I am doing a prediction of house trade money.
Here is the correlation matrix of features whose correlations are larger than 0.3 as follows:
train_corr_full.unstack().sort_values(kind='quicksort', ascending=False).reset_index() corr = corr[(corr['level_0'] != corr['level_1'])] corr level_0 level_1 0 13 pv uv 0.911879 14 uv pv 0.911879 15 area bathroom 0.721935 16 bathroom area 0.721935 17 area tradeMoney 0.687447 18 tradeMoney area 0.687447 19 bathroom tradeMoney 0.580745 20 tradeMoney bathroom 0.580745 21 bathroom room 0.421762 22 room bathroom 0.421762 23 remainNewNum totalNewTradeMoney 0.417114 24 totalNewTradeMoney remainNewNum 0.417114 25 buildYear totalFloor 0.393571 26 totalFloor buildYear 0.393571 27 remainNewNum tradeMeanPrice 0.314611 28 tradeMeanPrice remainNewNum 0.314611 corr[corr['level_0'] == 'tradeMoney'] level_0 level_1 0 18 tradeMoney area 0.687447 20 tradeMoney bathroom 0.580745 34 tradeMoney tradeMeanPrice 0.282720 45 tradeMoney totalFloor 0.249755 47 tradeMoney tradeNewMeanPrice 0.236713 55 tradeMoney room 0.215041 79 tradeMoney buildYear 0.123065 81 tradeMoney totalTradeMoney 0.122407 95 tradeMoney remainNewNum 0.100921 116 tradeMoney pv 0.072919 134 tradeMoney uv 0.040452 137 tradeMoney totalNewTradeMoney 0.038420
area: the area of the house.
room: the number of the room in this house.
bathroom: the number of the bathroom in this house.
totalFloor: total number of floors of this building.
tradeMeanPrice: the mean price of second-hand housing transaction price this month.
tradeNewMeanPrice: the mean price of new housing transaction price this month.
buildYear: the age of this house.
totalTradeMoney: the total price of second-hand housing transaction price this month.
totalNewTradeMoney: the total price of the new housing transaction price this month.
remainNewNum: the number of houses that haven't been sold this month.
pv: the number of times that the website was viewed by tenants.
uv: the number of tenants that viewed the website.
It shows that there is a high correlation with
uv but both of which has a low correlation with the target.
What I did before is that I would drop the features whose correlation is lower than about 0.15 and do more EDAs and feature engineerings in those high correlations feature.
And even if consider context I also think the
uv is not good enough for the prediction.
Could anyone give some hints on it?
Thanks in advance.