Ecommerce Customers - Project

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!

Import Libraries

In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

Get the Data

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:

  • Avg. Session Length: Average session of in-store style advice sessions.
  • Time on App: Average time spent on App in minutes
  • Time on Website: Average time spent on Website in minutes
  • Length of Membership: How many years the customer has been a member.
In [2]:
customers = pd.read_csv('Ecommerce Customers')
In [3]:
customers.head()
Out[3]:
Email Address Avatar Avg. Session Length Time on App Time on Website Length of Membership Yearly Amount Spent
0 mstephenson@fernandez.com 835 Frank Tunnel\nWrightmouth, MI 82180-9605 Violet 34.497268 12.655651 39.577668 4.082621 587.951054
1 hduke@hotmail.com 4547 Archer Common\nDiazchester, CA 06566-8576 DarkGreen 31.926272 11.109461 37.268959 2.664034 392.204933
2 pallen@yahoo.com 24645 Valerie Unions Suite 582\nCobbborough, D... Bisque 33.000915 11.330278 37.110597 4.104543 487.547505
3 riverarebecca@gmail.com 1414 David Throughway\nPort Jason, OH 22070-1220 SaddleBrown 34.305557 13.717514 36.721283 3.120179 581.852344
4 mstephens@davidson-herman.com 14023 Rodriguez Passage\nPort Jacobville, PR 3... MediumAquaMarine 33.330673 12.795189 37.536653 4.446308 599.406092
In [4]:
customers.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 500 entries, 0 to 499
Data columns (total 8 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   Email                 500 non-null    object 
 1   Address               500 non-null    object 
 2   Avatar                500 non-null    object 
 3   Avg. Session Length   500 non-null    float64
 4   Time on App           500 non-null    float64
 5   Time on Website       500 non-null    float64
 6   Length of Membership  500 non-null    float64
 7   Yearly Amount Spent   500 non-null    float64
dtypes: float64(5), object(3)
memory usage: 31.4+ KB
In [5]:
customers.describe()
Out[5]:
Avg. Session Length Time on App Time on Website Length of Membership Yearly Amount Spent
count 500.000000 500.000000 500.000000 500.000000 500.000000
mean 33.053194 12.052488 37.060445 3.533462 499.314038
std 0.992563 0.994216 1.010489 0.999278 79.314782
min 29.532429 8.508152 33.913847 0.269901 256.670582
25% 32.341822 11.388153 36.349257 2.930450 445.038277
50% 33.082008 11.983231 37.069367 3.533975 498.887875
75% 33.711985 12.753850 37.716432 4.126502 549.313828
max 36.139662 15.126994 40.005182 6.922689 765.518462

Exploratory Data Analysis

Let's explore the data!

For the rest of the exercise we'll only be using the numerical data of the csv file.

In [6]:
sns.set_palette('GnBu_d')
sns.set_style('whitegrid')
In [7]:
# More time on site, more money spent.
sns.jointplot(x='Time on Website',y='Yearly Amount Spent',data=customers)
Out[7]:
<seaborn.axisgrid.JointGrid at 0x1fc5c22e688>
In [8]:
sns.jointplot(x= 'Time on App', y= 'Yearly Amount Spent', data= customers)
Out[8]:
<seaborn.axisgrid.JointGrid at 0x1fc5b785c88>
In [9]:
sns.jointplot(x='Time on Website',y='Length of Membership',data=customers,kind ='hex')
Out[9]:
<seaborn.axisgrid.JointGrid at 0x1fc5bdd4748>
In [10]:
sns.pairplot(customers)
Out[10]:
<seaborn.axisgrid.PairGrid at 0x1fc5bd14988>

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

In [11]:
# Length of Membership
In [12]:
sns.lmplot(x='Length of Membership',y='Yearly Amount Spent',data=customers)
Out[12]:
<seaborn.axisgrid.FacetGrid at 0x1fc5d321e88>

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.

In [13]:
y = customers['Yearly Amount Spent']
In [14]:
X = customers[['Avg. Session Length', 'Time on App','Time on Website', 'Length of Membership']]
In [15]:
from sklearn.model_selection import train_test_split
In [55]:
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size= 0.3, random_state = 101)

Training the Model

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

In [56]:
from sklearn.linear_model import LinearRegression
In [57]:
lm = LinearRegression()
In [58]:
lm.fit(X_train,y_train)
Out[58]:
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)
In [59]:
# The coefficients
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!

In [61]:
predictions = lm.predict(X_test)
In [62]:
plt.scatter(y_test,predictions)
plt.xlabel('Y Test')
plt.ylabel('Predicted Y')
Out[62]:
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).

In [63]:
# 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)))

MAE: 7.228148653430838
MSE: 79.81305165097461
RMSE: 8.933815066978642

Residuals

In [64]:
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.

In [65]:
coeffecients = pd.DataFrame(lm.coef_,X.columns)
coeffecients.columns = ['Coeffecient']
coeffecients
Out[65]:
Coeffecient
Avg. Session Length 25.981550
Time on App 38.590159
Time on Website 0.190405
Length of Membership 61.279097

How can you interpret these coefficients?

Interpreting the coefficients:

  • Holding all other features fixed, a 1 unit increase in Avg. Session Length is associated with an increase of 25.98 total dollars spent.
  • Holding all other features fixed, a 1 unit increase in Time on App is associated with an increase of 38.59 total dollars spent.
  • Holding all other features fixed, a 1 unit increase in Time on Website is associated with an increase of 0.19 total dollars spent.
  • Holding all other features fixed, a 1 unit increase in Length of Membership is associated with an increase of 61.27 total dollars spent.

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, probably explore more relationships before coming to a conclusion!

Great !