Data-Visualization-with-Python

This repository contains a data visualization project exploring various datasets to extract meaningful insights through visual representations. The Jupyter Notebook (Visualization.ipynb) demonstrates the application of different plotting techniques and libraries to understand data patterns, relationships, and trends.

Skills Demonstrated:

• Data Visualization using Matplotlib
• Data Visualization using Seaborn
• Interactive Plotting (if applicable, e.g., using libraries like Plotly)
• Understanding of different plot types and their applications (e.g., scatter plots, histograms, bar charts, box plots, heatmaps, network graphs, geographical visualizations)
• Data Exploration and Interpretation through Visuals
• Handling different data formats (CSV, GeoJSON)

Project Description:

The Visualization.ipynb notebook analyzes several datasets to showcase a variety of visualization techniques. These analyses aim to:

• Explore relationships between car features (using Cars93.csv). Visualizations likely include scatter plots to see correlations between MPG, price, etc., and bar charts to compare categorical features.
• Analyze iPhone review sentiment (using IPhoneReview.csv). Visualizations could involve bar charts of sentiment distribution, word clouds of common positive and negative terms, and trends in ratings over time.
• Visualize a network of connections (using StackNetworkNodes.csv and StackNetworkLinks.csv). This likely involves creating a network graph to understand the relationships between different entities.
• Track the spread of COVID-19 in India (using covid_india_data.csv and india_states.geojson). Geographical visualizations on a map of India, as well as time series plots of cases and deaths, are likely included.
• Replicate and understand Anscombe's quartet (potentially related to anscombe data analysis.png). This demonstrates the importance of visualizing data beyond summary statistics.
• Analyze socio-economic indicators over time (using gapminder_socio_economic_data.csv). Visualizations could include animated scatter plots or line charts showing the evolution of different indicators.
• Investigate the relationship between car features and fuel efficiency (using mpg.csv). Similar to Cars93.csv, this likely involves scatter plots and other comparative visualizations.
• Explore the distribution of diabetes-related data (using diabetes.csv). Histograms, box plots, and potentially correlation heatmaps are likely used.

Code and Data:

• Visualization.ipynb: Jupyter Notebook containing the Python code for data visualization.
• Cars93.csv: Dataset containing information about different car models.
• IPhoneReview.csv: Dataset containing reviews for iPhones.
• StackNetworkLinks.csv: Dataset containing the links between nodes in a network.
• StackNetworkNodes.csv: Dataset containing the nodes in a network.
• covid_india_data.csv: Dataset containing COVID-19 data for India.
• india_states.geojson: GeoJSON file containing the geographical boundaries of Indian states.
• gapminder_socio_economic_data.csv: Dataset containing socio-economic data from the Gapminder project.
• mpg.csv: Dataset containing fuel efficiency data for cars.
• diabetes.csv: Dataset containing data related to diabetes.
• Uploaded Visualization Data/: This directory may contain the original zipped files and potentially other related files.
• Image files (Picture11683376825791.gif, anscombe data analysis.png, cloud.png, data analysis.jpeg, data visualization.jpeg, populous cities 1990 to 2020.gif, types of plots.png, varieties in data.png, visual statistics.png): These images likely illustrate specific visualizations or concepts explored in the notebook.

How to Run:

1. Open the Visualization.ipynb file in Jupyter Notebook or Google Colab.
2. Ensure that all the .csv files and the india_states.geojson file are located in the same directory as the notebook (or adjust the file paths in the notebook accordingly).
3. Run the cells in the notebook to reproduce the visualizations and analysis.

This project demonstrates the ability to effectively communicate data insights through a variety of visual techniques using Python and relevant libraries.