Machine Learning with scikit-learn and pandas

Includes machine learning classifier and regressors.

Computer_Vision_in_Agriculture

This project demonstrates a simple K-Nearest Neighbors (KNN) classifier applied to a fruit dataset. It shows how to use KNN for supervised classification, visualize decision boundaries, and evaluate model performance.

The project is based on an educational example from the Applied Machine Learning with Python course (Coursera), extended with clear plots and explanations.

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Jupyter Notebook

For a full walkthrough with code, outputs, and visualizations, see the Jupyter Notebook Computer_Vision_in_Agriculture.ipynb

Run the notebook online (no setup required):

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Project Overview

• Goal: Classify fruits based on features (weight, height, width, color score, etc.)
• Algorithm: K-Nearest Neighbors (KNN)
• Steps:
  1. Load the fruit dataset
  2. Split into training and test sets
  3. Standardize features
  4. Train a KNN classifier with different k values
  5. Visualize classification accuracy and decision boundaries
  6. Evaluate the model

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Dataset

The dataset contains fruit samples with the following attributes:

• Fruit label (apple, mandarin, orange, lemon)
• Features: height, width, mass, color score