Student Performance Data Validation and Analysis

Project Overview

This project analyzes student performance data using Python and pandas.
It validates assessment scores to ensure data quality and generates basic insights such as subject-wise average scores and pass/fail counts.

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Tech Stack

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📂 Dataset

The dataset (StudentsPerformance.csv) contains student assessment details including:

• Math score
• Reading score
• Writing score
• Demographic/background information (gender, race/ethnicity, parental education, lunch, test preparation)

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✅ Project Features

1️⃣ Data Loading

• Loaded student performance data from a CSV file using pandas.

2️⃣ Data Validation

• Checked for invalid scores (less than 0 or greater than 100) across math, reading, and writing columns.
• Ensured all assessment scores fall within acceptable ranges before analysis.

3️⃣ Data Analysis

• Calculated average Math, Reading, and Writing scores.
• Counted the number of students who passed and failed Math using a pass mark threshold.

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📊 Sample Output

The script prints:

• Invalid rows count (if any invalid scores exist)
• Average Math, Reading, and Writing scores
• Students passed math / failed math counts

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▶️ How to Run the Project

1. Clone the repository:

git clone https://github.com/VenkatM77797/student-performance-analytics

2. Navigate to the project folder:

cd student-performance-analytics

3. Install dependencies:

python -m pip install pandas

4. Run the script:

python check_invalid_marks.py

👤 Author

Venkat Mandarapu

LinkedIn: https://www.linkedin.com/in/venkat-mandarapu/