Iris Classification with Logistic Regression & Random Forest

This project demonstrates a classification task using two popular machine learning models:

• Logistic Regression
• Random Forest Classifier

The models are trained and evaluated on the Iris dataset, a classic dataset in machine learning.
The code also includes data visualization, evaluation metrics, and cross-validation for a complete workflow.

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Project Structure

• Data Loading: Load Iris dataset from CSV or sklearn datasets.
• Preprocessing: Split features (X) and labels (y).
• Model Training: Train Logistic Regression and Random Forest models.
• Evaluation: Print accuracy, confusion matrix, and classification report.
• Visualization: Pairplot, boxplots, heatmap for data exploration.
• Cross-Validation: Perform 5-fold CV for both models.

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Requirements

Make sure you have Python installed (>=3.8).
Install dependencies using:

pip install -r requirements.txt