Linear Regression ->Used for predicting continuous values. ->Models the relationship between dependent and independent variables by fitting a linear equation.
Logistic Regression ->Ideal for binary classification problems. ->Estimates the probability that an instance belongs to a particular class.
Decision Trees ->Splits data into subsets based on the value of input features. ->Easy to visualize and interpret but can be prone to overfitting.
Random Forest ->An ensemble method using multiple decision trees. ->Reduces overfitting and improves accuracy by averaging multiple trees.
Support Vector Machines (SVM) ->Finds the hyperplane that best separates different classes. ->Effective in high-dimensional spaces and for classification tasks.
k-Nearest Neighbors (k-NN) ->Classifies data based on the majority class among the k-nearest neighbors. ->Simple and intuitive but can be computationally intensive.
K-Means Clustering ->Partitions data into k clusters based on feature similarity. ->Useful for market segmentation, image compression, and more.
Naive Bayes ->Based on Bayes’ theorem with an assumption of independence among predictors. ->Particularly useful for text classification and spam filtering.
Neural Networks ->Mimic the human brain to identify patterns in data. ->Power deep learning applications, from image recognition to natural language processing.
Gradient Boosting Machines (GBM) ->Combines weak learners to create a strong predictive model. ->Used in various applications like ranking, classification, and regression.