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Predicting-house-prices-using-Linear-Regression

PROBLEM STATEMENT

  • Problem Statement – A real state agents want help to predict the house price for various regions. He gave you the dataset to work on and you decided to use the Linear Regression Model. Create a model that can accurately predict the price of a house, given the values of all variables.

INTRODUCTION

  • Linear Regression is a Supervised Machine Learning Model for finding the relationship between independent variables and dependent variable.
  • Linear regression performs the task to predict the response (dependent) variable value (y) based on a given (independent) explanatory variable (x). So, this regression technique finds out a linear relationship between x (input) and y (output).

FINAL REGRESSION EQUATION AND OUTPUT

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IMPORTANT VARIABLES

  • Crime rate
  • Air quality
  • Average no. of rooms in houses of that locality
  • Number of teachers per thousand population in the town
  • Proportion of poor population in town
  • Availability of airport in the city
  • Average distance from employment hub