multi-regression-and-valuation-model's Introduction

Multi-Regression-and-Valuation-Model

Home Price Predictions Model, Train Split Testing, LinearRegression, Seaborn, Plotly, and Matplotlib. Day 80 Python Learning

Welcome to Boston Massachusetts in the 1970s! Imagine you're working for a real estate development company. Your company wants to value any residential project before they start. You are tasked with building a model that can provide a price estimate based on a home's characteristics like:

The number of rooms
The distance to employment centres
How rich or poor the area is
How many students there are per teacher in local schools etc

Understand the Boston House Price Dataset

Characteristics:

:Number of Instances: 506 

:Number of Attributes: 13 numeric/categorical predictive. The Median Value (attribute 14) is the target.

:Attribute Information (in order):
    1. CRIM     per capita crime rate by town
    2. ZN       proportion of residential land zoned for lots over 25,000 sq.ft.
    3. INDUS    proportion of non-retail business acres per town
    4. CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
    5. NOX      nitric oxides concentration (parts per 10 million)
    6. RM       average number of rooms per dwelling
    7. AGE      proportion of owner-occupied units built prior to 1940
    8. DIS      weighted distances to five Boston employment centres
    9. RAD      index of accessibility to radial highways
    10. TAX      full-value property-tax rate per $10,000
    11. PTRATIO  pupil-teacher ratio by town
    12. B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
    13. LSTAT    % lower status of the population
    14. PRICE     Median value of owner-occupied homes in $1000's
    
:Missing Attribute Values: None

:Creator: Harrison, D. and Rubinfeld, D.L.

This is a copy of UCI ML housing dataset. This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. You can find the original research paper here.

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