https://github.com/tasninanika/coded_data_prediction-knn

K-Nearest Neighbors (KNN) is a supervised machine learning algorithm
https://github.com/tasninanika/coded_data_prediction-knn

knn pandas python3 scikit-learn

Last synced: 3 months ago
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K-Nearest Neighbors (KNN) is a supervised machine learning algorithm

• Host: GitHub
• URL: https://github.com/tasninanika/coded_data_prediction-knn
• Owner: tasninanika
• Created: 2025-06-27T16:14:16.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2025-06-28T09:12:04.000Z (12 months ago)
• Last Synced: 2025-08-16T11:22:57.455Z (10 months ago)
• Topics: knn, pandas, python3, scikit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 36.1 KB
• Stars: 1
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 🤖 Coded Data Prediction using K-Nearest Neighbors (KNN) Classifier

This project demonstrates how the **K-Nearest Neighbors (KNN)** algorithm can be used to build a simple and effective machine learning classifier. KNN is an easy-to-understand and powerful algorithm used for **classification** and **regression** problems.

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## 📌 What is KNN?

**K-Nearest Neighbors (KNN)** is a **supervised machine learning** algorithm that predicts the output class of a new data point by looking at the **'K' closest data points** in the training dataset. It works on the principle that similar data points are near each other.

For classification tasks:

- It checks the **K nearest neighbors** of a data point.

- Takes a **majority vote** from the neighbors' classes.

- Assigns the most common class as the prediction.

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## 📈 Key Steps in This Project

1. Import necessary libraries (`sklearn`, `pandas`, etc.)

2. Load and explore the dataset

3. Split data into training and testing sets

4. Train the KNN model using `KNeighborsClassifier`

5. Predict and evaluate accuracy using test data