total_bill   tip     sex smoker  day    time  size
0       16.99  1.01  Female     No  Sun  Dinner     2
1       10.34  1.66    Male     No  Sun  Dinner     3
2       21.01  3.50    Male     No  Sun  Dinner     3
3       23.68  3.31    Male     No  Sun  Dinner     2
4       24.59  3.61  Female     No  Sun  Dinner     4
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 244 entries, 0 to 243
Data columns (total 7 columns):
#   Column      Non-Null Count  Dtype   
---  ------      --------------  -----   
0   total_bill  244 non-null    float64 
1   tip         244 non-null    float64 
2   sex         244 non-null    category
3   smoker      244 non-null    category
4   day         244 non-null    category
5   time        244 non-null    category
6   size        244 non-null    int64   
dtypes: category(4), float64(2), int64(1)
memory usage: 7.4 KB

Now, we plot a scatterplot and see the relationship between the total bill and the tip. The scatterplot is already shown previously.

X_train, X_test, y_train, y_test = train_test_split(df[["total_bill"]], df["tip"], train_size=0.8, shuffle=True, random_state=1)

Now, we are using the train_test_split() function to split the dataset into training set and test set. Please note that we train a machine learning model on the training set. And after that, the machine learning model is run on the test set to see the performance of the model.

The train_size=0.8 parameter here indicates 80% of the total dataset is kept as training set. And 20% will be used as test set. The shuffle=True parameter indicates that we shuffle the data before splitting. And the random_state=1 parameter controls the random number generator that is used for shuffling the data.

After that, we are initializing the linear regressor.

linear_regressor = LinearRegression()
linear_regressor.fit(X_train, y_train)

Please note that using the linear_regressor.fit() function the model learns the coefficients of the linear regression from the training set. In other words, we run the linear_regression.fit() function on the training set and using this function, the model learns the coefficients of the linear regression.

Now, it is time to run the linear regression model on the test set and measure the performance…