Predictive Pricing Model for Airbnb Properties
The project tackled the business problem of identifying the most optimal investment strategy for acquiring, restoring, and listing old buildings on Airbnb. Utilizing Python and Machine Learning techniques, a linear regression as well as a clustering model was developed with data scraped from the Airbnb website. These models aimed to pinpoint the most influential features of apartments contributing to higher prices and inform strategic decision-making to maximise retrun on investment.
Tools used: Python, Machine Learning
Identified key factors influencing Airbnb listing prices with Machine Learning
Churn Prediction with Machine Learning
This project addressed the high churn rate of customers (14.5%) for a telecom company by developing a Machine Learning model to predict and understand churn reasons. Insights from the supervised classification model created were leveraged to devise targeted retention strategies to reduce the churn rate and generate an estimated $200,000 in revenue within the first month.
Tool used: Machine Learning
Identified methods to decrease churn and boost revenue using ML
