https://github.com/hariprasath-v/machinehack-renew-power-hiring-hackathon

Create a model to get an ideally functioning turbine’s expected rotor bearing temperature.
https://github.com/hariprasath-v/machinehack-renew-power-hiring-hackathon

exploratory-data-analysis machine-learning machinehack matplotlib pandas pycaret python seaborn sklearn

Last synced: 3 months ago
JSON representation

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

• Host: GitHub
• URL: https://github.com/hariprasath-v/machinehack-renew-power-hiring-hackathon
• Owner: hariprasath-v
• License: apache-2.0
• Created: 2022-10-10T14:46:33.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2022-10-10T14:58:29.000Z (almost 4 years ago)
• Last Synced: 2025-01-13T01:44:55.540Z (over 1 year ago)
• Topics: exploratory-data-analysis, machine-learning, machinehack, matplotlib, pandas, pycaret, python, seaborn, sklearn
• Language: Jupyter Notebook
• Homepage:
• Size: 1.69 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# 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.](https://github.com/hariprasath-v/Machinehack-Renew-Power-Hiring-Hackathon/blob/main/Approach_Machinehack-Renew-Power-Hiring-Hackathon.pdf)

     

### Xgboost Regressor Residual Plot

![Alt text](https://github.com/hariprasath-v/Machinehack-Renew-Power-Hiring-Hackathon/blob/main/Model%20Visualization%20Charts/Voting%20Regressor%20Residual%20Plot.png)

### Xgboost Prediction Error Plot

![Alt text](https://github.com/hariprasath-v/Machinehack-Renew-Power-Hiring-Hackathon/blob/main/Model%20Visualization%20Charts/Voting%20Regressor%20Prediction%20Error%20Plot.png)

### Top 3 Models

![Alt text](https://github.com/hariprasath-v/Machinehack-Renew-Power-Hiring-Hackathon/blob/main/Model%20Visualization%20Charts/Voting%20Regressor%20Models.PNG)

### Xgboost Feature Importance Plot

![Alt text](https://github.com/hariprasath-v/Machinehack-Renew-Power-Hiring-Hackathon/blob/main/Model%20Visualization%20Charts/Feature%20Importance%20Plot-%20%20%20Xgboost%20Regressor.png)

### SHAP - Xgboost Feature Importance Plot

![Alt text](https://github.com/hariprasath-v/Machinehack-Renew-Power-Hiring-Hackathon/blob/main/Model%20Visualization%20Charts/SHAP%20Feature%20Importance%20Plot%20-%20Xgboost%20Regressor.png)

### Rotor bearing temperature distribution - train and test data

![Alt text](https://github.com/hariprasath-v/Machinehack-Renew-Power-Hiring-Hackathon/blob/main/Model%20Visualization%20Charts/Rotor%20bearing%20temperature%20of%20train%20and%20test%20data.png)