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