Example use case: Classify churn or no churn
1. Data Cleaning:
- Handle missing values: Impute or remove missing data appropriately.
- Check for duplicate records and remove them if necessary.
- Eliminate irrelevant or redundant columns that do not contribute to churn prediction.
2. Data Transformation:
- Convert categorical variables into dummy/indicator variables using one-hot encoding.
- Scale numerical features to have a similar range, e.g., using Min-Max scaling or Standardization.
3. Outlier Detection and Handling:
- Identify and deal with outliers that could skew the model's performance.
- Consider techniques like trimming, winsorization (Replacing outliers with the nearest non-outlier values), imputation, deletion or statistical methods
4. Feature Selection:
- Conduct exploratory data analysis to identify potentially significant features related to churn prediction.
- Consider using domain knowledge and business understanding to select relevant features.
- Utilize techniques like correlation analysis, filter method, lasso regression, univariate feature selection, or recursive feature elimination to identify the most informative features.
5. Handling Class Imbalance:
- Check if the churn class is imbalanced (more churn or non-churn instances).
- Consider using techniques like SMOTE, ensembling method, resampling (e.g., oversampling, undersampling) or using class weights to balance the dataset.
6. Feature Engineering:
- Create new features from existing ones that could enhance the model's predictive power.
- Techniques like feature extraction or selection. Examples include calculating customer tenure, aggregating usage behavior, or deriving categorical features from numerical ones using binning.
7. Train-Test Split:
- Split the preprocessed data into training and testing sets to evaluate model performance accurately.
8. Model Selection:
- Choose an appropriate classification algorithm (e.g., logistic regression, random forest, support vector machine) based on the data characteristics and business requirements.
9. Hyperparameter Tuning:
- If using algorithms with hyperparameters, perform hyperparameter tuning (e.g., using grid search or random search) to optimize model performance.
10. Cross-Validation:
- Cross-validation is a resampling technique used to evaluate the performance of a machine learning model. It involves splitting the dataset into a training set and a test set, and then training the model on the training set and evaluating it on the test set. This process is repeated multiple times, and the results are averaged to get an estimate of the model's performance.
-Use techniques like k-fold cross-validation to get a more robust estimate of the model's performance or hold out, leave one out cross validation
11. Model Evaluation:
- Use evaluation metrics such as accuracy, precision, recall, F1-score, and ROC-AUC to assess the model's performance on the test dataset.
12. Model Interpretation:
- Interpret the model's predictions to understand the factors contributing to churn.
13. Business Interpretability:
- Consider the interpretability of the model's predictions for making business decisions.
- Provide insights on factors that contribute most to churn and their impact on the classification.
14. Updating and Maintenance:
- Periodically reevaluate the model's performance and update it with new data.
- Keep track of model drift and retrain the model when necessary.
15. Interpreting Feature Importances:
- For certain models (e.g., random forest), interpret feature importances to understand which features have the most significant influence on churn prediction.
Remember to adapt this checklist based on the specific characteristics of your dataset, the classification algorithm you are using, and the particular requirements of your churn prediction problem.
