Website XYZ, a music-listening social networking website, follows the "freemium" business model. The website offers basic services for free and provides a number of additional premium capabilities for a monthly subscription fee. The task is to build a predictive model that can identify users who are likely to convert from free users to premium subscribers in the next 6 months if they are targeted by the promotional campaign.
The provided dataset contains 58,077 records, each representing a different user of the XYZ website who was targeted in the previous marketing campaign. There are 25 attributes describing each record, including the outcome variable 'adopter' (whether a user became a subscriber within 6 months after the marketing campaign), user_id, age, gender, number of friends, average friend age, percentage of male friends, number of friends from different countries, number of friends who are subscribers of the premium service, total number of tracks listened, number of liked tracks, number of posts, number of playlists, number of wall posts received, user's country type, and tenure. Additionally, there are delta attributes representing the change in the corresponding attribute over the 3-month period before the marketing campaign.
- Use standard practices in building and evaluating predictive machine learning models, such as training-validation split or cross-validation
- Explore different modeling techniques and hyperparameter combinations
- Consider feature selection
- Deal with imbalanced data using sampling techniques
- Improve upon the provided baseline model that achieved an F-measure of 0.0707 for the 'adopter = 1' class.
To build a predictive model to identify users who are likely to convert from free users to premium subscribers in the next 6 months if targeted by the promotional campaign, you can follow the steps below:
Check for missing values in the dataset and decide on an appropriate strategy to handle them (e.g., drop missing values, use the mean or median value for continuous variables, or the mode for categorical variables). Encode categorical variables using one-hot encoding or ordinal encoding. Scale continuous variables to have the same range, either by normalizing or standardizing them.
Analyze the data to gain insights into the relationships between different features and the target variable (adopter). Plot histograms, boxplots, and scatterplots to visualize the data distribution and identify potential outliers.
Perform feature selection to identify the most important features that contribute to the model's predictive performance. Techniques like correlation analysis, recursive feature elimination (RFE), or regularization methods (Lasso, Ridge, or ElasticNet) can be used for this purpose.
Split the dataset into training and validation sets using a 70:30 or 80:20 ratio, or use k-fold cross-validation to train and validate the model on different subsets of the data.
Use sampling techniques such as oversampling the minority class (SMOTE) or undersampling the majority class (RandomUnderSampler) to balance the dataset and improve the model's performance on the minority class.
Test various machine learning algorithms such as logistic regression, support vector machines, random forests, gradient boosting, and neural networks to find the best model. Perform hyperparameter tuning using grid search or random search to find the optimal hyperparameters for the chosen model.
Evaluate the model's performance using appropriate metrics, such as precision, recall, F1-score, and area under the Receiver Operating Characteristic (ROC) curve. Ensure that the model's performance on the 'adopter = 1' class is improved compared to the baseline F-measure of 0.0707.
Once the final model is chosen and fine-tuned, deploy it to a production environment for use in identifying potential premium subscribers for the promotional campaign. By following these steps, you can build a robust predictive model that accurately identifies users who are likely to convert from free users to premium subscribers if targeted by the promotional campaign.