Congratulations! You just got some contract work with an Ecommerce company based in New York City that sells clothing online but they also have in-store style and clothing advice sessions. Customers come in to the store, have sessions/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. They've hired you on contract to help them figure it out! Let's get started!
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).
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
We'll work with the Ecommerce Customers csv file from the company. It has Customer info, suchas Email, Address, and their color Avatar. Then it also has numerical value columns:
Read in the Ecommerce Customers csv file as a DataFrame called customers.
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customers = pd.read_csv("Ecommerce Customers")
Check the head of customers, and check out its info() and describe() methods.
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customers.head()
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customers.describe()
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customers.info()
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sns.set_palette("GnBu_d")
sns.set_style('whitegrid')
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# More time on site, more money spent.
sns.jointplot(x='Time on Website',y='Yearly Amount Spent',data=customers)
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Do the same but with the Time on App column instead.
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sns.jointplot(x='Time on App',y='Yearly Amount Spent',data=customers)
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Use jointplot to create a 2D hex bin plot comparing Time on App and Length of Membership.
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sns.jointplot(x='Time on App',y='Length of Membership',kind='hex',data=customers)
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Let's explore these types of relationships across the entire data set. Use pairplot to recreate the plot below.(Don't worry about the the colors)
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sns.pairplot(customers)
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Based off this plot what looks to be the most correlated feature with Yearly Amount Spent?
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# Length of Membership
Create a linear model plot (using seaborn's lmplot) of Yearly Amount Spent vs. Length of Membership.
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sns.lmplot(x='Length of Membership',y='Yearly Amount Spent',data=customers)
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y = customers['Yearly Amount Spent']
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X = customers[['Avg. Session Length', 'Time on App','Time on Website', 'Length of Membership']]
Use model_selection.train_test_split from sklearn to split the data into training and testing sets. Set test_size=0.3 and random_state=101
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from sklearn.model_selection import train_test_split
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)
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from sklearn.linear_model import LinearRegression
Create an instance of a LinearRegression() model named lm.
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lm = LinearRegression()
Train/fit lm on the training data.
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lm.fit(X_train,y_train)
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Print out the coefficients of the model
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# The coefficients
print('Coefficients: \n', lm.coef_)
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predictions = lm.predict( X_test)
Create a scatterplot of the real test values versus the predicted values.
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plt.scatter(y_test,predictions)
plt.xlabel('Y Test')
plt.ylabel('Predicted Y')
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# calculate these metrics by hand!
from sklearn import metrics
print('MAE:', metrics.mean_absolute_error(y_test, predictions))
print('MSE:', metrics.mean_squared_error(y_test, predictions))
print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))
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sns.distplot((y_test-predictions),bins=50);
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.
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coeffecients = pd.DataFrame(lm.coef_,X.columns)
coeffecients.columns = ['Coeffecient']
coeffecients
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How can you interpret these coefficients?
Interpreting the coefficients:
Do you think the company should focus more on their mobile app or on their website?
This is tricky, there are two ways to think about this: Develop the Website to catch up to the performance of the mobile app, or develop the app more since that is what is working better. This sort of answer really depends on the other factors going on at the company, you would probably want to explore the relationship between Length of Membership and the App or the Website before coming to a conclusion!