Webshop - Assignment

Purchases distributed on the different age groups, from a webshop

Data

Data are in a Excel file: spend_age.xlsx

Tasks

It is your task to make the following:

Spend pr. group

You have to create a “Table” with the mean spend pr. Age group.

Group by Age:

group_age_spend = df[['Age', 'Spend']].groupby(['Age'], as_index=False).mean().sort_values(by='Age')
group_age_spend

Mean spend pr. age group:

plt.title('Mean spend pr. age group')
plt.bar(x='Age', height='Spend', data=group_age_spend)
plt.savefig("./image/spend_3.jpg")

Data Preprocessing

Now that we have imported the dataset, we will perform data preprocessing.

X = df.iloc[:,:-1].values  # Independent variable array
y = df.iloc[:,1].values    # Dependent variable vector

Splitting the dataset

X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=1/3,random_state=0)

Fitting linear regression model into the training set

regressor = LinearRegression()
regressor.fit(X_train, y_train) # Produces the linear eqn for the data

Predicting the test set results

y_pred = regressor.predict(X_test) 
y_pred

Show the test “set”

y_test

y_test is the real salary of the test set.
y_pred are the predicted salaries.

Visualizing the results

Let’s see what the results of our code will look like when we visualize it.

Plotting the points (observations)
Plotting the regression line

Plot for the Training set

plt.scatter(X_train, y_train, color='red') # Plotting the observation 
plt.plot(X_train, regressor.predict(X_train), color='blue') # Plotting the regression line
plt.title('Age vs. spend (Training set)')
plt.xlabel('Age')
plt.ylabel('Spend')
plt.savefig("./image/spend_4.jpg")
plt.show()

Linear regression

Plot for the Testing set

plt.scatter(X_test, y_test, color='red') # Plotting the observation 
plt.plot(X_train, regressor.predict(X_train), color='blue') # Plotting the regression line
plt.title('Age vs. spend (Testing sett)')
plt.xlabel('Age') 
plt.ylabel('Spend') 
plt.savefig("./image/spend_5.jpg")
plt.show()