End to End Machine Learning Project

Student Performance Prediction

Introduction About the Data :

The Dataset goal is to predict Math Score of students (Regression Analysis). It consists of 8 columns and 1000 rows.

It has 3 numerical features : ['math_score', 'reading_score', 'writing_score']

It has 5 categorical features : ['gender', 'race_ethnicity', 'parental_level_of_education', 'lunch', 'test_preparation_course']

There are 7 independent variable.

• 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: having lunch before test (standard or free/reduced)
• test_preparation_course: complete or not complete before test
• reading_score: score obtain by student in reading
• writing_score: score obtain by student in writing

Target Variable:

• math_score: score obtain by student in math subject

Data Source Link : (https://www.kaggle.com/datasets/spscientist/students-performance-in-exams?datasetId=74977) [https://www.kaggle.com/datasets/spscientist/students-performance-in-exams?datasetId=74977]

Screenshot of UI

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 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 OneHotEncoder 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.
   • After this hyperparameter tuning is performed on and Linear regression is the best model.
   • 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 Student Performance for Math score inside a Web Application.

Exploratory Data Analysis Notebook

Link : EDA Notebook

Model Training Approach Notebook

Link : Model Training Notebook