This project explores the various factors affecting student performance using Exploratory Data Analysis (EDA). The dataset, sourced from Kaggle, includes features related to demographics, social support, study habits, and parental factors that may influence academic outcomes. This analysis aims to uncover patterns and insights into how different factors relate to student exam scores.
This project performs in-depth analysis of student performance factors by examining relationships between attributes such as parental education, study time, and exam scores. The main objectives are:
- To understand how various factors correlate with exam scores.
- To identify significant features that could inform educational interventions.
- To visualize trends and distributions within the dataset.
The dataset used in this project is publicly available on Kaggle.
You can find the dataset here.
Import libraries such as Pandas, Numpy, Seaborn and Matplotlib
- Descriptive Statistics
- Univariate Analysis
- Bivariate Analysis
- Student Behavior Factors
- Parent Factors
- Personal Factors
- The distribution of exam scores is right-skewed.
- The number of hours studied shows a positive impact on exam scores.
- Participation in extracurricular activities has increased the number of top scorers compared to students not participating in these activities.
- Higher levels of parental education have positively influenced the minimum scores.
- High family income does not show any outliers for low exam scores.
- The number of low-scoring female students is lower compared to male students.
- Positive peer influence has helped students achieve better scores.