End-to-End Machine Learning Project: Student Performance Indicator

Author: Muhammad Adil Naeem

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Lifecycle of this Machine Learning Project

1. Understanding the Problem Statement
2. Data Exploration
3. Data Visualization
4. Data Cleaning
5. Data Preprocessing
6. Model Building
7. Model Evaluation
8. Choosing Best Machine Learning Model
9. Save the Best Machine Learning Model
10. Make prediction with Best Machine Learning Model
11. Creating a Web Interface for User Predictions

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Understanding Problem Statement

This project explores how students' performance (exam scores) is influenced by variables such as Gender, Ethnicity, Parental Level of Education, Lunch Type, and Test Preparation Course.

Data Exploration

• The dataset used in this project is obtained from Kaggle.
• The dataset contains 8 columns and 1000 rows.

Dataset Attributes:

Attribute	Description	Possible Values
gender	Sex of students	Male, Female
race_ethnicity	Ethnicity of students	Group A, Group B, Group C, Group D, Group 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 before test	Standard, Free/reduced
test_preparation_course	Completion of test preparation course	Complete, Not complete
math_score	Score in math	Numeric value
reading_score	Score in reading	Numeric value
writing_score	Score in writing	Numeric value

Libraries Used

• setuptools
• numpy
• pandas
• matplotlib
• seaborn
• openpyxl
• scikit-learn
• xgboost
• catboost

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Working of the Code

The project code includes data ingestion, preprocessing, model building, evaluation, and deployment steps. A web interface is provided to allow users to input their data and receive predictions.

smp.mp4

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Web Interface

The web interface built using Flask allows users to input various parameters to predict a student's math score. The interface is designed to be user-friendly and interactive.

Feel free to explore the project and use the interface to see how different variables can affect student performance in exams.