Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""Copy of Lab06.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1eKsEZ2OurE_fRyVw_cxMPq5cuYpUkSJM
+
+We will train an XGBoost model on the Adult's Income dataset and deploy it on Hugging Face spaces.
+"""
+
+!wget http://www.donlapark.cmustat.com/Income.csv
+
+import pandas as pd
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
+
+from xgboost import XGBClassifier
+
+
+EDU_DICT = {'Preschool': 1,
+            '1st-4th': 2,
+            '5th-6th': 3,
+            '7th-8th': 4,
+            '9th': 5,
+            '10th': 6,
+            '11th': 7,
+            '12th': 8,
+            'HS-grad': 9, 
+            'Some-college': 10,
+            'Assoc-voc': 11,
+            'Assoc-acdm': 12,
+            'Bachelors': 13,
+            'Masters': 14,
+            'Prof-school': 15,
+            'Doctorate': 16
+            }
+
+
+X_train = pd.read_csv('Income.csv')
+
+X_train
+
+y_train = X_train.pop("income")
+y_train = (y_train == ">50K").astype(int)
+X_train['education'].replace(EDU_DICT, inplace=True)
+
+# Names of numerical features
+num_col = X_train.select_dtypes(include=['int64', 'float64']).columns
+# Names of categorical features
+cat_col = X_train.select_dtypes(include=['object', 'bool']).columns
+
+print(num_col)
+print(cat_col)
+
+# print num_col and cat_col
+
+preprocessor = ColumnTransformer([("scaler", StandardScaler(), num_col), 
+                                  ("onehot", OneHotEncoder(sparse=False), cat_col)])
+
+model = Pipeline(steps=[('preprocessor', preprocessor), 
+                        ('classifier', XGBClassifier())])  
+
+model.fit(X_train, y_train)
+
+"""### Saving the model"""
+
+import joblib
+
+joblib.dump(model, 'model.joblib')
+
+unique_values = {col:X_train[col].unique() for col in cat_col}
+
+joblib.dump(unique_values, 'unique_values.joblib')