JohnUpgrad/Lead-Scoring-Case-Study: Lead Scoring Case Study Upgrad
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%. Now, although X Education gets a lot of leads, its lead conversion rate is very poor.
GitHub
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%. Now, although X Education gets a lot of leads, its lead conversion rate is very poor.
GitHub
Goals of the Case Study. 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 ...
Lead Scoring Case Study
If the issue persists, it's likely a problem on our side. Unexpected token < in JSON at position 4. keyboard_arrow_up. content_copy. SyntaxError: Unexpected token < in JSON at position 4. Refresh. Explore and run machine learning code with Kaggle Notebooks | Using data from Leads Dataset.
upGrad / Lead Scoring Case Study · GitLab
upGrad / Lead Scoring Case Study · GitLab ... GitLab.com
lead-scoring-case-study · GitHub Topics · GitHub
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garima2811/Lead_Scoring_Case_Study
In this repository, we are going to take a look at the UpGrad lead scoring case study and see how can we solve this problem through several supervised machine learning models. - garima2811/Lead_Scoring_Case_Study ... Also, make sure you include this in your final PPT where you'll make recommendations. Results Expected: A well-commented Jupyter ...
PDF Logistic-Regression-Lead-Scoring-Case-Study/Lead Score Case Study
The company needs a model wherein you a lead score is assigned to each of the leads such that the customers with higher lead score have a higher conversion chance and the customers with lower lead score have a lower conversion chance.The CEO, in particular, has given a ballpark of the target lead conversion rate to be around 80%.
Lead Scoring Case Study
Goals of the 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 ...
Lead-Scoring-Case-Study
The company requires you to build a model wherein you need to assign a lead score to each of the leads such that the customers with higher lead score have a higher conversion chance and the customers with lower lead score have a lower conversion chance. The CEO, in particular, has given a ballpark of the target lead conversion rate to be around ...
COMMENTS
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%. Now, although X Education gets a lot of leads, its lead conversion rate is very poor.
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%. Now, although X Education gets a lot of leads, its lead conversion rate is very poor.
Goals of the Case Study. 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 ...
If the issue persists, it's likely a problem on our side. Unexpected token < in JSON at position 4. keyboard_arrow_up. content_copy. SyntaxError: Unexpected token < in JSON at position 4. Refresh. Explore and run machine learning code with Kaggle Notebooks | Using data from Leads Dataset.
upGrad / Lead Scoring Case Study · GitLab ... GitLab.com
To associate your repository with the lead-scoring-case-study topic, visit your repo's landing page and select "manage topics." GitHub is where people build software. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects.
In this repository, we are going to take a look at the UpGrad lead scoring case study and see how can we solve this problem through several supervised machine learning models. - garima2811/Lead_Scoring_Case_Study ... Also, make sure you include this in your final PPT where you'll make recommendations. Results Expected: A well-commented Jupyter ...
The company needs a model wherein you a lead score is assigned to each of the leads such that the customers with higher lead score have a higher conversion chance and the customers with lower lead score have a lower conversion chance.The CEO, in particular, has given a ballpark of the target lead conversion rate to be around 80%.
Goals of the 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 ...
The company requires you to build a model wherein you need to assign a lead score to each of the leads such that the customers with higher lead score have a higher conversion chance and the customers with lower lead score have a lower conversion chance. The CEO, in particular, has given a ballpark of the target lead conversion rate to be around ...