In this Project: Introduction: Pass Rate - Linear Regression Project In this notebook, we will implement a complete machine learning project, walk through the whole machine learning process, cleaning up the data, exploring it for trends, establishing a basic model, by evaluating several machine learning approaches for comparisons, by interpreting the results, and presenting the results, proposals for improvement (model, data enrichment ...).
- Python 3.5
- Scikit-learn (1.20.1)
- Imbalanced-learn(0.7.0)
- Numpy
- Seaborn
- pymysql
- Matplotlib
- Dataset
- First install the required depedencies and run
pip install -r requirment.txt
- XGBoost Regressor
- SVM Regressor
- Linear Regressor
- ElasticNet Regressor
- Random Forest Regressor
- Extra Tree Regressor
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Dataset preprocessing must be improved further to produce better result.
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Using only the top best important features with algorithm can improve model performance
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Use different parameter with different values can also improve the model performance in future