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.