Student Test Score Prediction

Introduction About the Data :

Source

Dataset Source - source

The data consists of 8 column and 1000 rows.

The dataset The goal is to predict math score of given student (Regression Analysis).

There are 7 independent variables:

• 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
• writing score

Target variable:

• math score: Price of the given Diamond.

AWS Deployment Link :

AWS Elastic Beanstalk link : link

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 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 . The best model found was Linear regresion.
   • 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 gemstone prices inside a Web Application.

Exploratory Data Analysis Notebook

Link : EDA Notebook

Model Training Approach Notebook

Link : Model Training Notebook