Exploratory Data Analysis (EDA) on Student Performance Dataset

This repository contains an Exploratory Data Analysis (EDA) of the Student Performance dataset. The analysis covers data exploration, cleaning, and visualization to understand student performance metrics better.

Dataset

-The dataset used is StudentsPerformance.csv, which includes student performance data across different subjects, parental level of education, race/ethnicity, and gender.

  # Load the dataset
  data = pd.read_csv(r"D:\Projects\EDA\StudentsPerformance.csv")

  #link https://www.kaggle.com/datasets/spscientist/students-performance-in-exams

Steps and Analysis

• Loading Libraries and Dataset

  Imported necessary libraries for data manipulation and visualization.Loaded the dataset from a local file path.

• Initial Data Exploration

  Displayed the first and last few rows of the dataset to understand its structure.Checked the shape of the dataset and reviewed basic statistics. Examined column names and unique values in specific columns.Checked for missing values to assess data completeness.

• Data Cleaning

  Removed unnecessary columns that are not relevant for the analysis, specifically race/ethnicity and parental level of education.

• Correlation Analysis

  Selected numerical columns and computed their correlations.Visualized the correlation matrix using a heatmap to understand the relationships between different scores.

• Pair Plot

  Created pair plots to explore the relationships between math, reading, and writing scores.

• Relationship Plots

  Visualized relationships between different scores (math, reading, writing) with color-coded gender information to understand how gender affects performance.

• Distribution Plots

  Plotted histograms with Kernel Density Estimate (KDE) for math, reading, and writing scores to visualize their distributions.

• Box Plot

  Created a box plot for math scores to visualize the distribution and detect potential outliers.

Summary

This analysis provides insights into student performance through various visualizations, helping to understand the distribution of scores, relationships between different subjects, and the impact of gender on performance.