MLmodelIndividual.py

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

# Importing the dataset
dataset = pd.read_json('dataindividual.json')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values

# 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 = 0.2, random_state = 0)



#Featuring Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train[:,:] = sc.fit_transform(X_train[:,:])
X_test[:,:] = sc.transform(X_test[:,:])

# Training the Multiple Linear Regression model on the Training set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
 
 # Predicting the Test set results
y_pred = regressor.predict(X_test)
np.set_printoptions(precision=2)
print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))

import json

#printing all intercepts and constant
print(regressor.coef_)
print(regressor.intercept_)

# Open a file in write mode and use json.dump() to write the data to the file in JSON format
with open('resultindividual.json', 'w') as file:
    json.dump(data, file)