Multiple-Linear-Regression-Multivariate-Quantile-Regression-Ridge-Regression-Lasso-Regression-and

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Multiple-Linear-Regression-Multivariate-Quantile-Regression-Ridge-Regression-Lasso-Regression-and

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Multiple Linear Regression ,Multivariate Quantile Regression, Ridge Regression, Lasso Regression and Elastic Net Regression

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# Multiple Linear Regression ,Multivariate Quantile Regression, Ridge Regression, Lasso Regression and Elastic Net Regression
**Problem statement:**  Hass Consulting Company which is a real estate leader with over 25 years of experience. You have been tasked to study the factors that affect housing prices using the given information on real estate properties that was collected over the past few months. Later onwards, create a model that would allow the company to accurately predict the sale prices. 
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  <img src="image16.jpg" width="900" height="473" align="center">
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# Description
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An interrogation of the characteristics of the models on the coefficients is performed.The  performance of Each models is obtained in terms of MSE is obtained,
The best performing model is optimized in the problem solution
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# Context
The project code implements the following multivariate  regression techniques to predict house price:
* Multiple Linear Regression 
* Multivariate Quantile Regression
* Ridge Regression 
* Lasso Regression 
* Elastic Net Regression
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The [dataset](https://github.com/abel-keya/week_7_IP_Abel_Keya_hass_consulting-_company_with_quantile_and__regularization_based_regression_tech/blob/master/Independent%20Project%20Week%207%20-%20house_data.csv) has the following features:
# Data Features
* Id
* price - Price of the house
* bedrooms - Number of Bedrooms
* bathrooms - Number of Bathrooms
* sqft_living - Square feet area of living area
* sqft_lot - Square feet area of parking Layout
* floors - Number of Floors
* waterfront - Whether waterfront is there or not
* view - Number of Views
* grade - Grades
* sqft_above
* sqft_basement - Square feet area off basement
* yr_built - Year the house is built
* yr_renovated - Year the house is renovated
* zipcode - zipcode os the house
* lat : Latitude of the house
* lon : Longitude of the house
* sqft_living15
* sqft_lot15
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# Requirements
* Anaconda installation
* Google colab
* Setup instruction
> * Save a copy of the notebook in your drive and open it to access.
# Technologies used
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  <img src="DSrequirement.jpg" width="850" height="273" align="center" alt="DS" title="Requirements" />
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# Support
In case of any clarifications or suggestions with regards to this project email me at jumakeya@gmail.com

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