A simple machine learning project

Dataset Introduction

The goal is to predict the math score of a student based on other given values.

There are 7 different input variables:

1. Gender
2. Race/Ethnicity
3. Parental Education
4. Lunch
5. test preparation course
6. Reading Score
7. Writing Score

Target Variable: 2. Math Score

Aproach of this work

Data Ingestion :

In Data Ingestion phase the data is first read as csv. Then the data is split into training and testing and saved as csv file.

Data Transformation :

In this phase a ColumnTransformer Pipeline is created. For Numeric Variables first SimpleImputer is applied with strategy median , then Standard Scaling is performed on numeric data. For Categorical Variables SimpleImputer is applied with most frequent strategy, then ordinal encoding performed , after this data is scaled with Standard Scaler.

This preprocessor is saved as pickle file.

Model Training :

In this phase base model is tested . The best model found was Linear regressor by comparing with other algorithms and hyperparameter tuning.

This model is saved as pickle file.

Prediction Pipeline :

This pipeline converts given data into dataframe and has various functions to load pickle files and predict the final results in python.

Flask App creation :

Flask app is created with User Interface to predict the Math score of a student inside a Web Application.

UI output

AWS deployment using CD pipeline

changed app.py to application.py for AWS deployment

http://studentexamperformance-env.eba-gyfszm4z.ap-northeast-2.elasticbeanstalk.com/predictdata

AWS deployment UI