Merge pull request #417 from DarshAgrawal14/main

Added Models to predict Birth Rate

Time Series Analysis/Birth_rate_Forecasting/README.md

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+# Time Series Forecasting: Daily Female Births
+
+## Project Overview
+
+This project focuses on time series forecasting using various models to predict daily female births. The dataset used is "daily-total-female-births.csv", which contains information about daily female births in 1959.
+
+## Models Implemented
+
+1. Prophet
+2. ARIMA (AutoRegressive Integrated Moving Average)
+3. LSTM (Long Short-Term Memory)
+
+## Requirements
+
+To run this project, you'll need the following Python libraries:
+
+- pandas
+- numpy
+- prophet
+- statsmodels
+- matplotlib
+- seaborn
+- tensorflow
+- scikit-learn
+
+You can install these libraries using pip:
+
+```
+pip install pandas numpy prophet statsmodels matplotlib seaborn tensorflow scikit-learn
+```
+
+## File Structure
+
+- `daily-total-female-births.csv`: Input dataset
+- `main.py`: Main script containing all the code
+- `models/`: Directory containing saved models
+  - `prophet_model.pkl`: Saved Prophet model
+  - `arima_model.pkl`: Saved ARIMA model
+  - `lstm_model.h5`: Saved LSTM model
+  - `scaler.pkl`: Saved MinMaxScaler for LSTM
+
+## Usage
+
+1. Ensure you have all the required libraries installed.
+2. Place the `daily-total-female-births.csv` file in the same directory as the script.
+3. Run the main script:
+
+```
+python main.py
+```
+
+## Model Comparison
+
+The script will train and evaluate three different models:
+
+1. **Prophet**: A procedure for forecasting time series data based on an additive model.
+2. **ARIMA**: A statistical model for analyzing and forecasting time series data.
+3. **LSTM**: A type of recurrent neural network capable of learning long-term dependencies.
+
+The script will output performance metrics (MAE, MSE, RMSE) for each model and generate visualizations comparing the forecasts.
+
+## Visualizations
+
+The script generates several plots:
+
+1. Original data plot
+2. Prophet forecast and components
+3. ARIMA forecast
+4. LSTM predictions
+5. Comparison plot of all three models
+
+## Saved Models
+
+After training, the models are saved in the `models/` directory for future use.
+
+## Results
+
+The script will print a summary of the performance metrics for each model, allowing for easy comparison.
+
+## Future Improvements
+
+- Implement hyperparameter tuning for each model
+- Explore ensemble methods combining multiple models
+- Add more advanced deep learning models (e.g., Transformer-based models)
+- Incorporate external factors that might influence birth rates