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In statistics, linear regression is a linear approach to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). The case of one explanatory variable is called simple linear regression. There are two types of supervised machine learning algorithms: Regression and classification. The former predicts continuous value outputs while the latter predicts discrete outputs
The term “linearity” in algebra refers to a linear relationship between two or more variables. If we draw this relationship in a two-dimensional space (between two variables), we get a straight line. Linear regression performs the task to predict a dependent variable value (y) based on a given independent variable (x). So, this regression technique finds out a linear relationship between x (input) and y(output). Hence, the name is Linear Regression.
•In this task we will use K nearest algorithm and K means Clusteringto make prediction and understand its working.!
The k-Nearest Neighbors algorithm or KNN for short is a very simple technique. The entire training dataset is stored. When a prediction is required, the k-most similar records to a new record from the training dataset are then located. From these neighbors, a summarized prediction is made.
K-Means Clustering is an unsupervised machine learning algorithm. We graph the relationship between the number of clusters and Within Cluster Sum of Squares (WCSS) then we select the number of clusters where the change in WCSS begins to level off (elbow method).
Perform ‘Exploratory Data Analysis’ on dataset ‘Indian Premier League’
As a sports analysts, find out the most successful teams, players and factors contributing win or loss of a team.
Suggest teams or players a company should endorse for its products.
Perform ‘Exploratory Data Analysis’ on dataset ‘Indian Premier League’
As a sports analysts, find out the most successful teams, players and factors contributing win or loss of a team.
Suggest teams or players a company should endorse for its products.
•In this task we will use decision tree to make prediction and understand its working.!
A decision tree is a flowchart-like structure in which each internal node represents a "test" on an attribute (e.g. whether a coin flip comes up heads or tails), each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes).