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Completes UNB-2628 - pip install unboxapi on the tutorial notebooks
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‎examples/tabular-classification/documentation-tutorial/tabular-tutorial-part-1.ipynb

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"validation_set = pd.concat([X_val, y_val], axis=1)"
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]
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "945e2619",
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"metadata": {},
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"outputs": [],
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"source": [
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"# installing the Unbox Python API\n",
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"!pip install unboxapi"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,

‎examples/tabular-classification/documentation-tutorial/tabular-tutorial-part-2.ipynb

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"metadata": {},
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"outputs": [
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{
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"data": {
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"Gender Exited\n",
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"Female 0 20\n",
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"execution_count": 5,
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"metadata": {},
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],
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"training_set.groupby([\"Gender\", \"Exited\"])[\"Exited\"].count()"
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"text/plain": [
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" RowNumber CustomerId Surname CreditScore Geography Gender Age Tenure \\\n",
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"0 8274 15709643 Gray 675 France Male 32 1 \n",
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"1 8277 15775131 Bartlett 580 Spain Male 32 9 \n",
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"2 8281 15745716 McGregor 706 Spain Male 53 7 \n",
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"3 8282 15598485 Pinto 567 Spain Male 40 8 \n",
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"4 8284 15754569 Pagnotto 664 France Male 57 1 \n",
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"\n",
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" Balance NumOfProducts HasCrCard IsActiveMember EstimatedSalary \\\n",
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"0 0.00 3 1 0 85901.09 \n",
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"1 142188.20 2 0 1 128028.60 \n",
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"2 0.00 2 0 1 117939.17 \n",
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"3 28649.64 1 1 1 95140.62 \n",
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"new_prod_data = pd.read_csv(NEW_PROD_DATA_URL)\n",
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"new_prod_data.head()"
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" return model.predict_proba(encoded_df.to_numpy())"
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"execution_count": null,
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"id": "c7739e4e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# installing the Unbox Python API\n",
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"!pip install unboxapi"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,

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