In this codebase, we provide implementations of various segmentation techniques and temporal sequence prediction methods using neural nets on clickstream data. The aim is to provide consumer startups with tools and techniques for effectively segmenting and analyzing clickstream data and predicting future user behavior based on their past interactions.
The segmentation folder contains implementations of various models and techniques, including:
Logistic Regression Decision Tree Random Forest XGBoost LSTMs Clustering Models Markov Models Autoencoders
We have provided scripts for data preprocessing, feature engineering, and model training for each of these techniques. We also demonstrate how to implement a consensus approach, where the outputs of multiple models are combined to generate a final, more accurate segmentation.
The temporal_prediction folder contains implementations of various models and techniques, including:
LSTMs Markov Models Gated Recurrent Units (GRUs) Transformer Models Temporal Convolutional Networks (TCNs) Echo State Networks (ESNs) Sequence-to-Sequence (Seq2Seq) Models We have provided scripts for data preprocessing, feature engineering, and model training for each of these techniques. We also demonstrate how to predict future user behavior based on their past interactions.
The codebase requires the following dependencies:
Python 3.6 or higher pandas numpy scikit-learn tensorflow keras matplotlib seaborn To install the required dependencies, run the following command:
Copy code pip install -r requirements.txt
To use the codebase, follow these steps:
Prepare your clickstream data and place it in the data folder. Ensure that it includes a diverse range of user behaviors, travel package preferences, and demographic information. Run the preprocessing and feature engineering scripts to prepare the data for ingestion into the models. We have created two modules, data_preprocessing.py and feature_engineering.py, inside the src directory containing the necessary functions for these tasks. Train individual models using the provided scripts and evaluate their performance using cross-validation. Implement the consensus approach for model building, combining the outputs of multiple models for a more accurate and robust segmentation or prediction. Analyze the segments and use the insights to develop targeted marketing strategies or improve the user experience.
By effectively analyzing unstructured clickstream data, businesses can gain valuable insights into customer behavior, preferences, and trends, which can be leveraged to develop targeted marketing strategies, improve the user experience, and foster customer loyalty. By embracing advanced segmentation techniques and continuously refining their approach, startups can better understand their customers, stay ahead of the competition, and achieve lasting success in the ever-evolving digital marketplace