🌸 Iris Classification with Multiple ML Algorithms

This project explores the classic Iris dataset using a variety of supervised learning algorithms. It walks through data loading, visualization, model evaluation, and final prediction—all in a modular, beginner-friendly format.

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🚀 What’s Inside

1. Data Preparation

• Loads the Iris dataset using pandas
• Assigns column names for clarity
• Displays basic statistics and sample rows

2. Visualization

• Density plots for each feature
• Scatter matrix to explore feature relationships

3. Model Evaluation

• Splits data into training and validation sets (80/20)
• Tests six algorithms:
  • Logistic Regression
  • Linear Discriminant Analysis
  • K-Nearest Neighbors
  • Decision Tree
  • Naive Bayes
  • Support Vector Machine
• Uses 10-fold cross-validation to compare accuracy

4. Final Model

• Trains a KNN classifier on the training set
• Evaluates predictions on the validation set using:
  • Accuracy score
  • Confusion matrix
  • Classification report