🌸 Logistic Regression – Iris Dataset (Binary Classification)

This project demonstrates the practical implementation of Logistic Regression for binary classification using the Iris dataset. The focus is on classifying between Iris-versicolor and Iris-virginica using Scikit-learn and evaluating model performance with cross-validation and hyperparameter tuning.

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📂 Project Structure

• logistic_regression.ipynb – Jupyter notebook with complete implementation
• README.md – Project overview and usage guide

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✅ Features

• Load and filter the Iris dataset for binary classification
• Map categorical species to numerical labels
• Split data into training and testing sets
• Implement and tune Logistic Regression using GridSearchCV
• Evaluate model using classification report and accuracy
• Visualize data using Seaborn pair plots
• Display correlation matrix

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🛠️ Tools & Libraries

• Python
• NumPy
• Pandas
• Seaborn
• Matplotlib
• scikit-learn

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🧪 Model Training & Tuning

• Model: Logistic Regression
• Tuning Parameters:
  • penalty: l1, l2, elasticnet
  • C: Regularization strength
  • max_iter: Maximum iterations
• GridSearchCV used with cv=5 for cross-validation

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📊 Evaluation

• Metrics:
  • Accuracy Score
  • Classification Report (Precision, Recall, F1-Score)
• Best Parameters: Displayed using best_params_
• Best Score: Shown using best_score_

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🖼️ Visualizations

• Pair Plot colored by species
• Correlation matrix

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🚀 How to Run

1. Clone the repository:

git clone https://github.com/Sidra-009/logistic-regression-iris.git
cd logistic-regression-iris