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license: mit
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# 🏦 Bank Customer Churn Analysis

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## 🎥 Project Walkthrough (Video)

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

[![Watch the Video](https://img.youtube.com/vi/Syatg5hxqLc/0.jpg)](https://www.youtube.com/watch?v=Syatg5hxqLc)

> **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.

![Churn Rate by Geography](plot_geography_churn.png)

> **💡 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.

![Age Distribution by Churn Status](plot_age_density.png)

> **💡 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