This is a repo where I place some common data science algorithms and explainations of how they are used.
As a data scientist, it is important to have a strong understanding of the key algorithms that are commonly used in the field. Here are some of the most important algorithms that you should be familiar with as a data scientist:
Linear Regression: Linear regression is a supervised learning algorithm used for regression problems. It models the relationship between a dependent variable and one or more independent variables. It is a simple and widely used algorithm for understanding the relationship between variables and making predictions.
Logistic Regression: Logistic regression is another type of regression algorithm that is used for classification problems. Unlike linear regression, which outputs continuous values, logistic regression outputs binary values (i.e., 0 or 1) to represent the class of the target variable.
Decision Trees: Decision trees are a type of algorithm used for both classification and regression problems. They work by recursively splitting the data into smaller and smaller subsets based on the values of the input features. Each split results in a new node in the tree, and the final nodes represent the predictions.
Random Forests: Random forests are an extension of decision trees that use an ensemble of trees to make predictions. The idea is to create many decision trees and average their predictions to produce a more robust and accurate model.
Support Vector Machines (SVMs): SVMs are a type of algorithm used for classification problems. They work by finding the hyperplane that best separates the data into two classes. The hyperplane is chosen such that it maximizes the margin between the two classes, resulting in a model that is robust and less likely to overfit the data.
k-Nearest Neighbors (k-NN): k-NN is a simple and widely used algorithm for classification and regression problems. It works by finding the k closest data points to a given test sample and using their class labels or values to make a prediction.
Naive Bayes: Naive Bayes is a probabilistic algorithm used for classification problems. It works by calculating the probability of each class given the input features, and choosing the class with the highest probability as the prediction.
Gradient Boosting: Gradient boosting is a type of algorithm used for both classification and regression problems. It works by combining many weak decision trees to produce a strong model. The algorithm adjusts the weights of the trees at each iteration to improve the performance of the model.
Neural Networks: Neural networks are a type of machine learning algorithm inspired by the structure and function of the human brain. They are used for a wide range of problems, including image classification, natural language processing, and recommendation systems.
K-Means Clustering: K-Means is a type of unsupervised learning algorithm used for clustering problems. It works by partitioning the data into k clusters, where each cluster is represented by its centroid (i.e., the mean of the data points in the cluster).
These are some of the key algorithms that every data scientist should know. However, there are many more algorithms available, and the choice of algorithm will depend on the specific problem you are trying to solve. It's important to have a good understanding of the strengths and weaknesses of each algorithm so that you can choose the right one for your problem.
- ChaptGPT Examples
- Decision Trees
- Gradient Boosting
- K Nearest Neighbors
- K Means Clustering
- Linear Regression
- Logistic Regression
- Naive Bayes
- Neural Networks
- Support Vector Machine