Welcome to the Galaxy Bank Loan Conversion Enhancement project!
This project is aimed at revolutionizing Galaxy Bank's marketing strategy to boost conversion rates and effectively target potential loan customers.
We've combined data exploration, machine learning, and strategic decision-making to achieve this mission.
Our primary objectives for this project are:
Enhance Conversion Rates: We aim to increase the number of deposit customers accepting loan offers through data-driven insights.
Optimized Customer Targeting: Our strategy focuses on effectively identifying potential loan customers to make marketing campaigns more precise.
The project is organized into the following key steps:
Data Collection and Cleaning: After collecting the dataset, we performed data cleaning to ensure high data quality.
Data Exploration: We explored the dataset, gaining insights into customers' characteristics, behaviors, and potential loan acceptance factors.
Machine Learning: We experimented with various machine learning models, including Random Forest, to predict which deposit customers are most likely to accept a loan offer.
Recall Optimization: We emphasized recall as a metric, capturing as many true positives (actual acceptances) as possible.
Hyperparameter Tuning: To optimize the model's performance, we conducted hyperparameter tuning.
Model Validation: We validated the model's performance using cross-validation to ensure its ability to generalize to unseen data.
Communication: We prepared a comprehensive presentation for the bank's marketing team using slide and Medium, highlighting how our approach aligns with Galaxy Bank's mission and objectives.
To run this project,the following Python packages and tools were installed:
Seaborn Matplotlib Pandas NumPy scikit-learn (sklearn) Jupyter Notebook ipython
We have used various machine learning models to predict customer acceptance of loans based on their features.
The final model, a Random Forest model, is optimized for recall, emphasizing capturing as many potential loan customers as possible.
./my_project argument1 argument2
deniran_o
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