Description
Background & Context
Airbnb is an online platform that allows people to rent short-term accommodation. This ranges from regular people with a spare bedroom to property management firms who lease multiple rentals. On the one side, Airbnb enables owners to list their space and earn rental money. On the other side, it provides travelers easy access to renting private homes.
Airbnb receives commissions from two sources upon every booking, namely from the hosts and guests. For every booking, Airbnb charges the guest 6-12% of the booking fee. Moreover, Airbnb charges the host 3% for every successful transaction.
As a senior data scientist at Airbnb, you have to come up with a pricing model that can effectively predict the Rent for an accommodation and can help hosts, travelers, and also the business in devising profitable strategies.
Objective
Explore and visualize the dataset.
Build a linear regression model to predict the log of the rental price
Generate a set of insights and recommendations that will help the business.
Data Dictionary
1. id Property ID
2. room_type Type of Room in the property
3. accommodates How many adults can this property accomodate
4. bathrooms Number of bathrooms in the property
5. cancellation_policy Cancellation policy of the property
6. cleaning_fee This denotes whether the property's cleaning fee is included in the rent or not
7. instant_bookable It indicates whether an instant booking facility is available or not
8. review_scores_rating The review rating score of the property
9. bedrooms Number of bedrooms in the property
10. beds Total number of beds in the property
11. log_price Log of the rental price of the property for a fixed period
Best Practices for Notebook :
The notebook should be well-documented, with inline comments explaining the functionality of code and markdown cells containing comments on the observations and insights.
The notebook should be run from start to finish in a sequential manner before submission.
It is preferable to remove all warnings and errors before submission.
Scoring guide (Rubric) - Airbnb Project Rubric
Criteria Points
Define the problem and perform an Exploratory Data Analysis
- Problem definition, questions to be answered - Data background and contents - Univariate analysis - Bivariate analysis
Illustrate the insights based on EDA
Key meaningful observations on the relationship between variables
Data pre-processing
Data Preparation for modeling - Missing value Treatment - Outlier Treatment - Feature Engineering
Model building - Linear Regression
- Build the model and comment on the model statistics - Print model coefficients with column names in a data frame - Identify the key variables that have a strong relationship with dependent variable
Model performance evaluation
- Evaluate the model on different performance metrics - RMSE, MAE, Adjusted R-square - Comment on the performance measures and if there is any need to improve the model or not
Actionable Insights & Recommendations
Conclude with the key takeaways for the business - what would your advice be to grow the business?
Notebook - Overall Quality
- Structure and flow - Well commented code
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