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.
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Updated
Jan 20, 2021 - Jupyter Notebook
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.
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.
Lead Scoring Case Study using 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%.
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.
Airflow Pipeline for Lead Scoring to Maximize Profit with retraining pipeline and Development experimentation using mlflow
Lead-Scoring-Case-Study
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%.
Lead Score Case study solved using Logistic Regression Model
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.
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%.
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 th…
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.
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