☂️ Rainfall Prediction Using Machine Learning 🌧️

📖 Important Note

For a better understanding of the project, please check the Google Colab file 📄 uploaded in this repository. It contains detailed explanations and execution steps to help you grasp the workflow more effectively.

This repository contains a machine learning model that predicts rainfall based on various weather parameters. The model is built using a Random Forest Classifier and is fine-tuned using GridSearchCV to optimize performance. 👩‍💻🌍

🌟 Features

• 📊 Data preprocessing, including handling missing values and encoding categorical data
• 📊 Exploratory Data Analysis (EDA) with visualization
• 🎨 Model training and hyperparameter tuning
• ⚡️ Downsampling to handle class imbalance
• 🔢 Model evaluation using accuracy, confusion matrix, and classification report
• 📊 Prediction on new data inputs
• 📂 Model serialization using pickle for later use

🛠️ Requirements

Make sure you have the following dependencies installed:

pip install numpy pandas matplotlib seaborn scikit-learn

💪 Usage

1. Clone the repository:

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

2. Run the model training script:

   python rainfall_prediction.py

3. Load and use the saved model:

   import pickle
   import pandas as pd
   
   with open("rainfall_prediction_model.pkl", "rb") as file:
       model_data = pickle.load(file)
   
   model = model_data["model"]
   feature_names = model_data["feature_names"]
   
   input_data = (1015.9, 19.9, 95, 81, 0.0, 40.0, 13.7)
   input_df = pd.DataFrame([input_data], columns=feature_names)
   prediction = model.predict(input_df)
   print("Prediction result:", "☔️ Rainfall" if prediction[0] == 1 else "🌞 No Rainfall")

🏆 License

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