File size: 982 Bytes
fe8f546 cdccaab fe8f546 cdccaab fe8f546 cdccaab fe8f546 cdccaab fe8f546 1ac990b fe8f546 d8c411d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
st.title("Customer Lifetime Value App")
# Read the dataset
data = pd.read_csv('Online Retail.csv', encoding= 'unicode_escape')
st.write(data)
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'])
data["value"] = data.UnitPrice * data.Quantity
# Get the user id
user_id = st.selectbox('Select the user id :', data.CustomerID.unique())
# Get the data for the selected user id
user_data = data[data['CustomerID'] == user_id]
# Calculate the CLV
clv = (user_data.UnitPrice * user_data.Quantity).sum()
st.write('Customer lifetime value : ', clv)
# Plot the graphs
st.subheader('Purchase Trend')
gr = user_data.groupby(user_data["InvoiceDate"].dt.floor("30D")).sum()
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
plt.scatter(x=gr.index, y=gr["value"], c=gr["value"]<0)
st.write(fig)
# Risk of Churn
if clv <= 0:
st.write('Risk of Churn : Yes')
else:
st.write('Risk of Churn : No')
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