Regression Analysis_Success rate of high school

Introduction

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 ...).

Installation

Python 3.5
Scikit-learn (1.20.1)
Imbalanced-learn(0.7.0)
Numpy
Seaborn
pymysql
Matplotlib
Dataset

Running

To install dependencies:

First install the required depedencies and run pip install -r requirment.txt

Algorithms Used

XGBoost Regressor
SVM Regressor
Linear Regressor
ElasticNet Regressor
Random Forest Regressor
Extra Tree Regressor

Future Work

Dataset preprocessing must be improved further to produce better result.
Using only the top best important features with algorithm can improve model performance
Use different parameter with different values can also improve the model performance in future