Rainfall Prediction using Machine Learning

Project Overview

This project predicts whether rainfall will occur based on historical weather data collected from Kolkata. Multiple machine learning classification algorithms were implemented and compared to identify the most effective model for rainfall prediction.

Models Used

• Logistic Regression
• K-Nearest Neighbors (KNN)
• Decision Tree
• Random Forest

Dataset Features

• Minimum Temperature
• Maximum Temperature
• Temperature
• Dew Point
• Relative Humidity
• Heat Index
• Wind Speed
• Wind Direction
• Precipitation Cover
• Visibility
• Cloud Cover
• Sea Level Pressure
• Conditions

Data Preprocessing

• Missing value handling
• Feature engineering
• Label Encoding of categorical variables
• Feature scaling using StandardScaler
• Data cleaning and preparation

Model Evaluation

The models were evaluated using:

• Accuracy
• Precision
• Recall
• F1-Score
• Confusion Matrix

Best Performing Model

Random Forest achieved the highest performance among all implemented models for rainfall classification.

Technologies Used

• Python
• Pandas
• NumPy
• Scikit-Learn
• Matplotlib
• Seaborn
• Joblib

Author

Rahul Jana

M.Sc. Computer Science