Sheet1 idProperty ID room_typeType of Room in the property accommodatesHow many adults can this property accomodates bathroomsNumber of bathrooms in the property cancellation_policyCancellation...

Please see AirBNB_Project.docx for project details.


Sheet1 idProperty ID room_typeType of Room in the property accommodatesHow many adults can this property accomodates bathroomsNumber of bathrooms in the property cancellation_policyCancellation policy of the property cleaning_feeThis denotes whether propoerty cleaning fee is included in the rent or not instant_bookableIt indicates whether instant booking facility is available or not review_scores_ratingReview rating score of the property bedroomsNumber of bedrooms in the property bedsTotal number of beds in the property log_priceLog of rental price of the property for a fixed period 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 1. Explore and visualize the dataset. 2. Build a linear regression model to predict the log of rental price 3. 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    Submission Guidelines : 1. There are two parts to the submission:  1. A well commented Jupyter notebook [format - .ipynb] 2. A presentation as you would present to the top management/business leaders [format - .ppt /.pptx]  2. Any assignment found copied/ plagiarized with other groups will not be graded and awarded zero marks 3. Please ensure timely submission as any submission post-deadline will not be accepted for evaluation 4. Submission will not be evaluated if, 1. it is submitted post-deadline, or, 2. more than 2 files are submitted Happy Learning!! Scoring guide (Rubric) - Airbnb Project Rubric Criteria Points Perform an Exploratory Data Analysis on the data -Univariate analysis - Bivariate analysis - Use appropriate visualizations to identify the patterns and insights - Any other exploratory deep dive 5 Illustrate the insights based on EDA Key meaningful observations on the relationship between variables 5 Data pre-processing Prepare the data for analysis - Missing value Treatment, Outlier Treatment, Feature Engineering 15 Model building - Linear Regression - Build the model and comment on the model statistics - Identify the key variables that have a strong relationship with dependent variable 10 Test assumptions of linear regression model - Perform tests for the assumptions of the linear regression - Comment on the findings from the test - If any of the assumptions are violated, then take appropriate actions and try to make sure that all assumptions are satisfied 10 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 5 Actionable Insights & Recommendations - Conclude with the key takeaways for the business - what would your advice be to grow the business? 10 Points 60