E-commerce (Data_Science).py

#!/usr/bin/env python
# coding: utf-8

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#HACK_2022

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')


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customer = pd.read_csv('Ecommerce Customers')


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customer.head()


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customer.info()


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customer.describe()


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sns.jointplot(x='Time on Website',y='Yearly Amount Spent',data=customer)


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sns.jointplot(x='Time on App',y='Yearly Amount Spent', data=customer)


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sns.jointplot(x='Time on App',y='Length of Membership',data=customer,kind='hex')


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sns.pairplot(customer)


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


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customer.columns


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y = customer['Yearly Amount Spent']


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


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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


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lm = LinearRegression()


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lm.fit(X_train,y_train)


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lm.coef_


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predictions = lm.predict(X_test)


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


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from sklearn import metrics


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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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metrics.explained_variance_score(y_test,predictions)


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


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cdf = pd.DataFrame(lm.coef_,X.columns,columns=['Coef'])
cdf


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# now according to me company should focus on its website, or it should enhance it's app so that it can excel more !


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