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README.me

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 license: mit
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+## WATCH THE VIDEO HERE - (https://www.youtube.com/watch?v=Syatg5hxqLc)
+# 🏦 Bank Customer Churn Analysis
 
-# Project: Bank Customer Churn Analysis
+![Python](https://img.shields.io/badge/Python-3.8%2B-blue?style=for-the-badge&logo=python&logoColor=white)
+![Pandas](https://img.shields.io/badge/Pandas-Data%20Analysis-150458?style=for-the-badge&logo=pandas&logoColor=white)
+![Scikit-Learn](https://img.shields.io/badge/scikit--learn-Machine%20Learning-F7931E?style=for-the-badge&logo=scikit-learn&logoColor=white)
+![Status](https://img.shields.io/badge/Status-Completed-success?style=for-the-badge)
 
-This project is an end-to-end data science analysis of a bank customer dataset. The goal is to understand the factors that lead to customer churn (leaving the bank) and to build a predictive model.
+## 📌 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.
 
-This work was completed as part of the "Introduction to Data Science" course.
+> **Context:** This work was completed as **Assignment 1** for the "Introduction to Data Science" course.
 
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-## 1. The Dataset: Bank Customer Churn
+## 📂 1. The Dataset
+**Dataset:** Bank Customer Churn (Kaggle)  
+**Target Variable:** `Exited` (1 = Churned, 0 = Stayed)
 
-* **Source:** Kaggle (`Churn_Modelling.csv`)
-* **Size:** 10,000 rows (customers)
-* **Key Features:** `CreditScore`, `Geography`, `Gender`, `Age`, `Tenure`, `Balance`, `NumOfProducts`, `IsActiveMember`, `EstimatedSalary`.
-* **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` |
 
 ---
 
-## 2. Data Cleaning & Decisions
+## 🧹 2. Data Cleaning & Preprocessing
+A rigorous cleaning process was applied to ensure data quality. Below are the **critical decisions** made during this phase:
 
-A full data cleaning process was performed. Here are the key decisions:
-
-1.  **Irrelevant Columns:** Dropped `RowNumber`, `CustomerId`, and `Surname` as they are unique identifiers and not predictive features.
-2.  **Missing Values:** No missing values (NaN) were found in the dataset.
-3.  **Duplicate Entries:** No duplicate rows were found.
-4.  **Outlier Handling (Decision):** Outliers were **KEPT**.
-    * **Justification:** In a churn problem, outliers (e.g., very high `Age` or very low `CreditScore`) are often valid and highly informative data points. A 90-year-old customer is a real customer, and their behavior is important to model. Removing them would bias the model and lose valuable insights.
-5.  **Scaling Issues (Identification):** Numeric features (`Balance`, `Age`, `EstimatedSalary`) were identified as having vastly different scales. This was addressed during preprocessing (after the train-test split) using `StandardScaler`.
+* **❌ 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.
 
 ---
 
-## 3. Research Questions & Visual Insights
-
-We posed two key questions to understand churn behavior.
+## 📊 3. Exploratory Data Analysis (EDA)
+We focused on two main research questions to derive business insights.
 
-### Question 1: Does geography impact the churn rate?
+### 🌍 Q1: Does geography impact the churn rate?
+*Investigation:* Analyzing the proportion of churned customers across France, Germany, and Spain.
 
-To answer this, we visualized the count of churned vs. stayed customers across the three countries in the dataset.
-
-**Visualization:**
 ![Churn Rate by Geography](plot_geography_churn.png)
 
-**Answer & Insight:**
-Yes, geography has a clear impact. From the plot, we can see that while France has the most customers, **Germany has a visibly higher proportion of churned customers** compared to France and Spain. This suggests that the bank's operations or competition in Germany may be leading to higher customer dissatisfaction.
+> **💡 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.
 
-### Question 2: What is the relationship between customer age and the decision to churn?
-
-We used a Kernel Density Plot (KDE) to visualize the age distribution for customers who stayed versus those who churned.
-
-**Visualization:**
 ![Age Distribution by Churn Status](plot_age_density.png)
 
-**Answer & Insight:**
-Age is a major factor.
-* The 'Stayed' (0) customers show a large peak around **30-40 years old**.
-* The 'Churned' (1) customers show a distinct, separate peak around **45-55 years old**.
-* **Decision:** This insight is critical. It identifies a high-risk demographic (middle-aged customers) that the bank should target with retention campaigns.
+> **💡 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.
 
 ---
 
-## 4. Project Files
+## 🛠️ Tech Stack
+* **Language:** Python
+* **Libraries:** Pandas, Matplotlib, Seaborn, Scikit-learn
+* **Environment:** Jupyter Notebook
+
 
-* **Jupyter Notebook:** `Your_Notebook_Name.ipynb` (Change this to your .ipynb file name)
-* **Dataset:** `Churn_Modelling.csv`