Rainfall Analysis and Prediction System

A comprehensive Python-based system for analyzing and predicting rainfall patterns across different subdivisions of India using machine learning techniques.

Overview

This project provides tools for:

• Loading and cleaning rainfall data
• Visualizing rainfall patterns over time
• Analyzing monthly rainfall distributions
• Training machine learning models to predict rainfall patterns
• Generating rainfall forecasts using ARIMA models
• Creating various visualizations including heatmaps and 3D plots

Features

• Data Processing

  • Automated data cleaning and preprocessing
  • Handling missing values using median imputation
  • Feature engineering for rainfall classification

• Visualization

  • Yearly rainfall trends
  • Monthly rainfall distribution analysis
  • Correlation heatmaps
  • 3D rainfall visualization
  • Average rainfall heatmaps

• Machine Learning

  • Multiple model implementations (Random Forest, Gradient Boosting, XGBoost)
  • Model evaluation and comparison
  • Feature importance analysis
  • Rainfall classification

• Forecasting

  • ARIMA-based rainfall forecasting
  • Long-term rainfall predictions

Requirements

• Python 3.x
• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn
• xgboost
• statsmodels

Installation

1. Clone this repository
2. Install the required packages:

pip install pandas numpy matplotlib seaborn scikit-learn xgboost statsmodels

Usage

The main script rainfall.py contains several functions for different analyses:

# Load and clean data
data = load_and_clean("Sub_Division_IMD_2017.csv")

# Visualize yearly trends
plot_yearly_trend(data, "KERALA")

# Analyze monthly patterns
plot_monthly_spread(data)

# Train machine learning models
train_rain_model(data)

# Generate forecasts
forecast_annual_rainfall_arima(data, forecast_years=10)

Data

The project uses the Sub_Division_IMD_2017.csv dataset, which contains:

• Monthly rainfall data
• Annual rainfall totals
• Subdivision information
• Yearly records

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is open source and available under the MIT License.