This project implements a gender prediction model using various machine learning algorithms, including Logistic Regression, Multinomial Naive Bayes, and XGBoost. The model predicts gender based on names. The data is preprocessed by encoding the target variable and splitting it into training and validation sets.

1. Clone the repository.
2. Install the required libraries:

   pip install pandas scikit-learn xgboost joblib matplotlib seaborn

1. Open the Jupyter notebook gender prediction.ipynb.
2. Run the cells sequentially to load the data, preprocess it, build the models, and evaluate their performance.

The following models are implemented:

• Logistic Regression
• Multinomial Naive Bayes
• XGBoost

Performance metrics such as accuracy, precision, recall, F1-score, and AUC are calculated for each model. The evaluation process includes generating confusion matrices and ROC-AUC curves for visual representation.

The trained model is saved as logistic regression.pkl for future use.

App Link:- Click Here

This project is licensed under the MIT License.