Linear Regression

Amazon_cloths sells cloths online. Customers come in to the store, have meetings with a personal stylist, then they can go home and order either on a mobile app or website for the clothes they want.

The company is trying to decide whether to focus their efforts on their mobile app experience or their website. Following is predict is analysis for this company

Just follow the steps below to analyze the customer data (it's fake, don't worry I didn't give you real credit card numbers or emails).

Imports

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

Read in the Ecommerce Customers csv file as a DataFrame called customers.

customers = pd.read_csv('Ecommerce Customers')

customers.head()

customers.describe()

customers.info()

RangeIndex: 500 entries, 0 to 499 Data columns (total 8 columns): Email 500 non-null object Address 500 non-null object Avatar 500 non-null object Avg. Session Length 500 non-null float64 Time on App 500 non-null float64 Time on Website 500 non-null float64 Length of Membership 500 non-null float64 Yearly Amount Spent 500 non-null float64 dtypes: float64(5), object(3) memory usage: 31.3+ KB

Data Analysis

import seaborn as sns

sns.jointplot(customers['Time on Website' ],customers['Yearly Amount Spent'])

Do the same but with the Time on App column instead.

sns.jointplot(customers['Time on App'],customers['Yearly Amount Spent'])

Use jointplot to create a 2D hex bin plot comparing Time on App and Length of Membership.

sns.jointplot(customers['Time on App'],customers['Yearly Amount Spent'],kind='hex')

Let's explore these types of relationships across the entire data set

sns.pairplot(customers)

Based off this plot what looks to be the most correlated feature with Yearly Amount Spent?

#Length of Membership

Create a linear model plot (using seaborn's lmplot) of Yearly Amount Spent vs. Length of Membership.

sns.lmplot(x='Yearly Amount Spent',y ='Length of Membership', data=customers)

Training and Testing Data

Now that we've explored the data a bit, let's go ahead and split the data into training and testing sets. Set a variable X equal to the numerical features of the customers and a variable y equal to the "Yearly Amount Spent" column.

y = customers['Yearly Amount Spent']

X = customers[['Avg. Session Length', 'Time on App','Time on Website', 'Length of Membership']]

Use modelselection.traintestsplit from sklearn to split the data into training and testing sets. Set testsize=0.3 and random_state=101

from sklearn.modelselection import traintest_split

Xtrain, Xtest, ytrain, ytest = traintestsplit(X, y, testsize=0.3, randomstate=101)

Training the Model

Now its time to train our model on our training data!

Import LinearRegression from sklearn.linear_model

from sklearn.linear_model import LinearRegression

Create an instance of a LinearRegression() model named lm.

lm = LinearRegression()

Train/fit lm on the training data.

lm.fit(Xtrain,ytrain)

LinearRegression(copyX=True, fitintercept=True, n_jobs=1, normalize=False)

Print out the coefficients of the model

print('Coefficients: \n', lm.coef_)

Coefficients: [ 25.98154972 38.59015875 0.19040528 61.27909654]

Predicting Test Data

Now that we have fit our model, let's evaluate its performance by predicting off the test values!

Use lm.predict() to predict off the X_test set of the data.

predictions = lm.predict(X_test)

Create a scatterplot of the real test values versus the predicted values.

plt.scatter(y_test,predictions)
plt.xlabel('Y Test')
plt.ylabel('Predicted Y')

Text(0,0.5,'Predicted Y')

Evaluating the Model

Let's evaluate our model performance by calculating the residual sum of squares and the explained variance score (R^2).

Calculate the Mean Absolute Error, Mean Squared Error, and the Root Mean Squared Error.

from sklearn import metrics
print('MAE:', metrics.meanabsoluteerror(y_test, predictions)) print('MSE:', metrics.meansquarederror(y_test, predictions)) print('RMSE:', np.sqrt(metrics.meansquarederror(y_test, predictions)))

MAE: 7.22814865343 MSE: 79.813051651 RMSE: 8.93381506698

Residuals

Let's quickly explore the residuals to make sure everything was okay with our data.

Plot a histogram of the residuals and make sure it looks normally distributed. Use either seaborn distplot, or just plt.hist().

sns.distplot((y_test-predictions),bins=50);

Conclusion

We still want to figure out the answer to the original question, do we focus our efforst on mobile app or website development? Or maybe that doesn't even really matter, and Membership Time is what is really important. Let's see if we can interpret the coefficients at all to get an idea.

Recreate the dataframe below.

coeffecients = pd.DataFrame(lm.coef_,X.columns)
coeffecients.columns = ['Coeffecient']
coeffecients

Do you think the company should focus more on their mobile app or on their website?

Mobile App

Great Job!

We done it. Thank you