Data Visualization

A single notebook explaining all the important data visualization techniques.

Plots Covered

Univariate Plots:

1. Count Plot (Qualitative)
2. Pie Chart (Qualitative)
3. Histogram (Quantitative)
4. Discrete Plot (Quantitative)

Bivariate Plots:

For Quantitative vs Quantitative variable:

1. Scatter Plot
2. Heat Map

For Quantitative vs Qualitative variable:

1. Violin Plot
2. Box Plot
3. Faceting
4. Adapted Bar Chart
5. Swarm Plot

For Qualitative vs Qualitative variable:

1. Heat Map
2. Clustered Bar Chart
3. Line Plot OR Adapted Histogram

Plots Preview

Count Plot:

Histogram:

Scatter Plot:

Heat Map:

Scatter vs Heat Map:

Violin plot, Box plot and Adapted Bar Chart:

Adapted Bar Chart:

Facet:

Line Plot:

Clustered Bar Chart:

Getting Started

1. Clone or download the repository.
2. Open Visualization.ipynb in Jupyter Notebook or Jupyter Lab.
3. Ensure you have the required libraries installed.
4. Follow the code examples for each plot, including concepts of feature scaling, handling outliers, data wrangling, and comparison.

Contributions

Contributions are welcome - open issues or pull requests.