📊 InsightEDU & AQI Dashboard

Welcome to InsightEDU & AQI, a data analytics dashboard that explores the correlation between Student Performance and Air Pollution in India. Built as a capstone project for the Edunet Foundation SAP course, this dashboard leverages powerful Python libraries to visualize and interpret insights from two major datasets.

🚀 Live App

👉 Launch the Streamlit App

🧠 Project Objective

The aim of this project is to:

• Analyze literacy and performance data across districts
• Explore air quality metrics across major cities
• Correlate environmental conditions with student performance indicators

📂 Datasets Used

1. Education Dataset:
   Contains district-wise literacy rates and other academic indicators.

2. Pollution Dataset:
   Includes city-wise air quality data such as PM2.5, PM10, NO₂, etc.

📌 Features

📘 Education Analysis Dashboard

• View state-wise and district-wise literacy rates
• Key stats: average literacy rate, district count, min/max literacy
• Visuals: top 10 districts, distribution histogram, state-wise bar comparison
• 🚨 Low-literacy alert zone for districts under 60%
• 🧩 State & District Comparison Tool — compare literacy rates interactively

🌫️ Air Quality Insights Dashboard

• Analyze AQI trends across cities and dates
• Key metrics: average AQI, PM2.5, PM10, max AQI
• 📅 Monthly and yearly AQI trend charts
• 🔥 Pollutant correlation heatmap
• 🚨 High-pollution alert zone for AQI > 200
• 🧩 City Comparison Tool — side-by-side AQI and pollutant insights

⚙️ Technologies Used

• Python
• pandas, numpy
• matplotlib, seaborn
• streamlit
• Jupyter Notebook for initial exploration

🛠 Run Locally

git clone https://github.com/darshan02parmar/insightedu-aqi.git

cd insightedu-aqi

pip install -r requirements.txt

 streamlit run app.py