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94 changes: 35 additions & 59 deletions README.md
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After learned solutions by others,especially Little Boat,simple but effective!!amazing!

I spend a lot of time FE,and submit more times then Faron,Little Boat et.
# Kaggle Rethop Solution Ranking 1st

I'll thanks KazAnova who public the leak,otherwise I can't do this better.You are my idol.
## score: *Public*:0.50379 *Private*:0.50500

My English is poor,if I write confused,please tell me,many thanks.haha..

**FE:**
## Features Enginnering

I split the base features two classes:

manager:created,description,price,et

building:bathrooms,bedrooms,latitude,longitude,display_address,featuers,photos,et
1. manager:created,description,price,et

Then I link and compare them one by one.
2. building:bathrooms,bedrooms,latitude,longitude,display_address,featuers,photos,et

My best single model at: https://github.com/plantsgo/Rental-Listing-Inquiries
## Files

score:Public:0.50379 Private:0.50500
* *the jpgs.json* is the shape of each photos.

you should add train.json and test.json in the folder.
* *listing_image_time.csv* is the leak @KazAnova said.

the jpgs.json is the shape of each photos.
## How to Use

the listing_image_time.csv is the leak @KazAnova said.
1. *sigma.py* to create the csv file.

1.run sigma.py to create the csv file.
2. *script.py* to create the features what @gdy5 show.

2.run script.py to create the features what @gdy5 show.
3. *feature_tt.py* to create the base features.

3.run feature_tt.py to create the base features.
4. *feature_tt_long.py* to create four features which spend about four hours...but I have generate already which named timeout.csv,so you can skip it....

4.run feature_tt_long.py to create four features which spend long time,about four hours...but I have give it which named timeout.csv,so you can skip it....
5. *xgb.py* and will create the last result.

5.run xgb.py and will create the last result.
## Transform

**Transform：**
1. X

1.X
2. log10（X+1)

2.log10（X+1)
## Ensemble

My best nn model is log10(X+1) score LB:0.535 before add magic feature.
My best nn model is log10(X+1) score *LB:0.535* before add magic feature.

Ensemble:
Level 2:
### Level 2:

I have 4 datasets:

1.My best single model.

2.some features which not improve at my best model ,but can improve at model with base features.

3.@gdy5 's kernel with some of my features.

4.@Branden Murrayit 's kernel add some of my features.

①:each dateset I used [xgb,nn,gb,rf,et,lr,xgb_reg,lgb_reg,nn_reg] cv flod=5
1. My best single model.

the reg model have a good importance in my model.

②:and I merge high and medium level ,then userd[lgb,nn,lgb_reg,nn_reg,et,rf] in my best dataset. cv flod=5

③:[nn,nn_reg,xgb,gb,rf,et,lr,xgb_reg]@last three datasets cv flod=5
2. some features which not improve at my best model ,but can improve at model with base features.

④:[nn,nn_reg,xgb,gb,rf,et,lr,xgb_reg]add magic feature @last three datasets cv flod=5
3. @gdy5 's kernel with some of my features.

⑤:[nn,nn_reg,xgb,knn,gb,rf,et,lr,ada_reg,rf_reg,gb_reg,et_reg,xgb_reg]@last three datasets cv flod=10
4. @Branden Murrayit 's kernel add some of my features.


Level 3:
Each dateset I used [xgb,nn,gb,rf,et,lr,xgb_reg,lgb_reg,nn_reg] cv fold=5

1.user ①,②,③,④ as metefeatures with xgb,nn,et.

with a feature from description,Classify the source by description:
begin with " "

CooperCooper.com
the reg model have a good importance in my model.

p a website_redacted

li /ul /p
### Level 3:

it improved at public but turn bad at pravate.Maybe can remove it.
1. 1,2,3 metefeatures with xgb,nn,et.

pre=((xgb^0.65)*(nn^0.35))*0.85+et*0.15
then userd @weiwei 's Prior correction. but only improved 0.00001-0.00002
*pre=((xgb^0.65)*(nn^0.35))*0.85+et*0.15*
then userd @weiwei 's Prior correction. but only improved 0.00001-0.00002

2.user ①,②,⑤ as metefeatures with xgb,nn,et.
pre=((xgb^0.65)*(nn^0.35))*0.85+et*0.15
2. 1,2,3,4 metefeatures with xgb,nn,et.
*pre=((xgb^0.65)*(nn^0.35))*0.85+et*0.15*

Level 4:
50/50 average level 3
### Level 4:
50/50 average level 3


**Last,Thanks all shares,I learned many from the kernels and discussions.**
##*Last,Thanks all shares,I learned many from the kernels and discussions.*



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