- Developed a machine learning model to predict which deposit customers are most likely to accept a personal loan offer from Galaxy Bank, thereby improving marketing conversion rates.
- Utilized a multi-variable linear regression model on a large and complex dataset, involving data collection, cleaning, exploration, and visualization.
- Conducted in-depth analyses of customer demographics, including age, income, and other relevant factors, to understand their impact on loan acceptance.
- Implemented and fine-tuned various machine learning algorithms, including Logistic Regression, Naive Bayes, K-Nearest Neighbors, Decision Trees, Random Forests, and Support Vector Machines.
- Employed GridSearchCV for hyperparameter tuning to enhance model performance.
- Provided visualizations and insights to aid the marketing department in devising targeted campaigns and improving the success ratio with a minimal budget.
- Python
- Scikit-learn
- Pandas
- Matplotlib
- Seaborn
data/
: Contains datasets used in the project.notebooks/
: Jupyter notebooks for data cleaning, exploration, and model development.models/
: Saved machine learning models.visualizations/
: Visualizations generated during data analysis and model evaluation.