This project focuses on predicting customer churn for a bank using a variety of machine learning models. The dataset contains information on customers, and the goal is to identify which customers are likely to leave the bank.

The dataset used in this project is Churn_Modelling.csv, which contains the following features:

• CreditScore: Customer's credit score
• Geography: Customer's country
• Gender: Customer's gender
• Age: Customer's age
• Tenure: Number of years the customer has been with the bank
• Balance: Customer's account balance
• NumOfProducts: Number of products the customer has with the bank
• HasCrCard: Whether the customer has a credit card (1 = Yes, 0 = No)
• IsActiveMember: Whether the customer is an active member (1 = Yes, 0 = No)
• EstimatedSalary: Customer's estimated salary
• Exited: Whether the customer churned (1 = Yes, 0 = No)

The project is organized as follows:

1. Data Cleaning and Preprocessing: The data is cleaned, irrelevant columns are removed, categorical variables are encoded, and numerical features are standardized.
2. Exploratory Data Analysis: Various visualizations are created to understand the distribution and relationships of the features.
3. Model Evaluation: Multiple machine learning models are trained and evaluated, including:
   • Logistic Regression
   • Decision Tree
   • Random Forest
   • Support Vector Machine
   • K-Nearest Neighbors
   • Naive Bayes
   • Neural Networks
   • Ensemble Models (e.g., AdaBoost, Bagging, Gradient Boosting, etc.)
   • XGBoost
   • CatBoost
4. Model Selection: The best-performing model is selected based on accuracy, and a confusion matrix is plotted to visualize the results.

The following Python libraries are required for the project:

• pandas
• seaborn
• matplotlib
• scikit-learn
• xgboost
• catboost

You can install the necessary packages using:

pip install pandas seaborn matplotlib scikit-learn xgboost catboost

To run the Jupyter Notebook containing the project code, follow these steps:

Ensure you have Jupyter Notebook installed. If not, you can install it using pip:

pip install notebook

Navigate to the directory containing the Jupyter Notebook file (Banking_Customer_Churn_Prediction.ipynb) using the command line.

Launch Jupyter Notebook by running the following command:

jupyter notebook

This will open a new tab in your web browser showing the Jupyter file explorer. Click on the Banking_Customer_Churn_Prediction.ipynb file to open it.

Inside the notebook, you can execute each cell by clicking on it and then pressing Shift + Enter. Alternatively, you can run all cells at once by selecting Cell > Run All from the menu bar.

Follow the instructions and comments within the notebook to understand the workflow and analyze the results.

The best-performing model in this project was the CatBoost Classifier. The results were evaluated using accuracy and a confusion matrix was plotted to visualize the performance.

João Alves - up202108670@up.pt

Alberto Serra - up202103627@up.pt

Eduardo Sousa - up202103342@up.pt