https://github.com/aymen016/data-visualization
A collection of data analysis projects using Python, covering various datasets and domains. Includes EDA, preprocessing, data visualization (Matplotlib/Seaborn), and Power BI dashboards.
https://github.com/aymen016/data-visualization
dashboard eda jupyter-notebook matplotlib-pyplot matplotlib-python numpy pandas pandas-dataframe powerbi python seaborn seaborn-python visualization
Last synced: 5 days ago
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A collection of data analysis projects using Python, covering various datasets and domains. Includes EDA, preprocessing, data visualization (Matplotlib/Seaborn), and Power BI dashboards.
- Host: GitHub
- URL: https://github.com/aymen016/data-visualization
- Owner: Aymen016
- License: mit
- Created: 2024-03-06T18:15:59.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2025-07-24T08:27:52.000Z (11 months ago)
- Last Synced: 2025-07-24T11:37:09.032Z (11 months ago)
- Topics: dashboard, eda, jupyter-notebook, matplotlib-pyplot, matplotlib-python, numpy, pandas, pandas-dataframe, powerbi, python, seaborn, seaborn-python, visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 1.36 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
---
## 🔍 What’s Inside?
| 📁 File/Notebook | 📌 Description |
|------------------|----------------|
| `Analysis of Sales_Data.ipynb` | Complete sales data analysis: revenue trends, top products, and customer behavior. |
| `Basics of Visualisation.ipynb` | Introduction to basic visualization techniques using Matplotlib and Seaborn. |
| `Visualisation with matplotlib.ipynb` | Deep dive into Matplotlib for static and custom charts. |
| `Visualisation with Seaborn.ipynb` | Stylish, statistical plots using Seaborn – correlation heatmaps, distribution plots, etc. |
| `Visulising and Pre-processing.ipynb` | Data cleaning, preprocessing, and initial visualization for EDA. |
| `Sales Analysis.md` | Textual breakdown of insights found in the sales dataset. |
| `Report on Sales Analysis.pbix` | Power BI dashboard with interactive visuals. |
| `Report on Sales Data.pbix` | Another version of sales report – sliced and drilled down for business decisions. |
---
## 🛠️ Tools & Technologies Used
- **Python**
- Pandas
- NumPy
- Matplotlib
- Seaborn
- **Power BI**
- Interactive dashboards
- Data modeling
- DAX
- **Jupyter Notebooks** for exploratory analysis
- **CSV Datasets** for real-world practice
---
## 💡 Key Highlights
- Cleaned and visualized raw datasets using Python.
- Built clear and interactive dashboards using Power BI.
- Identified key sales drivers and customer trends.
- Applied EDA techniques to uncover hidden insights.
---
## 📈 Sample Visuals
Here are a few examples from the data visualizations created in this project:
### 🎻 Violin Plot of Product Prices by Category and Customer Country
This visualization shows the distribution and density of product prices across different categories, grouped by customer country. It highlights pricing trends and variability within each category, helping identify outliers and regional differences.
### 🚗 Average Car Prices by Model
This bar plot displays the average price of each car model. The x-axis represents different car models, while the y-axis shows their corresponding average prices. This visualization helps compare pricing trends across models and identify higher or lower-priced vehicles at a glance.
### 📈 Relationship Between Year, Mileage, and Other Features
This pairplot explores the relationships between numerical features such as year, mileage, and price. It visually reveals how mileage tends to decrease with newer car models, and highlights correlations (or lack thereof) between other variables.
---
## 🙋♂️ Author
**Aymen Baig**
📧 [ayemenbaig26@gmail.com](mailto:ayemenbaig26@gmail.com)
🔗 [Aymen016](https://github.com/Aymen016)
---
## 📝 License
This project is licensed under the **MIT License** — feel free to use, modify, and share with credits. 💙
---
## 🚀 Let's Connect!
Whether you're a recruiter, fellow data enthusiast, or curious learner — feel free to explore the notebooks and reach out if you'd like to collaborate or learn together.
Happy Analyzing! 📊✨