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.