House Price Prediction Machine Learning is a type of artificial intelligence that.enables self-learning from data and then applies that learning without the need for human intervention.Machine Learning is considered a subset of artificial.intelligence that is mainly concerned with the development of algorithms that allow a computer to learn from the data and past experiences.Arthur Samuel first introduced the term machine learning in 1959 applicatons Online Fraud Detection Virtual Personal Assistant Email Spam Self-driving Cars Product Recommendations Image Recognition Traffic Prediction Automatic Language Translation Social Media Features Sentiment Analysis Banking Domain Videos Surveillance Types of Machine Learning Supervised Learning, Unsupervised Learning, Semi Supervised Learning Supervised Learning: • Supervised Learning is the type of machine learning in which machines are trained using well "labelled" training data, and based on that data, machines predict the output. • The labelled data means some input data is already tagged with the correct output. Types of Supervised Learning 1)Classification The key objective of classification based tasks is to predict categorial output labels or responses for the given input data. Support Vector Machines Logistic Regression Random Forest Decision Trees 2)Regression The key objective of regression-based tasks is to predict output labels or continuous numeric responses for the given input data. Linear Regression Non-Linear Regression Regression Trees Polynomial Regression Bayesian Linear Regression Unsupervised Learning • Unsupervised learning is a type of machine learning in which models are trained using an unlabeled dataset and are allowed to act on that data without any supervision. • There are no labels or correct outputs. • The task is to discover the data structure: for example, grouping similar items to form “clusters” or reducing the data to a small number of important “dimensions”. • Data visualisation can also be considered unsupervised learning. Types of Unsupervised Learning 1)Clustering • Clustering is a method of grouping the objects into clusters.Objects with the most similarities remain in a group, and objects with less or no similarities remain in another group. K-Means Clustering Hierarchal Clustering Fuzzy K-Means Mixture of Gaussians Probabilistic Clustering Exclusive Clustering Overlapping Clustering
2)Association An association rule is an unsupervised learning method used to find the relationships between variables in the large database. • A typical example of the Association rule is Market Basket Analysis. Apriori Algorithm Eclat Algorithm F-P Growth Algorithm Semi-Supervised Learning • Semi-supervised learning is an approach to machine learning that combines a small amount of labelled data with a large amount of unlabeled data during training to create suitable ml model for prediction house price prediction, given dataset,using train_test_spilt using