SensorData-Project

The sequence of file is:

1. sensor_dataRead.ipynb
   Input: sensorswww_data.txt, Output: sensor_raw_data.csv
2. sensor_EDA.ipynb
   Input: sensor_raw_data.csv, Output: sensor_data_remove_missing.csv
3. sensor_featureEngineer.ipynb
   Input: sensor_data_remove_missing.csv, Output: sensor_data_engineered.csv
4. sensor_modelBuilding.ipynb

Summary of data analysis:
Part one: sensor_dataRead.ipynb
■ The original data is in the format of website log file. Each record is formatted in JSON style. Here I transfered the original data into Pandas data frame.

Part two: sensor_EDA.ipynb
Explored questions below.
■ Is 9419 a coincidence？
■ How active are the user weekly?
■ How to define conversion rate in this case?
■ Will the reference source have influence on conversion?
■ Performed funnel analysis for the signup flow and found the cell-phone verification step to be the bottleneck due to unclear format requirements.

Part three: sensor_featureEngineer.ipynb
■ Created and engineered features for conversion predictions.

Part four: sensor_modelBuilding.ipynb
■ Build machine learning models (GBDT and Logistic Regression) to predict user signup behaviors.
■ Tuned hyper parameters through grid search.
■ Evaluated model performance through AUC score and achieved 98.5% accuracy.
■ Correlated user retention with persona analysis service and correlated user churn with poor quality of introduction video.