MatanYehudaDataAnalyst/BankCustomerChurnPredictionOfMatan

🏦 Bank Customer Churn Analysis

🎥 Project Walkthrough (Video)

Click the image below to watch the full explanation of the data analysis:

In this video: I explain the data cleaning process, outlier handling, and the key insights about customer churn.

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📌 Project Overview

This project is an end-to-end data science analysis of a bank customer dataset. The primary objective is to investigate the factors contributing to customer attrition (churn) and to identify patterns that distinguish customers who leave from those who stay.

Context: This work was completed as Assignment 1 for the "Introduction to Data Science" course.

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📂 1. The Dataset

Dataset: Bank Customer Churn (Kaggle)
Target Variable: Exited (1 = Churned, 0 = Stayed)

Metric	Details
Source	Churn_Modelling.csv
Size	10,000 Rows
Key Features	CreditScore, Geography, Gender, Age, Tenure, Balance, NumOfProducts, EstimatedSalary

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🧹 2. Data Cleaning & Preprocessing

A rigorous cleaning process was applied to ensure data quality. Below are the critical decisions made during this phase:

• ❌ Dropping Irrelevants: Columns RowNumber, CustomerId, and Surname were removed as they are unique identifiers with no predictive power.
• ✅ Missing Values: Confirmed 0 null values in the dataset.
• ✅ Duplicates: Confirmed 0 duplicate rows.
• ⚠️ Outlier Strategy:
  • Decision: Outliers were KEPT.
  • Justification: In churn analysis, extreme values (e.g., older age, high balance) are often the most informative. Removing a 90-year-old customer would result in losing valid behavioral data for that demographic.
• ⚖️ Feature Scaling: Identified significant scale differences (e.g., Balance vs. Age). Applied StandardScaler to normalize features for model performance.

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📊 3. Exploratory Data Analysis (EDA)

We focused on two main research questions to derive business insights.

🌍 Q1: Does geography impact the churn rate?

Investigation: Analyzing the proportion of churned customers across France, Germany, and Spain.

💡 Insight: Geography is a strong predictor. While France has the highest volume of customers, Germany exhibits a significantly higher churn rate. This suggests potential issues with local competition, customer service, or market fit specifically in the German region.

👴 Q2: What is the relationship between Age and Churn?

Investigation: Using Kernel Density Estimation (KDE) to compare age distributions of retained vs. churned customers.

💡 Insight: Age is a critical factor.

• Stayed (0): Peak distribution around 30-40 years old.
• Churned (1): Distinct peak around 45-55 years old.
• Action Item: The bank is losing its mature, middle-aged customer base. Retention campaigns should specifically target the 45-60 age demographic.

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🛠️ Tech Stack

• Language: Python
• Libraries: Pandas, Matplotlib, Seaborn, Scikit-learn
• Environment: Jupyter Notebook