The objective of this assignment is to practice loading, analyzing, and visualizing data in Python. I used panda for data manipulation and matplotlib for visualization.
Dataset: Iris Dataset (from sklearn.datasets).
Steps:
Loaded dataset into a Pandas dataframe.
Inspected first rows using .head().
Checked dataset info with .info() and .isnull().sum().
Verified dataset is clean with no missing values.
Computed descriptive statistics using .describe().
Grouped data by species to compute average values for each flower type.
Created four different visualizations to analyze the dataset:
Line Chart – Shows sepal and petal length trends across samples.
Bar Chart – Compares the average petal length for each species.
Histogram – Displays the distribution of sepal Length.
Scatter plot – Visualizes relationship between sepal length and petal length, colored by species.
All plots include titles, axis labels, and legends for clarity.
Python 3
Pandas for data manipulation.
Matplotlib for visualization.
Seaborn for styling and advanced visualization.
Scikit-learn for dataset loading.
Install required libraries.
pip install pandas matplotlib seaborn scikit-learn.
Run the Python script.