Lead Score Case Study

Executive Summary

Target:
To find the potential lead for the company out of all leads.

Steps:

1. Cleaning Data:

   • Partial cleaning of the data, replacing null values and irrelevant options.
   • Filtered data to include only leads from India, USA, and UAE.

2. Dummy Variables:

   • Created dummy variables for categorical features.
   • Standardized data using StandardScaler.

3. EDA (Exploratory Data Analysis):

   • Quick EDA performed to check data condition.
   • Outliers in numerical variables were identified and removed.

4. Train-Test Split:

   • Data split into 70% train and 30% test sets with random_state = 100.

5. Model Building:

   • Used Recursive Feature Elimination (RFE) to select top 15 relevant variables.
   • Removed remaining variables based on VIF and p-value criteria.

6. Model Evaluation:

   • Confusion matrix created.
   • Optimum cut-off value determined using ROC curve.
   • Accuracy, sensitivity, and specificity evaluated (approx. 90% each).

7. Prediction:

   • Predicted on the test dataset using an optimum cut-off of 0.42.

Conclusions

• Manual segregation of leads required for nurturing potential customers.
• Model meets CEO's expectations with a target lead conversion rate of approx. 90%.
• Features contributing to lead conversion probability identified.

Summary

Train Set:

• Accuracy: 0.8945
• Specificity: 0.8846
• Sensitivity/Recall: 0.9083
• Precision: 0.8491

Test Set:

• Accuracy: 0.8914
• Specificity: 0.8724
• Sensitivity/Recall: 0.9181
• Precision: 0.8358

The model predicts conversion rate well, providing confidence to the CEO in making informed decisions.

Data Dictionary

Variable	Description
Prospect ID	A unique ID with which the customer is identified.
Lead Number	A lead number assigned to each lead procured.
Lead Origin	The origin identifier with which the customer was identified to be a lead. Includes API, Landing Page Submission, etc.
Lead Source	The source of the lead. Includes Google, Organic Search, Olark Chat, etc.
Do Not Email	An indicator variable selected by the customer indicating whether they want to be emailed about the course or not.
Do Not Call	An indicator variable selected by the customer indicating whether they want to be called about the course or not.
Converted	The target variable. Indicates whether a lead has been successfully converted or not.
TotalVisits	The total number of visits made by the customer on the website.
Total Time Spent on Website	The total time spent by the customer on the website.
Page Views Per Visit	Average number of pages on the website viewed during the visits.
Last Activity	Last activity performed by the customer. Includes Email Opened, Olark Chat Conversation, etc.
Country	The country of the customer.
Specialization	The industry domain in which the customer worked before. Includes 'Select Specialization' for customers who had not selected an option while filling the form.
How did you hear about X Education	The source from which the customer heard about X Education.
What is your current occupation	Indicates whether the customer is a student, unemployed, or employed.
What matters most to you in choosing this course	Indicates the main motivation behind choosing the course.
Search	Indicates whether the customer had seen an ad in any of the listed items.
Magazine	Indicates whether the customer had seen an ad in any of the listed items.
Newspaper Article	Indicates whether the customer had seen an ad in any of the listed items.
X Education Forums	Indicates whether the customer had seen an ad in any of the listed items.
Newspaper	Indicates whether the customer had seen an ad in any of the listed items.
Digital Advertisement	Indicates whether the customer had seen an ad in any of the listed items.
Through Recommendations	Indicates whether the customer came in through recommendations.
Receive More Updates About Our Courses	Indicates whether the customer chose to receive more updates about the courses.
Tags	Tags assigned to customers indicating the current status of the lead.
Lead Quality	Indicates the quality of lead based on data and intuition of the employee assigned to the lead.
Update me on Supply Chain Content	Indicates whether the customer wants updates on Supply Chain Content.
Get updates on DM Content	Indicates whether the customer wants updates on DM Content.
Lead Profile	A lead level assigned to each customer based on their profile.
City	The city of the customer.
Asymmetrique Activity Index	An index and score assigned to each customer based on their activity and profile.
Asymmetrique Profile Index	An index and score assigned to each customer based on their activity and profile.
Asymmetrique Activity Score	An index and score assigned to each customer based on their activity and profile.
Asymmetrique Profile Score	An index and score assigned to each customer based on their activity and profile.
I agree to pay the amount through cheque	Indicates whether the customer has agreed to pay the amount through cheque.
A free copy of Mastering The Interview	Indicates whether the customer wants a free copy of 'Mastering the Interview' or not.
Last Notable Activity	The last notable activity performed by the student.

Libraries Used

• numpy
• pandas
• matplotlib.pyplot
• seaborn
• sklearn
• statsmodels.api
• warnings