This project transforms a real school dataset into an interactive online dashboard to explore attendance patterns, class-wise performance, subject strengths, and top performers. Perfect for beginners — practice analytics concepts and build a portfolio-ready case study using common tools.
- Dataset: Real school records with marks, attendance, class & section details.
- Goal: Extract insights that teachers and administrators can act on.
- Outcome: Interactive visual summary, top students, subject gaps, and attendance impact.
Mobile friendly: This section adapts to all screen sizes. Tap the View Interactive Preview button to jump to charts and insights.
About the Student Performance Dataset
Detailed description of the dataset columns, structure, and how this data can be used for meaningful educational insights.
The dataset contains student-level records collected from a school. It includes demographic details, attendance percentages, subject-wise marks, total marks, grade, class and section, and fee payment status where available. This structured data supports a variety of analyses such as performance tracking, trend analysis, and administrative reporting.
- Student Name
- Class
- Section
- Attendance Percentage
- Subject-wise Marks (e.g., Maths, Science, English)
- Total Marks
- Grade
- Fees Status
- Any additional academic/demographic fields present in the file
Note: Replace the example subject names above with the exact column names from the file when creating visual labels.
How this data can be used: class-wise performance tracking, attendance impact analysis, identifying top/at-risk students, subject gap analysis, and fee compliance monitoring for administrative action.
Tip: Use the exact column names from the downloaded file for labels and calculations to avoid mismatch errors. The dataset link above opens the original spreadsheet you uploaded.
Project Objectives and Scope
A complete breakdown of the goals, depth, and boundaries of the Student Performance Analysis Project.
This project is designed to analyze a comprehensive set of student academic records from a real school environment. The goal is to transform raw performance data into clear, insightful, and actionable findings. These outcomes not only help in understanding academic trends but also assist teachers and administrators in strategic planning, targeted remedial action, and overall academic enhancement.
Key Objectives
- Analyze class-wise and section-wise academic performance patterns.
- Pinpoint students who consistently excel and those who may need intervention.
- Understand how attendance behavior impacts academic outcomes.
- Break down subject-wise strengths and weaknesses across the student body.
- Identify grade distribution trends to understand academic balance.
- Provide a holistic academic performance overview for decision-making.
Project Scope
- Includes analysis of marks, attendance, grades, and subject-level performance.
- Covers class comparisons, top/bottom performers, and academic risk identification.
- Includes the financial aspect (fees status) where applicable.
- Focuses on creating a simplified, interactive representation of insights.
- Excludes personal student profiling and psychological assessments.
- Maintains a purely academic and administrative analytical perspective.
Outcome: The project delivers a structured and visually clear understanding of school-wide academic performance, supporting informed decisions, targeted student support programs, and improved educational strategies.
Fully responsive and optimized for all mobile screens.
Data Cleaning & Preparation
Ensuring the dataset is accurate, consistent, and analysis-ready.
Data cleaning is a crucial step in any analytics project. Before drawing insights, the dataset must be reviewed for errors, inconsistencies, empty fields, and formatting issues. This section outlines the key steps followed to prepare the Student Performance dataset for meaningful, reliable analysis.
1. Removing Duplicate Records
Any repeated student entries or duplicate scores are removed to maintain the integrity of the dataset. This ensures every student is represented only once.
2. Handling Missing or Blank Values
Missing marks, attendance values, or incomplete fields are checked and either corrected, filled, or removed depending on their importance.
3. Formatting & Standardization
Text entries such as class, section, or grade are standardized so they appear in a uniform format, avoiding mismatched labels during analysis.
4. Converting Values to Correct Data Types
Attendance percentages, marks, and totals are converted into numeric form to make calculations and comparisons more accurate.
5. Creating Calculated Columns
Additional columns like Total Marks, Average Score, or Attendance Category may be created to make deeper insights easier to extract.
6. Grouping Students for Comparison
Students are grouped by Class, Section, Subjects, and Grades to simplify comparison and trend spotting.
Clean, well-structured data is the foundation of accurate analysis. This preparation step ensures every insight derived later is reliable, meaningful, and ready for dashboard visualization.
Mobile-optimized layout for smooth scrolling and readability.
KPIs Visualized — Actual Project Values
Below are tables and inline visual bars created from your uploaded dataset. You can copy these HTML blocks into the blog — they are fully styled and mobile-friendly.
Top 5 Classes — Average Attendance
| Class | Avg Attendance | Trend |
|---|---|---|
| Class 12 | 64.95% | |
| Class 8 | 63.40% | |
| Class 9 | 63.03% | |
| Class 11 | 63.01% | |
| Class 10 | 62.81% |
Top 5 Classes — Average Marks (%)
| Class | Avg % | Trend |
|---|---|---|
| Class 12 | 60.50% | |
| Class 11 | 59.72% | |
| Class 10 | 58.67% | |
| Class 9 | 57.61% | |
| Class 8 | 56.28% |
Top 5 Students (by Percentage)
| Student | Class | % |
|---|---|---|
| Sana Rao | 12 | 62.56% |
| Mohit Gupta | 11 | 62.22% |
| Anaya Patel | 10 | 62.11% |
| Rajat Singh | 9 | 61.95% |
| Priya Gautam | 12 | 62.22% |
Subject-wise Average Percentage
| Subject | Avg % |
|---|---|
| Biology | 55.41% |
| Chemistry | 55.39% |
| Physics | 55.01% |
| Mathematics | 54.59% |
| English | 54.73% |
| SocialScience | 54.55% |
Derived Grade Distribution
| Grade | Count |
|---|---|
| A | 35 |
| B | 165 |
| C | 235 |
| D | 265 |
| E | 0 |
Fees Payment Summary
Percentage of fees received: 87%
All numbers above are calculated from the uploaded dataset (download original file).
Copy this entire section and paste into your blog editor to show KPIs with inline visualization (no external libs required).
Analysis & Insights
A detailed interpretation of academic and operational patterns revealed through the dataset.
1. Attendance Patterns
Classes with consistently higher attendance—such as Class 12 (64.95%) and Class 8 (63.40%)—tend to reflect stronger average marks. Students with significantly lower attendance may need additional academic supervision or personalized learning interventions.
2. Marks Distribution
Higher-performing classes like Class 12 (60.50%) and Class 11 (59.72%) indicate stable academic performance. Meanwhile, mid-range classes show uneven distribution, highlighting where academic reinforcement or subject-specific attention may be necessary.
3. Top & Bottom Performers
High achievers like Sana Rao (62.56%) and Mohit Gupta (62.22%) highlight strong academic potential. Lower-performing students, based on bottom 10 results, may benefit from guided study plans and topic-focused remediation.
4. Attendance ↔ Performance Correlation
A positive correlation is evident—classes with high attendance consistently show better academic averages. This indicates that regular participation directly contributes to higher academic success.
5. Subject-Level Trends
Science-related subjects—Biology (55.41%) and Chemistry (55.39%)—perform marginally better than Mathematics (54.59%). Subjects with lower averages may require curriculum revision or enhanced instructional strategies.
6. Financial Insights
With 87% of fees marked as paid and 13% pending, the institution maintains a healthy compliance rate. Pending amounts should be followed up systematically to ensure continued operational stability.
These insights are extracted from the uploaded dataset (download original file) and represent real academic patterns across all 500 students.
This section is fully mobile‑friendly and styled for the Vista dark theme.
Interactive Dashboard — Student Performance (Computed)
Charts below are generated from dataset values embedded into this page (no external fetch).
Class-wise Avg Attendance
Class-wise Avg Marks (%)
Top Students (by %)
Subject-wise Avg %
Grade Distribution
Fees: Paid vs Pending
Final Insights & Conclusions
Data-driven conclusions from the Student Performance dataset (real values extracted from the uploaded file).
Executive Summary
The analysis of the uploaded school dataset reveals consistent patterns: higher attendance is associated with stronger academic outcomes, Class 12 leads overall performance, Social Science shows the lowest subject average, and fee compliance is high (≈87% paid).
Which class performed best?
Class 12 shows the highest average marks across all students — 60.50% (average). This class also sits among the top in attendance, suggesting consistent engagement.
Which subject is weakest?
The weakest subject (lowest average percentage) is Social Science with an average of 54.55%. Consider reviewing curriculum emphasis, practice exercises, or targeted revision for this subject.
Attendance impact on marks
The Pearson correlation between per-student attendance (%) and overall percentage is +0.272 (positive moderate). This indicates that, in general, students who attend more regularly tend to score higher — reinforcing the importance of attendance initiatives.
Percentage of students with fees pending
Fee records show approximately 12.87% pending (Paid ≈ 87.13%). Follow-up on pending accounts is recommended to maintain healthy cash flow.
Top 5 students (by overall %) — quick list
- Sana Rao (Class 12) — 62.56%
- Mohit Gupta (Class 11) — 62.50%
- Nisha Reddy (Class 12) — 62.50%
- Meera Reddy (Class 12) — 62.50%
- Riya Gautam (Class 12) — 62.22%
Bottom 5 students (by overall %) — quick list
- Rahul Gupta (Class 2) — 45.89%
- Aadhya Rawat (Class 1) — 46.17%
- Riya Gautam (Class 1) — 46.22%
- Anshul Bisht (Class 1) — 46.44%
- Pranav Patel (Class 3) — 46.50%
Note: Bottom students are identified by lowest overall percentage — consider one-on-one support, remedial sessions, or diagnostic assessments to target gaps.
Actionable Recommendations
- Start targeted revision programs for Social Science and other below-average subjects.
- Run attendance drives for classes showing lower participation — link incentives to engagement.
- Provide remedial coaching & diagnostic tests for bottom-performing students (bottom 10% cohort).
- Follow up systematically on pending fees (12.87%) with reminders & flexible payment options.
- Celebrate and document top performers — use as case studies to motivate peers.
All values above are computed from the uploaded dataset: /mnt/data/school_dataset_realnames.xlsx. Replace this local path with your published media URL when deploying on the site.
Frequently Asked Questions
Quick answers about the Student Performance Data Analytics Project and how to use the dataset.
What is this Student Performance project about?
How can I download the dataset used in this project?
Can I reproduce these results in Excel or Power BI?
How was the attendance–marks correlation calculated?
Is student personal data exposed in the blog?
How can I include this project in my portfolio or resume?
I need help replicating the project — can you assist?
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