📊 AI Attendance & Performance Insight System

AI for Good Hackathon | Snowflake + Cortex AI

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🧩 Problem Statement

Schools often struggle to identify students who need early academic attention due to irregular attendance or declining performance.
Manual monitoring is time-consuming, reactive, and error-prone, which delays timely intervention.

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💡 Solution

This project leverages Snowflake Cortex AI to analyze student attendance and performance data and generate human-readable explanations and recommendations for at-risk students.
The system enables educators to make early, data-driven decisions using AI-powered insights.

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🛠 Tech Stack

• Snowflake Data Warehouse
• Snowflake Cortex AI (snowflake-arctic)
• SQL
• Synthetic student dataset (CSV)

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📂 Dataset

Fields used:

• student_id
• student_name
• class
• date
• attendance
• marks

The dataset represents daily attendance and academic performance records for students.

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🧠 AI Features

• ✅ Attendance anomaly detection
• ✅ Performance risk identification
• ✅ Natural-language explanations using Snowflake Cortex AI
• ✅ Non-technical, easy-to-understand outputs

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🏗 Architecture / Workflow

1. Student attendance & performance data is stored in Snowflake
2. SQL queries aggregate attendance and marks
3. Snowflake Cortex AI generates explanations and recommendations
4. Educators receive clear, actionable insights

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🔍 Sample AI Analysis Query

SELECT
  student_name,
  SNOWFLAKE.CORTEX.COMPLETE(
    'snowflake-arctic',
    CONCAT(
      'Student risk analysis. ',
      'Absent days: ',
      COUNT_IF(attendance = ''Absent''),
      '. Average marks: ',
      ROUND(AVG(marks), 2),
      '. Provide a short recommendation.'
    )
  ) AS ai_recommendation
FROM SCHOOL_DB.PUBLIC.STUDENT_ATTENDANCE
GROUP BY student_name
HAVING COUNT_IF(attendance = 'Absent') >= 2
   OR AVG(marks) < 60;

## 🌍 Impact
- Enables early identification of at-risk students
- Reduces manual monitoring effort for educators
- Supports timely academic intervention using AI-driven insights

## 🚀 How to Run
1. Create a Snowflake account
2. Upload the provided CSV dataset
3. Run the SQL scripts from the repository
4. Execute the Cortex AI query to generate insights