model.py

## Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

## Importing the dataset"""
dataset = pd.read_csv('Wellbeing_and_lifestyle_data_Kaggle.csv')
X = dataset.iloc[:, 1:-1].values
y = dataset.iloc[:, -1].values

"""## One hot encoding """
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [-2])], remainder='passthrough')
X = np.array(ct.fit_transform(X))

"""## Splitting the dataset into the Training set and Test set"""
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3, random_state = 0)

"""## Training the Regression model on the Training set"""
from sklearn.svm import SVR
regressor = SVR(kernel = 'rbf')
regressor.fit(X, y)

"""## Predicting the Test set results"""
y_pred = regressor.predict(X_test)
from sklearn.metrics import r2_score
r2_score(y_test, y_pred)

"""## Loading the Model"""
import pickle
pickle.dump(regressor, open('model.pkl','wb'))
model = pickle.load(open('model.pkl','rb'))