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One of the most important part in Real estate business is variation of the land price over different factors. The above data has been collected over past few years in order to analyze the variation of price over different physical factors and predicting using a simple regression technique.

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Real Estate Analysis

Hello everyone!

In this notebook we start using ML algorithms to analyse a usual dataset. We are going to use Linear Regression method for create a model on dataset and then evaluate the results! 1_LEmBCYAttxS6uI6rEyPLMQ

In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.

If we have x feature for predicting y (target variable) linear regressions generate a Two-dimensional line equation and if we have n (more than 2) variables linear regressions generate a n-dimenshional line equation like this:

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We use Real Estate dataset to make a model that predict house prices based on 6 features. In the following, You can realize more about dataset.

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One of the most important part in Real estate business is variation of the land price over different factors. The above data has been collected over past few years in order to analyze the variation of price over different physical factors and predicting using a simple regression technique.

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