model.py

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pickle
from sklearn.model_selection import train_test_split
from sklearn import ensemble

from imblearn.over_sampling import SMOTE


dataset = pd.read_csv('train_df.csv')

X = dataset.iloc[:, :7]
y = dataset.iloc[:, -1]

smote = SMOTE(sampling_strategy = 'not majority')
x_sm, y_sm = smote.fit_resample(X, y)

X_train, X_test, y_train, y_test = train_test_split(x_sm, y_sm, test_size=0.2, random_state=0)

# Fit or Train the Model
params = {'n_estimators': 1000, 'max_depth': 4, "min_samples_split": 5, 'learning_rate':0.01}
lr_model = ensemble.GradientBoostingClassifier(**params)
lr_model.fit(X_train,y_train.values.ravel())

# Saving model to disk
pickle.dump(lr_model, open('model.pkl','wb'))

# Loading model to compare the results
model = pickle.load(open('model.pkl','rb'))
print(model.predict([[33,3, 3,1,3,3,3]]))