Project Title: Iris Flower Classification

Overview:

This project aims to build a predictive model for classifying iris flowers based on their measurements. It utilizes the famous Iris dataset and implements a machine learning model using scikit-learn. The model is trained on the dataset and evaluated for its accuracy in classifying different types of iris flowers.

Key Features:

• Uses the Iris dataset, which consists of measurements of various iris flowers.
• Implements a machine learning model for iris flower classification.
• Utilizes scikit-learn, a popular machine learning library in Python.
• Performs data preprocessing, including feature scaling.
• Evaluates the model's accuracy on a test set to assess its performance.

Project Files:

• model.py: Contains the code for building, training, and evaluating the predictive model.
• README.md: Provides an overview of the project, instructions to run the project, and details about the dataset and model.
• requirements.txt: Lists the dependencies required to run the project.

Instructions:

1. Clone the repository to your local machine.
2. Set up a Python environment and install the project dependencies using pip install -r requirements.txt.
3. Run the model.py script to train and evaluate the predictive model.
4. The accuracy of the model on the test set will be displayed.

Dependencies:

• Python
• NumPy
• scikit-learn

Results:

The model achieves an accuracy of XX% on the test set, demonstrating its effectiveness in classifying iris flowers based on their measurements.