examples/housing_price_prediction.deepnote
Rama main · réplica de lectura del repositorio original.
environment:
python:
version: '3.11.0'
packages:
pandas: '2.1.0'
numpy: '1.26.0'
scikit-learn: '1.3.0'
matplotlib: '3.8.0'
metadata:
createdAt: "2025-11-03T10:06:25.909Z"
modifiedAt: "2025-11-04T11:28:28.354Z"
project:
id: 524a81cf-7a4c-492c-be20-408cca8fd75b
name: 2_housing_prices_prediction
initNotebookId: a5356b1e77b34793a815faa71e75aad5
notebooks:
- blocks:
- blockGroup: b4d73fbb4ab44563a34f2d1251cfd5e2
content: |-
## Initialization Notebook
This is your initialization notebook.
**What's this for?**
You can put custom code you want us to run every time we setup your environment in here.
**Awesome! Anything I should not put in here?**
Please don't install `jupyter` or `jedi` packages, they would break your Deepnote environment. Also, no need to put `!pip install`s here, we already save those automatically!
**I want to learn more!**
Great! Just [head over to our docs](https://deepnote.com/docs/project-initialization).
id: a4d73fbb4ab44563a34f2d1251cfd5e2
metadata: {}
sortingKey: a0
type: markdown
- blockGroup: c8c0deee42454742961d5d623385af0c
content: |-
%%bash
# If your project has a 'requirements.txt' file, we'll install it here.
if test -f requirements.txt
then
pip install -r ./requirements.txt
else echo "There's no requirements.txt, so nothing to install."
fi
id: a8c0deee42454742961d5d623385af0c
metadata:
execution_start: 1759911417964
execution_millis: 3008
execution_context_id: 2cd84c6a-d7a9-46cf-a8a3-9bdee70d6c26
sortingKey: m
type: code
executionCount: 4
outputs: []
executionMode: block
id: a5356b1e77b34793a815faa71e75aad5
isModule: false
name: Init
- blocks:
- blockGroup: 1be438185453495caece9aad31ac2086
content: Housing Price Prediction Demo
id: 2c02cb54fe77f190fb93eafa64acd46f
metadata:
formattedRanges: []
sortingKey: a0
type: text-cell-h1
- blockGroup: 808a3f1913934d5cbdf27ba93c3337a7
content: In this notebook we generate synthetic housing data and use it to train a price prediction model.
id: 48625a220f6246489ad5eb5a18358f61
metadata:
formattedRanges: []
sortingKey: D
type: text-cell-p
- blockGroup: 437e267ccecd46fc93bf0ddebc496b35
content: |-
# Importing libraries
import numpy as np
import pandas as pd
from pathlib import Path
id: 0fecd816c4774a94b483b36d0c4458b4
metadata:
execution_start: 1762164828331
execution_millis: 2
execution_context_id: 74476248-3f22-4f61-bf4b-0790bf71c9b0
sortingKey: I
type: code
executionCount: 1
outputs: []
- blockGroup: 98230a7a1544458492a0221e4617a651
content: |-
# Random number generation
rng = np.random.default_rng(17)
id: 87cd42344c68449a9f48384507bd155f
metadata:
execution_start: 1762164898659
execution_millis: 0
execution_context_id: 74476248-3f22-4f61-bf4b-0790bf71c9b0
sortingKey: R
type: code
executionCount: 10
outputs: []
- blockGroup: e6e997ed94e147818d28f41d6f17adfe
content: |-
# This means we are creating a dataset with 300 entries or rows.
n = 300
id: 37e66ec28ab74b84b8bb703b29e2509a
metadata:
execution_start: 1762164899960
execution_millis: 0
execution_context_id: 74476248-3f22-4f61-bf4b-0790bf71c9b0
sortingKey: V
type: code
executionCount: 13
outputs: []
- blockGroup: 38253fb8dfc4461c9db1b0d3f81077fe
content: |-
size_sqft = rng.uniform(350, 3500, size=n) # 350 to 3500 square feet
age_years = rng.uniform(0, 80, size=n) # 0 to 80 years old
beds = rng.integers(1, 6, size=n) # 1 to 5 bedrooms
baths = np.clip(beds + rng.normal(0.0, 0.6, size=n), 1, None) # 1 to 5 bathrooms
baths = np.round(baths * 2) / 2 # round to nearest half
dist_city_km = rng.gamma(shape=2.0, scale=4.0, size=n) # gamma distribution
has_garage = rng.random(n) < 0.55 # 55% have a garage
floor = rng.integers(0, 12, size=n) # 0 to 12 floors
id: 390be651a5d34490ba3dcffdbec6c302
metadata:
execution_start: 1762164902842
execution_millis: 1
execution_context_id: 74476248-3f22-4f61-bf4b-0790bf71c9b0
sortingKey: X
type: code
executionCount: 16
outputs: []
- blockGroup: 59f5fa0c64854ec5a5061dedcb865be9
content: |-
# Market effects (USD)
# Base price + 180 USD/ft² + (beds,baths) premiums - age depreciation - distance penalty + noise
base = 60_000
price_true = (
base
+ 180 * size_sqft
+ 12_000 * beds
+ 8_000 * baths
- 1_100 * age_years
- 2_500 * np.log1p(dist_city_km) # diminishing penalty by distance
+ 6_000 * has_garage.astype(int)
+ 1_200 * floor
+ rng.normal(0, 45_000, size=n) # market noise
)
# Keep prices reasonable and positive
price_true = np.clip(price_true, 40_000, None)
df_csv = pd.DataFrame({
"size_sqft": size_sqft,
"age_years": age_years,
"beds": beds,
"baths": baths,
"dist_city_km": dist_city_km,
"has_garage": has_garage.astype(int), # 0/1
"floor": floor,
"price_usd": price_true
})
id: 59f5fa0c64854ec5a5061dedcb865be9
metadata:
execution_start: 1762164919866
execution_millis: 2
execution_context_id: 74476248-3f22-4f61-bf4b-0790bf71c9b0
sortingKey: a0
type: code
executionCount: 19
outputs: []
- blockGroup: 48fce827cafd434fb919e016dd678852
content: df_csv
id: c81d9b8712774c68b96e9cd075ede87f
metadata:
execution_start: 1762164928369
execution_millis: 1
execution_context_id: 74476248-3f22-4f61-bf4b-0790bf71c9b0
sortingKey: m
type: code
executionCount: 22
outputs:
- data:
text/html: |-
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<th>...</th>
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<td>...</td>
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<td>...</td>
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<p>300 rows × 8 columns</p>
</div>
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text/plain: |2-
size_sqft age_years beds baths dist_city_km has_garage floor \
0 3011.985597 50.726792 5 5.5 2.631902 1 1
1 857.065237 30.281690 5 4.5 6.583170 0 11
2 2106.895324 53.778601 5 6.0 3.780098 1 6
3 1509.451816 59.536168 1 1.5 3.016834 0 0
4 1027.067185 57.537558 3 3.0 9.326278 1 7
.. ... ... ... ... ... ... ...
295 1398.629565 16.089740 1 1.0 3.379627 1 3
296 3458.153920 49.157737 5 4.5 7.299976 1 5
297 2423.124765 13.743865 4 3.5 2.090731 1 6
298 3255.327884 21.417663 5 4.5 9.126624 1 4
299 1045.412903 70.484253 4 4.5 4.211601 0 7
price_usd
0 670269.961426
1 265318.093025
2 605300.572210
3 364601.524773
4 229267.771995
.. ...
295 344537.835850
296 784651.168874
297 535543.668982
298 717094.142698
299 182502.213011
[300 rows x 8 columns]
execution_count: 6
output_type: execute_result
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execution_start: 1762164928369
execution_millis: 1
execution_context_id: 74476248-3f22-4f61-bf4b-0790bf71c9b0
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content: |-
# Optional: round for nicer CSV
df_csv = df_csv.round({
"size_sqft": 1,
"age_years": 1,
"baths": 1,
"dist_city_km": 2,
"price_usd": 0
})
id: 8b2999bf4fdb4bf5b6bcc159ec5c519f
metadata:
execution_start: 1762164942644
execution_millis: 0
execution_context_id: 74476248-3f22-4f61-bf4b-0790bf71c9b0
sortingKey: s
type: code
executionCount: 25
outputs: []
- blockGroup: 64e43cc200ed47d6bc6a3c759733a482
content: df_csv
id: 440cdf8daede40ec996763840703a80a
metadata:
execution_start: 1762164951911
execution_millis: 17
execution_context_id: 74476248-3f22-4f61-bf4b-0790bf71c9b0
sortingKey: t
type: code
executionCount: 28
outputs:
- data:
text/html: |-
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>size_sqft</th>
<th>age_years</th>
<th>beds</th>
<th>baths</th>
<th>dist_city_km</th>
<th>has_garage</th>
<th>floor</th>
<th>price_usd</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>3012.0</td>
<td>50.7</td>
<td>5</td>
<td>5.5</td>
<td>2.63</td>
<td>1</td>
<td>1</td>
<td>670270.0</td>
</tr>
<tr>
<th>1</th>
<td>857.1</td>
<td>30.3</td>
<td>5</td>
<td>4.5</td>
<td>6.58</td>
<td>0</td>
<td>11</td>
<td>265318.0</td>
</tr>
<tr>
<th>2</th>
<td>2106.9</td>
<td>53.8</td>
<td>5</td>
<td>6.0</td>
<td>3.78</td>
<td>1</td>
<td>6</td>
<td>605301.0</td>
</tr>
<tr>
<th>3</th>
<td>1509.5</td>
<td>59.5</td>
<td>1</td>
<td>1.5</td>
<td>3.02</td>
<td>0</td>
<td>0</td>
<td>364602.0</td>
</tr>
<tr>
<th>4</th>
<td>1027.1</td>
<td>57.5</td>
<td>3</td>
<td>3.0</td>
<td>9.33</td>
<td>1</td>
<td>7</td>
<td>229268.0</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>295</th>
<td>1398.6</td>
<td>16.1</td>
<td>1</td>
<td>1.0</td>
<td>3.38</td>
<td>1</td>
<td>3</td>
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<tr>
<th>296</th>
<td>3458.2</td>
<td>49.2</td>
<td>5</td>
<td>4.5</td>
<td>7.30</td>
<td>1</td>
<td>5</td>
<td>784651.0</td>
</tr>
<tr>
<th>297</th>
<td>2423.1</td>
<td>13.7</td>
<td>4</td>
<td>3.5</td>
<td>2.09</td>
<td>1</td>
<td>6</td>
<td>535544.0</td>
</tr>
<tr>
<th>298</th>
<td>3255.3</td>
<td>21.4</td>
<td>5</td>
<td>4.5</td>
<td>9.13</td>
<td>1</td>
<td>4</td>
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<tr>
<th>299</th>
<td>1045.4</td>
<td>70.5</td>
<td>4</td>
<td>4.5</td>
<td>4.21</td>
<td>0</td>
<td>7</td>
<td>182502.0</td>
</tr>
</tbody>
</table>
<p>300 rows × 8 columns</p>
</div>
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rows:
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age_years: 50.7
beds: 5
baths: 5.5
dist_city_km: 2.63
has_garage: 1
floor: 1
price_usd: 670270
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- size_sqft: 857.1
age_years: 30.3
beds: 5
baths: 4.5
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has_garage: 0
floor: 11
price_usd: 265318
_deepnote_index_column: 1
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beds: 5
baths: 6
dist_city_km: 3.78
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floor: 6
price_usd: 605301
_deepnote_index_column: 2
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dist_city_km: 3.02
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floor: 0
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_deepnote_index_column: 3
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age_years: 57.5
beds: 3
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has_garage: 1
floor: 7
price_usd: 229268
_deepnote_index_column: 4
- size_sqft: 1565.3
age_years: 22
beds: 3
baths: 3.5
dist_city_km: 0.55
has_garage: 1
floor: 7
price_usd: 315690
_deepnote_index_column: 5
- size_sqft: 1698.7
age_years: 70.1
beds: 5
baths: 5.5
dist_city_km: 1.3
has_garage: 1
floor: 2
price_usd: 448008
_deepnote_index_column: 6
- size_sqft: 2275.7
age_years: 30.2
beds: 4
baths: 4.5
dist_city_km: 6.01
has_garage: 0
floor: 1
price_usd: 536164
_deepnote_index_column: 7
- size_sqft: 2669.6
age_years: 46.6
beds: 4
baths: 4.5
dist_city_km: 7.6
has_garage: 1
floor: 5
price_usd: 580187
_deepnote_index_column: 8
- size_sqft: 398.2
age_years: 75.4
beds: 2
baths: 2.5
dist_city_km: 4.78
has_garage: 0
floor: 11
price_usd: 116067
_deepnote_index_column: 9
type: dataframe
text/plain: |2-
size_sqft age_years beds baths dist_city_km has_garage floor \
0 3012.0 50.7 5 5.5 2.63 1 1
1 857.1 30.3 5 4.5 6.58 0 11
2 2106.9 53.8 5 6.0 3.78 1 6
3 1509.5 59.5 1 1.5 3.02 0 0
4 1027.1 57.5 3 3.0 9.33 1 7
.. ... ... ... ... ... ... ...
295 1398.6 16.1 1 1.0 3.38 1 3
296 3458.2 49.2 5 4.5 7.30 1 5
297 2423.1 13.7 4 3.5 2.09 1 6
298 3255.3 21.4 5 4.5 9.13 1 4
299 1045.4 70.5 4 4.5 4.21 0 7
price_usd
0 670270.0
1 265318.0
2 605301.0
3 364602.0
4 229268.0
.. ...
295 344538.0
296 784651.0
297 535544.0
298 717094.0
299 182502.0
[300 rows x 8 columns]
execution_count: 8
output_type: execute_result
metadata:
cellId: 440cdf8daede40ec996763840703a80a
execution_start: 1762164951911
execution_millis: 17
execution_context_id: 74476248-3f22-4f61-bf4b-0790bf71c9b0
cellIndex: 9
outputType: execute_result
id: 440cdf8daede40ec996763840703a80a
__hadOutputs: true
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type: code
table_state_spec: "{}"
metadata:
table_state_spec: "{}"
- blockGroup: e21066166f514e698e0d5fce3cf82a0f
content: |-
# Save the data to a CSV file
csv_path = Path("houses.csv")
df_csv.to_csv(csv_path, index=False)
id: 50cf9b0d8d7e460faabbf13e93e1b650
metadata:
execution_start: 1762165027192
execution_millis: 341
execution_context_id: 74476248-3f22-4f61-bf4b-0790bf71c9b0
sortingKey: v
type: code
executionCount: 40
outputs: []
executionMode: block
id: b2063cdb7aac4a21aae4e28a0a2f4c49
isModule: false
name: 1. Housing data generation and clean-up
- blocks:
- blockGroup: ea362a2a7dec4cc7b9e18d73108ab3e8
content: Training a model
id: d7eccc0efe60445ba3aad9fa14c6ba2a
metadata:
formattedRanges: []
sortingKey: I
type: text-cell-h1
- blockGroup: ee61b1d7545c4fd5bc976dc384c8059d
content: |-
# Import libraries
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score, mean_absolute_error
import matplotlib.pyplot as plt
import seaborn as sns
executionCount: 13
id: ee61b1d7545c4fd5bc976dc384c8059d
metadata:
execution_start: 1762165153181
execution_millis: 394
execution_context_id: 5954a2ce-a03a-4dd8-9183-2d7fdecb3a8d
outputs: []
sortingKey: a0
type: code
- blockGroup: 7f80b0408b34454db9cc55db8170ee67
content: |-
# load the dataset from house.csv
df = pd.read_csv("houses.csv")
executionCount: 4
id: a73c51565eeb448e9f2d230875e42a93
metadata:
execution_start: 1762165108788
execution_millis: 75
execution_context_id: 5954a2ce-a03a-4dd8-9183-2d7fdecb3a8d
outputs: []
sortingKey: m
type: code
- blockGroup: 95141b0bcefa4df1ad0b3cc7c5a8d65d
content: |-
feature_cols = ["size_sqft", "age_years", "beds", "baths", "dist_city_km", "has_garage", "floor"]
X = df[feature_cols].values
y = df["price_usd"].values
# Fit linear regression
model = LinearRegression()
model.fit(X, y)
# Predict and evaluate
y_pred = model.predict(X)
r2 = r2_score(y, y_pred)
mae = mean_absolute_error(y, y_pred)
# Create results dataframe for visualization
res = df.copy()
res["price_pred_usd"] = y_pred
res["residual_usd"] = res["price_usd"] - res["price_pred_usd"]
res["age_bucket"] = res["age_years"].apply(
lambda x: '≤5' if x <= 5 else '6–15' if x <= 15 else '16–30' if x <= 30 else '31–50' if x <= 50 else '51–100'
)
executionCount: 106
id: 784efe0811c0458f88d5f82d9ac9d27b
metadata:
execution_start: 1762165985108
execution_millis: 1
execution_context_id: 5954a2ce-a03a-4dd8-9183-2d7fdecb3a8d
outputs: []
sortingKey: p
type: code
- blockGroup: 8acf64ad76084111a191efe121a69ea0
content: |-
SELECT CASE
WHEN age_years <= 5 THEN '≤5'
WHEN age_years <= 15 THEN '6–15'
WHEN age_years <= 30 THEN '16–30'
WHEN age_years <= 50 THEN '31–50'
ELSE '51–100'
END AS age_bucket,
COUNT(price_usd) AS count,
AVG(price_usd) AS mean,
STDDEV(price_usd) AS std
FROM df
GROUP BY age_bucket
ORDER BY age_bucket;
executionCount: 61
id: f2c6824f0aa54e4cb06b9c90870ed4a4
metadata:
execution_start: 1762165622029
execution_millis: 0
sql_integration_id: deepnote-dataframe-sql
execution_context_id: 5954a2ce-a03a-4dd8-9183-2d7fdecb3a8d
deepnote_variable_name: df_1
outputs:
- output_type: execute_result
execution_count: 61
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categories: null
- name: _deepnote_index_column
dtype: int64
row_count: 5
preview_row_count: 5
rows:
- age_bucket: ≤5
count: 21
mean: 485297.14285714284
std: 160946.224373014
_deepnote_index_column: 0
- age_bucket: 16–30
count: 51
mean: 441124.5294117647
std: 186732.5985914996
_deepnote_index_column: 1
- age_bucket: 31–50
count: 78
mean: 428479.03846153844
std: 175809.38208217267
_deepnote_index_column: 2
- age_bucket: 51–100
count: 108
mean: 371924.74074074073
std: 159264.9997615724
_deepnote_index_column: 3
- age_bucket: 6–15
count: 42
mean: 460843.0714285714
std: 167605.6366561018
_deepnote_index_column: 4
type: dataframe
text/plain: |2-
age_bucket count mean std
0 ≤5 21 485297.142857 160946.224373
1 16–30 51 441124.529412 186732.598591
2 31–50 78 428479.038462 175809.382082
3 51–100 108 371924.740741 159264.999762
4 6–15 42 460843.071429 167605.636656
text/html: |-
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>age_bucket</th>
<th>count</th>
<th>mean</th>
<th>std</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>≤5</td>
<td>21</td>
<td>485297.142857</td>
<td>160946.224373</td>
</tr>
<tr>
<th>1</th>
<td>16–30</td>
<td>51</td>
<td>441124.529412</td>
<td>186732.598591</td>
</tr>
<tr>
<th>2</th>
<td>31–50</td>
<td>78</td>
<td>428479.038462</td>
<td>175809.382082</td>
</tr>
<tr>
<th>3</th>
<td>51–100</td>
<td>108</td>
<td>371924.740741</td>
<td>159264.999762</td>
</tr>
<tr>
<th>4</th>
<td>6–15</td>
<td>42</td>
<td>460843.071429</td>
<td>167605.636656</td>
</tr>
</tbody>
</table>
</div>
metadata: {}
sortingKey: t
type: sql
- blockGroup: b0f5f1aac72c480ab5fe70bfbdf6a936
content: |-
sns.set(style="whitegrid", context="notebook")
fig, axes = plt.subplots(1, 2, figsize=(14, 5))
sns.scatterplot(data=res, x="price_pred_usd", y="price_usd", hue="beds", palette="viridis", ax=axes[0], alpha=0.8)
low = min(res["price_pred_usd"].min(), res["price_usd"].min())
high = max(res["price_pred_usd"].max(), res["price_usd"].max())
axes[0].plot([low, high], [low, high], "--", color="crimson", label="Ideal")
axes[0].set(title="Actual vs predicted price by bedrooms (USD)", xlabel="Predicted (USD)", ylabel="Actual (USD)")
axes[0].legend()
sns.boxplot(data=res, x="age_bucket", y="residual_usd", ax=axes[1], hue="age_bucket", palette="Set2", legend=False)
axes[1].axhline(0, color="black", linestyle="--", linewidth=1)
axes[1].set(title="Residuals by age bucket", xlabel="Age bucket (years)", ylabel="Residual (USD)")
plt.tight_layout()
plt.show()
executionCount: 100
id: 79ef20a02ccb4f68b2cc85cd3d7e980d
metadata:
execution_start: 1762165936099
execution_millis: 466
execution_context_id: 5954a2ce-a03a-4dd8-9183-2d7fdecb3a8d
outputs:
- data:
text/plain: <Figure size 1400x500 with 2 Axes>
image/png: 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
metadata:
image/png:
width: 1384
height: 484
output_type: display_data
sortingKey: v
type: code
executionMode: block
id: 239dfad31ad94a36ad61c35967aec832
isModule: false
name: 2. Model training and visualization
settings:
requirements:
- scikit-learn
- matplotlib
- seaborn
version: 1.0.0