📊 Analyzing Data with Pandas & Visualizing with Matplotlib 📌 Overview

This project demonstrates how to analyze and visualize a dataset using Pandas for data analysis and Matplotlib/Seaborn for data visualization. The classic Iris dataset is used for this assignment.

🎯 Objectives

Load and explore a dataset (Iris).

Perform basic data analysis (statistics, groupings, patterns).

Create visualizations (line chart, bar chart, histogram, scatter plot).

Handle missing values and errors gracefully.

🛠️ Requirements

Install the dependencies before running:

pip install pandas matplotlib seaborn scikit-learn

🚀 How to Run Jupyter Notebook

Open the provided .ipynb file in Jupyter Notebook or JupyterLab.

Run the cells step by step to see the analysis and visualizations.

Python Script

If you have the .py version:

python analysis.py

📊 Visualizations Included

Line Chart – Trend of sepal length across samples.

Bar Chart – Average petal length per species.

Histogram – Distribution of sepal width.

Scatter Plot – Sepal length vs petal length (with species color-coded).

📝 Findings

No missing values in the Iris dataset.

Petal length & width are strong indicators of flower species.

Setosa species clearly separates from others in scatter plots.

Virginica shows larger petal dimensions compared to Versicolor and Setosa.

✅ Ubuntu-Inspired Principles Applied

Community: Used a globally shared dataset.

Respect: Handled potential errors in loading/cleaning data.

Sharing: Visualizations make findings accessible to others.