KNN Classifier — Iris Flower Species Prediction

A supervised machine learning project that implements and tunes a K-Nearest Neighbors (KNN) classifier to predict the species of iris flowers based on physical measurements.

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Overview

This notebook walks through the full ML workflow:

1. Libraries — import all required dependencies
2. Data Extraction — load and preview the Iris dataset
3. EDA — explore class balance, feature distributions, and pairwise relationships
4. Model Building — build a pipeline (StandardScaler → KNN) and tune hyperparameters via GridSearchCV
5. Evaluation — assess performance using accuracy, precision, recall, F1-score, and a confusion matrix

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Dataset

The dataset (iris_dataset.csv) contains 150 samples from 3 iris species:

Feature	Description
sepal_length	Sepal length in cm
sepal_width	Sepal width in cm
petal_length	Petal length in cm
petal_width	Petal width in cm
species	Target class: setosa, versicolor, virginica

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Tech Stack

Library	Purpose
pandas	Data manipulation
numpy	Numerical operations
matplotlib / seaborn	Visualization
scikit-learn	Model, pipeline, tuning, metrics
scipy	Statistical utilities

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How to Run

1. Clone the repository:

   git clone <repo-url>
   cd <repo-folder>

2. Create and activate a virtual environment:

   python -m venv venv
   # Windows
   venv\Scripts\activate
   # macOS/Linux
   source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

4. Launch the notebook:

   jupyter notebook

5. Open knn_iris.ipynb and run all cells.

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Model Details

• Algorithm: K-Nearest Neighbors (KNeighborsClassifier)
• Preprocessing: StandardScaler (feature normalization — essential for distance-based algorithms)
• Hyperparameter tuning: GridSearchCV over n_neighbors (odd values 1–10) and weights (uniform / distance)
• Cross-validation: 10-fold StratifiedKFold
• Scoring metric: Weighted F1-Score

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Results

After grid search and cross-validation, the best model is evaluated on a held-out test set (30% of the data). Metrics reported: Accuracy, Precision, Recall, F1-Score, and Confusion Matrix.

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Project Structure

├── knn_iris.ipynb       # Main notebook
├── iris_dataset.csv     # Dataset
├── requirements.txt     # Python dependencies (optional)
└── README.md            # Project documentation

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License

This project is intended for educational purposes.