https://github.com/mj-awad17/school-daily-attendance-eda
Aims to uncover trends, identify influencing factors, and provide actionable insights
https://github.com/mj-awad17/school-daily-attendance-eda
eda expolatory-data-analysis python seaborn
Last synced: 3 months ago
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Aims to uncover trends, identify influencing factors, and provide actionable insights
- Host: GitHub
- URL: https://github.com/mj-awad17/school-daily-attendance-eda
- Owner: mj-awad17
- Created: 2025-03-14T16:16:48.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-03-14T18:02:52.000Z (3 months ago)
- Last Synced: 2025-03-14T18:38:37.419Z (3 months ago)
- Topics: eda, expolatory-data-analysis, python, seaborn
- Homepage: https://www.kaggle.com/mjawad17
- Size: 136 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# School Student Daily Attendance EDA
## 📚 Overview
Welcome to the **School Student Daily Attendance EDA** notebook! This comprehensive exploratory data analysis aims to delve into student attendance patterns, providing valuable insights for educators and administrators. By analyzing attendance data, I identify trends, understand influencing factors, and formulate strategies to enhance student engagement.
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## 🎯 Objectives
- **Analyze Attendance Patterns**: Uncover trends in student attendance over time.
- **Identify Influencing Factors**: Explore demographics and other variables affecting attendance.
- **Visualize Insights**: Create impactful visualizations to present findings.
- **Provide Actionable Recommendations**: Offer strategies for improving attendance rates based on data-driven insights.
---
## 📊 Data Source
The dataset utilized for this analysis is derived from a school attendance database. It encompasses daily attendance records for students across various grades and demographics over a specified period. [Link](https://www.kaggle.com/datasets/sahirmaharajj/school-student-daily-attendance)
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## 🔍 Key Analysis Steps
### 1. **Data Cleaning**
- **Standardization**: Ensure consistency in attendance records.
- **Missing Values**: Identify and handle any gaps in the data.
### 2. **Descriptive Statistics**
- Summarize attendance data to provide a foundational understanding.
- Analyze attendance rates segmented by demographics (e.g., grade level, gender).
### 3. **Data Visualization**
- Create engaging visual representations of attendance trends.
- Compare attendance rates among different student groups.
### 4. **Correlation Analysis**
- Investigate potential relationships between attendance and other factors (e.g., academic performance, behavior).
### 5. **Predictive Modeling** (if applicable)
- Develop predictive models to forecast future attendance based on historical trends.
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## 🌟 Expected Results
Upon completion of this analysis, you will gain:
- Clear insights into attendance trends over time.
- An understanding of the factors influencing student attendance.
- Visualizations that effectively communicate key findings.
- Practical recommendations for enhancing attendance strategies.
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## 🚀 Usage Instructions
To utilize this notebook:
1. Ensure that all required libraries are installed, including `pandas`, `matplotlib`, and `seaborn`.
2. Load the dataset and execute the cells sequentially to perform the analysis.
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## 🌟 A Huge Thank You to Everyone Who Reviewed This! 🌟
I’m truly grateful for your time and support! It means so much that you took the time to go through this project. I genuinely hope you found the information helpful and insightful. 🚀
## 🤝 Connect with the Author
Feel free to reach out for collaboration, feedback, or inquiries:
[LinkedIn](https://www.linkedin.com/in/mjawad17/)
[Kaggle](https://www.kaggle.com/mjawad17)
---
💬 Your thoughts and feedback are always welcome! If you have any questions or suggestions, feel free to reach out—I’d love to hear from you! 💡✨