Customer Insights

Investigating customer data to determine how to best encourage customers to spend more for the duration of their customer lifecycle.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn import metrics
sns.set()

%matplotlib inline

# Load data and confirm it was loaded
customers = pd.read_csv('Fake Ecommerce Customers.csv')
customers.head()

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	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

# Take a look at our data's characteristics
customers.describe()

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	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

# Assess the relationship between the time people spend on the website and purchasing
sns.jointplot(x='Time on Website', y='Yearly Amount Spent', data=customers)

<seaborn.axisgrid.JointGrid at 0x1a1ffa9690>

# Assess the relationship between the time people spend on the app and purchasing
sns.jointplot(x='Time on App', y='Yearly Amount Spent', data=customers)

<seaborn.axisgrid.JointGrid at 0x1a2091dbd0>

# Look for any clear correlations
sns.pairplot(customers)

<seaborn.axisgrid.PairGrid at 0x1a2108c7d0>

# Set up independent features as X and the dependent feature as Y 
X = customers[['Avg. Session Length', 'Time on App',
       'Time on Website', 'Length of Membership']]
y = customers['Yearly Amount Spent']

# Set up my testing and training datasets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

# Create and train the mobel
lm = LinearRegression()
lm.fit(X_train,y_train)

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)

#Create some predictions based on the X test dataset
predictions = lm.predict(X_test)

# Compare our predicitons of Y based on the X test dataset to the actual Y test dataset
ax = sns.scatterplot(y=y_test,x=predictions)
ax.set(xlabel='Y Test', ylabel='Predicted Y')
plt.show()

# Assess the module performance
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: 8.097631371748907
MSE: 104.90160739775463
RMSE: 10.24214857330993

# Evaluate the independent features with the most impact on the purchasing amount of a customer
pd.DataFrame(lm.coef_,X.columns,columns=['Coefficient'])

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	Coefficient
Avg. Session Length	25.745354
Time on App	38.252715
Time on Website	0.894086
Length of Membership	61.575456

chiedo/simple-customer-insights

Customer Insights