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Dataset | Count | Parameter | Descriptor | |
---|---|---|---|---|
Apartments | 15k | N | Airbnb | |
Restaurants | 15k | M | DOH | |
Subway Stations | 300 | M | MTA | |
Hotspots | 10 | M | Red Diamonds | |
Cost Epicenter | 1 | M | Orange Star |
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Feature | Relative Importance | |
---|---|---|
accommodates | 1.0 | |
accommodates _per_bathrooms | -0.428 | |
accommodates _per_bedrooms | -0.28 | |
food_density | 0.248 | |
is_brooklyn | -0.231 | |
center_dist | -0.102 | |
density | -0.086 |
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accommodates | accommodates _per_bathrooms | accommodates _per_bedrooms | accommodates bedrooms | center_dist _is_brooklyn | food_density | food_density _is_brooklyn | is_brooklyn | ||
---|---|---|---|---|---|---|---|---|---|
All NYC | 34 | -14 | -9 | -4 | -6 | 8 | 16 | -9 | |
Manhattan | 51 | -15 | -13 | -13 | - | 9 | - | - | |
Brooklyn | 43 | -19 | -16 | -7 | - | 16 | - | - | |
Linear SVM | 25 | -11 | -7 | -6 | -1 | 3 | 21 | -25 | |
XGBoost | 7 | 1 | 1 | 38 | 29 | 22 | 1 | 0 | |
AdaBoost | 4 | 1 | 0 | 25 | 14 | 51 | 4 | 0 | |
Extra Forest | 6 | 4 | 4 | 8 | 16 | 45 | 11 | 7 | |
Random Forest | 2 | 1 | 2 | 26 | 11 | 56 | 3 | 0 |
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cur_data = price_data.copy() | |
cur_data = cur_data.merge(rental_data, on="id") | |
cur_data = cur_data.merge(location_data, on="id") | |
cur_data = cur_data.merge(geo_data, on="id") | |
X_train, X_test, y_train, y_test = \ | |
custom_train_test_split(cur_data) | |
pass_cols = ["is_brooklyn", "density"] | |
drop_cols = ["year", "geometry", "zipcode"] |
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<img src="img/missing-image.png" onerror="javascript:this.src='img/default.png'"> |
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global_colors = Dict( | |
"aliceblue" => [240, 248, 255], | |
"antiquewhite" => [250, 235, 215], | |
"aqua" => [0, 255, 255], | |
"aquamarine" => [127, 255, 212], | |
"azure" => [240, 255, 255], | |
"beige" => [245, 245, 220], | |
"bisque" => [255, 228, 196], | |
"black" => [0, 0, 0], | |
"blanchedalmond" => [255, 235, 205], |
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