Business Objective - To create a pricing strategy for Airbnb, which helps Airbnb hosts set the right price for their Airbnb listing and provides customers, the benefit of cost.
Type of Problem - Regression Analysis (Dependent Variable is 'Airbnb listing price per night (in USD)' which is regressed against bunch of independent variables (listing attributes - accommodates, bedrooms, bathrooms, beds, amenities provided, neighbourhood and the room type).
Metrics - RMSE (Root Mean Squared Error) to check which ML model provides accurate predictions, Cross Validation Score for hyperparameter tuning in certain ML models, R^2 and Adjusted R^2 for explainability power and to check model fit.
Data Science Lifecycle:
-
Business Understanding
-
Data Acquisition and Understanding
2.1 Raw to Relevant Data
2.2 Data Type Inspection and Conversion
2.3 Dirty Data due to Constraints
2.4 Outlier Detection and Treatment
2.5 Handling Missing Values
2.6 Exploratory Data Analysis (Correlation Analysis, Visualizations & Statistical Analysis)
2.7 Handling Categorical Variables for ML Modeling (Text Encoding)
2.8 Train and Test Split and Data Scaling/Feature Scaling
-
Modeling
-
Deployment
-
Customer Acceptance