onlyoneuche/efiko

Predict students math score in exams based on economic, personal and social factors.

Dataset Description:

The aim of the dataset is to understand the influence of various factors like economic, personal and social on a student's performance.

Dataset Features:

• gender
• race/ethnicity
• parental level of education
• lunch
• test preparation course
• math score
• reading score
• writing score

Target Feature: math score

Dataset Source Link : https://www.kaggle.com/code/spscientist/student-performance-in-exams/notebook

Project Objective:

The objective of this project is to predict the math score of a student based on the given features.

Project Tasks:

Data Ingestion

• Read the data as a CSV file.
• Split the data into training and testing sets.
• Save the training and testing sets as CSV files.

Data Transformation

• Create a ColumnTransformer pipeline.
• Apply SimpleImputer with median strategy for numeric variables.
• Scale numeric data with StandardScaler.
• Apply SimpleImputer with most frequent strategy for categorical variables.
• Perform ordinal encoding on categorical variables.
• Scale categorical data with StandardScaler.
• Save the preprocessor as a pickle file.

Model Training

• Evaluate the performance of base models.
• Identify the best performing model
• Perform hyperparameter tuning
• Save the result as a pickle file.

Prediction Pipeline

• Convert input data into a DataFrame.
• Load pickle files for data preprocessing and model prediction.
• Predict final results.

API Creation

• Develop a Flask application with a user interface for predicting the students math score in a web application.

How to run the project:

• Clone the repository
• Create a virtual environment
• Install the dependencies
• Run the application.py file
• Open the localhost link in the browser
• Enter the values in the form and click on the predict button to get the predicted math score.