This project focuses on analyzing student results using Python and data visualization techniques. The goal is to extract meaningful insights from student performance data and present them in an easy-to-understand manner using various Python libraries like Pandas, Matplotlib, and Seaborn.
Data Cleaning: Handles missing values and incorrect data entries. Descriptive Analysis: Provides summary statistics such as mean, median, and mode for each student result attribute. Visualizations: Generates various types of charts (e.g., bar charts, histograms) to represent student performance trends. Result Classification: Classifies students into categories such as "Pass", "Fail", "Distinction" based on their scores. Comparison Analysis: Compares student performance based on different categories such as subject, gender, or class.
The dataset used in this project includes the following attributes: Student ID Name Subjects Marks obtained in each subject Total marks Pass/Fail status
Python Pandas Matplotlib Seaborn Jupyter Notebook
#Python #DataAnalysis #StudentResults #MachineLearning #Jupyter #DataScience #EdTech