hariprasath-v/Machinehack-Renew-Power-Hiring-Hackathon

Create a model to get an ideally functioning turbine’s expected rotor bearing temperature.

Machinehack-Renew-Power-Hiring-Hackathon

Competition hosted on Machinehack.com

About

Create a model to get an ideally functioning turbine’s expected rotor bearing temperature.

Final Score is 0.01852

Evaluation Metric is MAPE.

File information

• machinehack-renew-power-hiring-hackathon_eda.ipynb

  Basic Exploratory Data Analysis

  Packages Used,

   * seaborn
   * Pandas
   * Numpy
   * Matplotlib
  

• machinehack-renew-power-hiring-hackathon-model.ipynb

  Data Pre-processing and model.

  Packages Used,

    * Sklearn
    * Pandas
    * Numpy
    * Matplotlib
    * pycaret    
  

  Compared multiple regression models using pycaret’s compare_models function. Then took the top 3 models based on the MAPE then blend the model by using pycaret blend_models function.

  For more detailed information about the model.

Xgboost Regressor Residual Plot

Xgboost Prediction Error Plot

Top 3 Models

Xgboost Feature Importance Plot

SHAP - Xgboost Feature Importance Plot

Rotor bearing temperature distribution - train and test data