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Datasets:
misikoff
/
zillow

Tasks:
Tabular Regression
Time Series Forecasting
Languages:
English
License:
Dataset card Files Community
misikoff commited on
Commit
53fe7a6
•
1 Parent(s): c29711e

fix: update readme and move testing stuff into a different directory

Browse files
Files changed (4) hide show
  1. README.md +416 -7
  2. checker.ipynb +470 -219
  3. dataset_infos.json → test-stuff/dataset_infos.json +0 -0
  4. test.json → test-stuff/test.json +0 -0
README.md CHANGED
@@ -1,13 +1,422 @@
1
  ---
2
- pretty_name: "Zillow"
3
- license: "other"
4
  language:
5
- - en
 
6
  task_categories:
7
- - tabular-regression
8
- - time-series-forecasting
9
- description: "This dataset is comprised of seven different configurations of data covering different aspects of the housing market in the United States. All data is provided by Zillow. The seven configurations are: home_values_forecasts, new_construction, for_sale_listings, rentals, sales, home_values, and days_on_market. Each configuration has a different set of features and target variables. The data is provided in JSONL format."
10
- homepage: "https://www.zillow.com/research/data/"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
11
  ---
12
 
13
  # Housing Data Provided by Zillow (In Progress)
 
1
  ---
 
 
2
  language:
3
+ - en
4
+ license: other
5
  task_categories:
6
+ - tabular-regression
7
+ - time-series-forecasting
8
+ pretty_name: Zillow
9
+ description: 'This dataset is comprised of seven different configurations of data
10
+ covering different aspects of the housing market in the United States. All data
11
+ is provided by Zillow. The seven configurations are: home_values_forecasts, new_construction,
12
+ for_sale_listings, rentals, sales, home_values, and days_on_market. Each configuration
13
+ has a different set of features and target variables. The data is provided in JSONL
14
+ format.'
15
+ homepage: https://www.zillow.com/research/data/
16
+ dataset_info:
17
+ - config_name: days_on_market
18
+ features:
19
+ - name: Region ID
20
+ dtype: string
21
+ id: Region ID
22
+ - name: Size Rank
23
+ dtype: int32
24
+ id: Size Rank
25
+ - name: Region
26
+ dtype: string
27
+ id: Region
28
+ - name: Region Type
29
+ dtype:
30
+ class_label:
31
+ names:
32
+ '0': country
33
+ '1': msa
34
+ - name: State
35
+ dtype: string
36
+ id: State
37
+ - name: Home Type
38
+ dtype:
39
+ class_label:
40
+ names:
41
+ '0': SFR
42
+ '1': all homes (SFR + Condo)
43
+ - name: Date
44
+ dtype: string
45
+ id: Date
46
+ - name: Mean Listings Price Cut Amount (Smoothed)
47
+ dtype: float32
48
+ id: Mean Listings Price Cut Amount (Smoothed)
49
+ - name: Percent Listings Price Cut
50
+ dtype: float32
51
+ id: Percent Listings Price Cut
52
+ - name: Mean Listings Price Cut Amount
53
+ dtype: float32
54
+ id: Mean Listings Price Cut Amount
55
+ - name: Percent Listings Price Cut (Smoothed)
56
+ dtype: float32
57
+ id: Percent Listings Price Cut (Smoothed)
58
+ - name: Median Days on Pending (Smoothed)
59
+ dtype: float32
60
+ id: Median Days on Pending (Smoothed)
61
+ - name: Median Days on Pending
62
+ dtype: float32
63
+ id: Median Days on Pending
64
+ splits:
65
+ - name: train
66
+ num_bytes: 53627604
67
+ num_examples: 586714
68
+ download_size: 232641668
69
+ dataset_size: 53627604
70
+ - config_name: for_sale_listings
71
+ features:
72
+ - name: Region ID
73
+ dtype: string
74
+ id: Region ID
75
+ - name: Size Rank
76
+ dtype: int32
77
+ id: Size Rank
78
+ - name: Region
79
+ dtype: string
80
+ id: Region
81
+ - name: Region Type
82
+ dtype:
83
+ class_label:
84
+ names:
85
+ '0': country
86
+ '1': msa
87
+ - name: State
88
+ dtype: string
89
+ id: State
90
+ - name: Home Type
91
+ dtype:
92
+ class_label:
93
+ names:
94
+ '0': SFR
95
+ '1': all homes
96
+ - name: Date
97
+ dtype: string
98
+ id: Date
99
+ - name: Median Listing Price
100
+ dtype: float32
101
+ id: Median Listing Price
102
+ - name: Median Listing Price (Smoothed)
103
+ dtype: float32
104
+ id: Median Listing Price (Smoothed)
105
+ - name: New Listings
106
+ dtype: int32
107
+ id: New Listings
108
+ - name: New Listings (Smoothed)
109
+ dtype: int32
110
+ id: New Listings (Smoothed)
111
+ - name: New Pending (Smoothed)
112
+ dtype: int32
113
+ id: New Pending (Smoothed)
114
+ - name: New Pending
115
+ dtype: int32
116
+ id: New Pending
117
+ splits:
118
+ - name: train
119
+ num_bytes: 52884116
120
+ num_examples: 578653
121
+ download_size: 179627939
122
+ dataset_size: 52884116
123
+ - config_name: gem_data_split
124
+ features:
125
+ - name: gem_id
126
+ dtype: string
127
+ - name: id
128
+ dtype: string
129
+ - name: title
130
+ dtype: string
131
+ - name: context
132
+ dtype: string
133
+ - name: question
134
+ dtype: string
135
+ - name: target
136
+ dtype: string
137
+ - name: references
138
+ list: string
139
+ - name: answers
140
+ sequence:
141
+ - name: text
142
+ dtype: string
143
+ - name: answer_start
144
+ dtype: int32
145
+ splits:
146
+ - name: test
147
+ num_bytes: 14716686
148
+ num_examples: 13922
149
+ download_size: 159207580
150
+ dataset_size: 150788555
151
+ - config_name: home_values
152
+ features:
153
+ - name: Region ID
154
+ dtype: string
155
+ id: Region ID
156
+ - name: Size Rank
157
+ dtype: int32
158
+ id: Size Rank
159
+ - name: Region
160
+ dtype: string
161
+ id: Region
162
+ - name: Region Type
163
+ dtype:
164
+ class_label:
165
+ names:
166
+ '0': state
167
+ - name: State
168
+ dtype: string
169
+ id: State
170
+ - name: Home Type
171
+ dtype:
172
+ class_label:
173
+ names:
174
+ '0': all homes (SFR/condo)
175
+ '1': SFR
176
+ '2': condo
177
+ - name: Bedroom Count
178
+ dtype:
179
+ class_label:
180
+ names:
181
+ '0': 1-Bedroom
182
+ '1': 2-Bedrooms
183
+ '2': 3-Bedrooms
184
+ '3': 4-Bedrooms
185
+ '4': 5+-Bedrooms
186
+ '5': All Bedrooms
187
+ - name: Date
188
+ dtype: string
189
+ id: Date
190
+ - name: Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)
191
+ dtype: float32
192
+ id: Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)
193
+ - name: Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)
194
+ dtype: float32
195
+ id: Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)
196
+ - name: Top Tier ZHVI (Smoothed) (Seasonally Adjusted)
197
+ dtype: float32
198
+ id: Top Tier ZHVI (Smoothed) (Seasonally Adjusted)
199
+ splits:
200
+ - name: train
201
+ num_bytes: 10085231
202
+ num_examples: 117912
203
+ download_size: 42082517
204
+ dataset_size: 10085231
205
+ - config_name: home_values_forecasts
206
+ features:
207
+ - name: Region ID
208
+ dtype: string
209
+ id: Region ID
210
+ - name: Size Rank
211
+ dtype: int32
212
+ id: Size Rank
213
+ - name: Region
214
+ dtype: string
215
+ id: Region
216
+ - name: Region Type
217
+ dtype:
218
+ class_label:
219
+ names:
220
+ '0': zip
221
+ '1': country
222
+ '2': msa
223
+ - name: State
224
+ dtype: string
225
+ id: State
226
+ - name: City
227
+ dtype: string
228
+ id: City
229
+ - name: Metro
230
+ dtype: string
231
+ id: Metro
232
+ - name: County
233
+ dtype: string
234
+ id: County
235
+ - name: Date
236
+ dtype: string
237
+ id: Date
238
+ - name: Month Over Month % (Smoothed) (Seasonally Adjusted)
239
+ dtype: float32
240
+ id: Month Over Month % (Smoothed) (Seasonally Adjusted)
241
+ - name: Quarter Over Quarter % (Smoothed) (Seasonally Adjusted)
242
+ dtype: float32
243
+ id: Quarter Over Quarter % (Smoothed) (Seasonally Adjusted)
244
+ - name: Year Over Year % (Smoothed) (Seasonally Adjusted)
245
+ dtype: float32
246
+ id: Year Over Year % (Smoothed) (Seasonally Adjusted)
247
+ - name: Month Over Month %
248
+ dtype: float32
249
+ id: Month Over Month %
250
+ - name: Quarter Over Quarter %
251
+ dtype: float32
252
+ id: Quarter Over Quarter %
253
+ - name: Year Over Year %
254
+ dtype: float32
255
+ id: Year Over Year %
256
+ splits:
257
+ - name: train
258
+ num_bytes: 4167993
259
+ num_examples: 31854
260
+ download_size: 14050125
261
+ dataset_size: 4167993
262
+ - config_name: new_construction
263
+ features:
264
+ - name: Region ID
265
+ dtype: string
266
+ id: Region ID
267
+ - name: Size Rank
268
+ dtype: int32
269
+ id: Size Rank
270
+ - name: Region
271
+ dtype: string
272
+ id: Region
273
+ - name: Region Type
274
+ dtype:
275
+ class_label:
276
+ names:
277
+ '0': country
278
+ '1': msa
279
+ - name: State
280
+ dtype: string
281
+ id: State
282
+ - name: Home Type
283
+ dtype:
284
+ class_label:
285
+ names:
286
+ '0': SFR
287
+ '1': all homes
288
+ '2': condo/co-op only
289
+ - name: Date
290
+ dtype: string
291
+ id: Date
292
+ - name: Median Sale Price
293
+ dtype: float32
294
+ id: Median Sale Price
295
+ - name: Median Sale Price per Sqft
296
+ dtype: float32
297
+ id: Sale Price per Sqft
298
+ - name: Sales Count
299
+ dtype: int32
300
+ id: Sales Count
301
+ splits:
302
+ - name: train
303
+ num_bytes: 3921553
304
+ num_examples: 49487
305
+ download_size: 10903095
306
+ dataset_size: 3921553
307
+ - config_name: rentals
308
+ features:
309
+ - name: Region ID
310
+ dtype: string
311
+ id: Region ID
312
+ - name: Size Rank
313
+ dtype: int32
314
+ id: Size Rank
315
+ - name: Region
316
+ dtype: string
317
+ id: Region
318
+ - name: Region Type
319
+ dtype:
320
+ class_label:
321
+ names:
322
+ '0': county
323
+ '1': city
324
+ '2': zip
325
+ '3': country
326
+ '4': msa
327
+ - name: State
328
+ dtype: string
329
+ id: State
330
+ - name: Home Type
331
+ dtype:
332
+ class_label:
333
+ names:
334
+ '0': all homes plus multifamily
335
+ '1': SFR
336
+ '2': multifamily
337
+ - name: Date
338
+ dtype: string
339
+ id: Date
340
+ - name: Rent (Smoothed)
341
+ dtype: float32
342
+ id: Rent (Smoothed)
343
+ - name: Rent (Smoothed) (Seasonally Adjusted)
344
+ dtype: float32
345
+ id: Rent (Smoothed) (Seasonally Adjusted)
346
+ splits:
347
+ - name: train
348
+ num_bytes: 100467121
349
+ num_examples: 1258740
350
+ download_size: 446166329
351
+ dataset_size: 100467121
352
+ - config_name: sales
353
+ features:
354
+ - name: Region ID
355
+ dtype: string
356
+ id: Region ID
357
+ - name: Size Rank
358
+ dtype: int32
359
+ id: Size Rank
360
+ - name: Region
361
+ dtype: string
362
+ id: Region
363
+ - name: Region Type
364
+ dtype:
365
+ class_label:
366
+ names:
367
+ '0': country
368
+ '1': msa
369
+ - name: State
370
+ dtype: string
371
+ id: State
372
+ - name: Home Type
373
+ dtype:
374
+ class_label:
375
+ names:
376
+ '0': SFR
377
+ '1': all homes
378
+ - name: Date
379
+ dtype: string
380
+ id: Date
381
+ - name: Mean Sale to List Ratio (Smoothed)
382
+ dtype: float32
383
+ id: Mean Sale to List Ratio (Smoothed)
384
+ - name: Median Sale to List Ratio
385
+ dtype: float32
386
+ id: Median Sale to List Ratio
387
+ - name: Median Sale Price
388
+ dtype: float32
389
+ id: Median Sale Price
390
+ - name: Median Sale Price (Smoothed) (Seasonally Adjusted)
391
+ dtype: float32
392
+ id: Median Sale Price (Smoothed) (Seasonally Adjusted)
393
+ - name: Median Sale Price (Smoothed)
394
+ dtype: float32
395
+ id: Median Sale Price (Smoothed)
396
+ - name: Median Sale to List Ratio (Smoothed)
397
+ dtype: float32
398
+ id: Median Sale to List Ratio (Smoothed)
399
+ - name: '% Sold Below List'
400
+ dtype: float32
401
+ id: '% Sold Below List'
402
+ - name: '% Sold Below List (Smoothed)'
403
+ dtype: float32
404
+ id: '% Sold Below List (Smoothed)'
405
+ - name: '% Sold Above List'
406
+ dtype: float32
407
+ id: '% Sold Above List'
408
+ - name: '% Sold Above List (Smoothed)'
409
+ dtype: float32
410
+ id: '% Sold Above List (Smoothed)'
411
+ - name: Mean Sale to List Ratio
412
+ dtype: float32
413
+ id: Mean Sale to List Ratio
414
+ splits:
415
+ - name: train
416
+ num_bytes: 28618183
417
+ num_examples: 255024
418
+ download_size: 139042553
419
+ dataset_size: 28618183
420
  ---
421
 
422
  # Housing Data Provided by Zillow (In Progress)
checker.ipynb CHANGED
@@ -12,7 +12,7 @@
12
  },
13
  {
14
  "cell_type": "code",
15
- "execution_count": 27,
16
  "metadata": {},
17
  "outputs": [
18
  {
@@ -40,103 +40,127 @@
40
  " <th>Size Rank</th>\n",
41
  " <th>Region</th>\n",
42
  " <th>Region Type</th>\n",
43
- " <th>Home Type</th>\n",
44
  " <th>State</th>\n",
45
- " <th>Metro</th>\n",
46
- " <th>State Code FIPS</th>\n",
47
- " <th>Municipal Code FIPS</th>\n",
48
  " <th>Date</th>\n",
49
- " <th>Rent (Smoothed)</th>\n",
50
- " <th>Rent (Smoothed) (Seasonally Adjusted)</th>\n",
51
- " <th>City</th>\n",
52
- " <th>County</th>\n",
 
 
 
 
 
 
 
53
  " </tr>\n",
54
  " </thead>\n",
55
  " <tbody>\n",
56
  " <tr>\n",
57
  " <th>0</th>\n",
58
- " <td>66</td>\n",
59
- " <td>146</td>\n",
60
- " <td>Ada County</td>\n",
61
- " <td>county</td>\n",
62
- " <td>all homes plus multifamily</td>\n",
63
- " <td>Ada County</td>\n",
64
- " <td>Boise City, ID</td>\n",
65
- " <td>16.0</td>\n",
66
- " <td>1.0</td>\n",
67
- " <td>2015-01-31</td>\n",
68
- " <td>927.493763</td>\n",
69
- " <td>927.493763</td>\n",
70
  " <td>None</td>\n",
71
- " <td>Ada County</td>\n",
 
 
 
 
 
 
 
 
 
 
 
 
72
  " </tr>\n",
73
  " <tr>\n",
74
  " <th>1</th>\n",
75
- " <td>66</td>\n",
76
- " <td>146</td>\n",
77
- " <td>Ada County</td>\n",
78
- " <td>county</td>\n",
79
- " <td>all homes plus multifamily</td>\n",
80
- " <td>Ada County</td>\n",
81
- " <td>Boise City, ID</td>\n",
82
- " <td>16.0</td>\n",
83
- " <td>1.0</td>\n",
84
- " <td>2015-02-28</td>\n",
85
- " <td>931.690623</td>\n",
86
- " <td>931.690623</td>\n",
87
  " <td>None</td>\n",
88
- " <td>Ada County</td>\n",
 
 
 
 
 
 
 
 
 
 
 
 
89
  " </tr>\n",
90
  " <tr>\n",
91
  " <th>2</th>\n",
92
- " <td>66</td>\n",
93
- " <td>146</td>\n",
94
- " <td>Ada County</td>\n",
95
- " <td>county</td>\n",
96
- " <td>all homes plus multifamily</td>\n",
97
- " <td>Ada County</td>\n",
98
- " <td>Boise City, ID</td>\n",
99
- " <td>16.0</td>\n",
100
- " <td>1.0</td>\n",
101
- " <td>2015-03-31</td>\n",
102
- " <td>932.568601</td>\n",
103
- " <td>932.568601</td>\n",
104
  " <td>None</td>\n",
105
- " <td>Ada County</td>\n",
 
 
 
 
 
 
 
 
 
 
 
 
106
  " </tr>\n",
107
  " <tr>\n",
108
  " <th>3</th>\n",
109
- " <td>66</td>\n",
110
- " <td>146</td>\n",
111
- " <td>Ada County</td>\n",
112
- " <td>county</td>\n",
113
- " <td>all homes plus multifamily</td>\n",
114
- " <td>Ada County</td>\n",
115
- " <td>Boise City, ID</td>\n",
116
- " <td>16.0</td>\n",
117
- " <td>1.0</td>\n",
118
- " <td>2015-04-30</td>\n",
119
- " <td>933.148134</td>\n",
120
- " <td>933.148134</td>\n",
121
  " <td>None</td>\n",
122
- " <td>Ada County</td>\n",
 
 
 
 
 
 
 
 
 
 
 
 
123
  " </tr>\n",
124
  " <tr>\n",
125
  " <th>4</th>\n",
126
- " <td>66</td>\n",
127
- " <td>146</td>\n",
128
- " <td>Ada County</td>\n",
129
- " <td>county</td>\n",
130
- " <td>all homes plus multifamily</td>\n",
131
- " <td>Ada County</td>\n",
132
- " <td>Boise City, ID</td>\n",
133
- " <td>16.0</td>\n",
134
- " <td>1.0</td>\n",
135
- " <td>2015-05-31</td>\n",
136
- " <td>941.045724</td>\n",
137
- " <td>941.045724</td>\n",
138
  " <td>None</td>\n",
139
- " <td>Ada County</td>\n",
 
 
 
 
 
 
 
 
 
 
 
 
140
  " </tr>\n",
141
  " <tr>\n",
142
  " <th>...</th>\n",
@@ -154,189 +178,239 @@
154
  " <td>...</td>\n",
155
  " <td>...</td>\n",
156
  " <td>...</td>\n",
 
 
 
 
157
  " </tr>\n",
158
  " <tr>\n",
159
- " <th>1258735</th>\n",
160
- " <td>857850</td>\n",
161
- " <td>713</td>\n",
162
- " <td>Cherry Hill</td>\n",
163
- " <td>city</td>\n",
164
- " <td>all homes plus multifamily</td>\n",
165
- " <td>Camden County</td>\n",
166
- " <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
167
- " <td>NaN</td>\n",
168
- " <td>NaN</td>\n",
169
- " <td>2023-08-31</td>\n",
170
- " <td>2291.604800</td>\n",
171
- " <td>2244.961006</td>\n",
172
- " <td>Cherry Hill</td>\n",
173
- " <td>None</td>\n",
 
 
 
 
174
  " </tr>\n",
175
  " <tr>\n",
176
- " <th>1258736</th>\n",
177
- " <td>857850</td>\n",
178
- " <td>713</td>\n",
179
- " <td>Cherry Hill</td>\n",
180
- " <td>city</td>\n",
181
- " <td>all homes plus multifamily</td>\n",
182
- " <td>Camden County</td>\n",
183
- " <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
184
- " <td>NaN</td>\n",
185
- " <td>NaN</td>\n",
186
- " <td>2023-09-30</td>\n",
187
- " <td>2296.188906</td>\n",
188
- " <td>2254.213172</td>\n",
189
- " <td>Cherry Hill</td>\n",
190
- " <td>None</td>\n",
 
 
 
 
191
  " </tr>\n",
192
  " <tr>\n",
193
- " <th>1258737</th>\n",
194
- " <td>857850</td>\n",
195
- " <td>713</td>\n",
196
- " <td>Cherry Hill</td>\n",
197
- " <td>city</td>\n",
198
- " <td>all homes plus multifamily</td>\n",
199
- " <td>Camden County</td>\n",
200
- " <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
201
- " <td>NaN</td>\n",
202
- " <td>NaN</td>\n",
203
- " <td>2023-10-31</td>\n",
204
- " <td>2292.270938</td>\n",
205
- " <td>2261.540446</td>\n",
206
- " <td>Cherry Hill</td>\n",
207
- " <td>None</td>\n",
 
 
 
 
208
  " </tr>\n",
209
  " <tr>\n",
210
- " <th>1258738</th>\n",
211
- " <td>857850</td>\n",
212
- " <td>713</td>\n",
213
- " <td>Cherry Hill</td>\n",
214
- " <td>city</td>\n",
215
- " <td>all homes plus multifamily</td>\n",
216
- " <td>Camden County</td>\n",
217
- " <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
218
- " <td>NaN</td>\n",
219
- " <td>NaN</td>\n",
220
- " <td>2023-11-30</td>\n",
221
- " <td>2253.417140</td>\n",
222
- " <td>2257.956024</td>\n",
223
- " <td>Cherry Hill</td>\n",
224
- " <td>None</td>\n",
 
 
 
 
225
  " </tr>\n",
226
  " <tr>\n",
227
- " <th>1258739</th>\n",
228
- " <td>857850</td>\n",
229
- " <td>713</td>\n",
230
- " <td>Cherry Hill</td>\n",
231
- " <td>city</td>\n",
232
- " <td>all homes plus multifamily</td>\n",
233
- " <td>Camden County</td>\n",
234
- " <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
235
- " <td>NaN</td>\n",
236
- " <td>NaN</td>\n",
237
- " <td>2023-12-31</td>\n",
238
- " <td>2280.830303</td>\n",
239
- " <td>2280.830303</td>\n",
240
- " <td>Cherry Hill</td>\n",
241
- " <td>None</td>\n",
 
 
 
 
242
  " </tr>\n",
243
  " </tbody>\n",
244
  "</table>\n",
245
- "<p>1258740 rows × 14 columns</p>\n",
246
  "</div>"
247
  ],
248
  "text/plain": [
249
- " Region ID Size Rank Region Region Type \\\n",
250
- "0 66 146 Ada County county \n",
251
- "1 66 146 Ada County county \n",
252
- "2 66 146 Ada County county \n",
253
- "3 66 146 Ada County county \n",
254
- "4 66 146 Ada County county \n",
255
- "... ... ... ... ... \n",
256
- "1258735 857850 713 Cherry Hill city \n",
257
- "1258736 857850 713 Cherry Hill city \n",
258
- "1258737 857850 713 Cherry Hill city \n",
259
- "1258738 857850 713 Cherry Hill city \n",
260
- "1258739 857850 713 Cherry Hill city \n",
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
261
  "\n",
262
- " Home Type State \\\n",
263
- "0 all homes plus multifamily Ada County \n",
264
- "1 all homes plus multifamily Ada County \n",
265
- "2 all homes plus multifamily Ada County \n",
266
- "3 all homes plus multifamily Ada County \n",
267
- "4 all homes plus multifamily Ada County \n",
268
- "... ... ... \n",
269
- "1258735 all homes plus multifamily Camden County \n",
270
- "1258736 all homes plus multifamily Camden County \n",
271
- "1258737 all homes plus multifamily Camden County \n",
272
- "1258738 all homes plus multifamily Camden County \n",
273
- "1258739 all homes plus multifamily Camden County \n",
274
  "\n",
275
- " Metro State Code FIPS \\\n",
276
- "0 Boise City, ID 16.0 \n",
277
- "1 Boise City, ID 16.0 \n",
278
- "2 Boise City, ID 16.0 \n",
279
- "3 Boise City, ID 16.0 \n",
280
- "4 Boise City, ID 16.0 \n",
281
- "... ... ... \n",
282
- "1258735 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
283
- "1258736 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
284
- "1258737 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
285
- "1258738 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
286
- "1258739 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
287
  "\n",
288
- " Municipal Code FIPS Date Rent (Smoothed) \\\n",
289
- "0 1.0 2015-01-31 927.493763 \n",
290
- "1 1.0 2015-02-28 931.690623 \n",
291
- "2 1.0 2015-03-31 932.568601 \n",
292
- "3 1.0 2015-04-30 933.148134 \n",
293
- "4 1.0 2015-05-31 941.045724 \n",
294
- "... ... ... ... \n",
295
- "1258735 NaN 2023-08-31 2291.604800 \n",
296
- "1258736 NaN 2023-09-30 2296.188906 \n",
297
- "1258737 NaN 2023-10-31 2292.270938 \n",
298
- "1258738 NaN 2023-11-30 2253.417140 \n",
299
- "1258739 NaN 2023-12-31 2280.830303 \n",
300
  "\n",
301
- " Rent (Smoothed) (Seasonally Adjusted) City County \n",
302
- "0 927.493763 None Ada County \n",
303
- "1 931.690623 None Ada County \n",
304
- "2 932.568601 None Ada County \n",
305
- "3 933.148134 None Ada County \n",
306
- "4 941.045724 None Ada County \n",
307
- "... ... ... ... \n",
308
- "1258735 2244.961006 Cherry Hill None \n",
309
- "1258736 2254.213172 Cherry Hill None \n",
310
- "1258737 2261.540446 Cherry Hill None \n",
311
- "1258738 2257.956024 Cherry Hill None \n",
312
- "1258739 2280.830303 Cherry Hill None \n",
313
  "\n",
314
- "[1258740 rows x 14 columns]"
315
  ]
316
  },
317
- "execution_count": 27,
318
  "metadata": {},
319
  "output_type": "execute_result"
320
  }
321
  ],
322
  "source": [
323
  "# read the data\n",
324
- "x = pd.read_json(\"processed/rentals/final5.jsonl\", lines=True)\n",
325
  "x"
326
  ]
327
  },
328
  {
329
  "cell_type": "code",
330
- "execution_count": 28,
331
  "metadata": {},
332
  "outputs": [
333
  {
334
  "data": {
335
  "text/plain": [
336
- "array(['county', 'city', 'zip', 'country', 'msa'], dtype=object)"
337
  ]
338
  },
339
- "execution_count": 28,
340
  "metadata": {},
341
  "output_type": "execute_result"
342
  }
@@ -348,16 +422,16 @@
348
  },
349
  {
350
  "cell_type": "code",
351
- "execution_count": 29,
352
  "metadata": {},
353
  "outputs": [
354
  {
355
  "data": {
356
  "text/plain": [
357
- "array(['all homes plus multifamily', 'SFR', 'multifamily'], dtype=object)"
358
  ]
359
  },
360
- "execution_count": 29,
361
  "metadata": {},
362
  "output_type": "execute_result"
363
  }
@@ -386,6 +460,183 @@
386
  "source": [
387
  "x[\"Bedroom Count\"].unique()"
388
  ]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
389
  }
390
  ],
391
  "metadata": {
 
12
  },
13
  {
14
  "cell_type": "code",
15
+ "execution_count": 30,
16
  "metadata": {},
17
  "outputs": [
18
  {
 
40
  " <th>Size Rank</th>\n",
41
  " <th>Region</th>\n",
42
  " <th>Region Type</th>\n",
 
43
  " <th>State</th>\n",
44
+ " <th>Home Type</th>\n",
 
 
45
  " <th>Date</th>\n",
46
+ " <th>Median Sale to List Ratio</th>\n",
47
+ " <th>Median Sale Price</th>\n",
48
+ " <th>Median Sale Price (Smoothed) (Seasonally Adjusted)</th>\n",
49
+ " <th>Median Sale Price (Smoothed)</th>\n",
50
+ " <th>% Sold Below List (Smoothed)</th>\n",
51
+ " <th>Median Sale to List Ratio (Smoothed)</th>\n",
52
+ " <th>% Sold Above List</th>\n",
53
+ " <th>Mean Sale to List Ratio (Smoothed)</th>\n",
54
+ " <th>Mean Sale to List Ratio</th>\n",
55
+ " <th>% Sold Below List</th>\n",
56
+ " <th>% Sold Above List (Smoothed)</th>\n",
57
  " </tr>\n",
58
  " </thead>\n",
59
  " <tbody>\n",
60
  " <tr>\n",
61
  " <th>0</th>\n",
62
+ " <td>102001</td>\n",
63
+ " <td>0</td>\n",
64
+ " <td>United States</td>\n",
65
+ " <td>country</td>\n",
 
 
 
 
 
 
 
 
66
  " <td>None</td>\n",
67
+ " <td>SFR</td>\n",
68
+ " <td>2008-02-02</td>\n",
69
+ " <td>NaN</td>\n",
70
+ " <td>172000.0</td>\n",
71
+ " <td>NaN</td>\n",
72
+ " <td>NaN</td>\n",
73
+ " <td>NaN</td>\n",
74
+ " <td>NaN</td>\n",
75
+ " <td>NaN</td>\n",
76
+ " <td>NaN</td>\n",
77
+ " <td>NaN</td>\n",
78
+ " <td>NaN</td>\n",
79
+ " <td>NaN</td>\n",
80
  " </tr>\n",
81
  " <tr>\n",
82
  " <th>1</th>\n",
83
+ " <td>102001</td>\n",
84
+ " <td>0</td>\n",
85
+ " <td>United States</td>\n",
86
+ " <td>country</td>\n",
 
 
 
 
 
 
 
 
87
  " <td>None</td>\n",
88
+ " <td>SFR</td>\n",
89
+ " <td>2008-02-09</td>\n",
90
+ " <td>NaN</td>\n",
91
+ " <td>165400.0</td>\n",
92
+ " <td>NaN</td>\n",
93
+ " <td>NaN</td>\n",
94
+ " <td>NaN</td>\n",
95
+ " <td>NaN</td>\n",
96
+ " <td>NaN</td>\n",
97
+ " <td>NaN</td>\n",
98
+ " <td>NaN</td>\n",
99
+ " <td>NaN</td>\n",
100
+ " <td>NaN</td>\n",
101
  " </tr>\n",
102
  " <tr>\n",
103
  " <th>2</th>\n",
104
+ " <td>102001</td>\n",
105
+ " <td>0</td>\n",
106
+ " <td>United States</td>\n",
107
+ " <td>country</td>\n",
 
 
 
 
 
 
 
 
108
  " <td>None</td>\n",
109
+ " <td>SFR</td>\n",
110
+ " <td>2008-02-16</td>\n",
111
+ " <td>NaN</td>\n",
112
+ " <td>168000.0</td>\n",
113
+ " <td>NaN</td>\n",
114
+ " <td>NaN</td>\n",
115
+ " <td>NaN</td>\n",
116
+ " <td>NaN</td>\n",
117
+ " <td>NaN</td>\n",
118
+ " <td>NaN</td>\n",
119
+ " <td>NaN</td>\n",
120
+ " <td>NaN</td>\n",
121
+ " <td>NaN</td>\n",
122
  " </tr>\n",
123
  " <tr>\n",
124
  " <th>3</th>\n",
125
+ " <td>102001</td>\n",
126
+ " <td>0</td>\n",
127
+ " <td>United States</td>\n",
128
+ " <td>country</td>\n",
 
 
 
 
 
 
 
 
129
  " <td>None</td>\n",
130
+ " <td>SFR</td>\n",
131
+ " <td>2008-02-23</td>\n",
132
+ " <td>NaN</td>\n",
133
+ " <td>167600.0</td>\n",
134
+ " <td>NaN</td>\n",
135
+ " <td>167600.0</td>\n",
136
+ " <td>NaN</td>\n",
137
+ " <td>NaN</td>\n",
138
+ " <td>NaN</td>\n",
139
+ " <td>NaN</td>\n",
140
+ " <td>NaN</td>\n",
141
+ " <td>NaN</td>\n",
142
+ " <td>NaN</td>\n",
143
  " </tr>\n",
144
  " <tr>\n",
145
  " <th>4</th>\n",
146
+ " <td>102001</td>\n",
147
+ " <td>0</td>\n",
148
+ " <td>United States</td>\n",
149
+ " <td>country</td>\n",
 
 
 
 
 
 
 
 
150
  " <td>None</td>\n",
151
+ " <td>SFR</td>\n",
152
+ " <td>2008-03-01</td>\n",
153
+ " <td>NaN</td>\n",
154
+ " <td>168100.0</td>\n",
155
+ " <td>NaN</td>\n",
156
+ " <td>168100.0</td>\n",
157
+ " <td>NaN</td>\n",
158
+ " <td>NaN</td>\n",
159
+ " <td>NaN</td>\n",
160
+ " <td>NaN</td>\n",
161
+ " <td>NaN</td>\n",
162
+ " <td>NaN</td>\n",
163
+ " <td>NaN</td>\n",
164
  " </tr>\n",
165
  " <tr>\n",
166
  " <th>...</th>\n",
 
178
  " <td>...</td>\n",
179
  " <td>...</td>\n",
180
  " <td>...</td>\n",
181
+ " <td>...</td>\n",
182
+ " <td>...</td>\n",
183
+ " <td>...</td>\n",
184
+ " <td>...</td>\n",
185
  " </tr>\n",
186
  " <tr>\n",
187
+ " <th>255019</th>\n",
188
+ " <td>845160</td>\n",
189
+ " <td>198</td>\n",
190
+ " <td>Prescott Valley, AZ</td>\n",
191
+ " <td>msa</td>\n",
192
+ " <td>AZ</td>\n",
193
+ " <td>all homes</td>\n",
194
+ " <td>2023-11-11</td>\n",
195
+ " <td>0.985132</td>\n",
196
+ " <td>515000.0</td>\n",
197
+ " <td>480020.0</td>\n",
198
+ " <td>480020.0</td>\n",
199
+ " <td>0.651221</td>\n",
200
+ " <td>0.982460</td>\n",
201
+ " <td>0.080000</td>\n",
202
+ " <td>0.978546</td>\n",
203
+ " <td>0.983288</td>\n",
204
+ " <td>0.680000</td>\n",
205
+ " <td>0.119711</td>\n",
206
  " </tr>\n",
207
  " <tr>\n",
208
+ " <th>255020</th>\n",
209
+ " <td>845160</td>\n",
210
+ " <td>198</td>\n",
211
+ " <td>Prescott Valley, AZ</td>\n",
212
+ " <td>msa</td>\n",
213
+ " <td>AZ</td>\n",
214
+ " <td>all homes</td>\n",
215
+ " <td>2023-11-18</td>\n",
216
+ " <td>0.972559</td>\n",
217
+ " <td>510000.0</td>\n",
218
+ " <td>476901.0</td>\n",
219
+ " <td>476901.0</td>\n",
220
+ " <td>0.659583</td>\n",
221
+ " <td>0.980362</td>\n",
222
+ " <td>0.142857</td>\n",
223
+ " <td>0.972912</td>\n",
224
+ " <td>0.958341</td>\n",
225
+ " <td>0.625000</td>\n",
226
+ " <td>0.120214</td>\n",
227
  " </tr>\n",
228
  " <tr>\n",
229
+ " <th>255021</th>\n",
230
+ " <td>845160</td>\n",
231
+ " <td>198</td>\n",
232
+ " <td>Prescott Valley, AZ</td>\n",
233
+ " <td>msa</td>\n",
234
+ " <td>AZ</td>\n",
235
+ " <td>all homes</td>\n",
236
+ " <td>2023-11-25</td>\n",
237
+ " <td>0.979644</td>\n",
238
+ " <td>484500.0</td>\n",
239
+ " <td>496540.0</td>\n",
240
+ " <td>496540.0</td>\n",
241
+ " <td>0.669387</td>\n",
242
+ " <td>0.979179</td>\n",
243
+ " <td>0.088235</td>\n",
244
+ " <td>0.971177</td>\n",
245
+ " <td>0.973797</td>\n",
246
+ " <td>0.705882</td>\n",
247
+ " <td>0.107185</td>\n",
248
  " </tr>\n",
249
  " <tr>\n",
250
+ " <th>255022</th>\n",
251
+ " <td>845160</td>\n",
252
+ " <td>198</td>\n",
253
+ " <td>Prescott Valley, AZ</td>\n",
254
+ " <td>msa</td>\n",
255
+ " <td>AZ</td>\n",
256
+ " <td>all homes</td>\n",
257
+ " <td>2023-12-02</td>\n",
258
+ " <td>0.978261</td>\n",
259
+ " <td>538000.0</td>\n",
260
+ " <td>510491.0</td>\n",
261
+ " <td>510491.0</td>\n",
262
+ " <td>0.678777</td>\n",
263
+ " <td>0.978899</td>\n",
264
+ " <td>0.126761</td>\n",
265
+ " <td>0.970576</td>\n",
266
+ " <td>0.966876</td>\n",
267
+ " <td>0.704225</td>\n",
268
+ " <td>0.109463</td>\n",
269
  " </tr>\n",
270
  " <tr>\n",
271
+ " <th>255023</th>\n",
272
+ " <td>845160</td>\n",
273
+ " <td>198</td>\n",
274
+ " <td>Prescott Valley, AZ</td>\n",
275
+ " <td>msa</td>\n",
276
+ " <td>AZ</td>\n",
277
+ " <td>all homes</td>\n",
278
+ " <td>2023-12-09</td>\n",
279
+ " <td>0.981498</td>\n",
280
+ " <td>485000.0</td>\n",
281
+ " <td>503423.0</td>\n",
282
+ " <td>503423.0</td>\n",
283
+ " <td>0.658777</td>\n",
284
+ " <td>0.977990</td>\n",
285
+ " <td>0.100000</td>\n",
286
+ " <td>0.970073</td>\n",
287
+ " <td>0.981278</td>\n",
288
+ " <td>0.600000</td>\n",
289
+ " <td>0.114463</td>\n",
290
  " </tr>\n",
291
  " </tbody>\n",
292
  "</table>\n",
293
+ "<p>255024 rows × 18 columns</p>\n",
294
  "</div>"
295
  ],
296
  "text/plain": [
297
+ " Region ID Size Rank Region Region Type State \\\n",
298
+ "0 102001 0 United States country None \n",
299
+ "1 102001 0 United States country None \n",
300
+ "2 102001 0 United States country None \n",
301
+ "3 102001 0 United States country None \n",
302
+ "4 102001 0 United States country None \n",
303
+ "... ... ... ... ... ... \n",
304
+ "255019 845160 198 Prescott Valley, AZ msa AZ \n",
305
+ "255020 845160 198 Prescott Valley, AZ msa AZ \n",
306
+ "255021 845160 198 Prescott Valley, AZ msa AZ \n",
307
+ "255022 845160 198 Prescott Valley, AZ msa AZ \n",
308
+ "255023 845160 198 Prescott Valley, AZ msa AZ \n",
309
+ "\n",
310
+ " Home Type Date Median Sale to List Ratio Median Sale Price \\\n",
311
+ "0 SFR 2008-02-02 NaN 172000.0 \n",
312
+ "1 SFR 2008-02-09 NaN 165400.0 \n",
313
+ "2 SFR 2008-02-16 NaN 168000.0 \n",
314
+ "3 SFR 2008-02-23 NaN 167600.0 \n",
315
+ "4 SFR 2008-03-01 NaN 168100.0 \n",
316
+ "... ... ... ... ... \n",
317
+ "255019 all homes 2023-11-11 0.985132 515000.0 \n",
318
+ "255020 all homes 2023-11-18 0.972559 510000.0 \n",
319
+ "255021 all homes 2023-11-25 0.979644 484500.0 \n",
320
+ "255022 all homes 2023-12-02 0.978261 538000.0 \n",
321
+ "255023 all homes 2023-12-09 0.981498 485000.0 \n",
322
+ "\n",
323
+ " Median Sale Price (Smoothed) (Seasonally Adjusted) \\\n",
324
+ "0 NaN \n",
325
+ "1 NaN \n",
326
+ "2 NaN \n",
327
+ "3 NaN \n",
328
+ "4 NaN \n",
329
+ "... ... \n",
330
+ "255019 480020.0 \n",
331
+ "255020 476901.0 \n",
332
+ "255021 496540.0 \n",
333
+ "255022 510491.0 \n",
334
+ "255023 503423.0 \n",
335
  "\n",
336
+ " Median Sale Price (Smoothed) % Sold Below List (Smoothed) \\\n",
337
+ "0 NaN NaN \n",
338
+ "1 NaN NaN \n",
339
+ "2 NaN NaN \n",
340
+ "3 167600.0 NaN \n",
341
+ "4 168100.0 NaN \n",
342
+ "... ... ... \n",
343
+ "255019 480020.0 0.651221 \n",
344
+ "255020 476901.0 0.659583 \n",
345
+ "255021 496540.0 0.669387 \n",
346
+ "255022 510491.0 0.678777 \n",
347
+ "255023 503423.0 0.658777 \n",
348
  "\n",
349
+ " Median Sale to List Ratio (Smoothed) % Sold Above List \\\n",
350
+ "0 NaN NaN \n",
351
+ "1 NaN NaN \n",
352
+ "2 NaN NaN \n",
353
+ "3 NaN NaN \n",
354
+ "4 NaN NaN \n",
355
+ "... ... ... \n",
356
+ "255019 0.982460 0.080000 \n",
357
+ "255020 0.980362 0.142857 \n",
358
+ "255021 0.979179 0.088235 \n",
359
+ "255022 0.978899 0.126761 \n",
360
+ "255023 0.977990 0.100000 \n",
361
  "\n",
362
+ " Mean Sale to List Ratio (Smoothed) Mean Sale to List Ratio \\\n",
363
+ "0 NaN NaN \n",
364
+ "1 NaN NaN \n",
365
+ "2 NaN NaN \n",
366
+ "3 NaN NaN \n",
367
+ "4 NaN NaN \n",
368
+ "... ... ... \n",
369
+ "255019 0.978546 0.983288 \n",
370
+ "255020 0.972912 0.958341 \n",
371
+ "255021 0.971177 0.973797 \n",
372
+ "255022 0.970576 0.966876 \n",
373
+ "255023 0.970073 0.981278 \n",
374
  "\n",
375
+ " % Sold Below List % Sold Above List (Smoothed) \n",
376
+ "0 NaN NaN \n",
377
+ "1 NaN NaN \n",
378
+ "2 NaN NaN \n",
379
+ "3 NaN NaN \n",
380
+ "4 NaN NaN \n",
381
+ "... ... ... \n",
382
+ "255019 0.680000 0.119711 \n",
383
+ "255020 0.625000 0.120214 \n",
384
+ "255021 0.705882 0.107185 \n",
385
+ "255022 0.704225 0.109463 \n",
386
+ "255023 0.600000 0.114463 \n",
387
  "\n",
388
+ "[255024 rows x 18 columns]"
389
  ]
390
  },
391
+ "execution_count": 30,
392
  "metadata": {},
393
  "output_type": "execute_result"
394
  }
395
  ],
396
  "source": [
397
  "# read the data\n",
398
+ "x = pd.read_json(\"processed/sales/final5.jsonl\", lines=True)\n",
399
  "x"
400
  ]
401
  },
402
  {
403
  "cell_type": "code",
404
+ "execution_count": 33,
405
  "metadata": {},
406
  "outputs": [
407
  {
408
  "data": {
409
  "text/plain": [
410
+ "array(['country', 'msa'], dtype=object)"
411
  ]
412
  },
413
+ "execution_count": 33,
414
  "metadata": {},
415
  "output_type": "execute_result"
416
  }
 
422
  },
423
  {
424
  "cell_type": "code",
425
+ "execution_count": 32,
426
  "metadata": {},
427
  "outputs": [
428
  {
429
  "data": {
430
  "text/plain": [
431
+ "array(['SFR', 'all homes'], dtype=object)"
432
  ]
433
  },
434
+ "execution_count": 32,
435
  "metadata": {},
436
  "output_type": "execute_result"
437
  }
 
460
  "source": [
461
  "x[\"Bedroom Count\"].unique()"
462
  ]
463
+ },
464
+ {
465
+ "cell_type": "code",
466
+ "execution_count": 34,
467
+ "metadata": {},
468
+ "outputs": [
469
+ {
470
+ "name": "stderr",
471
+ "output_type": "stream",
472
+ "text": [
473
+ "/Users/misikoff/opt/anaconda3/envs/sta663/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
474
+ " from .autonotebook import tqdm as notebook_tqdm\n"
475
+ ]
476
+ }
477
+ ],
478
+ "source": [
479
+ "from datasets import load_dataset"
480
+ ]
481
+ },
482
+ {
483
+ "cell_type": "code",
484
+ "execution_count": 35,
485
+ "metadata": {},
486
+ "outputs": [
487
+ {
488
+ "name": "stdout",
489
+ "output_type": "stream",
490
+ "text": [
491
+ "home_values_forecasts\n"
492
+ ]
493
+ },
494
+ {
495
+ "name": "stderr",
496
+ "output_type": "stream",
497
+ "text": [
498
+ "Downloading builder script: 100%|██████████| 26.8k/26.8k [00:00<00:00, 15.9MB/s]\n",
499
+ "Downloading readme: 100%|██████████| 13.2k/13.2k [00:00<00:00, 19.4MB/s]\n",
500
+ "Downloading data: 100%|██████████| 14.1M/14.1M [00:01<00:00, 11.0MB/s]\n",
501
+ "Generating train split: 31854 examples [00:01, 27592.53 examples/s]\n"
502
+ ]
503
+ },
504
+ {
505
+ "name": "stdout",
506
+ "output_type": "stream",
507
+ "text": [
508
+ "new_construction\n"
509
+ ]
510
+ },
511
+ {
512
+ "name": "stderr",
513
+ "output_type": "stream",
514
+ "text": [
515
+ "Downloading builder script: 100%|██████████| 26.8k/26.8k [00:00<00:00, 7.13MB/s]\n",
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+ "Downloading readme: 100%|██████████| 13.2k/13.2k [00:00<00:00, 10.4MB/s]\n",
517
+ "Downloading data: 100%|██████████| 10.9M/10.9M [00:01<00:00, 5.63MB/s]\n",
518
+ "Generating train split: 49487 examples [00:01, 42744.19 examples/s]\n"
519
+ ]
520
+ },
521
+ {
522
+ "name": "stdout",
523
+ "output_type": "stream",
524
+ "text": [
525
+ "for_sale_listings\n"
526
+ ]
527
+ },
528
+ {
529
+ "name": "stderr",
530
+ "output_type": "stream",
531
+ "text": [
532
+ "Downloading builder script: 100%|██████████| 26.8k/26.8k [00:00<00:00, 5.22MB/s]\n",
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+ "Downloading readme: 100%|██████████| 13.2k/13.2k [00:00<00:00, 4.72MB/s]\n",
534
+ "Downloading data: 100%|██████████| 180M/180M [00:06<00:00, 28.9MB/s] \n",
535
+ "Generating train split: 578653 examples [00:16, 34651.20 examples/s]\n"
536
+ ]
537
+ },
538
+ {
539
+ "name": "stdout",
540
+ "output_type": "stream",
541
+ "text": [
542
+ "rentals\n"
543
+ ]
544
+ },
545
+ {
546
+ "name": "stderr",
547
+ "output_type": "stream",
548
+ "text": [
549
+ "Downloading builder script: 100%|██████████| 26.8k/26.8k [00:00<00:00, 6.18MB/s]\n",
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+ "Downloading readme: 100%|██████████| 13.2k/13.2k [00:00<00:00, 13.3MB/s]\n",
551
+ "Downloading data: 100%|██████████| 446M/446M [00:11<00:00, 38.2MB/s] \n",
552
+ "Generating train split: 1258740 examples [00:28, 44189.96 examples/s]\n"
553
+ ]
554
+ },
555
+ {
556
+ "name": "stdout",
557
+ "output_type": "stream",
558
+ "text": [
559
+ "sales\n"
560
+ ]
561
+ },
562
+ {
563
+ "name": "stderr",
564
+ "output_type": "stream",
565
+ "text": [
566
+ "Downloading builder script: 100%|██████████| 26.8k/26.8k [00:00<00:00, 12.7MB/s]\n",
567
+ "Downloading readme: 100%|██████████| 13.2k/13.2k [00:00<00:00, 13.5MB/s]\n",
568
+ "Downloading data: 100%|██████████| 139M/139M [00:04<00:00, 34.2MB/s] \n",
569
+ "Generating train split: 255024 examples [00:09, 26686.54 examples/s]\n"
570
+ ]
571
+ },
572
+ {
573
+ "name": "stdout",
574
+ "output_type": "stream",
575
+ "text": [
576
+ "home_values\n"
577
+ ]
578
+ },
579
+ {
580
+ "name": "stderr",
581
+ "output_type": "stream",
582
+ "text": [
583
+ "Downloading builder script: 100%|██████████| 26.8k/26.8k [00:00<00:00, 2.18MB/s]\n",
584
+ "Downloading readme: 100%|██████████| 13.2k/13.2k [00:00<00:00, 8.86MB/s]\n",
585
+ "Downloading data: 100%|██████████| 42.1M/42.1M [00:01<00:00, 32.4MB/s]\n",
586
+ "Generating train split: 117912 examples [00:03, 37382.19 examples/s]\n"
587
+ ]
588
+ },
589
+ {
590
+ "name": "stdout",
591
+ "output_type": "stream",
592
+ "text": [
593
+ "days_on_market\n"
594
+ ]
595
+ },
596
+ {
597
+ "name": "stderr",
598
+ "output_type": "stream",
599
+ "text": [
600
+ "Downloading builder script: 100%|██████████| 26.8k/26.8k [00:00<00:00, 12.7MB/s]\n",
601
+ "Downloading readme: 100%|██████████| 13.2k/13.2k [00:00<00:00, 8.86MB/s]\n",
602
+ "Downloading data: 100%|██████████| 233M/233M [00:06<00:00, 34.9MB/s] \n",
603
+ "Generating train split: 586714 examples [00:17, 34104.98 examples/s]\n"
604
+ ]
605
+ }
606
+ ],
607
+ "source": [
608
+ "dataset_dict = {}\n",
609
+ "\n",
610
+ "configs = [\n",
611
+ " \"home_values_forecasts\",\n",
612
+ " \"new_construction\",\n",
613
+ " \"for_sale_listings\",\n",
614
+ " \"rentals\",\n",
615
+ " \"sales\",\n",
616
+ " \"home_values\",\n",
617
+ " \"days_on_market\",\n",
618
+ "]\n",
619
+ "for config in configs:\n",
620
+ " print(config)\n",
621
+ " dataset_dict[config] = load_dataset(\n",
622
+ " \"misikoff/zillow\",\n",
623
+ " config,\n",
624
+ " trust_remote_code=True,\n",
625
+ " download_mode=\"force_redownload\",\n",
626
+ " cache_dir=\"~/desktop/cache\",\n",
627
+ " )"
628
+ ]
629
+ },
630
+ {
631
+ "cell_type": "code",
632
+ "execution_count": null,
633
+ "metadata": {},
634
+ "outputs": [],
635
+ "source": [
636
+ "df = pd.read_feather(\n",
637
+ " \"~/desktop/cache/misikoff___zillow/sales/1.1.0/c70d9545e9cef7612b795e19b5393a565f297e17856ab372df6f4026ecc498ae/zillow-train.arrow\"\n",
638
+ ")"
639
+ ]
640
  }
641
  ],
642
  "metadata": {
dataset_infos.json → test-stuff/dataset_infos.json RENAMED
File without changes
test.json → test-stuff/test.json RENAMED
File without changes