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| # importing libraries | |
| #for versions checkout requirements.txt | |
| import numpy as np | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| #importing and reading data sets | |
| data = pd.read_csv("bmi_and_life.csv") | |
| #look at top 5 rows in data set | |
| data.head() | |
| #delete/drop 'Country' variable. | |
| data = data.drop(['Country'], axis = 1) | |
| #reading into variables | |
| X = data.iloc[:, :-1].values | |
| Y = data.iloc[:, 1].values | |
| from sklearn.model_selection import train_test_split | |
| X_train,X_test,y_train,y_test = train_test_split(X,Y, test_size = 20, random_state = 0) | |
| #import linear regression | |
| from sklearn.linear_model import LinearRegression | |
| lr = LinearRegression() | |
| #fitting the model | |
| lr.fit(X_train,y_train) | |
| LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False) | |
| # Predicting the Test set results | |
| y_pred = lr.predict(X_test) |
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