https://github.com/abshar-shihab/fruit_prediction_using_knn_classification_model

This repository contains a Python implementation of the K-Nearest Neighbors (KNN) algorithm for predicting the class of fruits based on features such as size, weight, and colour.
https://github.com/abshar-shihab/fruit_prediction_using_knn_classification_model

knn-algorithm knn-classification machine-learning prediction prediction-model

Last synced: about 1 month ago
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This repository contains a Python implementation of the K-Nearest Neighbors (KNN) algorithm for predicting the class of fruits based on features such as size, weight, and colour.

• Host: GitHub
• URL: https://github.com/abshar-shihab/fruit_prediction_using_knn_classification_model
• Owner: Abshar-Shihab
• Created: 2024-11-21T11:08:12.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-11-21T11:22:58.000Z (about 1 month ago)
• Last Synced: 2024-11-21T12:24:07.369Z (about 1 month ago)
• Topics: knn-algorithm, knn-classification, machine-learning, prediction, prediction-model
• Language: Python
• Homepage:
• Size: 6.84 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Fruit_prediction_using_KNN_Classification_model

This repository contains a Python implementation of the K-Nearest Neighbors (KNN) algorithm for predicting the class of fruits based on features such as size, weight, and colour.

---

## Description: Fruit Prediction using K-Nearest Neighbors (KNN)

This repository contains a Python implementation of the K-Nearest Neighbors (KNN) algorithm for predicting the class of fruits based on features such as size, weight, and color. The KNN algorithm is a simple, non-parametric supervised learning method that classifies data points based on the majority class of their nearest neighbors in the feature space.

### Features

- **KNN Classifier:** Implements the core KNN logic, including calculating distances, finding neighbors, and predicting classes.

- **Euclidean Distance Metric:** Measures the similarity between data points.

- **CSV File Support:** Reads fruit data from a CSV file and uses it for training and testing the model.

- **Dynamic K-Value:** Allows selection of the number of nearest neighbors (K) to tune classification performance.

### Dataset

The dataset consists of fruit characteristics:

- **Features:** Mass, width, height, color_score

- **Labels:** Class of fruit (1, 2, 3, 4)

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## Usage

1. Place your dataset in the same directory as the script and name it `fruit_data_with_colours.csv`.

2. Run the script:

   ```bash

   python knn_fruit_prediction.py

   ```

3. Modify the number of neighbors (K) in the script to observe how it affects the predictions.

---

## Steps in KNN

### Step 1: Distance Calculation

- The Euclidean distance is used to measure similarity between data points:

  \[

  \text{Distance} = \sqrt{\sum_{i=1}^n (x_i - y_i)^2}

  \]

### Step 2: Finding K Nearest Neighbors

- Distances between the test point and all training points are calculated and sorted.

- The K points with the smallest distances are selected as the neighbors.

### Step 3: Predicting the Class

- The most common class among the neighbors is assigned to the test data.

---

## Dataset

The dataset should be in CSV format, with the following structure:

| fruit_label | Mass | Width  | height      | color_score |

|-------------|------|--------|-------------|--------|

|      1      | 192  | 8.4    | 7.3         | 0.55   |

|      2      | 86   | 6.2    | 4.7         | 0.77   |

|    ...      | ...  | ...    | ...         | ...    |

Replace `fruit_data_with_colours.csv` with your own dataset if needed.

## Contributing

Contributions are welcome! Feel free to submit issues or pull requests.

---