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TimTemi/Predicting-Conversion-Model-for-Bank

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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.

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conversion model prediction

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