744,0.234809 124 | phon_R01_S31_1,138.19000,203.52200,83.34000,0.00704,0.00005,0.00406,0.00398,0.01218,0.04479,0.44100,0.02587,0.02567,0.03220,0.07761,0.01968,18.30500,1,0.538016,0.741480,-5.418787,0.160267,2.090438,0.229892 125 | phon_R01_S31_2,182.01800,197.17300,79.18700,0.00842,0.00005,0.00506,0.00449,0.01517,0.02503,0.23100,0.01372,0.01580,0.01931,0.04115,0.01813,18.78400,1,0.589956,0.732903,-5.445140,0.142466,2.174306,0.215558 126 | phon_R01_S31_3,156.23900,195.10700,79.82000,0.00694,0.00004,0.00403,0.00395,0.01209,0.02343,0.22400,0.01289,0.01420,0.01720,0.03867,0.02020,19.19600,1,0.618663,0.728421,-5.944191,0.143359,1.929715,0.181988 127 | phon_R01_S31_4,145.17400,198.10900,80.63700,0.00733,0.00005,0.00414,0.00422,0.01242,0.02362,0.23300,0.01235,0.01495,0.01944,0.03706,0.01874,18.85700,1,0.637518,0.735546,-5.594275,0.127950,1.765957,0.222716 128 | phon_R01_S31_5,138.14500,197.23800,81.11400,0.00544,0.00004,0.00294,0.00327,0.00883,0.02791,0.24600,0.01484,0.01805,0.02259,0.04451,0.01794,18.17800,1,0.623209,0.738245,-5.540351,0.087165,1.821297,0.214075 129 | phon_R01_S31_6,166.88800,198.96600,79.51200,0.00638,0.00004,0.00368,0.00351,0.01104,0.02857,0.25700,0.01547,0.01859,0.02301,0.04641,0.01796,18.33000,1,0.585169,0.736964,-5.825257,0.115697,1.996146,0.196535 130 | phon_R01_S32_1,119.03100,127.53300,109.21600,0.00440,0.00004,0.00214,0.00192,0.00641,0.01033,0.09800,0.00538,0.00570,0.00811,0.01614,0.01724,26.84200,1,0.457541,0.699787,-6.890021,0.152941,2.328513,0.112856 131 | phon_R01_S32_2,120.07800,126.63200,105.66700,0.00270,0.00002,0.00116,0.00135,0.00349,0.01022,0.09000,0.00476,0.00588,0.00903,0.01428,0.00487,26.36900,1,0.491345,0.718839,-5.892061,0.195976,2.108873,0.183572 132 | phon_R01_S32_3,120.28900,128.14300,100.20900,0.00492,0.00004,0.00269,0.00238,0.00808,0.01412,0.12500,0.00703,0.00820,0.01194,0.02110,0.01610,23.94900,1,0.467160,0.724045,-6.135296,0.203630,2.539724,0.169923 133 | phon_R01_S32_4,120.25600,125.30600,104.77300,0.00407,0.00003,0.00224,0.00205,0.00671,0.01516,0.13800,0.00721,0.00815,0.01310,0.02164,0.01015,26.01700,1,0.468621,0.735136,-6.112667,0.217013,2.527742,0.170633 134 | phon_R01_S32_5,119.05600,125.21300,86.79500,0.00346,0.00003,0.00169,0.00170,0.00508,0.01201,0.10600,0.00633,0.00701,0.00915,0.01898,0.00903,23.38900,1,0.470972,0.721308,-5.436135,0.254909,2.516320,0.232209 135 | phon_R01_S32_6,118.74700,123.72300,109.83600,0.00331,0.00003,0.00168,0.00171,0.00504,0.01043,0.09900,0.00490,0.00621,0.00903,0.01471,0.00504,25.61900,1,0.482296,0.723096,-6.448134,0.178713,2.034827,0.141422 136 | phon_R01_S33_1,106.51600,112.77700,93.10500,0.00589,0.00006,0.00291,0.00319,0.00873,0.04932,0.44100,0.02683,0.03112,0.03651,0.08050,0.03031,17.06000,1,0.637814,0.744064,-5.301321,0.320385,2.375138,0.243080 137 | phon_R01_S33_2,110.45300,127.61100,105.55400,0.00494,0.00004,0.00244,0.00315,0.00731,0.04128,0.37900,0.02229,0.02592,0.03316,0.06688,0.02529,17.70700,1,0.653427,0.706687,-5.333619,0.322044,2.631793,0.228319 138 | phon_R01_S33_3,113.40000,133.34400,107.81600,0.00451,0.00004,0.00219,0.00283,0.00658,0.04879,0.43100,0.02385,0.02973,0.04370,0.07154,0.02278,19.01300,1,0.647900,0.708144,-4.378916,0.300067,2.445502,0.259451 139 | phon_R01_S33_4,113.16600,130.27000,100.67300,0.00502,0.00004,0.00257,0.00312,0.00772,0.05279,0.47600,0.02896,0.03347,0.04134,0.08689,0.03690,16.74700,1,0.625362,0.708617,-4.654894,0.304107,2.672362,0.274387 140 | phon_R01_S33_5,112.23900,126.60900,104.09500,0.00472,0.00004,0.00238,0.00290,0.00715,0.05643,0.51700,0.03070,0.03530,0.04451,0.09211,0.02629,17.36600,1,0.640945,0.701404,-5.634576,0.306014,2.419253,0.209191 141 | phon_R01_S33_6,116.15000,131.73100,109.81500,0.00381,0.00003,0.00181,0.00232,0.00542,0.03026,0.26700,0.01514,0.01812,0.02770,0.04543,0.01827,18.80100,1,0.624811,0.696049,-5.866357,0.233070,2.445646,0.184985 142 | phon_R01_S34_1,170.36800,268.79600,79.54300,0.00571,0.00003,0.00232,0.00269,0.00696,0.03273,0.28100,0.01713,0.01964,0.02824,0.05139,0.02485,18.54000,1,0.677131,0.685057,-4.796845,0.397749,2.963799,0.277227 143 | phon_R01_S34_2,208.08300,253.79200,91.80200,0.00757,0.00004,0.00428,0.00428,0.01285,0.06725,0.57100,0.04016,0.04003,0.04464,0.12047,0.04238,15.64800,1,0.606344,0.665945,-5.410336,0.288917,2.665133,0.231723 144 | phon_R01_S34_3,198.45800,219.29000,148.69100,0.00376,0.00002,0.00182,0.00215,0.00546,0.03527,0.29700,0.02055,0.02076,0.02530,0.06165,0.01728,18.70200,1,0.606273,0.661735,-5.585259,0.310746,2.465528,0.209863 145 | phon_R01_S34_4,202.80500,231.50800,86.23200,0.00370,0.00002,0.00189,0.00211,0.00568,0.01997,0.18000,0.01117,0.01177,0.01506,0.03350,0.02010,18.68700,1,0.536102,0.632631,-5.898673,0.213353,2.470746,0.189032 146 | phon_R01_S34_5,202.54400,241.35000,164.16800,0.00254,0.00001,0.00100,0.00133,0.00301,0.02662,0.22800,0.01475,0.01558,0.02006,0.04426,0.01049,20.68000,1,0.497480,0.630409,-6.132663,0.220617,2.576563,0.159777 147 | phon_R01_S34_6,223.36100,263.87200,87.63800,0.00352,0.00002,0.00169,0.00188,0.00506,0.02536,0.22500,0.01379,0.01478,0.01909,0.04137,0.01493,20.36600,1,0.566849,0.574282,-5.456811,0.345238,2.840556,0.232861 148 | phon_R01_S35_1,169.77400,191.75900,151.45100,0.01568,0.00009,0.00863,0.00946,0.02589,0.08143,0.82100,0.03804,0.05426,0.08808,0.11411,0.07530,12.35900,1,0.561610,0.793509,-3.297668,0.414758,3.413649,0.457533 149 | phon_R01_S35_2,183.52000,216.81400,161.34000,0.01466,0.00008,0.00849,0.00819,0.02546,0.06050,0.61800,0.02865,0.04101,0.06359,0.08595,0.06057,14.36700,1,0.478024,0.768974,-4.276605,0.355736,3.142364,0.336085 150 | phon_R01_S35_3,188.62000,216.30200,165.98200,0.01719,0.00009,0.00996,0.01027,0.02987,0.07118,0.72200,0.03474,0.04580,0.06824,0.10422,0.08069,12.29800,1,0.552870,0.764036,-3.377325,0.335357,3.274865,0.418646 151 | phon_R01_S35_4,202.63200,565.74000,177.25800,0.01627,0.00008,0.00919,0.00963,0.02756,0.07170,0.83300,0.03515,0.04265,0.06460,0.10546,0.07889,14.98900,1,0.427627,0.775708,-4.892495,0.262281,2.910213,0.270173 152 | phon_R01_S35_5,186.69500,211.96100,149.44200,0.01872,0.00010,0.01075,0.01154,0.03225,0.05830,0.78400,0.02699,0.03714,0.06259,0.08096,0.10952,12.52900,1,0.507826,0.762726,-4.484303,0.340256,2.958815,0.301487 153 | phon_R01_S35_6,192.81800,224.42900,168.79300,0.03107,0.00016,0.01800,0.01958,0.05401,0.11908,1.30200,0.05647,0.07940,0.13778,0.16942,0.21713,8.44100,1,0.625866,0.768320,-2.434031,0.450493,3.079221,0.527367 154 | phon_R01_S35_7,198.11600,233.09900,174.47800,0.02714,0.00014,0.01568,0.01699,0.04705,0.08684,1.01800,0.04284,0.05556,0.08318,0.12851,0.16265,9.44900,1,0.584164,0.754449,-2.839756,0.356224,3.184027,0.454721 155 | phon_R01_S37_1,121.34500,139.64400,98.25000,0.00684,0.00006,0.00388,0.00332,0.01164,0.02534,0.24100,0.01340,0.01399,0.02056,0.04019,0.04179,21.52000,1,0.566867,0.670475,-4.865194,0.246404,2.013530,0.168581 156 | phon_R01_S37_2,119.10000,128.44200,88.83300,0.00692,0.00006,0.00393,0.00300,0.01179,0.02682,0.23600,0.01484,0.01405,0.02018,0.04451,0.04611,21.82400,1,0.651680,0.659333,-4.239028,0.175691,2.451130,0.247455 157 | phon_R01_S37_3,117.87000,127.34900,95.65400,0.00647,0.00005,0.00356,0.00300,0.01067,0.03087,0.27600,0.01659,0.01804,0.02402,0.04977,0.02631,22.43100,1,0.628300,0.652025,-3.583722,0.207914,2.439597,0.206256 158 | phon_R01_S37_4,122.33600,142.36900,94.79400,0.00727,0.00006,0.00415,0.00339,0.01246,0.02293,0.22300,0.01205,0.01289,0.01771,0.03615,0.03191,22.95300,1,0.611679,0.623731,-5.435100,0.230532,2.699645,0.220546 159 | phon_R01_S37_5,117.96300,134.20900,100.75700,0.01813,0.00015,0.01117,0.00718,0.03351,0.04912,0.43800,0.02610,0.02161,0.02916,0.07830,0.10748,19.07500,1,0.630547,0.646786,-3.444478,0.303214,2.964568,0.261305 160 | phon_R01_S37_6,126.14400,154.28400,97.54300,0.00975,0.00008,0.00593,0.00454,0.01778,0.02852,0.26600,0.01500,0.01581,0.02157,0.04499,0.03828,21.53400,1,0.635015,0.627337,-5.070096,0.280091,2.892300,0.249703 161 | phon_R01_S39_1,127.93000,138.75200,112.17300,0.00605,0.00005,0.00321,0.00318,0.00962,0.03235,0.33900,0.01360,0.01650,0.03105,0.04079,0.02663,19.65100,1,0.654945,0.675865,-5.498456,0.234196,2.103014,0.216638 162 | phon_R01_S39_2,114.23800,124.39300,77.02200,0.00581,0.00005,0.00299,0.00316,0.00896,0.04009,0.40600,0.01579,0.01994,0.04114,0.04736,0.02073,20.43700,1,0.653139,0.694571,-5.185987,0.259229,2.151121,0.244948 163 | phon_R01_S39_3,115.32200,135.73800,107.80200,0.00619,0.00005,0.00352,0.00329,0.01057,0.03273,0.32500,0.01644,0.01722,0.02931,0.04933,0.02810,19.38800,1,0.577802,0.684373,-5.283009,0.226528,2.442906,0.238281 164 | phon_R01_S39_4,114.55400,126.77800,91.12100,0.00651,0.00006,0.00366,0.00340,0.01097,0.03658,0.36900,0.01864,0.01940,0.03091,0.05592,0.02707,18.95400,1,0.685151,0.719576,-5.529833,0.242750,2.408689,0.220520 165 | phon_R01_S39_5,112.15000,131.66900,97.52700,0.00519,0.00005,0.00291,0.00284,0.00873,0.01756,0.15500,0.00967,0.01033,0.01363,0.02902,0.01435,21.21900,1,0.557045,0.673086,-5.617124,0.184896,1.871871,0.212386 166 | phon_R01_S39_6,102.27300,142.83000,85.90200,0.00907,0.00009,0.00493,0.00461,0.01480,0.02814,0.27200,0.01579,0.01553,0.02073,0.04736,0.03882,18.44700,1,0.671378,0.674562,-2.929379,0.396746,2.560422,0.367233 167 | phon_R01_S42_1,236.20000,244.66300,102.13700,0.00277,0.00001,0.00154,0.00153,0.00462,0.02448,0.21700,0.01410,0.01426,0.01621,0.04231,0.00620,24.07800,0,0.469928,0.628232,-6.816086,0.172270,2.235197,0.119652 168 | phon_R01_S42_2,237.32300,243.70900,229.25600,0.00303,0.00001,0.00173,0.00159,0.00519,0.01242,0.11600,0.00696,0.00747,0.00882,0.02089,0.00533,24.67900,0,0.384868,0.626710,-7.018057,0.176316,1.852402,0.091604 169 | phon_R01_S42_3,260.10500,264.91900,237.30300,0.00339,0.00001,0.00205,0.00186,0.00616,0.02030,0.19700,0.01186,0.01230,0.01367,0.03557,0.00910,21.08300,0,0.440988,0.628058,-7.517934,0.160414,1.881767,0.075587 170 | phon_R01_S42_4,197.56900,217.62700,90.79400,0.00803,0.00004,0.00490,0.00448,0.01470,0.02177,0.18900,0.01279,0.01272,0.01439,0.03836,0.01337,19.26900,0,0.372222,0.725216,-5.736781,0.164529,2.882450,0.202879 171 | phon_R01_S42_5,240.30100,245.13500,219.78300,0.00517,0.00002,0.00316,0.00283,0.00949,0.02018,0.21200,0.01176,0.01191,0.01344,0.03529,0.00965,21.02000,0,0.371837,0.646167,-7.169701,0.073298,2.266432,0.100881 172 | phon_R01_S42_6,244.99000,272.21000,239.17000,0.00451,0.00002,0.00279,0.00237,0.00837,0.01897,0.18100,0.01084,0.01121,0.01255,0.03253,0.01049,21.52800,0,0.522812,0.646818,-7.304500,0.171088,2.095237,0.096220 173 | phon_R01_S43_1,112.54700,133.37400,105.71500,0.00355,0.00003,0.00166,0.00190,0.00499,0.01358,0.12900,0.00664,0.00786,0.01140,0.01992,0.00435,26.43600,0,0.413295,0.756700,-6.323531,0.218885,2.193412,0.160376 174 | phon_R01_S43_2,110.73900,113.59700,100.13900,0.00356,0.00003,0.00170,0.00200,0.00510,0.01484,0.13300,0.00754,0.00950,0.01285,0.02261,0.00430,26.55000,0,0.369090,0.776158,-6.085567,0.192375,1.889002,0.174152 175 | phon_R01_S43_3,113.71500,116.44300,96.91300,0.00349,0.00003,0.00171,0.00203,0.00514,0.01472,0.13300,0.00748,0.00905,0.01148,0.02245,0.00478,26.54700,0,0.380253,0.766700,-5.943501,0.192150,1.852542,0.179677 176 | phon_R01_S43_4,117.00400,144.46600,99.92300,0.00353,0.00003,0.00176,0.00218,0.00528,0.01657,0.14500,0.00881,0.01062,0.01318,0.02643,0.00590,25.44500,0,0.387482,0.756482,-6.012559,0.229298,1.872946,0.163118 177 | phon_R01_S43_5,115.38000,123.10900,108.63400,0.00332,0.00003,0.00160,0.00199,0.00480,0.01503,0.13700,0.00812,0.00933,0.01133,0.02436,0.00401,26.00500,0,0.405991,0.761255,-5.966779,0.197938,1.974857,0.184067 178 | phon_R01_S43_6,116.38800,129.03800,108.97000,0.00346,0.00003,0.00169,0.00213,0.00507,0.01725,0.15500,0.00874,0.01021,0.01331,0.02623,0.00415,26.14300,0,0.361232,0.763242,-6.016891,0.109256,2.004719,0.174429 179 | phon_R01_S44_1,151.73700,190.20400,129.85900,0.00314,0.00002,0.00135,0.00162,0.00406,0.01469,0.13200,0.00728,0.00886,0.01230,0.02184,0.00570,24.15100,1,0.396610,0.745957,-6.486822,0.197919,2.449763,0.132703 180 | phon_R01_S44_2,148.79000,158.35900,138.99000,0.00309,0.00002,0.00152,0.00186,0.00456,0.01574,0.14200,0.00839,0.00956,0.01309,0.02518,0.00488,24.41200,1,0.402591,0.762508,-6.311987,0.182459,2.251553,0.160306 181 | phon_R01_S44_3,148.14300,155.98200,135.04100,0.00392,0.00003,0.00204,0.00231,0.00612,0.01450,0.13100,0.00725,0.00876,0.01263,0.02175,0.00540,23.68300,1,0.398499,0.778349,-5.711205,0.240875,2.845109,0.192730 182 | phon_R01_S44_4,150.44000,163.44100,144.73600,0.00396,0.00003,0.00206,0.00233,0.00619,0.02551,0.23700,0.01321,0.01574,0.02148,0.03964,0.00611,23.13300,1,0.352396,0.759320,-6.261446,0.183218,2.264226,0.144105 183 | phon_R01_S44_5,148.46200,161.07800,141.99800,0.00397,0.00003,0.00202,0.00235,0.00605,0.01831,0.16300,0.00950,0.01103,0.01559,0.02849,0.00639,22.86600,1,0.408598,0.768845,-5.704053,0.216204,2.679185,0.197710 184 | phon_R01_S44_6,149.81800,163.41700,144.78600,0.00336,0.00002,0.00174,0.00198,0.00521,0.02145,0.19800,0.01155,0.01341,0.01666,0.03464,0.00595,23.00800,1,0.329577,0.757180,-6.277170,0.109397,2.209021,0.156368 185 | phon_R01_S49_1,117.22600,123.92500,106.65600,0.00417,0.00004,0.00186,0.00270,0.00558,0.01909,0.17100,0.00864,0.01223,0.01949,0.02592,0.00955,23.07900,0,0.603515,0.669565,-5.619070,0.191576,2.027228,0.215724 186 | phon_R01_S49_2,116.84800,217.55200,99.50300,0.00531,0.00005,0.00260,0.00346,0.00780,0.01795,0.16300,0.00810,0.01144,0.01756,0.02429,0.01179,22.08500,0,0.663842,0.656516,-5.198864,0.206768,2.120412,0.252404 187 | phon_R01_S49_3,116.28600,177.29100,96.98300,0.00314,0.00003,0.00134,0.00192,0.00403,0.01564,0.13600,0.00667,0.00990,0.01691,0.02001,0.00737,24.19900,0,0.598515,0.654331,-5.592584,0.133917,2.058658,0.214346 188 | phon_R01_S49_4,116.55600,592.03000,86.22800,0.00496,0.00004,0.00254,0.00263,0.00762,0.01660,0.15400,0.00820,0.00972,0.01491,0.02460,0.01397,23.95800,0,0.566424,0.667654,-6.431119,0.153310,2.161936,0.120605 189 | phon_R01_S49_5,116.34200,581.28900,94.24600,0.00267,0.00002,0.00115,0.00148,0.00345,0.01300,0.11700,0.00631,0.00789,0.01144,0.01892,0.00680,25.02300,0,0.528485,0.663884,-6.359018,0.116636,2.152083,0.138868 190 | phon_R01_S49_6,114.56300,119.16700,86.64700,0.00327,0.00003,0.00146,0.00184,0.00439,0.01185,0.10600,0.00557,0.00721,0.01095,0.01672,0.00703,24.77500,0,0.555303,0.659132,-6.710219,0.149694,1.913990,0.121777 191 | phon_R01_S50_1,201.77400,262.70700,78.22800,0.00694,0.00003,0.00412,0.00396,0.01235,0.02574,0.25500,0.01454,0.01582,0.01758,0.04363,0.04441,19.36800,0,0.508479,0.683761,-6.934474,0.159890,2.316346,0.112838 192 | phon_R01_S50_2,174.18800,230.97800,94.26100,0.00459,0.00003,0.00263,0.00259,0.00790,0.04087,0.40500,0.02336,0.02498,0.02745,0.07008,0.02764,19.51700,0,0.448439,0.657899,-6.538586,0.121952,2.657476,0.133050 193 | phon_R01_S50_3,209.51600,253.01700,89.48800,0.00564,0.00003,0.00331,0.00292,0.00994,0.02751,0.26300,0.01604,0.01657,0.01879,0.04812,0.01810,19.14700,0,0.431674,0.683244,-6.195325,0.129303,2.784312,0.168895 194 | phon_R01_S50_4,174.68800,240.00500,74.28700,0.01360,0.00008,0.00624,0.00564,0.01873,0.02308,0.25600,0.01268,0.01365,0.01667,0.03804,0.10715,17.88300,0,0.407567,0.655683,-6.787197,0.158453,2.679772,0.131728 195 | phon_R01_S50_5,198.76400,396.96100,74.90400,0.00740,0.00004,0.00370,0.00390,0.01109,0.02296,0.24100,0.01265,0.01321,0.01588,0.03794,0.07223,19.02000,0,0.451221,0.643956,-6.744577,0.207454,2.138608,0.123306 196 | phon_R01_S50_6,214.28900,260.27700,77.97300,0.00567,0.00003,0.00295,0.00317,0.00885,0.01884,0.19000,0.01026,0.01161,0.01373,0.03078,0.04398,21.20900,0,0.462803,0.664357,-5.724056,0.190667,2.555477,0.148569 197 | -------------------------------------------------------------------------------- /colab_files_to_train_models/Multiple disease prediction system - Parkinsons.ipynb: -------------------------------------------------------------------------------- 1 | {"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Multiple disease prediction system - Parkinsons.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyPgH5xu9ZLOpcMFNkcpInRX"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"9B5Zl1UOBMAJ"},"source":["Importing the Dependencies"]},{"cell_type":"code","metadata":{"id":"YOCpZ1Vm6cfW","executionInfo":{"status":"ok","timestamp":1653200307851,"user_tz":-330,"elapsed":2162,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["import numpy as np\n","import pandas as pd\n","from sklearn.model_selection import train_test_split\n","from sklearn import svm\n","from sklearn.metrics import accuracy_score"],"execution_count":1,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"PZm-USrtB_q4"},"source":["Data Collection & Analysis"]},{"cell_type":"code","metadata":{"id":"5YC2lGuVBiZA","executionInfo":{"status":"ok","timestamp":1653200307854,"user_tz":-330,"elapsed":23,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["# loading the data from csv file to a Pandas DataFrame\n","parkinsons_data = pd.read_csv('/content/parkinsons.csv')"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":299},"id":"Iw8z6w60Djd2","executionInfo":{"status":"ok","timestamp":1653200307855,"user_tz":-330,"elapsed":23,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"ca177b83-79f9-46c5-89c0-42985b1923ba"},"source":["# printing the first 5 rows of the dataframe\n","parkinsons_data.head()"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" name MDVP:Fo(Hz) MDVP:Fhi(Hz) MDVP:Flo(Hz) MDVP:Jitter(%) \\\n","0 phon_R01_S01_1 119.992 157.302 74.997 0.00784 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nameMDVP:Fo(Hz)MDVP:Fhi(Hz)MDVP:Flo(Hz)MDVP:Jitter(%)MDVP:Jitter(Abs)MDVP:RAPMDVP:PPQJitter:DDPMDVP:Shimmer...Shimmer:DDANHRHNRstatusRPDEDFAspread1spread2D2PPE
0phon_R01_S01_1119.992157.30274.9970.007840.000070.003700.005540.011090.04374...0.065450.0221121.03310.4147830.815285-4.8130310.2664822.3014420.284654
1phon_R01_S01_2122.400148.650113.8190.009680.000080.004650.006960.013940.06134...0.094030.0192919.08510.4583590.819521-4.0751920.3355902.4868550.368674
2phon_R01_S01_3116.682131.111111.5550.010500.000090.005440.007810.016330.05233...0.082700.0130920.65110.4298950.825288-4.4431790.3111732.3422590.332634
3phon_R01_S01_4116.676137.871111.3660.009970.000090.005020.006980.015050.05492...0.087710.0135320.64410.4349690.819235-4.1175010.3341472.4055540.368975
4phon_R01_S01_5116.014141.781110.6550.012840.000110.006550.009080.019660.06425...0.104700.0176719.64910.4173560.823484-3.7477870.2345132.3321800.410335
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5 rows × 24 columns

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Shimmer:DDA NHR HNR status \\\n","count 195.000000 ... 195.000000 195.000000 195.000000 195.000000 \n","mean 0.282251 ... 0.046993 0.024847 21.885974 0.753846 \n","std 0.194877 ... 0.030459 0.040418 4.425764 0.431878 \n","min 0.085000 ... 0.013640 0.000650 8.441000 0.000000 \n","25% 0.148500 ... 0.024735 0.005925 19.198000 1.000000 \n","50% 0.221000 ... 0.038360 0.011660 22.085000 1.000000 \n","75% 0.350000 ... 0.060795 0.025640 25.075500 1.000000 \n","max 1.302000 ... 0.169420 0.314820 33.047000 1.000000 \n","\n"," RPDE DFA spread1 spread2 D2 PPE \n","count 195.000000 195.000000 195.000000 195.000000 195.000000 195.000000 \n","mean 0.498536 0.718099 -5.684397 0.226510 2.381826 0.206552 \n","std 0.103942 0.055336 1.090208 0.083406 0.382799 0.090119 \n","min 0.256570 0.574282 -7.964984 0.006274 1.423287 0.044539 \n","25% 0.421306 0.674758 -6.450096 0.174351 2.099125 0.137451 \n","50% 0.495954 0.722254 -5.720868 0.218885 2.361532 0.194052 \n","75% 0.587562 0.761881 -5.046192 0.279234 2.636456 0.252980 \n","max 0.685151 0.825288 -2.434031 0.450493 3.671155 0.527367 \n","\n","[8 rows x 23 columns]"],"text/html":["\n","
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MDVP:Fo(Hz)MDVP:Fhi(Hz)MDVP:Flo(Hz)MDVP:Jitter(%)MDVP:Jitter(Abs)MDVP:RAPMDVP:PPQJitter:DDPMDVP:ShimmerMDVP:Shimmer(dB)...Shimmer:DDANHRHNRstatusRPDEDFAspread1spread2D2PPE
count195.000000195.000000195.000000195.000000195.000000195.000000195.000000195.000000195.000000195.000000...195.000000195.000000195.000000195.000000195.000000195.000000195.000000195.000000195.000000195.000000
mean154.228641197.104918116.3246310.0062200.0000440.0033060.0034460.0099200.0297090.282251...0.0469930.02484721.8859740.7538460.4985360.718099-5.6843970.2265102.3818260.206552
std41.39006591.49154843.5214130.0048480.0000350.0029680.0027590.0089030.0188570.194877...0.0304590.0404184.4257640.4318780.1039420.0553361.0902080.0834060.3827990.090119
min88.333000102.14500065.4760000.0016800.0000070.0006800.0009200.0020400.0095400.085000...0.0136400.0006508.4410000.0000000.2565700.574282-7.9649840.0062741.4232870.044539
25%117.572000134.86250084.2910000.0034600.0000200.0016600.0018600.0049850.0165050.148500...0.0247350.00592519.1980001.0000000.4213060.674758-6.4500960.1743512.0991250.137451
50%148.790000175.829000104.3150000.0049400.0000300.0025000.0026900.0074900.0229700.221000...0.0383600.01166022.0850001.0000000.4959540.722254-5.7208680.2188852.3615320.194052
75%182.769000224.205500140.0185000.0073650.0000600.0038350.0039550.0115050.0378850.350000...0.0607950.02564025.0755001.0000000.5875620.761881-5.0461920.2792342.6364560.252980
max260.105000592.030000239.1700000.0331600.0002600.0214400.0195800.0643300.1190801.302000...0.1694200.31482033.0470001.0000000.6851510.825288-2.4340310.4504933.6711550.527367
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8 rows × 23 columns

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\n"," "]},"metadata":{},"execution_count":7}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3O8AclzwExyH","executionInfo":{"status":"ok","timestamp":1653200308701,"user_tz":-330,"elapsed":21,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"6a330028-c2a3-431a-f1cb-b529137cdcc7"},"source":["# distribution of target Variable\n","parkinsons_data['status'].value_counts()"],"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1 147\n","0 48\n","Name: status, dtype: int64"]},"metadata":{},"execution_count":8}]},{"cell_type":"markdown","metadata":{"id":"L1srlxtEFYfN"},"source":["1 --> Parkinson's Positive\n","\n","0 --> Healthy\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":237},"id":"zUrPan7CFTMq","executionInfo":{"status":"ok","timestamp":1653200308702,"user_tz":-330,"elapsed":18,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"9addca6f-f25f-4cde-aa11-266fbece8b9f"},"source":["# grouping the data bas3ed on the target variable\n","parkinsons_data.groupby('status').mean()"],"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" MDVP:Fo(Hz) MDVP:Fhi(Hz) MDVP:Flo(Hz) MDVP:Jitter(%) \\\n","status \n","0 181.937771 223.636750 145.207292 0.003866 \n","1 145.180762 188.441463 106.893558 0.006989 \n","\n"," MDVP:Jitter(Abs) MDVP:RAP MDVP:PPQ Jitter:DDP MDVP:Shimmer \\\n","status \n","0 0.000023 0.001925 0.002056 0.005776 0.017615 \n","1 0.000051 0.003757 0.003900 0.011273 0.033658 \n","\n"," MDVP:Shimmer(dB) ... MDVP:APQ Shimmer:DDA NHR HNR \\\n","status ... \n","0 0.162958 ... 0.013305 0.028511 0.011483 24.678750 \n","1 0.321204 ... 0.027600 0.053027 0.029211 20.974048 \n","\n"," RPDE DFA spread1 spread2 D2 PPE \n","status \n","0 0.442552 0.695716 -6.759264 0.160292 2.154491 0.123017 \n","1 0.516816 0.725408 -5.333420 0.248133 2.456058 0.233828 \n","\n","[2 rows x 22 columns]"],"text/html":["\n","
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MDVP:Fo(Hz)MDVP:Fhi(Hz)MDVP:Flo(Hz)MDVP:Jitter(%)MDVP:Jitter(Abs)MDVP:RAPMDVP:PPQJitter:DDPMDVP:ShimmerMDVP:Shimmer(dB)...MDVP:APQShimmer:DDANHRHNRRPDEDFAspread1spread2D2PPE
status
0181.937771223.636750145.2072920.0038660.0000230.0019250.0020560.0057760.0176150.162958...0.0133050.0285110.01148324.6787500.4425520.695716-6.7592640.1602922.1544910.123017
1145.180762188.441463106.8935580.0069890.0000510.0037570.0039000.0112730.0336580.321204...0.0276000.0530270.02921120.9740480.5168160.725408-5.3334200.2481332.4560580.233828
\n","

2 rows × 22 columns

\n","
\n"," \n"," \n"," \n","\n"," \n","
\n","
\n"," "]},"metadata":{},"execution_count":9}]},{"cell_type":"markdown","metadata":{"id":"8RY6c0waGSs7"},"source":["Data Pre-Processing"]},{"cell_type":"markdown","metadata":{"id":"We7sRYu7Gc4q"},"source":["Separating the features & Target"]},{"cell_type":"code","metadata":{"id":"UAcz8jFnFuzH","executionInfo":{"status":"ok","timestamp":1653200308702,"user_tz":-330,"elapsed":16,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["X = parkinsons_data.drop(columns=['name','status'], axis=1)\n","Y = parkinsons_data['status']"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"guRof_8WG1Yn","executionInfo":{"status":"ok","timestamp":1653200308702,"user_tz":-330,"elapsed":16,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"531b55ec-5615-47e6-dde6-51295bfe8945"},"source":["print(X)"],"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":[" MDVP:Fo(Hz) MDVP:Fhi(Hz) MDVP:Flo(Hz) MDVP:Jitter(%) \\\n","0 119.992 157.302 74.997 0.00784 \n","1 122.400 148.650 113.819 0.00968 \n","2 116.682 131.111 111.555 0.01050 \n","3 116.676 137.871 111.366 0.00997 \n","4 116.014 141.781 110.655 0.01284 \n",".. ... ... ... ... \n","190 174.188 230.978 94.261 0.00459 \n","191 209.516 253.017 89.488 0.00564 \n","192 174.688 240.005 74.287 0.01360 \n","193 198.764 396.961 74.904 0.00740 \n","194 214.289 260.277 77.973 0.00567 \n","\n"," MDVP:Jitter(Abs) MDVP:RAP MDVP:PPQ Jitter:DDP MDVP:Shimmer \\\n","0 0.00007 0.00370 0.00554 0.01109 0.04374 \n","1 0.00008 0.00465 0.00696 0.01394 0.06134 \n","2 0.00009 0.00544 0.00781 0.01633 0.05233 \n","3 0.00009 0.00502 0.00698 0.01505 0.05492 \n","4 0.00011 0.00655 0.00908 0.01966 0.06425 \n",".. ... ... ... ... ... \n","190 0.00003 0.00263 0.00259 0.00790 0.04087 \n","191 0.00003 0.00331 0.00292 0.00994 0.02751 \n","192 0.00008 0.00624 0.00564 0.01873 0.02308 \n","193 0.00004 0.00370 0.00390 0.01109 0.02296 \n","194 0.00003 0.00295 0.00317 0.00885 0.01884 \n","\n"," MDVP:Shimmer(dB) ... MDVP:APQ Shimmer:DDA NHR HNR RPDE \\\n","0 0.426 ... 0.02971 0.06545 0.02211 21.033 0.414783 \n","1 0.626 ... 0.04368 0.09403 0.01929 19.085 0.458359 \n","2 0.482 ... 0.03590 0.08270 0.01309 20.651 0.429895 \n","3 0.517 ... 0.03772 0.08771 0.01353 20.644 0.434969 \n","4 0.584 ... 0.04465 0.10470 0.01767 19.649 0.417356 \n",".. ... ... ... ... ... ... ... \n","190 0.405 ... 0.02745 0.07008 0.02764 19.517 0.448439 \n","191 0.263 ... 0.01879 0.04812 0.01810 19.147 0.431674 \n","192 0.256 ... 0.01667 0.03804 0.10715 17.883 0.407567 \n","193 0.241 ... 0.01588 0.03794 0.07223 19.020 0.451221 \n","194 0.190 ... 0.01373 0.03078 0.04398 21.209 0.462803 \n","\n"," DFA spread1 spread2 D2 PPE \n","0 0.815285 -4.813031 0.266482 2.301442 0.284654 \n","1 0.819521 -4.075192 0.335590 2.486855 0.368674 \n","2 0.825288 -4.443179 0.311173 2.342259 0.332634 \n","3 0.819235 -4.117501 0.334147 2.405554 0.368975 \n","4 0.823484 -3.747787 0.234513 2.332180 0.410335 \n",".. ... ... ... ... ... \n","190 0.657899 -6.538586 0.121952 2.657476 0.133050 \n","191 0.683244 -6.195325 0.129303 2.784312 0.168895 \n","192 0.655683 -6.787197 0.158453 2.679772 0.131728 \n","193 0.643956 -6.744577 0.207454 2.138608 0.123306 \n","194 0.664357 -5.724056 0.190667 2.555477 0.148569 \n","\n","[195 rows x 22 columns]\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xSNrvkJoG3cY","executionInfo":{"status":"ok","timestamp":1653200308703,"user_tz":-330,"elapsed":14,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"db156ede-5d9e-4ab4-de6d-ead138a71faf"},"source":["print(Y)"],"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["0 1\n","1 1\n","2 1\n","3 1\n","4 1\n"," ..\n","190 0\n","191 0\n","192 0\n","193 0\n","194 0\n","Name: status, Length: 195, dtype: int64\n"]}]},{"cell_type":"markdown","metadata":{"id":"WDeqEaaHHBAS"},"source":["Splitting the data to training data & Test data"]},{"cell_type":"code","metadata":{"id":"4c6nrCiVG6NB","executionInfo":{"status":"ok","timestamp":1653200309503,"user_tz":-330,"elapsed":810,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)"],"execution_count":13,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6OqUka96H35c","executionInfo":{"status":"ok","timestamp":1653200309504,"user_tz":-330,"elapsed":16,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"47eb1e86-5aa5-41f1-deb2-e02d9d2bffe7"},"source":["print(X.shape, X_train.shape, X_test.shape)"],"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["(195, 22) (156, 22) (39, 22)\n"]}]},{"cell_type":"markdown","metadata":{"id":"QIOAtx35JUMg"},"source":["Model Training"]},{"cell_type":"markdown","metadata":{"id":"fWlsaBNuJV5g"},"source":["Support Vector Machine Model"]},{"cell_type":"code","metadata":{"id":"IDInA1u5JCZ9","executionInfo":{"status":"ok","timestamp":1653200309504,"user_tz":-330,"elapsed":16,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["model = svm.SVC(kernel='linear')"],"execution_count":15,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"F01DNpqWKmaW","executionInfo":{"status":"ok","timestamp":1653200309505,"user_tz":-330,"elapsed":16,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"a681ba26-aa79-4b09-a0a3-ddd653726c52"},"source":["# training the SVM model with training data\n","model.fit(X_train, Y_train)"],"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["SVC(kernel='linear')"]},"metadata":{},"execution_count":16}]},{"cell_type":"markdown","metadata":{"id":"1z_-nZfuLJrH"},"source":["Model Evaluation"]},{"cell_type":"markdown","metadata":{"id":"Rj3XAnF8LMF4"},"source":["Accuracy Score"]},{"cell_type":"code","metadata":{"id":"5LwxNgnqK1Za","executionInfo":{"status":"ok","timestamp":1653200309505,"user_tz":-330,"elapsed":14,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["# accuracy score on training data\n","X_train_prediction = model.predict(X_train)\n","training_data_accuracy = accuracy_score(Y_train, X_train_prediction)"],"execution_count":17,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-dS9tcGdLm41","executionInfo":{"status":"ok","timestamp":1653200309506,"user_tz":-330,"elapsed":15,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"bbc0b0cd-ed16-4430-f137-8fdaf1f2183d"},"source":["print('Accuracy score of training data : ', training_data_accuracy)"],"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy score of training data : 0.8717948717948718\n"]}]},{"cell_type":"code","metadata":{"id":"rNUO2uHmLtjY","executionInfo":{"status":"ok","timestamp":1653200309506,"user_tz":-330,"elapsed":14,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["# accuracy score on training data\n","X_test_prediction = model.predict(X_test)\n","test_data_accuracy = accuracy_score(Y_test, X_test_prediction)"],"execution_count":19,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BsF3UnQ2L_aR","executionInfo":{"status":"ok","timestamp":1653200309506,"user_tz":-330,"elapsed":14,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"80347c36-1481-4ba3-9cf3-7ed635ca5a85"},"source":["print('Accuracy score of test data : ', test_data_accuracy)"],"execution_count":20,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy score of test data : 0.8717948717948718\n"]}]},{"cell_type":"markdown","metadata":{"id":"QlR4JG4YMfOR"},"source":["Building a Predictive System"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"w0FjSoO1MGBU","executionInfo":{"status":"ok","timestamp":1653200309507,"user_tz":-330,"elapsed":13,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"a4715754-198f-4927-9df3-e8a104a9b154"},"source":["input_data = (197.07600,206.89600,192.05500,0.00289,0.00001,0.00166,0.00168,0.00498,0.01098,0.09700,0.00563,0.00680,0.00802,0.01689,0.00339,26.77500,0.422229,0.741367,-7.348300,0.177551,1.743867,0.085569)\n","\n","# changing input data to a numpy array\n","input_data_as_numpy_array = np.asarray(input_data)\n","\n","# reshape the numpy array\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","\n","if (prediction[0] == 0):\n"," print(\"The Person does not have Parkinsons Disease\")\n","\n","else:\n"," print(\"The Person has Parkinsons\")\n"],"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["[0]\n","The Person does not have Parkinsons Disease\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n"," \"X does not have valid feature names, but\"\n"]}]},{"cell_type":"markdown","metadata":{"id":"FCHCMHpshHU4"},"source":["Saving the trained model"]},{"cell_type":"code","metadata":{"id":"cdmTOR4MhHCB","executionInfo":{"status":"ok","timestamp":1653200309507,"user_tz":-330,"elapsed":12,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["import pickle"],"execution_count":22,"outputs":[]},{"cell_type":"code","metadata":{"id":"4gN09lokhKuZ","executionInfo":{"status":"ok","timestamp":1653200309508,"user_tz":-330,"elapsed":13,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["filename = 'parkinsons_model.sav'\n","pickle.dump(model, open(filename, 'wb'))"],"execution_count":23,"outputs":[]},{"cell_type":"code","metadata":{"id":"IKW4D5CqhP5X","executionInfo":{"status":"ok","timestamp":1653200309510,"user_tz":-330,"elapsed":14,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["# loading the saved model\n","loaded_model = pickle.load(open('parkinsons_model.sav', 'rb'))"],"execution_count":24,"outputs":[]},{"cell_type":"code","source":["for column in X.columns:\n"," print(column)"],"metadata":{"id":"m8FO1U8hRVm_","executionInfo":{"status":"ok","timestamp":1653200309511,"user_tz":-330,"elapsed":15,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"079be68d-fb39-4544-9100-c6388b397091","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":25,"outputs":[{"output_type":"stream","name":"stdout","text":["MDVP:Fo(Hz)\n","MDVP:Fhi(Hz)\n","MDVP:Flo(Hz)\n","MDVP:Jitter(%)\n","MDVP:Jitter(Abs)\n","MDVP:RAP\n","MDVP:PPQ\n","Jitter:DDP\n","MDVP:Shimmer\n","MDVP:Shimmer(dB)\n","Shimmer:APQ3\n","Shimmer:APQ5\n","MDVP:APQ\n","Shimmer:DDA\n","NHR\n","HNR\n","RPDE\n","DFA\n","spread1\n","spread2\n","D2\n","PPE\n"]}]},{"cell_type":"code","source":[""],"metadata":{"id":"JPyuHFeDRXZU","executionInfo":{"status":"ok","timestamp":1653200309512,"user_tz":-330,"elapsed":15,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"execution_count":25,"outputs":[]}]} --------------------------------------------------------------------------------