This project provides tools and methods for balancing imbalanced datasets, which is crucial for improving the performance and fairness of machine learning models. The included Jupyter Notebook demonstrates techniques for analyzing, visualizing, and rebalancing datasets using various approaches.
- Data Analysis: Visualizes the distribution of classes to identify imbalances.
- Balancing Techniques: Implements methods such as oversampling, undersampling, and synthetic data generation (e.g., SMOTE).
- Custom Balancing Strategies: Provides flexibility to experiment with different balancing ratios and techniques.
- Model Training Compatibility: Ensures rebalanced datasets are ready for machine learning pipelines.
Ensure you have the following Python libraries installed:
pip install pandas numpy matplotlib seaborn scikit-learn imbalanced-learn-
Clone the Repository
git clone https://github.com/mahmoodalikhan1999/balancingdata.git cd Data_Balancing_Project -
Prepare the Dataset
- Place your dataset in CSV format in the project directory.
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Run the Jupyter Notebook
jupyter notebook Balancing_data.ipynb
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Select Balancing Techniques
- Choose appropriate methods based on your dataset and model requirements.
- Experiment with different approaches to determine the most effective technique.
- Oversampling: Replicates minority class samples to balance the dataset.
- Undersampling: Reduces majority class samples to achieve class balance.
- Synthetic Data Generation: Utilizes algorithms like SMOTE to generate synthetic samples for the minority class.
- Fraud Detection: Balance data for better detection of rare fraudulent transactions.
- Medical Diagnosis: Improve model accuracy for rare disease detection.
- Customer Churn Prediction: Ensure balanced predictions in business analytics.
This project is available for use and modification in accordance with the repository's license.