Customer Churn Analysis

 Customer Churn Analysis 

1. Introduction

Objective: Predict customer churn for a telecom or banking service provider using classification techniques.
Purpose: Help organizations identify at-risk customers and implement retention strategies.

2. Project Workflow

1. Problem Definition:
   - Predict whether a customer will churn based on historical data.
   - Key questions:
     - What are the key factors contributing to customer churn?
     - How accurately can we predict churn?
2. Data Collection:
   - Source: Public datasets (e.g., Kaggle, UCI ML Repository, or company-specific data).
   - Example: A dataset containing attributes like `Customer ID`, `Tenure`, `Monthly Charges`, `Contract Type`, `Payment Method`, and `Churn`.
3. Data Preprocessing:
   - Clean and preprocess the data.
   - Handle missing values, encode categorical variables, and normalize features.
4. Modeling and Evaluation:
   - Train classification models and evaluate their performance.
5. Insights and Recommendations:
   - Identify actionable factors for churn reduction.

3. Technical Requirements

- Programming Language: Python
- Libraries/Tools:
  - Data Handling: Pandas, NumPy
  - Visualization: Matplotlib, Seaborn
  - Machine Learning: Scikit-learn
  - Model Evaluation: Scipy, Statsmodels

4. Implementation Steps

Step 1: Setup Environment

Install required libraries:
```
pip install pandas numpy matplotlib seaborn scikit-learn
```

Step 2: Load and Explore Dataset

Load the churn dataset:
```
import pandas as pd

df = pd.read_csv('customer_churn.csv')
```
Explore the dataset:
```
print(df.head())
print(df.info())
```

Step 3: Data Cleaning and Preprocessing

Handle missing values:
```
df.fillna(df.median(), inplace=True)
```
Encode categorical variables:
```
df = pd.get_dummies(df, drop_first=True)
```
Normalize numerical features:
```
from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
numerical_features = ['Tenure', 'MonthlyCharges']
df[numerical_features] = scaler.fit_transform(df[numerical_features])
```

Step 4: Train-Test Split

Split the data into training and testing sets:
```
from sklearn.model_selection import train_test_split

X = df.drop('Churn', axis=1)
y = df['Churn']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```

Step 5: Build and Evaluate Models

Train a logistic regression model:
```
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

model = LogisticRegression()
model.fit(X_train, y_train)
predictions = model.predict(X_test)

print("Accuracy:", accuracy_score(y_test, predictions))
print(confusion_matrix(y_test, predictions))
print(classification_report(y_test, predictions))
```
Try other models (e.g., Decision Trees, Random Forests, Gradient Boosting):
```
from sklearn.ensemble import RandomForestClassifier

rf_model = RandomForestClassifier(random_state=42)
rf_model.fit(X_train, y_train)
rf_predictions = rf_model.predict(X_test)

print("Random Forest Accuracy:", accuracy_score(y_test, rf_predictions))
```

Step 6: Generate Reports and Insights

Export model performance metrics:
```
import json

results = {
    "Logistic Regression Accuracy": accuracy_score(y_test, predictions),
    "Random Forest Accuracy": accuracy_score(y_test, rf_predictions)
}

with open('churn_model_performance.json', 'w') as file:
    json.dump(results, file)
```
Save visualizations for feature importance or performance metrics.

5. Expected Outcomes

1. Identification of key factors affecting customer churn.
2. Trained classification models with performance metrics.
3. Insights into actionable retention strategies.

6. Additional Suggestions

- Advanced Techniques:
  - Use grid search for hyperparameter tuning.
  - Implement ensemble learning for better predictions.
- Explainable AI:
  - Use SHAP or LIME to interpret model predictions.
- Dashboard Integration:
  - Develop an interactive dashboard for real-time churn prediction using Streamlit or Flask.