Project Description

Overview

• 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.

Technologies Used

• Python
• Scikit-learn
• Pandas
• Matplotlib
• Seaborn

Link To Project Presentation

Presentation

Folder Structure

• 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.