https://github.com/parthds02/analyzing-student-success-with-data

Discover key factors influencing student performance through data analysis and visualization. Explore gender, parental education, sports, and ethnicity impacts.
https://github.com/parthds02/analyzing-student-success-with-data

data datascience jupyter-notebook kaggle python pythonlibraries

Last synced: 7 days ago
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Discover key factors influencing student performance through data analysis and visualization. Explore gender, parental education, sports, and ethnicity impacts.

• Host: GitHub
• URL: https://github.com/parthds02/analyzing-student-success-with-data
• Owner: ParthDS02
• License: mit
• Created: 2024-10-11T11:51:55.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-12-12T11:24:59.000Z (10 days ago)
• Last Synced: 2024-12-12T12:28:33.977Z (10 days ago)
• Topics: data, datascience, jupyter-notebook, kaggle, python, pythonlibraries
• Language: Jupyter Notebook
• Homepage:
• Size: 1.96 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Analyzing-Student-Success

Discover key factors influencing student performance through data analysis and visualization. Explore gender, parental education, sports, and ethnicity impacts.

![Project_Flow drawio](https://github.com/user-attachments/assets/4630fc23-461e-4aa3-bfb2-c0e114f38c6b)

# Steps perform in this project

**1. Understanding the Problem**

   

   I aimed to analyze how students perform in their academics based on factors like gender, parents' education, sports participation, and ethnicity. This helps identify patterns and key influences on student success

   

**2. Collecting and Loading Data**

   

   I used a dataset of student scores and information on their background. This data was loaded into the analysis environment (Python) using libraries like Pandas for easier manipulation.

   

**3. Data Cleanup**

   Some unnecessary columns or irrelevant data (like "Unnamed" columns) were removed to make the dataset more usable.

   

**4. Visualizing the Data (Graphs)**

   I created various graphs to understand the data better:

   

      •	Bar Plot for Gender Distribution: Shows how many students are male or female.

      

      •	Heatmaps: Visualized how parents' education and marital status relate to student scores.

      

      •	Boxplots for Scores: Showed the distribution of scores in Math, Reading, and Writing.

      •	Pie Chart for Ethnic Group Distribution: Showed the proportion of students from different ethnic backgrounds.

   

**5. Analyzing the Graphs**

   Each graph was examined to identify patterns or insights. For example:

   

      •	Higher parent education often leads to better student performance.

      

      •	Students who participate in sports tend to have better academic results.

      

      •	The score ranges across subjects (Math, Reading, Writing) were compared.

   

**6. Drawing Conclusions**

   Based on the graphs, I summarized that parental involvement (education and marital status) and extracurricular activities (like sports) play important roles in academic success.