Linear Regressions

How to run Linear regression in Python scikit-Learn

Scikit-learn is a strong Python machine learning tool. Regression, classification, clustering, model selection, and data preprocessing are all functions included. “Methods intended for regression in which the target value is supposed to be a linear combination of the input variables” are found in the sklearn.linear model package.

we can import scikit-learn library As:

import sklearn

• with scikit-learn library we can predict the output according to the data provided. for that we need to import Linear Regressions. we can do it as:

from sklearn.linear_model import LinearRegression

Important functions to keep in mind while fitting a linear regression model are:

lm.fit() -> fits a linear model

lm.predict() -> Predict Y using the linear model with estimated coefficients

lm.score() -> Returns the coefficient of determination (R^2). A measure of how well observed outcomes are replicated by the model, as the proportion of total variation of outcomes explained by the model.

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Linear Regression in Python

• What Is Regression? Simple linear regression is an approach for predicting a response using a single feature. It is assumed that the two variables are linearly related.

• When Do You Need Regression? to answer whether and how some phenomenon influences the other or how several variables are related. For example, you can use it to determine if and to what extent the experience or gender impact salaries.

• Linear Regression Linear regression is probably one of the most important and widely used regression techniques. It’s among the simplest regression methods. One of its main advantages is the ease of interpreting results.