This project is part of the Data Mining course and focuses on predicting customer churn for a telecommunications company. Customer churn, or customer attrition, refers to the phenomenon where customers stop doing business with a company. By accurately predicting which customers are at risk of churning, the company can take proactive measures to retain them.
- Analyze the telco company's customer dataset to identify patterns and factors contributing to churn.
- Develop and compare multiple machine learning models to predict customer churn.
- Provide actionable insights to reduce customer churn based on the analysis and model results.
The project uses a dataset from a telecommunications company containing various customer attributes, including:
- Customer demographics (age, gender, etc.)
- Account information (contract type, payment method, etc.)
- Services used (phone, internet, streaming, etc.)
- Usage patterns
- Churn status (target variable)
-
Data Preprocessing:
- Handle missing values
- Encode categorical variables
- Feature scaling
-
Exploratory Data Analysis (EDA):
- Visualize relationships between features and churn
- Identify key factors influencing customer churn
-
Feature Engineering:
- Create new features based on domain knowledge and EDA insights
-
Model Development:
- Split data into training and testing sets
- Implement and compare multiple algorithms, such as:
- Logistic Regression
- Random Forest
- Gradient Boosting
- Support Vector Machines
-
Model Evaluation:
- Use metrics like accuracy, precision, recall, and F1-score
- Perform cross-validation to ensure model robustness
-
Interpretation and Insights:
- Analyze feature importance
- Provide actionable recommendations based on model results
- Clone this repository
- Install required packages:
pip install -r requirements.txt - Run Jupyter notebooks
project.ipynbto see the analysis and modeling process