This project demonstrates the process of loading, cleaning, analyzing, and visualizing a dataset using Python. The analysis uses the Iris dataset as an example, employing pandas for data manipulation and matplotlib/seaborn for visualization. The goal is to extract insights and showcase fundamental data science workflows with clear, reproducible code.
To run this project, Python 3.x is required along with the following libraries:
- pandas
- matplotlib
- seaborn
Install the necessary packages via pip:
The Iris dataset is used and is loaded directly from the seaborn library, which contains measurements of iris flowers from three species. This eliminates the need for external datasets but the code can be adapted to load any CSV file.
data_analysis.ipynb(or.py): This script/notebook contains the full workflow:- Loading and exploring the dataset
- Handling missing values
- Computing basic statistics and group-wise summaries
- Creating visualizations: line chart, bar chart, histogram, and scatter plot
- Dataset first rows and info displayed to understand structure and data types.
- Missing values checked and handled appropriately (none in Iris dataset).
- Statistical summaries (mean, median, std) provided for numerical data.
- Grouping by species revealed notable differences in average measurements.
- Visualizations highlighted trends and relationships:
- Sepal length trends
- Average petal length per species comparison
- Distribution of sepal width
- Correlation between sepal length and petal length
Run the notebook to reproduce all analyses and plots. Adapt the code to different datasets by modifying the file loading section and relevant column names.
- Extend analysis to other datasets and more complex transformations.
- Implement predictive modeling and classification algorithms.
- Add interactive visualizations using modern JavaScript libraries.
- Iris dataset courtesy of the seaborn Python library.
- Python, pandas, matplotlib, and seaborn for powerful data processing and visualization.