Machine Learning Algorithms ⚙️

A repository to document the various machine learning algorithms and their applications in a detailed way

The file 'Machine Learning Algorithms.ipynb' contains the syntax and applications

The individual files contain a problem solved by the algorithm on a dataset

List of Algorithms

Linear Regression - Regression

1. Contains syntax of creating a Linear Regression Model and predicting using Linear Regression to predict prices of the Boston Housing Dataset and calculating the accuracy.
2. The application of the model using it to predict the salary based on the years of experience and understanding by plotting the actual and predicted values

Logistic Regression - Regression

1. Contains syntax of creating a Logistic Regression Model and predicting using Logistic Regression to predict the outcome of the Cancer Dataset
2. The application of the model using it to predict the outcome of whether the client will subscribe a term deposit of a bank and calculate the accuracy using some new methods such as classification report and confusion matrix

Decision Tree 1 - Iterative Dichotomiser 3 - Classification

1. Contains syntax of creating a Decision Tree Model (using entropy - ID3) and to predict the outcome of the Iris Dataset
2. The application of the algorithm (Iterative Dichotomiser 3 - ID3) using it to predict the type of iris based on entered values and visualizing the data and the Decision tree

Decision Tree 2 - CART (Classification & Regression Tree) - Classification & Regression

1. Contains syntax of creating a Decision Tree Model (using gini impurity) and predicting using Decision Tree to predict the outcome of the Iris Dataset
2. The application of the algorithm (CART - Classification & Regression Tree) using it to predict the type of iris based on entered values and visualizing the data and the Decision Tree

Gaussian Naive Bayes Algorithm - Classification

1. Contains syntax of creating a basic Gaussian Naive Bayes Model and predicting the outcome of the Iris dataset
2. The application of the Gaussian Naive Bayes Model and understanding how it predicts the type of iris and visualizing the data and calculating the accuracy

KMeans Clustering Algorithm - Clustering

1. Contains syntax of creating a basic KMeans Clustering Model and predicting the outcome of the penguins dataset
2. The application of the KMeans Clustering Model and understanding how it predicts the type of penguin and visualizing the data and calculating the accuracy using silhouette samples

KNN (K-Nearest Neighbours) Classifier - Classification

1. Contains syntax of creating a basic KNN(K-Nearest Neighbours) Classifier Model and predicting the outcome of the wine dataset
2. The application of the KNN(K-Nearest Neighbours) Classifier Model and understanding how it predicts the type of wine and visualizing the testing and training accuracy

KNN (K-Nearest Neighbours) Regressor - Classification

1. Contains syntax of creating a basic KNN (K-Nearest Neighbours) Regressor Model and predicting the outcome on a dataset created using make_regression.
2. The application of the KNN (K-Nearest Neighbours) Regressor Model and understanding how it predicts the price of a car based on the given values

Support Vector Classifier (SVC) - Classification

1. Contains syntax of creating a basic Support Vector Classifier (SVC) Model and predicting the outcome of the breast cancer dataset
2. The application of the Support Vector Classifier (SVC) Model and understanding how it predicts the type of iris and visualizing the data

Support Vector Regressor (SVR) - Regression

1. Contains syntax of creating a basic Support Vector Regressor (SVR) Model and predicting the outcome of the diabetes dataset
2. The application of the Support Vector Classifier (SVC) Model and understanding how it predicts the productivity of a worker based on the given features

Random Forest Classifier - Classification

1. Contains syntax of creating a basic Random Forest Classifier Model and predicting the outcome of the iris dataset
2. The application of the Random Forest Classifier Model and understanding how it predicts the type of penguin and visualizing the data

Gradient Boosting Classifier - Classification

1. Contains the syntax of creating a basic Gradient Boosting Classifier Model and predicting the outcome of the breast cancer dataset
2. The application of the Gradient Boosting Classifier Model and understanding how it predicts the outcome of the PIMA India Diabetes dataset

Gradient Boosting Regressor - Regression

1. Contains the syntax of creating a basic Gradient Boosting Regressor Model and predicting the outcome of a dataset created using the make_regression function of sklearn.datasets
2. The application of the Gradient Boosting Regressor Model and understanding how it predicts the price of a car based on the given values

Ada Boost Classifier - Classification

1. Contains the syntax of creating a basic Ada Boost Classifier Model and predicting the outcome on a dataset created using make_classification function of sklearn.dataset
2. The application of the Ada Boost Classifier Model and understanding how it predicts if a person will get a stroke or not based on the data provided

Ada Boost Regressor - Regression

1. Contains the syntax of creating a Ada Boost Regressor model and predicting the outcome on a dataset created using make_regression function of sklearn.dataset
2. The application of the Ada Boost Regressor Model and understanding how it predicts the productivity of a worker based on the given features