🌦️ KNN Weather Classifier

An interactive machine learning web application that uses the K-Nearest Neighbors (KNN) algorithm to classify weather conditions based on environmental features.

🚀 Overview

This project demonstrates a supervised learning classification model. Users can manipulate environmental variables to see how a KNN model draws boundaries between "Sunny" and "Rainy" weather conditions.

🛠️ Tech Stack

• Framework: Streamlit (UI/UX)
• Machine Learning: Scikit-Learn (KNeighborsClassifier)
• Data Processing: NumPy
• Visualization: Matplotlib

💡 Key Features

• Live Prediction: Real-time classification updates as you move the temperature and humidity sliders.
• Dynamic K-Value: Adjust the number of neighbors (K) to see how it affects model confidence and classification.
• Visual Decision Plot: An interactive scatter plot showing the training data points and where the new input sits in the feature space.
• Probability Metrics: Displays the mathematical confidence (probability) for both Sunny and Rainy labels.

📖 How the Model Works

The model utilizes a small training set of temperature and humidity pairings:

1. Distance Calculation: Measures the Euclidean distance between the user input and stored training points.
2. Neighbor Selection: Identifies the $K$ closest points.
3. Voting: Assigns the label based on the majority class among the $K$ neighbors.

Model Logic: Generally, higher temperatures and lower humidity lead to a "Sunny" prediction, while lower temperatures and higher humidity trend toward "Rainy".

🏃 Getting Started

1. Clone this repository.
2. Install dependencies:

   pip install -r requirements.txt

3. Run the app:

   streamlit run main.py

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Made with 💗 by Manas Shukla