Spaces:
Runtime error
A newer version of the Streamlit SDK is available:
1.37.1
Customer Conversion Prediction with Markov Chain Classifier
Bussiness Requirement: For online users, conversion generally refers to the user action that results in some tangible gain for a business e.g., a user opening an account or a user making his or her first purchase. Next to drawing a large number of users to a website, getting a user to convert is the most critical event in a user’s relationship with an online business. Being able to predict when a user will convert to become a customer should be an important tool that online businesses should have at their disposal. A business could initiate a targeted marketing campaign based on the prediction result.
There are many relevant attributes for the web session. We will be considering only the following as part of the demo.
- Time elapsed since the last visit
- Time spent in the session
To keep the state transition matrix manageable, we will discretize the attributes into 3 levels; High, Medium, and Low. With two attributes, we will end up with 9 states in our problem. Each session will be characterized by two symbols, which stand for a state. For example, HM will imply that time elapsed since the last session is high and time spent in the current session is medium.
Here is some sample input data: 4F014156K07N,LL,ML,HH,HL,LL,HM,HL,LH,ML,HH,HL,LH G7C0M9H5SUZ1,HL,LM,HL,MH,HH,HH,ML,HL GWBX875AD31D,LL,HM,HL,HL,HM KRO2F24JUDE5,HL,HM,HM,HL,HM,MH,HM,HL,HL 3J0G4BB9BI1Q,LM,LH,LH,MH,LM,MH,LH
Here is the output for the above data: 4F014156K07N,F,LL,ML,HH,HL,LL,HM,HL,LH,ML,HH,HL,LH G7C0M9H5SUZ1,F,HL,LM,HL,MH,HH,HH,ML,HL GWBX875AD31D,F,LL,HM,HL,HL,HM KRO2F24JUDE5,T,HL,HM,HM,HL,HM,MH,HM,HL,HL 3J0G4BB9BI1Q,F,LM,LH,LH,MH,LM,MH,LH
Each line in our output will consist of the following
- Cookie ID (or User ID)
- Class variable indicating whether the user converted or not (True or False)
- Sequence of session data where each element of the sequence is a 2 alphabet symbol
Setup
Install matumizi which is a package for data exploration and various other utilities pip3 install -i https://test.pypi.org/simple/ matumizi==0.0.3
Make sure you have the supv directory at the same level as your working directory containing visit_history.py mcclf_cc.properties
Generate training data
python3 visit_history.py --op gen --nuser 1000 --crate 10 --label true >> cc_tr.txt
nuser = num of users crate = conversion rate label = whether class label should be created
Train model
python3 visit_history.py --op train --mlfpath mcclf_cc.properties
Generate prediction data
python3 visit_history.py --op gen --nuser 100 --crate 10 --label false >> cc_pr.txt
Predict
python3 visit_history.py --op pred --mlfpath mcclf_cc.properties