The Telecom Customer Churn Prediction project aimed to develop a machine learning model using PySpark to accurately predict customer churn in a telecom company. The goal was to achieve a minimum accuracy of 0.8 to enable the company to proactively identify and retain customers at risk of leaving.
The project utilized a telecom customer dataset that included relevant features such as customer demographics, usage patterns, service plans, call details, customer complaints, and churn status. The dataset was preprocessed to handle missing values, encode categorical variables, and create new features.
- Missing Values: Any rows with missing values were dropped from the dataset.
- Feature Engineering: New features were created, including call duration, average monthly spend, customer tenure, and customer satisfaction scores.
- Categorical Encoding: Categorical variables such as gender and contract type were encoded using StringIndexer.
- Feature Scaling: Features were scaled using StandardScaler to ensure they were on a similar scale.
Four machine learning algorithms were considered for churn prediction: Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Machines (LinearSVC). The models were trained and evaluated using PySpark's MLlib library. Hyperparameter tuning and experimentation were performed to achieve the desired accuracy of 0.8.
The trained models were evaluated on both the training and testing datasets using multiple evaluation metrics:
- Accuracy: The overall accuracy of the model's predictions.
- Precision: The ability of the model to correctly identify churned customers.
- Recall: The ability of the model to correctly capture all churned customers.
- F1-score: The harmonic mean of precision and recall, providing a balanced evaluation metric.
After evaluating the models, the best performing model was identified based on the accuracy metric on the testing dataset. The chosen model achieved an accuracy of X.XX on the testing dataset. The precision, recall, and F1-score of the model were also high, indicating its effectiveness in predicting customer churn.
Throughout the project, we encountered several challenges:
- Limited dataset: The dataset provided limited information, which required careful feature engineering and selection to improve the model's performance.
- Imbalanced classes: The churned customers were significantly fewer than the non-churned customers, leading to imbalanced classes. Techniques such as stratified sampling and class weighting were employed to address this issue.
This project provided valuable insights and lessons learned:
- Feature engineering is crucial: Creating new features that capture relevant information can significantly improve the model's performance.
- Handling imbalanced classes: Imbalanced class distribution requires appropriate techniques to ensure the model can accurately predict churn.
- Model selection and hyperparameter tuning: Experimenting with different models and hyperparameter configurations is essential to find the best performing model.
The Telecom Customer Churn Prediction project successfully developed a PySpark machine learning model to predict customer churn in the telecom industry. The best model achieved a high accuracy of X.XX, enabling the company to proactively identify and retain customers at risk of leaving. The project demonstrated the effectiveness of PySpark's distributed computing capabilities and provided valuable insights for future churn prediction initiatives.