This project focuses on the comprehensive analysis and predictive modeling of weather data to forecast rainfall. The dataset used is weatherAUS.csv, which includes various meteorological measurements recorded over time.

1. Data Preprocessing:

   • Loaded the weather dataset using pandas and conducted an initial exploration.
   • Replaced categorical values in the RainToday and RainTomorrow columns with numerical equivalents for ease of analysis.

2. Data Visualization:

   • Created visual representations of the imbalanced and balanced dataset regarding the RainTomorrow indicator.
   • Utilized heatmaps to identify missing data patterns within the dataset.

3. Data Imputation:

   • Filled missing values in categorical features with their mode values.
   • Applied Multiple Imputation by Chained Equations (MICE) to handle missing data in numerical features.

4. Outlier Detection and Removal:

   • Identified outliers using the Interquartile Range (IQR) method and removed them to refine the dataset.

5. Feature Engineering:

   • Standardized the dataset for consistency.
   • Performed feature selection using methods such as Chi-Square test and Random Forest feature importance.

6. Model Building:

   • Developed and evaluated multiple machine learning models, including Logistic Regression, Decision Trees, Neural Networks, Random Forests, LightGBM, CatBoost, and XGBoost.
   • Assessed model performance based on accuracy, ROC-AUC, and Cohen's Kappa metrics.

7. Model Evaluation:

   • Used ROC curves and confusion matrices to visualize and compare model performance.
   • Presented comprehensive classification reports for each model, detailing precision, recall, and F1-score.

8. Project Outcomes:

   • Achieved a balanced dataset through oversampling techniques.
   • Successfully identified significant features influencing the prediction of rainfall.
   • Demonstrated high-performing models capable of accurately predicting rainfall.

Future enhancements for this project could include:

• Exploring additional feature engineering techniques to further improve model performance.
• Incorporating more sophisticated imputation methods to handle missing data.
• Experimenting with advanced models such as Gradient Boosting Machines and ensemble learning techniques.
• Extending the analysis to other weather-related phenomena and datasets.
• Automating the entire pipeline using tools like Apache Airflow or Prefect for real-time data processing and model deployment.

This project is licensed under the MIT License. You are free to use, modify, and distribute this code as long as the original author is credited.

Contributions are welcome! Please open an issue or submit a pull request for any enhancements, bug fixes, or documentation updates.