An education company named X Education sells online courses to industry professionals. On any given day, many professionals who are interested in the courses land on their website and browse for courses.
The company markets its courses on several websites and search engines like Google. Once these people land on the website, they might browse the courses or fill up a form for the course or watch some videos. When these people fill up a form providing their email address or phone number, they are classified to be a lead. Moreover, the company also gets leads through past referrals. Once these leads are acquired, employees from the sales team start making calls, writing emails, etc. Through this process, some of the leads get converted while most do not. The typical lead conversion rate at X education is around 30%.
Solution: For this case study we're going to use several classification model to predict whether the lead is going to quantify as a hot lead. The steps involved for this case study are mentioned below:
Data Loading
Data Exploration a.k.a Exploratory Data Analysis
Preprocessing
Feature Engineering
Outlier Analysis
Model Building
Model Performance Benchmarking
Model Performance Evaluation
Cross Validation + Hyperparameter Tuning
Model Diagnosis Using Probability Calibration, ROC AUC Curve, Precision-Recall Curve
There are quite a few goals for this case study:
Build a logistic regression model to assign a lead score between 0 and 100 to each of the leads which can be used by the company to target potential leads. A higher score would mean that the lead is hot, i.e. is most likely to convert whereas a lower score would mean that the lead is cold and will mostly not get converted.
There are some more problems presented by the company which your model should be able to adjust to if the company's requirement changes in the future so you will need to handle these as well. These problems are provided in a separate doc file. Please fill it based on the logistic regression model you got in the first step. Also, make sure you include this in your final PPT where you'll make recommendations.