https://github.com/chaitanya1436/student_performance_analysis

A project focused on analyzing college student performance using data on department, assessment scores, and performance labels. Implemented in Google Colab, the analysis includes data preprocessing, feature scaling, and exploratory data analysis to uncover insights and prepare the data for further analysis or modeling.
https://github.com/chaitanya1436/student_performance_analysis

ata-preprocessing data-preparation exploratory-data-analysis feature-scaling google-colab numpy pandas scikit-learn

Last synced: 1 day ago
JSON representation

A project focused on analyzing college student performance using data on department, assessment scores, and performance labels. Implemented in Google Colab, the analysis includes data preprocessing, feature scaling, and exploratory data analysis to uncover insights and prepare the data for further analysis or modeling.

• Host: GitHub
• URL: https://github.com/chaitanya1436/student_performance_analysis
• Owner: Chaitanya1436
• Created: 2024-08-18T18:22:47.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-08-22T08:39:22.000Z (about 1 month ago)
• Last Synced: 2024-09-23T06:02:57.832Z (5 days ago)
• Topics: ata-preprocessing, data-preparation, exploratory-data-analysis, feature-scaling, google-colab, numpy, pandas, scikit-learn
• Language: Jupyter Notebook
• Homepage: https://colab.research.google.com/drive/1RTAmx_KtluLA_wByNqjQVB8M6G6SiKiP#scrollTo=yIq3HJemqeLQ
• Size: 127 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
## Project Description

The **College Student Performance Analysis** project focuses on evaluating and analyzing the academic performance of college students based on various attributes such as their department, assessment scores, and performance labels. The analysis is conducted using a Google Colab notebook, which provides an interactive environment for performing data preprocessing, feature scaling, and exploratory data analysis.

### Objectives

- **Data Preprocessing**: Clean and prepare the dataset by handling missing values and encoding categorical features.

- **Feature Scaling**: Standardize numerical features to bring them to a common scale, improving the performance of machine learning models.

- **Exploratory Data Analysis**: Analyze the relationships between different features and their impact on student performance.

### Key Features

- **Handling Missing Values**: Impute missing values in both categorical and numerical columns to ensure a complete dataset.

- **Categorical Encoding**: Convert categorical data, such as department names, into numerical format using one-hot encoding.

- **Standardization**: Apply standard scaling to numerical features, ensuring they have a mean of 0 and a standard deviation of 1 for better model performance.

- **Interactive Analysis**: Utilize Google Colab for an interactive approach to data analysis, allowing for real-time adjustments and visualizations.

The project serves as a foundation for understanding and preparing educational data for further analysis or machine learning applications. It provides practical examples of data preprocessing techniques and showcases the use of Google Colab for data science tasks.