End to End ML (Machine Learning) Project

Prediction of Students Performance in Exams

Introduction About the Data :

The kaggle dataset The goal is to predict Maths Score of a given student provided various features (Regression Analysis).

Context Marks secured by the students

Content This data set consists of the marks secured by the students in various subjects.

Inspiration To understand the influence of the parents background, test preparation etc on students performance.

There are 7 features:

• gender :
• race_ethnicity :
• parental_level_of_education :
• lunch :
• test_preparation_course :
• reading_score :
• writing_score :

Target variable:

• Maths Score: Price of the given Diamond.

Kaggle Dataset Source Link : [https://www.kaggle.com/datasets/spscientist/students-performance-in-exams/data]

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 ordinal 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 for RandomForestRegressor,DecisionTreeRegressor,GradientBoostingRegressor,LinearRegression,XGBRegressor, CatBoostRegressor, and AdaBoostRegressor. The best model found was Linear Regression.
   • After this hyperparameter tuning is performed on each of the models, again the best model found was Linear Regression.
   • A final Regressor is created for 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 Student's Performance on Math Score inside a Web Application.

Exploratory Data Analysis Notebook

Link : EDA Notebook

Model Training Approach Notebook

Link : Model Training Notebook

Microsoft Azure Deployment using Github Actions:

Microsoft Azure link : [https://stdntperformance.azurewebsites.net/predictdata]

Screenshot of UI

Filling features in the Homepage

Predcted Maths Score :