Analysis of the Student performance

Data set obtained from kaggle : https://www.kaggle.com/datasets/lainguyn123/student-performance-factors/ Number of data rows: 6607
Number of columns: 20\

The code is written in python with the following modules:

• pandas
• numpy
• matplotlib.pyplot
• seaborn
• scipy

Information contained in the data about the student

• Hours Studied
• Attendance
• Parental Involvement
• Access to Resources
• Extracurricular Activities
• Sleep Hours
• Previous Scores
• Motivation Level
• Internet Access
• Tutoring Sessions
• Family Income
• Teacher Quality
• School Type
• Peer Influence
• Physical Activity
• Learning Disabilities
• Parental Education Level
• Distance from Home
• Gender
• Exam Score

Analysis strategy

The first step in this analysis is an exploration of the data set found in the file data_exploration.ipynb
The second step is to determine which features or columns are related to the overall score in the file data_analysis.ipynb
The third step is to train a linear regression model to predict the exam scores in model_training.ipynb