Raghavendra Choudhary

Prediction using Supervised ML

First Question comes to mind What is the Supervised Machine Learning (ML) ?

Supervised learning, also known as supervised machine learning, is a subcategory of machine learning and artificial intelligence.

It is defined by its use of labeled datasets to train algorithms that to classify data or predict outcomes accurately. As input data is fed into the model, it adjusts its weights until the model has been fitted appropriately, which occurs as part of the cross validation process.

Supervised learning helps organizations solve for a variety of real-world problems at scale, such as classifying spam in a separate folder from your inbox.

Example of Supervised Learning Algorithms:

Linear Regression
Logistic Regression
Nearest Neighbor
Gaussian Naive Bayes
Decision Trees
Support Vector Machine (SVM)
Random Forest

So We are using the Linear Regression for the Assignment .

So What is Linear Regression ?

Linear regression is used to identify the relationship between a dependent variable and one or more independent variables and is typically leveraged to make predictions about future outcomes.

When there is only one independent variable and one dependent variable, it is known as simple linear regression.

As the number of independent variables increases, it is referred to as multiple linear regression.

For each type of linear regression, it seeks to plot a line of best fit, which is calculated through the method of least squares. However, unlike other regression models, this line is straight when plotted on a graph.

Equation Of Linear Regression :-

Y = M*X + C