This repository contains implementations and brief descriptions of essential data science algorithms. These algorithms are fundamental in solving various machine learning and data analysis problems.
Linear Regression is a simple algorithm used for predicting a continuous target variable based on one or more predictor variables. It models the relationship between the dependent and independent variables by fitting a linear equation to observed data.
Logistic Regression is used for binary classification problems. It predicts the probability of a categorical dependent variable by using a logistic function to model the data and estimate the likelihood of an event occurring.
Decision Trees are non-parametric supervised learning algorithms used for classification and regression. They split the dataset into subsets based on the value of input features, creating a tree-like model of decisions and their possible consequences.
Random Forest is an ensemble learning method that constructs multiple decision trees during training and outputs the mode of the classes for classification or mean prediction for regression. It improves accuracy and reduces overfitting.
SVMs are powerful classifiers that find the optimal hyperplane which maximizes the margin between different classes in the feature space. They are effective in high-dimensional spaces and when the number of dimensions exceeds the number of samples.
KNN is a simple, instance-based learning algorithm used for classification and regression. It assigns a class to a data point based on the majority class of its K nearest neighbors in the feature space.
K-Means Clustering is an unsupervised learning algorithm used for partitioning a dataset into K distinct, non-overlapping subsets (clusters). It aims to minimize the variance within each cluster, grouping similar data points together.
PCA is a dimensionality reduction technique that transforms high-dimensional data into fewer dimensions by projecting it onto new axes (principal components). It captures the maximum variance in the data with the least number of components.