https://github.com/rizz1406/customer-churn-analysis
Telco Customer Churn Analysis - Data analysis and visualization to identify churn patterns in telecom customers. Includes EDA, feature engineering, and optional machine learning modeling to predict churn and provide business insights.
https://github.com/rizz1406/customer-churn-analysis
churn-analysis dataanalysis dataanalysisusingpython datacleaning jupyter-notebook python visualization
Last synced: 4 months ago
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Telco Customer Churn Analysis - Data analysis and visualization to identify churn patterns in telecom customers. Includes EDA, feature engineering, and optional machine learning modeling to predict churn and provide business insights.
- Host: GitHub
- URL: https://github.com/rizz1406/customer-churn-analysis
- Owner: rizz1406
- Created: 2025-03-08T07:07:52.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-03-08T07:23:46.000Z (4 months ago)
- Last Synced: 2025-03-08T08:20:08.508Z (4 months ago)
- Topics: churn-analysis, dataanalysis, dataanalysisusingpython, datacleaning, jupyter-notebook, python, visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 2.28 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 📊 Telco Customer Churn Analysis
## 📌 Overview
This project analyzes customer churn in a **telecommunications company**. The dataset contains customer demographics, service usage, and contract details to identify patterns associated with churn.
The goal is to:
- Perform **Exploratory Data Analysis (EDA)** to identify trends.
- Handle **data preprocessing** and feature engineering.
- Use **visualizations** for insights.
- Optionally, apply **machine learning models** to predict churn.
---
## 🗂️ Dataset Description
The dataset includes:
- **Customer ID**: Unique identifier.
- **Demographics**: Gender, senior citizen status, partner, and dependents.
- **Service Information**: Internet service, online security, streaming TV, etc.
- **Contract Details**: Contract type, paperless billing, payment method.
- **Churn Label**: Whether the customer left the service (`Yes` or `No`).
📌 **Data Cleaning Steps:**
- Handled missing values.
- Converted categorical variables.
- Engineered new features for analysis.
---
## ⚙️ Installation & Setup
### **1️⃣ Clone the Repository**
```bash
git clone https://github.com/rizz1406/Customer-Churn-Analysis.git
cd Customer-Churn-Analysis
```
### **2️⃣ Install Dependencies**
Ensure you have **Python 3.x** installed, then install required libraries:
```bash
pip install pandas numpy matplotlib seaborn scikit-learn
```
### **3️⃣ Run the Jupyter Notebook**
```bash
jupyter notebook
```
Open `Telco Customer Churn.ipynb` and execute all cells.
---
## 🔍 Exploratory Data Analysis (EDA)
### **1️⃣ Data Summary**
```python
import pandas as pd
df = pd.read_csv("Customer Churn.csv")
print(df.info()) # Dataset structure
print(df.describe()) # Statistical summary
print(df.isnull().sum()) # Check missing values
```
### **2️⃣ Churn Distribution**
```python
import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(6,4))
sns.countplot(x='Churn', data=df, palette='coolwarm')
plt.title("Customer Churn Distribution")
plt.show()
```
📊 **Insight**: Helps understand the proportion of customers who churned vs. stayed.
---
### **3️⃣ Correlation Heatmap**
```python
plt.figure(figsize=(10,6))
sns.heatmap(df.corr(), annot=True, cmap='Blues')
plt.title("Feature Correlation Heatmap")
plt.show()
```
📊 **Insight**: Identifies relationships between different variables.
---
## ✨ Feature Engineering
Some feature transformations:
- **Encoding categorical variables** (`Yes/No`, `Male/Female` → `0/1`).
- **Creating new aggregated features**.
- **Removing redundant columns**.
Example transformation:
```python
df['SeniorCitizen'] = df['SeniorCitizen'].map({0: 'No', 1: 'Yes'})
df = pd.get_dummies(df, drop_first=True) # Convert categorical to numerical
```
---
## 📈 Predicting Customer Churn (Optional)
### **1️⃣ Splitting Data for Modeling**
```python
from sklearn.model_selection import train_test_split
X = df.drop(columns=['Churn'])
y = df['Churn']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
### **2️⃣ Applying a Machine Learning Model**
```python
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Model Accuracy: {accuracy:.2f}")
```
📊 **Insight**: This gives a baseline model to predict churn.
---
## 📊 Key Visualizations
### **1️⃣ Churn by Contract Type**
```python
plt.figure(figsize=(8,5))
sns.countplot(x='Contract', hue='Churn', data=df)
plt.title("Churn Rate by Contract Type")
plt.show()
```
📊 **Insight**: Customers with **month-to-month contracts** have a higher churn rate.
### **2️⃣ Monthly Charges vs. Churn**
```python
plt.figure(figsize=(8,5))
sns.boxplot(x="Churn", y="MonthlyCharges", data=df)
plt.title("Monthly Charges vs Churn")
plt.show()
```
📊 **Insight**: Higher monthly charges correlate with increased churn.
---
## 🏆 Results & Insights
- **Customers with month-to-month contracts are more likely to churn.**
- **Senior citizens have a slightly higher churn rate.**
- **Paperless billing customers churn more frequently.**
- **Long-term contract customers are more loyal.**
📢 **Business Recommendation**: Offer incentives for long-term contracts to reduce churn.
---
## 🏗️ Future Improvements
- ✅ Improve feature selection for better model accuracy.
- ✅ Implement hyperparameter tuning for the ML model.
- ✅ Deploy the model via Flask or Streamlit.
---
## 🤝 Contribution & License
- Feel free to **contribute** by submitting pull requests.
- Licensed under **MIT License**.
---