Supervised Learning

Introduction

Supervised learning is a type of machine learning where the model is trained on labeled data. The goal is to learn a mapping from inputs to outputs based on the provided examples.

Types of Supervised Learning Algorithms

1. Linear Regression
   • Used for predicting a continuous target variable based on one or more input features.
2. Logistic Regression
   • Used for binary classification problems where the target variable has two possible outcomes.
3. Decision Trees
   • A tree-like model used for both classification and regression tasks.
4. Support Vector Machines (SVM)
   • Used for classification tasks by finding the hyperplane that best separates the classes.
5. K-Nearest Neighbors (KNN)
   • A simple, instance-based learning algorithm used for both classification and regression.
6. Neural Networks
   • Complex models inspired by the human brain, used for a variety of tasks including image and speech recognition.

Getting Started

1. Install Required Libraries
   • Python: pip install numpy pandas scikit-learn matplotlib
2. Load Your Data
   • Use pandas to load and preprocess your dataset.
3. Choose an Algorithm
   • Select an appropriate algorithm based on your problem type (regression or classification).
4. Train Your Model
   • Split your data into training and testing sets and train your model using the training set.
5. Evaluate Your Model
   • Use metrics like accuracy, precision, recall, and F1-score to evaluate your model's performance.