DataScience-Task1-IrisFlowerClassification

Iris Flower Classification

Objective

Develop a machine learning model to classify Iris flowers into Setosa, Versicolor, and Virginica species using flower measurements.

Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-Learn
• Jupyter Notebook

Dataset

The Iris dataset was loaded directly from Scikit-Learn using:

from sklearn.datasets import load_iris

Exploratory Data Analysis

Performed:

• Dataset shape analysis
• Data type inspection
• Null value checking
• Descriptive statistics
• Pairplot visualization
• Boxplot visualization

Models Implemented

Logistic Regression

Accuracy: 100%

K-Nearest Neighbors

Accuracy: 100%

Results

Model	Accuracy
Logistic Regression	100%
KNN	100%

Both models successfully classified all test samples.

Project Structure

DataScience-Task1-IrisFlowerClassification/
├── Iris_Flower_Classification.ipynb
├── README.md
├── screenshots/
└── output/

Author

Muhammad Danish