Rain Prediction Model

Project Overview

This project aims to predict rainfall using historical and current weather data. The model integrates three datasets:

1. Daily Rainfall Data from January 2024.
2. District-Wise Rainfall Normals for various districts.
3. Historical Rainfall Data from 1901 to 2015.

The goal is to develop a predictive model using Linear Regression to forecast average rainfall based on various features.

Why It's a Test Project

This project is primarily a proof of concept and test model. The predictions from this model may not be practically accurate due to:

• Limited and possibly inaccurate data.
• Simplistic approach using linear regression without more advanced techniques.
• Preliminary preprocessing and feature engineering.
• Less cleaning up and pre-processing of data.

The results obtained are:

• Mean Absolute Error (MAE): 0.0704
• Root Mean Squared Error (RMSE): 0.2222
• R^2 Score: 0.0147

These results suggest that the model may not perform well in real-world scenarios.

Tools and Technologies

• Python: Programming language used for data processing and modeling.
• Pandas: Library for data manipulation and analysis.
• Scikit-Learn: Library for machine learning, used for creating and evaluating the Linear Regression model.
• Jupyter Notebook: Development environment used for running the code and visualizing results.

Steps and Methodology

1. Data Loading and Inspection:

   • Loaded datasets from CSV files.
   • Inspected the datasets to understand their structure and contents.

2. Data Transformation:

   • Converted datasets to long format using pd.melt() to facilitate merging.
   • Processed the daily rainfall data to extract relevant features.

3. Data Merging:

   • Merged daily rainfall data with monthly norms and historical rainfall data based on geographical and temporal features.

4. Data Preprocessing:

   • Handled missing values by filling them with zeros.
   • Defined features (Day, Monthly_Rainfall, Historical_Rainfall) and target variable (Avg_rainfall).

5. Model Training and Evaluation:

   • Split the data into training and testing sets.
   • Trained a Linear Regression model on the training data.
   • Evaluated the model's performance using MAE, RMSE, and R^2 Score.

Future Improvements

• Advanced Modeling: Explore other machine learning models such as Decision Trees, Random Forests, or Gradient Boosting.
• Feature Engineering: Improve feature selection and engineering to enhance model performance.
• Data Quality: Use more accurate and comprehensive datasets for better predictions.
• Hyperparameter Tuning: Optimize model parameters for better results.

How to Contribute

If you'd like to contribute to this project:

1. Fork the repository.
2. Clone your fork to your local machine.
3. Create a new branch for your changes.
4. Commit your changes and push them to your fork.
5. Create a pull request describing your changes and improvements.

Installation and Usage

To use this project, follow these steps:

1. Clone the Repository:

   git clone https://github.com/yourusername/rain-prediction-model.git

2. Navigate to the Project Directory:

   cd rain-prediction-model

3. Set Up a Virtual Environment:

   python -m venv env
   source env/bin/activate  # On Windows, use `env\Scripts\activate`

4. Install Dependencies:

   pip install -r requirements.txt

5. Run the Jupyter Notebook:

   jupyter notebook

Additional Information

For the datasets used in this project, visit Data.gov.in.