Next Purchase Prediction with Machine Learning

Inspiration The inspiration behind this project came from the need to predict customer behavior in e-commerce. With businesses constantly looking for ways to optimize marketing strategies, predicting a customer's next purchase can provide valuable insights, helping businesses personalize their offerings and increase sales.

What it does The Next Purchase Prediction with Machine Learning project predicts whether a participant will make their next purchase based on demographic and behavioral data. The model outputs the likelihood of a participant purchasing in the near future, enabling businesses to target their marketing efforts more effectively.

How we built it Data Preprocessing: We loaded and cleaned the training data, handling missing values in both categorical and numeric columns. We used One-Hot Encoding for categorical features and filled missing numeric values with the mean of the column. Model Training: We used a Random Forest Classifier, an ensemble learning model, to predict the target variable, "Next_Purchase." We split the data into training and validation sets to evaluate model performance. Prediction: After training, the model was used to make predictions on the test dataset. The predictions were then saved in a submission file. Challenges we ran into Handling Missing Data: Dealing with missing values in both numeric and categorical columns was a challenge. Ensuring that the data used for training and testing was consistent required a lot of attention. Model Performance: Fine-tuning the model to achieve optimal accuracy required experimenting with different hyperparameters and feature engineering techniques. Accomplishments that we're proud of Successfully built a Random Forest model that predicts participants' next purchase with good accuracy. Effectively handled missing data and implemented a robust preprocessing pipeline using One-Hot Encoding and imputation strategies. Generated a submission file as per the competition requirements. What we learned Feature Engineering and Data Preprocessing: How to handle missing data and categorical features to improve model performance. Model Selection and Tuning: Gained experience in using ensemble models like Random Forest and fine-tuning hyperparameters to improve predictive performance. What's next for Next Purchase Prediction with Machine Learning In the future, we plan to improve the model's accuracy by experimenting with other algorithms like XGBoost or Gradient Boosting. We also aim to incorporate additional features and explore more advanced preprocessing techniques to handle larger datasets and improve prediction reliability.

Built With

• and-random-forest-classifier-for-machine-learning-and-prediction-tasks
• built-with-python
• csv
• data
• files
• for
• joblib
• pandas
• scikit-learn
• using