Supervised Learning Algorithms

This repository contains implementations of key supervised learning algorithms used in predictive modeling. Each algorithm is applied to solve a regression or classification problem, with code files available for Linear Regression, Logistic Regression, and K-Nearest Neighbors (KNN).

Contents

1. Linear Regression

   • Purpose: Predict continuous numerical outcomes based on input features.
   • Use Case: Predicting values such as house prices, salaries, etc.
   • Key Concepts:
     • Assumes a linear relationship between the input features and the output.
     • Includes steps such as fitting the model, calculating the coefficients, and evaluating performance using metrics like Mean Squared Error (MSE).

2. Logistic Regression

   • Purpose: Classify categorical outcomes, typically binary (e.g., 0 or 1).
   • Use Case: Predicting binary outcomes such as email spam detection or loan approval.
   • Key Concepts:
     • Uses the logistic function (sigmoid) to map predicted values to probabilities.
     • Includes steps such as model fitting, threshold determination, and evaluation using accuracy, precision, and recall.

3. K-Nearest Neighbors (KNN)

   • Purpose: Predict outcomes by finding the majority class (classification) or the average (regression) of the k-nearest data points in the feature space.
   • Use Case: Classifying data like customer segmentation or predicting product ratings.
   • Key Concepts:
     • Relies on distance metrics (e.g., Euclidean distance) to find the closest points.
     • Includes hyperparameter tuning to select the best value of "k" and evaluates performance using confusion matrix or other relevant metrics.

Requirements

• Python 3.x
• Libraries: numpy, pandas, scikit-learn, matplotlib, seaborn

Install the required libraries using:

pip install numpy pandas scikit-learn matplotlib seaborn

How to Use

1. Clone the repository:

   git clone https://github.com/Kainattkhan/Supervised-Learning-Algorithms.git

2. Open the Jupyter Notebook files (.ipynb) in your environment (e.g., Jupyter Lab, Google Colab).
3. Run the cells to see how each algorithm is implemented and applied to datasets.