mohamedsilima53-droid/tanzania-life-standard-predictor

machine learning model to predict life standard of people within the country # 🏠 Tanzania Life Standard Predictor - Streamlit App ## 📋 Overview This is a web application that predicts life standard in Tanzania (Tajiri, Hali ya Kawaida, or Maskini) based on socioeconomic factors. ## 🚀 How to Run Locally ### Prerequisites - Python 3.8 or higher - pip ### Installation Steps 1. **Install required packages:** ```bash pip install -r requirements.txt ``` 2. **Make sure you have the model files in the same directory:** - `model.pkl` - `label_encoders.pkl` - `scaler.pkl` *(These files are generated by running the Jupyter notebook: `ml_project.ipynb`)* 3. **Run the Streamlit app:** ```bash streamlit run app.py ``` 4. **Open your browser:** - The app will automatically open at `localhost` - If it doesn't open automatically, navigate to that URL manually ## 📦 Files Needed for Deployment ``` your-project-folder/ │ ├── app.py # Main Streamlit application ├── requirements.txt # Python dependencies ├── model.pkl # Trained ML model ├── label_encoders.pkl # Categorical encoders ├── scaler.pkl # Feature scaler └── README.md # This file ``` ## 🌐 Deploy to Streamlit Cloud (FREE!) ### Step 1: Prepare Files 1. Make sure all files are in your project folder 2. Run the Jupyter notebook first to generate the .pkl files ### Step 2: Push to GitHub ```bash # Initialize git (if not already done) git init # Add all files git add . # Commit git commit -m "Tanzania Life Standard Predictor App" # Create a new repository on GitHub # Then push to GitHub git remote add origin github.com git branch -M main git push -u origin main ``` ### Step 3: Deploy on Streamlit Cloud 1. Go to share.streamlit.io 2. Sign in with GitHub 3. Click "New app" 4. Select your repository 5. Choose the branch (main) 6. Set the main file path: `app.py` 7. Click "Deploy"! ### Your app will be live at: ``` your_username-your_ …