🌧️ Rainfall Prediction Classifier

Welcome to the Rainfall Prediction project! 🌦️ This project aims to predict whether it will rain the following day using various machine learning classification algorithms. The dataset is sourced from the Australian Government's Bureau of Meteorology, providing historical weather data to train and evaluate our models.

🎯 Objectives

• 📊 Preprocess and clean the rainfall dataset to ensure high-quality input.
• 🤖 Implement and compare multiple classification algorithms for rainfall prediction.
• 📈 Evaluate model performance using various evaluation metrics.

🛠️ Algorithms Used

1. Linear Regression 📉
2. K-Nearest Neighbors (KNN) 🔍
3. Decision Trees 🌳
4. Logistic Regression 🧑‍💼
5. Support Vector Machines (SVM) 🔲

📊 Evaluation Metrics

The models are evaluated using the following metrics:

• ✅ Accuracy Score: Measures how often the model is correct.
• ⚖️ Jaccard Index: Evaluates the similarity between predicted and actual labels.
• 📏 F1-Score: Combines precision and recall for classification accuracy.
• 📉 Log Loss: Measures the uncertainty of predictions.
• 🧮 Mean Absolute Error (MAE): Calculates the average of absolute errors.
• 🔢 Mean Squared Error (MSE): Evaluates the average squared difference between predicted and actual values.
• 📈 R² Score: Indicates how well the model explains the variance in the dataset.

📝 Requirements

To run this project, you will need the following Python libraries:

• pandas 📑
• numpy 🔢
• scikit-learn 🧑‍💻

🙏 Acknowledgments

Special thanks to the Australian Government's Bureau of Meteorology 🌏 for providing the rainfall dataset used in this project.

Thank you for checking out the Rainfall Prediction Classifier project! 🌟 Feel free to contribute, open issues, or suggest improvements! 🚀