https://github.com/wb-az/ml-airbnb-paris-analytics-and-price-prediction

Airbnb Paris - analytics and accommodation price prediction
https://github.com/wb-az/ml-airbnb-paris-analytics-and-price-prediction

cleansing-data deep-learning ensemble-machine-learning feature-selection gradient-boosting gridsearchcv machine-learning neural-network pipeline random-forest svr-regression-prediction visualization wrangling-cleaning xai-shap xgboost-regression

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Airbnb Paris - analytics and accommodation price prediction

Host: GitHub
URL: https://github.com/wb-az/ml-airbnb-paris-analytics-and-price-prediction
Owner: Wb-az
Created: 2022-10-12T15:03:33.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2024-09-16T21:43:49.000Z (about 1 year ago)
Last Synced: 2024-09-17T02:51:52.865Z (about 1 year ago)
Topics: cleansing-data, deep-learning, ensemble-machine-learning, feature-selection, gradient-boosting, gridsearchcv, machine-learning, neural-network, pipeline, random-forest, svr-regression-prediction, visualization, wrangling-cleaning, xai-shap, xgboost-regression
Language: Jupyter Notebook
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Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

          # Airbnb visualisations, analytics and price prediction

## Airbnb Paris quarters Q3 - Q4 2022 scrapes (March-Dec 2022)

## 1. Introduction

Airbnb, Inc. is an American San Francisco-based company operating an online marketplace for short-term homestays and experiences. The company acts as a broker and charges a commission from each booking. The company not only have revolutionized the tourism industry but has also facilitated an unaffordable increase in home rents and a lack of regulation (https://en.wikipedia.org/wiki/Airbnb).

With this in mind, the objectives of this project were:

- Visualise the properties offered in the 20 Arrondissements of Paris during Quarters Q2 - Q4 of 2022

- Find features that impact the price of a listing

- Identify potential inactive listing with outlier prices - noise

- Identify the most expensive and cheapest neighbourhoods

- Identyfy the type of accommodations and properties more often offered by Airbnb

- Identify listings that offered for long-term stays

- Processing the data to predict the listing price per night with Machine Learning algorithms 

- Optimize the best model hyperparameters

- Best model explainability - XAI Shapley number computation

Airbnb datasets Q2-Q4 2022 can be sourced from http://insideairbnb.com/get-the-data/

## 2. Libraries  and frameworks used

* pandas

* numpy

* matplotlib

* sklearn

* xgboost

* lightgbm

* skorch

* missigno

* joblib

* shap

GIS  libraries

* geopandas

* contextily

* folium

Frameworks

* PyTorch

## 2. Geographical listing distribution

- Interactive map available for ``` airbnb_paris.ipynb ``` https://ace-aitech.github.io

  

  

      
Listings density per neighbourhood

  

## 3. Properties, rooms type quaters density and price frequency

* 75 % of the listing has minimum_nights up to 4. 

* There are 7232 listings in airbnb Paris that required a minimun_nights stay of 30 nights covering the 8.74% unique listings from Q2-Q4 2022.

* The most common property_type  is **rental unit**

* Most common type of room **Entire home/apt**



**Note** The listings were recategorised in short and long-term if the minimum_nights was >=30

  

## 3.Price visualisation

* The most expensive room type is **Hotel room**

* The cheapest accommodation is  **Shared room**

* Most expensive properties are floor and Villa 

* Cheapest neighbourhoods Ménilmontant $101.787, Buttes-Chaumont $116.75

* The most expensive neighbourhoods are Élysée $260.112, Louvre $257.11









There are 7232 listings in airbnb Paris that required a minimun_nights stay of 30 nights covering the 8.74% unique listings from Q2-Q4 2022.

## 3. Top ten amenities

#### **Top 10 amenities**

|amenity|frequency|

|---|---|

|wifi|88783|

|kitchen|85683|

|essentials|83260|

|heating|83255|

|long term stays allowed|77452|

|smoke alarm|74465|

|hot water|73666|

|hair dryer|73414|

|dishes and silverware|71057|

|washer|70354|

## 4. Features selection

The dataset after wrangling, cleaning and encoding contained 215 features. SelectPercentile with mutual_info_regression was used to select only the 50 percentile. The table below only shows the top 10 features.

|Feature | score |

|-----------------------------|---------|

|latitude                     |0.432559|

|longitude                    |0.432547 |

|accommodates                 |0.218494 |

|reviews_per_month            |0.158682|

|private_bathroom             |0.157154 |

|bedrooms                     |0.147699 |

|review_scores_value          |0.145960 |

|beds                         |0.144976 |

|review_scores_cleanliness    |0.143778 |

|review_scores_rating         |0.134326 |

Note: this table only shows the top ten features

## 4. Algorithms to process the date

**Linear Models**

* LinearRegression

* Ridge

* Bayesian Ridge Regression

**Support Vector Machines**

* SVR - Performed well with sparse data (hot-encoded)

**Trees**

* DecisionTreeRegressor

**Emssembles**

* RandomForestRegressor

* GradientBoostingRegressor

* HistGradientBoostingRegressor

* XGBRegressor

* LGBMRegressor

**ANN**

* Three hidden layers Neural Network

### 4.1 Performance metrics

* r²

* mae

* mape

* mse

* rmse

## 5. Results

|model|linear\_regression|lasso|ridge|svr|decision\_tree|random\_forrest|gradient\_boosting|hist\_gradient\_boosting|xgb|LGBM|ann\_regressor|

|---|---|---|---|---|---|---|---|---|---|---|---|

|train\_r2|0\.5544|0\.4966|0\.5544|0\.6094|0\.6435|0\.6564|0\.896|0\.7041|0\.9565|0\.8016|0\.9491|

|val\_r2|0\.5568|0\.498|0\.5568|0\.6002|0\.5877|0\.6279|0\.78|0\.6824|0\.803|0\.7422|0\.7845|

|test\_r2|0\.5603|0\.5012|0\.5603|0\.6077|0\.5965|0\.6362|0\.7863|0\.694|0\.8111|0\.7533|0\.7942|

|mean\_yhat\_val|155\.944|156\.1785|155\.9431|142\.8075|155\.889|155\.8143|155\.5398|155\.985|155\.6422|155\.786|145\.6884|

|mean\_yhat\_test|155\.7705|155\.7204|155\.7696|142\.5344|155\.4132|155\.9366|155\.724|156\.0552|155\.6028|155\.7839|145\.4348|

|val\_mae|0\.3843|0\.3997|0\.3843|0\.2811|0\.3434|0\.3332|0\.2425|0\.306|0\.2263|0\.2728|0\.2141|

|test\_mae|0\.3817|0\.3939|0\.3817|0\.2811|0\.3419|0\.332|0\.2418|0\.3029|0\.225|0\.2698|0\.2118|

|val\_mse|5427\.405|6147\.953|5427\.3707|4895\.9498|5049\.7012|4557\.1913|2694\.7748|3889\.0085|2412\.8689|3156\.8245|2639\.4353|

|test\_mse|5252\.9704|5959\.0155|5252\.9621|4687\.2693|4820\.9274|4346\.8873|2553\.1095|3656\.1552|2256\.5164|2947\.1248|2459\.0037|

|val\_rmse|73\.6709|78\.4089|73\.6707|69\.9711|71\.0612|67\.507|51\.9112|62\.3619|49\.121|56\.1856|51\.3754|

|test\_rmse|72\.4774|77\.1947|72\.4773|68\.4636|69\.4329|65\.9309|50\.5283|60\.4661|47\.5028|54\.2874|49\.5883|

## 5.1 Ranked Models

|Model|mean\_yhat\_val|mean\_yhat\_test|train\_r2|val\_r2|test\_r2|val\_mae|test\_mae|val\_mape|test\_mape|val\_mse|test\_mse|val\_rmse|test\_rmse|

|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

|linear\_regression|155\.4831|156\.4836|10\.0|10\.0|10\.5|11\.0|11\.0|9\.5|9\.5|9\.0|11\.0|9\.0|11\.0|

|ridge|155\.4825|156\.483|10\.0|10\.0|10\.5|10\.0|10\.0|9\.5|9\.5|10\.0|10\.0|10\.0|10\.0|

|bayesian\_ridge|155\.4768|156\.4773|10\.0|10\.0|9\.0|9\.0|9\.0|8\.0|8\.0|11\.0|9\.0|11\.0|9\.0|

|svr|168\.6909|169\.1026|5\.0|6\.0|6\.0|8\.0|8\.0|11\.0|11\.0|6\.0|6\.0|6\.0|6\.0|

|decision\_tree|154\.8135|156\.4564|8\.0|8\.0|8\.0|7\.0|7\.0|7\.0|7\.0|8\.0|8\.0|8\.0|8\.0|

|random\_forest|155\.1075|156\.3841|7\.0|7\.0|7\.0|6\.0|6\.0|6\.0|6\.0|7\.0|7\.0|7\.0|7\.0|

|gradient\_boosting|155\.1426|156\.318|3\.0|2\.0|3\.0|3\.0|3\.0|3\.0|3\.0|2\.0|3\.0|2\.0|3\.0|

|hist\_gradient\_boosting|155\.2242|156\.4411|6\.0|5\.0|5\.0|5\.0|5\.0|5\.0|5\.0|5\.0|5\.0|5\.0|5\.0|

|xgb|155\.4133|156\.6328|__1\.0__|__1\.0__|__1\.0__|__1\.0__|__1\.0__|__1\.0__|__1\.0__|__1\.0__|__1\.0__|__1\.0__|__1\.0__|

|LGBM|155\.0745|156\.3721|4\.0|4\.0|4\.0|4\.0|4\.0|4\.0|4\.0|4\.0|4\.0|4\.0|4\.0|

|ann\_regressor|153\.1353|154\.2809|2\.0|3\.0|2\.0|2\.0|2\.0|2\.0|2\.0|3\.0|2\.0|3\.0|2\.0|

## 5.2 Hyper-parameters tuning for the best model - XGBoost

---

Best Hyperparameters: 

* learning_rate: 0.05

* max_depth: 10 

* n_estimators: 700

---

Metrics

* RMSE: 46.73

* r²: 0.96

* mean price: 155.65

# 6. Explainability - XAI Shapley values computation

### Largest contributors to the price (mean values contribution):

-  **longitude** is the feature mean highest contribution to the price

- **private bathroom**

- **accommodates**

- **latitude**

- **bedrooms**

---

### Largest contributors to the price absolute value:

- **accommodates**

- **private bathroom**

- **longitude** 

- **latitude**

- **bedrooms**



  



      Features contribution towards the prices - SHAP value






     Features absolute contribution towards the prices sorted by the maximum absolute value SHAP value




 Waterfall example of features contribution to a listing price




 Features clustering mean SHAP value