app.py

# -*- coding: utf-8 -*-
"""Untitled0.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1TrLYru7HIkMCSYavUVhf6DZ-5lYqp3zI
"""

import pandas as pd
import numpy as np
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import  StandardScaler
from sklearn.tree import DecisionTreeClassifier

df=pd.read_csv('wine_red.csv')
df.drop(['residual_sugar','pH','free_sulfur_dioxide'],axis=1,inplace=True)
X_train = df.drop('quality',axis=1)
y_train = df.pop("quality")

num_col = X_train.select_dtypes(include=['int64', 'float64']).columns
preprocessor = ColumnTransformer([("scaler", StandardScaler(), num_col)])
num_col = X_train.select_dtypes(include=['int64', 'float64']).columns
preprocessor = ColumnTransformer([("scaler", StandardScaler(), num_col)])
model = Pipeline(steps=[('preprocessor', preprocessor), ('decisiontree', DecisionTreeClassifier())]) 
model.fit(X_train, y_train)

model = Pipeline(steps=[('scaler', StandardScaler()), 
                ('decisiontree', DecisionTreeClassifier())])

model.fit(X_train, y_train)

"""### Saving the model"""

import joblib

joblib.dump(model,'model.joblib')