🌧️ Rainfall Prediction using Machine Learning

This project applies machine learning algorithms to predict rainfall based on meteorological data. The code is written in Python and structured within a Jupyter Notebook.

📌 Features

• Data cleaning and preprocessing
• Exploratory Data Analysis (EDA)
• Classification model training and testing
• Visualization of insights and model performance
• Prediction of rainfall occurrence (Yes/No)

🛠️ Tech Stack

• Language: Python
• Environment: Jupyter Notebook
• Libraries:
  • pandas
  • numpy
  • matplotlib
  • seaborn
  • scikit-learn

📁 Files

• Rainfall Prediction.ipynb: Main notebook with full implementation
• requirements.txt: List of required Python packages

🚀 Getting Started

1. Clone this repository:

   git clone https://github.com/your-username/rainfall-prediction.git
   cd rainfall-prediction

2. Install dependencies:

   pip install -r requirements.txt

3. Launch Jupyter Notebook:

   jupyter notebook

4. Open and run Rainfall Prediction.ipynb.

📈 Model Overview

• Multiple machine learning classifiers are used
• Evaluation metrics include accuracy, confusion matrix, etc.

📊 Output Highlights

• Cleaned dataset preview
• Correlation heatmap
• Model accuracy and predictions

📄 License

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