lead-scoring-case-study
There are 3 repositories under lead-scoring-case-study topic.
mukulsinghal001 / lead-scoring-model-python
Lead Scoring is such a powerful metric when it comes to quantifying the lead & it is nowadays used by every CRM. 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.
Language:Jupyter Notebook 45amity024 / Lead_Score_Logistic_Regression
This case study involves helping X Education, an education company, improve its lead conversion rate by building a logistic regression model to assign lead scores. The aim is to identify potential leads with the highest chances of converting to paying customers and handling future problems to achieve a target conversion rate of 80%.
Language:Jupyter Notebook 4garima2811 / 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.
Language:Jupyter Notebook 3abhiram-ds / lead_scoring_logistic_regression
Lead Scoring Case Study using Logistic Regression
Language:Jupyter Notebook 2sahidul-shaikh / lead-scoring-for-education-company
Build a machine learning model for identifying the set of leads of X Education so that the lead conversion rate should go up and the sales team of the company focus more on communication with the potential leads rather than making calls to every customer.
Language:Jupyter Notebook 1- Lead-scoring-casestudy
sailyshah / Lead-scoring-casestudy
Lead Score Case study solved using Logistic Regression Model
Language:Jupyter Notebook 1 shaiasi / Lead_Scoring_Logistic_Regression-Project
This case study involves helping X Education, an education company, improve its lead conversion rate by building a logistic regression model to assign lead scores. The aim is to identify potential leads with the highest chances of converting to paying customers and handling future problems to achieve a target conversion rate of 80%.
Language:Jupyter Notebook 1sukhijapiyush / CodePro-mlops-using-airflow-mlflow
Airflow Pipeline for Lead Scoring to Maximize Profit with retraining pipeline and Development experimentation using mlflow
Language:Jupyter Notebook 1SumitSatam / Lead_Scoring_Case_Study
X Education has appointed you to help them select the most promising leads, i.e. the leads that are most likely to convert into paying customers.
Language:Jupyter Notebook 1imumi17 / Lead-Scoring-Case-Study
Lead-Scoring-Case-Study
Language:Jupyter Notebook 0VyasBhaumik / EdTech_Lead_Scoring_Analysis
An education company named X Education sells online courses to industry professionals. Now, although X Education gets a lot of leads, its lead conversion rate is very poor. The objective is to build a model to identify the hot/potential leads and achieve lead conversion rate to 80%.
Language:Jupyter Notebook 0- Language:Jupyter Notebook
RishikeshRai / Lead-Scoring-Case-Study
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%. Now, although X Education gets a lot of leads, its lead conversion rate is very poor. For example, if, say, they acquire 100 leads in a day, only about 30 of them are converted. To make this process more efficient, the company wishes to identify the most potential leads, also known as ‘Hot Leads’. If they successfully identify this set of leads, the lead conversion rate should go up as the sales team will now be focusing more on communicating with the potential leads rather than making calls to everyone. A typical lead conversion process can be represented using the following funnel:
Language:Jupyter Notebooksaptarshim7 / Lead_Scoring_Case_Study
This github repository contains a logistic regression model built for X Education to help the company prioritize potential leads based on their likelihood of conversion. It includes code for data preprocessing, feature selection, and model evaluation, as well as recommendations for utilizing the model effectively.
Language:Jupyter Notebook
