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Student Performance Indicator

Information About the Dataset:

The dataset The goal of this project is to understand the influence of the parents background, test preparation, and various other variables on the students math score.

There are 8 independent variables:

  • gender : Sex of a student (Male/Female)
  • race/ethnicity : Ethnicity of a student (Group A,B,C,D,E)
  • parental level of education : parents' final education (bachelor's degree,some college,master's degree,associate's degree,high school)
  • lunch : What type of lunch the student had before test (standard or free/reduced)
  • test preparation course : Whether the student completed any preparation course before the test.
  • reading score : Reading score obtained by the student.
  • writing score : Writing score obtained by the student.

Target variable:

  • math score: Math score of a student.

Dataset Source Link : https://www.kaggle.com/datasets/spscientist/students-performance-in-exams?resource=download

Animation of UI

HomepageUI

Project Development Approach

  1. 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.

  2. Data Transformation :

    • In this phase a ColumnTransformer Pipeline is created.
    • for Numeric Variables first SimpleImputer is applied with strategy median (because there were some outliers in the data), then standard scaling is performed on numeric data.
    • for Categorical Variables SimpleImputer is applied with most frequent strategy, then one-hot-encoding is performed, after this data is scaled with standard scaler.
    • This preprocessor is saved as pkl file inside the artifacts folder.

  3. Model Training :

    • In this phase, all the models are trained and evaluated. The best model found was Linear Regression.
    • After this hyperparameter tuning is also performed prior to selecting the best model.
    • This model is saved as pickle file to be used for the predict pipeline.

  4. Prediction Pipeline :

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

  5. Flask App creation :

    • Flask app is created with User Interface to predict the math score of a student given the required features inside a Web Application.

Exploratory Data Analysis Notebook

Link : EDA Notebook

Model Training Approach Notebook

Link : Model Training Notebook

Usage:

  1. conda create -p std python=3.8 -y
  2. conda activate std/
  3. pip install -r requirements.txt
  4. Execute app.py
  5. Access http://127.0.0.1:5000/