{"id":24525797,"url":"https://github.com/mehboob14/nextdayrainprediction","last_synced_at":"2026-05-19T11:05:36.679Z","repository":{"id":273178833,"uuid":"918598078","full_name":"mehboob14/NextDayRainPrediction","owner":"mehboob14","description":"As part of a course project in Data Science, I worked on the Nextday Rain Predictions dataset sourced from Kaggle. The project involved end-to-end processes, including data preprocessing, handling class imbalance, and model evaluation. 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The project includes data preprocessing, handling class imbalance, model implementation, and evaluation.\nTable of Contents\n\n    Overview\n    Dataset\n    Technologies Used\n    Project Workflow\n    Installation and Setup\n    Usage\n    Results\n    Contributing\n    License\n\nOverview\n\nThe project demonstrates the end-to-end data science pipeline, from data preprocessing to model evaluation. Various techniques were applied to improve prediction accuracy, including:\n\n    Data preprocessing\n    Addressing class imbalance\n    Feature engineering\n    Model training and evaluation\n\nModels explored:\n\n    Logistic Regression\n    Random Forest\n\nDataset\n\nThe dataset used for this project is the Netday Rain Predictions dataset, which is available on Kaggle. It includes features that help predict rain events based on historical weather data.\nFiles in this Repository:\n\n    *.ipynb: Jupyter notebooks containing the step-by-step implementation of the project.\n    *.csv: Dataset files used in this project.\n\nTechnologies Used\n\n    Python: Core programming language\n    Pandas: For data manipulation and analysis\n    NumPy: For numerical computations\n    Scikit-learn: For implementing machine learning models\n    Matplotlib/Seaborn: For data visualization\n    Jupyter Notebook: For interactive development\n\nProject Workflow\n\nThe workflow of this project includes:\n\n    Data Preprocessing\n        Handling missing values\n        Encoding categorical features\n        Scaling numerical features\n    Class Imbalance\n        Techniques such as oversampling and undersampling were used.\n    Feature Selection\n        Correlation analysis and feature importance methods.\n    Model Training\n        Logistic Regression\n        Random Forest\n    Model Evaluation\n        Metrics such as accuracy, precision, recall, F1-score, and ROC-AUC.\n\nInstallation and Setup\n\n    Clone the repository:\n\ngit clone https://github.com/your-repo/netday-rain-predictions.git\ncd netday-rain-predictions\n\nCreate a virtual environment (optional but recommended):\n\npython -m venv env\nsource env/bin/activate  # On Windows: env\\Scripts\\activate\n\nInstall the required dependencies:\n\npip install -r requirements.txt\n\nLaunch the Jupyter Notebook:\n\n    jupyter notebook\n\nUsage\n\n    Open the Jupyter notebooks to view the step-by-step implementation.\n    Place the dataset files (*.csv) in the same directory as the notebooks or update the file paths in the code.\n    Run each cell sequentially to reproduce the results.\n\nResults\n\n    The Random Forest model outperformed Logistic Regression in terms of accuracy and precision.\n    Key metrics:\n        Accuracy: 85%\n        Precision: 82%\n        Recall: 79%\n        ROC-AUC: 88%\n\nContributing\n\nIf you'd like to contribute to this project, feel free to submit a pull request. For major changes, please open an issue first to discuss the changes.\nLicense\n\nThis project is licensed under the MIT License. See the LICENSE file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmehboob14%2Fnextdayrainprediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmehboob14%2Fnextdayrainprediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmehboob14%2Fnextdayrainprediction/lists"}