Delete pages/15KNN Alogrithm.py

pages/15KNN Alogrithm.py

@@ -1,137 +0,0 @@
-import streamlit as st
-
-# Page configuration
-st.set_page_config(page_title="KNN Overview", page_icon="📊", layout="wide")
-
-# Custom CSS styling for a cleaner, light-colored interface
-st.markdown("""
-    <style>
-        .stApp {
-            background-color: #f2f6fa;
-        }
-        h1, h2, h3 {
-            color: #1a237e;
-        }
-        .custom-font, p {
-            font-family: 'Arial', sans-serif;
-            font-size: 18px;
-            color: #212121;
-            line-height: 1.6;
-        }
-    </style>
-""", unsafe_allow_html=True)
-
-# Title
-st.markdown("<h1 style='color: #1a237e;'>Understanding K-Nearest Neighbors (KNN)</h1>", unsafe_allow_html=True)
-
-# Introduction to KNN
-st.write("""
-K-Nearest Neighbors (KNN) is a fundamental machine learning method suitable for both **classification** and **regression** problems. It makes predictions by analyzing the `K` closest data points in the training set.
-
-Key features:
-- KNN is a non-parametric model.
-- It memorizes training data instead of learning a model.
-- Distance metrics like **Euclidean** help determine similarity between data points.
-""")
-
-# How KNN Works
-st.markdown("<h2 style='color: #1a237e;'>How KNN Functions</h2>", unsafe_allow_html=True)
-
-st.subheader("Training Phase")
-st.write("""
-- KNN doesn't train a model in the traditional sense.
-- It stores the dataset and uses it during prediction.
-""")
-
-st.subheader("Prediction - Classification")
-st.write("""
-1. Set the value of `k`.
-2. Calculate the distance between the input and each point in the training data.
-3. Identify the `k` nearest neighbors.
-4. Use majority voting to assign the class label.
-""")
-
-st.subheader("Prediction - Regression")
-st.write("""
-1. Choose `k`.
-2. Find the distances to all training points.
-3. Pick the closest `k` neighbors.
-4. Predict using the **average** or **weighted average** of their values.
-""")
-
-# Overfitting and Underfitting
-st.subheader("Model Behavior")
-st.write("""
-- **Overfitting**: Occurs when the model captures noise by using very low values of `k`.
-- **Underfitting**: Happens when the model oversimplifies, often with high `k` values.
-- **Optimal Fit**: Found by balancing both, often using cross-validation.
-""")
-
-# Training vs CV Error
-st.subheader("Error Analysis")
-st.write("""
-- **Training Error**: Error on the dataset used for fitting.
-- **Cross-Validation Error**: Error on separate validation data.
-- Ideal models show low error in both.
-""")
-
-# Hyperparameter Tuning
-st.subheader("Hyperparameter Choices")
-st.write("""
-Important tuning options for KNN include:
-- `k`: Number of neighbors
-- `weights`: `uniform` or `distance`
-- `metric`: Distance formula like Euclidean or Manhattan
-- `n_jobs`: Parallel processing support
-""")
-
-# Scaling
-st.subheader("Why Scaling is Crucial")
-st.write("""
-KNN relies heavily on distances, so it's essential to scale features. Use:
-- **Min-Max Normalization** to compress values between 0 and 1.
-- **Z-score Standardization** to center data.
-
-Always scale training and testing data separately.
-""")
-
-# Weighted KNN
-st.subheader("Weighted KNN")
-st.write("""
-In Weighted KNN, closer neighbors have more influence on the result. It improves accuracy, especially in noisy or uneven data.
-""")
-
-# Decision Regions
-st.subheader("Decision Boundaries")
-st.write("""
-KNN creates boundaries based on training data:
-- Small `k` = complex, sensitive regions (risk of overfitting).
-- Large `k` = smoother regions (risk of underfitting).
-""")
-
-# Cross Validation
-st.subheader("Cross-Validation")
-st.write("""
-Cross-validation helps evaluate models effectively. For example:
-- **K-Fold CV** divides data into parts and tests each part.
-- Ensures model generalization.
-""")
-
-# Hyperparameter Optimization Techniques
-st.subheader("Tuning Methods")
-st.write("""
-- **Grid Search**: Tests all combinations of parameters.
-- **Random Search**: Picks random combinations for faster tuning.
-- **Bayesian Search**: Uses previous results to make better guesses on parameter selection.
-""")
-
-# Notebook Link
-st.markdown("<h2 style='color: #1a237e;'>KNN Implementation Notebook</h2>", unsafe_allow_html=True)
-st.markdown(
-    "<a href='https://colab.research.google.com/drive/11wk6wt7sZImXhTqzYrre3ic4oj3KFC4M?usp=sharing' target='_blank' style='font-size: 16px; color: #1a237e;'>Click here to open the Colab notebook</a>",
-    unsafe_allow_html=True
-)
-
-st.write("""
-KNN is intuitive and effective when combined with proper preprocessing and hyperparameter tuning. Use cross-validation to find the sweet spot and avoid overfitting or underfitting.
-""")