Boston-houses-prediction

This work contains a set of different models, that predict the price of Boston houses for Kaggle completion.

Short description of the dataset, that was used (https://www.kaggle.com/c/boston-housing):

Feature	Description	Type	Example
crim	per capita crime rate by town.	Numeric	0.00632
zn	proportion of residential land zoned for lots over 25,000 sq.ft.	Numeric	18
indus	proportion of non-retail business acres per town.	Numeric	2.31
chas	Charles River dummy variable	Boolean	0
nox	nitrogen oxides concentration (parts per 10 million).	Numeric	538
rm	average number of rooms per dwelling.	Numeric	6575
age	proportion of owner-occupied units built prior to 1940.	Numeric	65.2
dis	weighted mean of distances to five Boston employment centres.	Numeric	4.09
rad	index of accessibility to radial highways.	Numeric	1
tax	full-value property-tax rate per $10,000.	Numeric	296
ptratio	pupil-teacher ratio by town.	Numeric	15.3
black	1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town.	Numeric	396.9
lstat	lower status of the population (percent).	Numeric	4.98
medv	median value of owner-occupied homes in $1000s.	Numeric	24

Outcome binary-classes models metrics:

Model name	Accuracy	Precision	Recall	F-measure
Logistic regression	91 %	91 %	91 %	91 %
Bagged decision tree	90 %	90 %	90 %	90 %
KNN classifier with 9 NN	89 %	91 %	90 %	90 %
Random forest with 70 trees	90 %	89 %	90 %	89 %
Linear regression	82 %	91 %	88 %	89 %
KNN classifier with 3 NN	80 %	89 %	84 %	85 %
Decision tree	76 %	82 %	76 %	79 %

Outcome 10-classes models metrics:

Model name	Accuracy	Precision	Recall	F-measure
Random forest with 580 trees	58 %	72 %	58 %	62 %
Bagged decision tree	55 %	69 %	55 %	59 %
KNN classifier with 3 NN	50 %	71 %	48 %	56 %
Logistic regression	43 %	55 %	43 %	46 %
Linear regression	73 %	41 %	31 %	30 %
Decision tree	36 %	40 %	36 %	35 %

Outcome clustering models metrics: