BANK-CUSTOMER-CHURN-PREDICTION

Bank Customer Churn Analysis

Overview

This project analyzes customer churn in a bank using a dataset containing customer information. We aim to understand the factors that contribute to customer churn and create visualizations to present our findings.

Dataset

The dataset used in this project is "Bank Churn Modelling.csv," which includes the following columns:

• CustomerId
• Surname
• CreditScore
• Geography
• Gender
• Age
• Tenure
• Balance
• Num Of Products
• Has Credit Card
• Is Active Member
• Estimated Salary
• Churn (Target Variable: 0 for not churned, 1 for churned)

Code

Data Preprocessing

• Imported the necessary libraries, including pandas, numpy, matplotlib, and seaborn.
• Read the dataset into a pandas DataFrame.
• Processed and cleaned the data, including handling missing values and converting categorical variables into numerical form.

Data Visualization

• Created various visualizations to explore and understand the dataset:
  • Churn distribution (bar chart)
  • Gender distribution (bar chart)
  • Age distribution (box plot)
  • Distribution of other features (bar charts)

Data Resampling

• Utilized the imbalanced-learn library to perform random under-sampling and random over-sampling to balance the dataset.

Machine Learning Model (Random Forest)

• Trained a Random Forest Classifier on both the under-sampled and over-sampled datasets.
• Evaluated the model's performance using accuracy and classification reports.

Results

• Foundings and insights from the data analysis.
• Model performance metrics for both under-sampled and over-sampled datasets.