"DUST, DATA AND DISCOVERY: AQI ANALYSIS UNDER THE MICROSCOPE"

📊 Data Analysis Project using NumPy, Pandas, Matplotlib & Seaborn This project is a comprehensive data analysis task performed using Python libraries such as NumPy, Pandas, Matplotlib, and Seaborn. It involves data cleaning, exploratory data analysis (EDA), and statistical visualization to uncover insights from a real-world dataset.

🚀 Project Highlights

✅ Cleaned and preprocessed the dataset for analysis

✅ Performed statistical summary and value counts

✅ Visualized distributions using histograms and boxplots

✅ Explored correlation and relationships using heatmaps and scatter plots

✅ Used Seaborn to generate appealing and insightful visualizations

✅ Demonstrated good use of NumPy for efficient numeric operations

✅ Implemented creativity in presenting the data and drawing conclusions

🧰 Libraries Used NumPy – for numeric computations

Pandas – for data handling and preprocessing

Matplotlib – for plotting basic visualizations

Seaborn – for advanced and aesthetic statistical graphics

🧪 How to Run

1.Clone the repository

git clone https://github.com/yourusername/your-repo-name.git

2.Navigate into the project directory

cd your-repo-name

3.Open the notebook

jupyter notebook Final_CA_Project.ipynb

5.Ensure that you have installed the required libraries:

pip install numpy pandas matplotlib seaborn

📌 Use Case

This project can serve as a template for EDA projects and helps beginners understand how to approach a dataset from scratch — cleaning, analyzing, and visualizing it efficiently using the Python data stack.

📎 Dataset The dataset used is publicly available from data.gov.in, containing commodity prices across various Indian mandis (markets).

📈 Sample Visuals Histogram showing distribution of prices

Boxplots comparing commodities

Heatmaps indicating correlation

Line charts for trends over time

🏁 Final Thoughts This project helped in:

Improving proficiency in pandas and seaborn

Strengthening the understanding of real-world data structures

Practicing data visualization to communicate insights clearly

🌟 Show Some Love If you found this helpful, please ⭐ the repo and share your thoughts!