This project focuses on analyzing and predicting customer churn in the telecommunications industry. Customer churn refers to customers who stop using a company's services.
The objective of this project is to analyze customer information, identify the factors that influence churn, and build a machine learning model that can predict whether a customer is likely to churn.
The dataset includes customer demographics, account information, services subscribed to, billing details, and the churn status of each customer.
- Analyze customer behavior and churn patterns.
- Perform Exploratory Data Analysis (EDA).
- Identify important factors affecting customer churn.
- Clean and preprocess the dataset.
- Build machine learning models for churn prediction.
- Evaluate and compare model performance.
- Generate insights that can help improve customer retention.
The dataset contains information related to:
- Demographics: Gender, Senior Citizen, Partner, and Dependents.
- Customer Services: Phone Service, Multiple Lines, Internet Service, Online Security, Online Backup, Device Protection, Tech Support, Streaming TV, and Streaming Movies.
- Account Information: Tenure and Contract type.
- Billing Information: Paperless Billing, Payment Method, Monthly Charges, and Total Charges.
- Target Variable: Churn, indicating whether a customer has left the service.
customerID is a unique customer identifier, while Churn is the binary target variable used for prediction.
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
- Jupyter Notebook
Data Collection
↓
Data Understanding
↓
Data Cleaning
↓
Exploratory Data Analysis
↓
Data Preprocessing
↓
Feature Encoding & Scaling
↓
Model Building
↓
Model Evaluation
↓
Customer Churn Prediction
The project analyzes different factors that may influence customer churn, including:
- Customer tenure
- Contract type
- Internet service
- Monthly charges
- Total charges
- Payment method
- Online security
- Technical support
- Other subscribed services
These analyses help identify patterns and relationships between customer characteristics and churn behavior.
The dataset can be preprocessed and used to train machine learning classification models for churn prediction.
Possible models include:
- Logistic Regression
- Decision Tree Classifier
- Random Forest Classifier
- K-Nearest Neighbors
- Support Vector Machine
The performance of the models can be evaluated using appropriate classification metrics.
The following metrics can be used to evaluate model performance:
- Accuracy Score
- Precision
- Recall
- F1-Score
- Confusion Matrix
Telco-Customer-Churn/
│
├── data/
│ └── Telco_Customer_Churn.csv
│
├── notebooks/
│ └── Telco_Customer_Churn_Analysis.ipynb
│
├── README.md
└── requirements.txt
git clone <your-repository-url>cd Telco-Customer-Churnpip install pandas numpy matplotlib seaborn scikit-learnOpen the Jupyter Notebook:
jupyter notebookThen open:
Telco_Customer_Churn_Analysis.ipynb
This project aims to provide insights into:
- Which customers are more likely to churn.
- Which services and contract types are associated with higher churn.
- How tenure and monthly charges relate to customer retention.
- Which customer characteristics can help predict churn.
- Perform hyperparameter tuning.
- Improve feature engineering.
- Compare additional machine learning models.
- Deploy the model using Streamlit or Flask.
- Create an interactive dashboard using Power BI or Streamlit.
Kushagra Tewari
B.Tech CSE Student | Data Science & Machine Learning Enthusiast