Student Score Prediction

Introduction About the Data : The dataset The goal is to predict the score of a student (Regression Analysis).

There are 9 independent variables (including id):

• id: the unique identifier of each student
• 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 the test (standard or free/reduced)
• test preparation course: complete or not complete before the test
• math score
• reading score
• writing score

Target variable: predict any score with the help of two other provided scores

1) Problem statement

• This project understands how the student's performance (test scores) is affected by other variables such as Gender, Ethnicity, Parental level of education, Lunch and Test preparation course.

2) Data Collection

• Dataset Source - https://www.kaggle.com/datasets/spscientist/students-performance-in-exams?datasetId=74977
• The data consists of 8 columns and 1000 rows.