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  1. README.md +204 -0
  2. config.json +137 -0
  3. model.pkl +3 -0
  4. prediction_error.png +0 -0
README.md ADDED
@@ -0,0 +1,204 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: mit
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+ library_name: sklearn
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+ tags:
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+ - sklearn
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+ - skops
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+ - tabular-classification
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+ model_format: pickle
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+ model_file: model.pkl
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+ widget:
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+ structuredData:
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+ BsmtFinSF1:
13
+ - 1280
14
+ - 1464
15
+ - 0
16
+ BsmtUnfSF:
17
+ - 402
18
+ - 536
19
+ - 795
20
+ Condition2:
21
+ - Norm
22
+ - Norm
23
+ - Norm
24
+ ExterQual:
25
+ - Ex
26
+ - Gd
27
+ - Gd
28
+ Foundation:
29
+ - PConc
30
+ - PConc
31
+ - PConc
32
+ GarageCars:
33
+ - 3
34
+ - 3
35
+ - 1
36
+ GarageType:
37
+ - BuiltIn
38
+ - Attchd
39
+ - Detchd
40
+ Heating:
41
+ - GasA
42
+ - GasA
43
+ - GasA
44
+ HeatingQC:
45
+ - Ex
46
+ - Ex
47
+ - TA
48
+ HouseStyle:
49
+ - 2Story
50
+ - 1Story
51
+ - 2.5Fin
52
+ MSSubClass:
53
+ - 60
54
+ - 20
55
+ - 75
56
+ MasVnrArea:
57
+ - 272.0
58
+ - 246.0
59
+ - 0.0
60
+ MasVnrType:
61
+ - Stone
62
+ - Stone
63
+ - .nan
64
+ MiscFeature:
65
+ - .nan
66
+ - .nan
67
+ - .nan
68
+ MoSold:
69
+ - 8
70
+ - 7
71
+ - 3
72
+ OverallQual:
73
+ - 10
74
+ - 8
75
+ - 4
76
+ Street:
77
+ - Pave
78
+ - Pave
79
+ - Pave
80
+ TotalBsmtSF:
81
+ - 1682
82
+ - 2000
83
+ - 795
84
+ YearRemodAdd:
85
+ - 2008
86
+ - 2005
87
+ - 1950
88
+ YrSold:
89
+ - 2008
90
+ - 2007
91
+ - 2006
92
+ ---
93
+
94
+ # Model description
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+
96
+ [More Information Needed]
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+
98
+ ## Intended uses & limitations
99
+
100
+ This model is not ready to be used in production.
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+
102
+ ## Training Procedure
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+
104
+ [More Information Needed]
105
+
106
+ ### Hyperparameters
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+
108
+ <details>
109
+ <summary> Click to expand </summary>
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+
111
+ | Hyperparameter | Value |
112
+ |-----------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
113
+ | memory | |
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+ | steps | [('columntransformer', ColumnTransformer(transformers=[('pipeline',<br /> Pipeline(steps=[('standardscaler',<br /> StandardScaler()),<br /> ('simpleimputer',<br /> SimpleImputer(add_indicator=True))]),<br /> <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF5D97B7C0>),<br /> ('onehotencoder',<br /> OneHotEncoder(handle_unknown='ignore'),<br /> <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF128511E0>)])), ('lassocv', LassoCV())] |
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+ | verbose | False |
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+ | columntransformer | ColumnTransformer(transformers=[('pipeline',<br /> Pipeline(steps=[('standardscaler',<br /> StandardScaler()),<br /> ('simpleimputer',<br /> SimpleImputer(add_indicator=True))]),<br /> <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF5D97B7C0>),<br /> ('onehotencoder',<br /> OneHotEncoder(handle_unknown='ignore'),<br /> <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF128511E0>)]) |
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+ | lassocv | LassoCV() |
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+ | columntransformer__n_jobs | |
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+ | columntransformer__remainder | drop |
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+ | columntransformer__sparse_threshold | 0.3 |
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+ | columntransformer__transformer_weights | |
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+ | columntransformer__transformers | [('pipeline', Pipeline(steps=[('standardscaler', StandardScaler()),<br /> ('simpleimputer', SimpleImputer(add_indicator=True))]), <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF5D97B7C0>), ('onehotencoder', OneHotEncoder(handle_unknown='ignore'), <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF128511E0>)] |
123
+ | columntransformer__verbose | False |
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+ | columntransformer__verbose_feature_names_out | True |
125
+ | columntransformer__pipeline | Pipeline(steps=[('standardscaler', StandardScaler()),<br /> ('simpleimputer', SimpleImputer(add_indicator=True))]) |
126
+ | columntransformer__onehotencoder | OneHotEncoder(handle_unknown='ignore') |
127
+ | columntransformer__pipeline__memory | |
128
+ | columntransformer__pipeline__steps | [('standardscaler', StandardScaler()), ('simpleimputer', SimpleImputer(add_indicator=True))] |
129
+ | columntransformer__pipeline__verbose | False |
130
+ | columntransformer__pipeline__standardscaler | StandardScaler() |
131
+ | columntransformer__pipeline__simpleimputer | SimpleImputer(add_indicator=True) |
132
+ | columntransformer__pipeline__standardscaler__copy | True |
133
+ | columntransformer__pipeline__standardscaler__with_mean | True |
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+ | columntransformer__pipeline__standardscaler__with_std | True |
135
+ | columntransformer__pipeline__simpleimputer__add_indicator | True |
136
+ | columntransformer__pipeline__simpleimputer__copy | True |
137
+ | columntransformer__pipeline__simpleimputer__fill_value | |
138
+ | columntransformer__pipeline__simpleimputer__keep_empty_features | False |
139
+ | columntransformer__pipeline__simpleimputer__missing_values | nan |
140
+ | columntransformer__pipeline__simpleimputer__strategy | mean |
141
+ | columntransformer__pipeline__simpleimputer__verbose | deprecated |
142
+ | columntransformer__onehotencoder__categories | auto |
143
+ | columntransformer__onehotencoder__drop | |
144
+ | columntransformer__onehotencoder__dtype | <class 'numpy.float64'> |
145
+ | columntransformer__onehotencoder__handle_unknown | ignore |
146
+ | columntransformer__onehotencoder__max_categories | |
147
+ | columntransformer__onehotencoder__min_frequency | |
148
+ | columntransformer__onehotencoder__sparse | deprecated |
149
+ | columntransformer__onehotencoder__sparse_output | True |
150
+ | lassocv__alphas | |
151
+ | lassocv__copy_X | True |
152
+ | lassocv__cv | |
153
+ | lassocv__eps | 0.001 |
154
+ | lassocv__fit_intercept | True |
155
+ | lassocv__max_iter | 1000 |
156
+ | lassocv__n_alphas | 100 |
157
+ | lassocv__n_jobs | |
158
+ | lassocv__positive | False |
159
+ | lassocv__precompute | auto |
160
+ | lassocv__random_state | |
161
+ | lassocv__selection | cyclic |
162
+ | lassocv__tol | 0.0001 |
163
+ | lassocv__verbose | False |
164
+
165
+ </details>
166
+
167
+ ### Model Plot
168
+
169
+ <style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,ColumnTransformer(transformers=[(&#x27;pipeline&#x27;,Pipeline(steps=[(&#x27;standardscaler&#x27;,StandardScaler()),(&#x27;simpleimputer&#x27;,SimpleImputer(add_indicator=True))]),&lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001CF5D97B7C0&gt;),(&#x27;onehotencoder&#x27;,OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),&lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001CF128511E0&gt;)])),(&#x27;lassocv&#x27;, LassoCV())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,ColumnTransformer(transformers=[(&#x27;pipeline&#x27;,Pipeline(steps=[(&#x27;standardscaler&#x27;,StandardScaler()),(&#x27;simpleimputer&#x27;,SimpleImputer(add_indicator=True))]),&lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001CF5D97B7C0&gt;),(&#x27;onehotencoder&#x27;,OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),&lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001CF128511E0&gt;)])),(&#x27;lassocv&#x27;, LassoCV())])</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">columntransformer: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[(&#x27;pipeline&#x27;,Pipeline(steps=[(&#x27;standardscaler&#x27;,StandardScaler()),(&#x27;simpleimputer&#x27;,SimpleImputer(add_indicator=True))]),&lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001CF5D97B7C0&gt;),(&#x27;onehotencoder&#x27;,OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),&lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001CF128511E0&gt;)])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">pipeline</label><div class="sk-toggleable__content"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001CF5D97B7C0&gt;</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer(add_indicator=True)</pre></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-6" type="checkbox" ><label for="sk-estimator-id-6" class="sk-toggleable__label sk-toggleable__label-arrow">onehotencoder</label><div class="sk-toggleable__content"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001CF128511E0&gt;</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-7" type="checkbox" ><label for="sk-estimator-id-7" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-8" type="checkbox" ><label for="sk-estimator-id-8" class="sk-toggleable__label sk-toggleable__label-arrow">LassoCV</label><div class="sk-toggleable__content"><pre>LassoCV()</pre></div></div></div></div></div></div></div>
170
+
171
+ ## Evaluation Results
172
+
173
+ | Metric | Value |
174
+ |----------|----------|
175
+ | R2 score | 0.753308 |
176
+ | MAE | 0.112742 |
177
+
178
+ # How to Get Started with the Model
179
+
180
+ [More Information Needed]
181
+
182
+ # Model Card Authors
183
+
184
+ This model card is written by following authors:
185
+
186
+ [More Information Needed]
187
+
188
+ # Model Card Contact
189
+
190
+ You can contact the model card authors through following channels:
191
+ [More Information Needed]
192
+
193
+ # Citation
194
+
195
+ Below you can find information related to citation.
196
+
197
+ **BibTeX:**
198
+ ```
199
+ [More Information Needed]
200
+ ```
201
+
202
+ # prediction-error
203
+
204
+ ![prediction-error](prediction_error.png)
config.json ADDED
@@ -0,0 +1,137 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "sklearn": {
3
+ "columns": [
4
+ "YrSold",
5
+ "HeatingQC",
6
+ "Street",
7
+ "YearRemodAdd",
8
+ "Heating",
9
+ "MasVnrType",
10
+ "BsmtUnfSF",
11
+ "Foundation",
12
+ "MasVnrArea",
13
+ "MSSubClass",
14
+ "ExterQual",
15
+ "Condition2",
16
+ "GarageCars",
17
+ "GarageType",
18
+ "OverallQual",
19
+ "TotalBsmtSF",
20
+ "BsmtFinSF1",
21
+ "HouseStyle",
22
+ "MiscFeature",
23
+ "MoSold"
24
+ ],
25
+ "environment": [
26
+ "scikit-learn=1.2.2"
27
+ ],
28
+ "example_input": {
29
+ "BsmtFinSF1": [
30
+ 1280,
31
+ 1464,
32
+ 0
33
+ ],
34
+ "BsmtUnfSF": [
35
+ 402,
36
+ 536,
37
+ 795
38
+ ],
39
+ "Condition2": [
40
+ "Norm",
41
+ "Norm",
42
+ "Norm"
43
+ ],
44
+ "ExterQual": [
45
+ "Ex",
46
+ "Gd",
47
+ "Gd"
48
+ ],
49
+ "Foundation": [
50
+ "PConc",
51
+ "PConc",
52
+ "PConc"
53
+ ],
54
+ "GarageCars": [
55
+ 3,
56
+ 3,
57
+ 1
58
+ ],
59
+ "GarageType": [
60
+ "BuiltIn",
61
+ "Attchd",
62
+ "Detchd"
63
+ ],
64
+ "Heating": [
65
+ "GasA",
66
+ "GasA",
67
+ "GasA"
68
+ ],
69
+ "HeatingQC": [
70
+ "Ex",
71
+ "Ex",
72
+ "TA"
73
+ ],
74
+ "HouseStyle": [
75
+ "2Story",
76
+ "1Story",
77
+ "2.5Fin"
78
+ ],
79
+ "MSSubClass": [
80
+ 60,
81
+ 20,
82
+ 75
83
+ ],
84
+ "MasVnrArea": [
85
+ 272.0,
86
+ 246.0,
87
+ 0.0
88
+ ],
89
+ "MasVnrType": [
90
+ "Stone",
91
+ "Stone",
92
+ NaN
93
+ ],
94
+ "MiscFeature": [
95
+ NaN,
96
+ NaN,
97
+ NaN
98
+ ],
99
+ "MoSold": [
100
+ 8,
101
+ 7,
102
+ 3
103
+ ],
104
+ "OverallQual": [
105
+ 10,
106
+ 8,
107
+ 4
108
+ ],
109
+ "Street": [
110
+ "Pave",
111
+ "Pave",
112
+ "Pave"
113
+ ],
114
+ "TotalBsmtSF": [
115
+ 1682,
116
+ 2000,
117
+ 795
118
+ ],
119
+ "YearRemodAdd": [
120
+ 2008,
121
+ 2005,
122
+ 1950
123
+ ],
124
+ "YrSold": [
125
+ 2008,
126
+ 2007,
127
+ 2006
128
+ ]
129
+ },
130
+ "model": {
131
+ "file": "model.pkl"
132
+ },
133
+ "model_format": "pickle",
134
+ "task": "tabular-classification",
135
+ "use_intelex": false
136
+ }
137
+ }
model.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:6469fabf16e1ded7d4787edeafa7814989635cef3fc28c17965326604547ef09
3
+ size 9589
prediction_error.png ADDED