app.py

# -*- 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')