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End to End Machine Learning Project

Student Performance Indicator

Introduction About the Data :

Problem Statement

The objective of this project is to investigate how various factors influence student performance (test scores). We will explore the relationship between test scores and the following variables:

  1. Gender: Analyze whether there are gender-based differences in performance.
  2. Ethnicity: Investigate how ethnicity impacts test scores.
  3. Parental Level of Education: Explore correlations between parents’ education levels and student performance.
  4. Lunch: Examine whether the type of lunch (standard or free/reduced) affects test scores.
  5. Test Preparation Course: Understand the impact of completing a test preparation course on student outcomes.
Dataset Information

The dataset includes the following columns:

  • Gender: Sex of students (Male/Female)
  • Race/Ethnicity: Ethnicity of students (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: Type of lunch (Standard or Free/Reduced)
  • Test Preparation Course: Whether the test preparation course was completed or not
  • Math Score
  • Reading Score
  • Writing Score

Dataset Source Link : https://www.kaggle.com/datasets/spscientist/students-performance-in-exams?datasetId=74977

The data consists of 8 column and 1000 rows.

Approach for the project

  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 , then Standard Scaling is performed on numeric data.
    • for Categorical Variables one hot encoding performed , after this data is scaled with Standard Scaler.
    • This preprocessor is saved as pickle file.

  3. Model Training :

    • In this phase base model is tested . The best model found was linear regression.
    • This model is saved as pickle file.

  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 inside a Web Application.

      Screenshot 2024-08-20 220514