Week1-StudentPerformance-EDA

Project Overview

This project analyzes student exam scores to uncover patterns based on gender, parental education level, lunch type, and test preparation course. The analysis is performed on the Students Performance in Exams dataset from Kaggle.

The goal is to practice data handling, exploratory data analysis (EDA), and data visualization using Python.

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Objective

• Explore the dataset structure and understand data types.
• Analyze how categorical features affect numerical scores (Math, Reading, Writing).
• Visualize trends and distributions with bar plots, box plots, and histograms.
• Derive meaningful insights to summarize student performance.

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🛠 Tools & Technologies

• Python (Programming language)
• Google Colab (Notebook environment)
• Libraries:
  • pandas – data handling
  • matplotlib & seaborn – visualization

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Dataset

Source: Kaggle – Students Performance in Exams

Key Features:

• gender – Male or Female
• race/ethnicity
• parental level of education – e.g., Bachelor's degree, High school
• lunch – Standard or Free/Reduced
• test preparation course – None or Completed
• math score , reading score , writing score – Student exam scores

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Stepwise Approach

Step 1: Upload and Load Dataset

• Used files.upload() in Colab to upload the CSV file.
• Loaded the dataset into a pandas DataFrame.

Step 2: Explore the Data

• Checked dataset info (df.info()) and summary statistics (df.describe()).
• Identified unique values in categorical columns.

Step 3: Check for Missing Values

• Verified dataset has no missing/null values.

Step 4: Analyze Score Patterns

• Calculated average scores grouped by:
  • Gender
  • Lunch type
  • Test preparation course
  • Parental education level

Step 5: Visualize the Data

• Boxplots for gender vs math scores.
• Bar plots for lunch type, test preparation, and parental education vs average scores.
• Histograms for distribution of all scores.

Step 6: Summarize Insights

Key Findings:

• Gender Difference: Females perform better in reading and writing; males slightly better in math.
• Lunch Effect: Students with standard lunch score higher than those with free/reduced lunch.
• Test Preparation: Completing the test preparation course boosts reading and writing scores.
• Parental Education: Higher parental education correlates with better student performance.
• Score Distribution: Most students score between 60–80, with a few outliers.

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Visualization

All visualizations are created using matplotlib and seaborn:

• Boxplots, bar plots, and histograms.
• Clear titles, axis labels, and color schemes for readability.

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How to Use

1. Clone or download the repository.
2. Open the notebook Week1-StudentPerformance-EDA.ipynb in Google Colab.
3. Upload StudentsPerformance.csv when prompted.
4. Run all cells to view analysis and visualizations.

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