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 1 | "DUST, DATA AND DISCOVERY: AQI ANALYSIS UNDER THE MICROSCOPE"
 2 | 
 3 | 📊 Data Analysis Project using NumPy, Pandas, Matplotlib & Seaborn
 4 | 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.
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 6 | 
 7 | 🚀 Project Highlights
 8 | 
 9 | ✅ Cleaned and preprocessed the dataset for analysis
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11 | ✅ Performed statistical summary and value counts
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13 | ✅ Visualized distributions using histograms and boxplots
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15 | ✅ Explored correlation and relationships using heatmaps and scatter plots
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17 | ✅ Used Seaborn to generate appealing and insightful visualizations
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19 | ✅ Demonstrated good use of NumPy for efficient numeric operations
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21 | ✅ Implemented creativity in presenting the data and drawing conclusions
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23 | 
24 | 🧰 Libraries Used
25 | NumPy – for numeric computations
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27 | Pandas – for data handling and preprocessing
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29 | Matplotlib – for plotting basic visualizations
30 | 
31 | Seaborn – for advanced and aesthetic statistical graphics
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33 | 
34 | 🧪 How to Run
35 | 
36 | 1.Clone the repository
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38 | git clone https://github.com/yourusername/your-repo-name.git
39 | 
40 | 2.Navigate into the project directory
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42 | cd your-repo-name
43 | 
44 | 3.Open the notebook
45 | 
46 | jupyter notebook Final_CA_Project.ipynb
47 | 
48 | 5.Ensure that you have installed the required libraries:
49 | 
50 | pip install numpy pandas matplotlib seaborn
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52 | 
53 | 📌 Use Case
54 | 
55 | 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.
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57 | 
58 | 📎 Dataset
59 | The dataset used is publicly available from data.gov.in, containing commodity prices across various Indian mandis (markets).
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61 | 
62 | 📈 Sample Visuals
63 | Histogram showing distribution of prices
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65 | Boxplots comparing commodities
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67 | Heatmaps indicating correlation
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69 | Line charts for trends over time
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71 | 
72 | 🏁 Final Thoughts
73 | This project helped in:
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75 | Improving proficiency in pandas and seaborn
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77 | Strengthening the understanding of real-world data structures
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79 | Practicing data visualization to communicate insights clearly
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81 | 
82 | 🌟 Show Some Love
83 | If you found this helpful, please ⭐ the repo and share your thoughts!
84 | 


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