analysis-of-airbnb-data-new-york-city.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":611395,"sourceType":"datasetVersion","datasetId":268833}],"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# New York City Airbnb Open Data\n#### Airbnb listings and metrics in NYC, NY, USA (2019)\nAirbnb is a paid community platform for renting and booking private accommodation founded in 2008. Airbnb allows individuals to rent all or part of their own home as extra accommodation. The site offers a search and booking platform between the person offering their accommodation and the vacationer who wishes to rent it. It covers more than 1.5 million advertisements in more than 34,000 cities and 191 countries. From creation, inaugust 2008, until June 2012, more than 10 million nights have been booked on Airbnb.\n","metadata":{},"attachments":{"35072368-31872050.jpg":{"image/jpeg":"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"}}},{"cell_type":"markdown","source":"## Importing Libraries","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n%matplotlib inline\nsns.set_style(\"whitegrid\")\nplt.style.use(\"fivethirtyeight\")","metadata":{"execution":{"iopub.status.busy":"2024-12-18T06:28:35.900610Z","iopub.execute_input":"2024-12-18T06:28:35.900975Z","iopub.status.idle":"2024-12-18T06:28:37.254219Z","shell.execute_reply.started":"2024-12-18T06:28:35.900942Z","shell.execute_reply":"2024-12-18T06:28:37.253010Z"},"trusted":true},"outputs":[],"execution_count":2},{"cell_type":"markdown","source":"## Loading the data","metadata":{}},{"cell_type":"code","source":"df = pd.read_csv(\"../input/new-york-city-airbnb-open-data/AB_NYC_2019.csv\")\n# df.index.name = None\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2024-12-18T06:28:40.600525Z","iopub.execute_input":"2024-12-18T06:28:40.601034Z","iopub.status.idle":"2024-12-18T06:28:40.945612Z","shell.execute_reply.started":"2024-12-18T06:28:40.600998Z","shell.execute_reply":"2024-12-18T06:28:40.944596Z"},"trusted":true},"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"     id                                              name  host_id  \\\n0  2539                Clean & quiet apt home by the park     2787   \n1  2595                             Skylit Midtown Castle     2845   \n2  3647               THE VILLAGE OF HARLEM....NEW YORK !     4632   \n3  3831                   Cozy Entire Floor of Brownstone     4869   \n4  5022  Entire Apt: Spacious Studio/Loft by central park     7192   \n\n     host_name neighbourhood_group neighbourhood  latitude  longitude  \\\n0         John            Brooklyn    Kensington  40.64749  -73.97237   \n1     Jennifer           Manhattan       Midtown  40.75362  -73.98377   \n2    Elisabeth           Manhattan        Harlem  40.80902  -73.94190   \n3  LisaRoxanne            Brooklyn  Clinton Hill  40.68514  -73.95976   \n4        Laura           Manhattan   East Harlem  40.79851  -73.94399   \n\n         room_type  price  minimum_nights  number_of_reviews last_review  \\\n0     Private room    149               1                  9  2018-10-19   \n1  Entire home/apt    225               1                 45  2019-05-21   \n2     Private room    150               3                  0         NaN   \n3  Entire home/apt     89               1                270  2019-07-05   \n4  Entire home/apt     80              10                  9  2018-11-19   \n\n   reviews_per_month  calculated_host_listings_count  availability_365  \n0               0.21                               6               365  \n1               0.38                               2               355  \n2                NaN                               1               365  \n3               4.64                               1               194  \n4               0.10                               1                 0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>name</th>\n      <th>host_id</th>\n      <th>host_name</th>\n      <th>neighbourhood_group</th>\n      <th>neighbourhood</th>\n      <th>latitude</th>\n      <th>longitude</th>\n      <th>room_type</th>\n      <th>price</th>\n      <th>minimum_nights</th>\n      <th>number_of_reviews</th>\n      <th>last_review</th>\n      <th>reviews_per_month</th>\n      <th>calculated_host_listings_count</th>\n      <th>availability_365</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2539</td>\n      <td>Clean &amp; quiet apt home by the park</td>\n      <td>2787</td>\n      <td>John</td>\n      <td>Brooklyn</td>\n      <td>Kensington</td>\n      <td>40.64749</td>\n      <td>-73.97237</td>\n      <td>Private room</td>\n      <td>149</td>\n      <td>1</td>\n      <td>9</td>\n      <td>2018-10-19</td>\n      <td>0.21</td>\n      <td>6</td>\n      <td>365</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2595</td>\n      <td>Skylit Midtown Castle</td>\n      <td>2845</td>\n      <td>Jennifer</td>\n      <td>Manhattan</td>\n      <td>Midtown</td>\n      <td>40.75362</td>\n      <td>-73.98377</td>\n      <td>Entire home/apt</td>\n      <td>225</td>\n      <td>1</td>\n      <td>45</td>\n      <td>2019-05-21</td>\n      <td>0.38</td>\n      <td>2</td>\n      <td>355</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3647</td>\n      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n      <td>4632</td>\n      <td>Elisabeth</td>\n      <td>Manhattan</td>\n      <td>Harlem</td>\n      <td>40.80902</td>\n      <td>-73.94190</td>\n      <td>Private room</td>\n      <td>150</td>\n      <td>3</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>365</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3831</td>\n      <td>Cozy Entire Floor of Brownstone</td>\n      <td>4869</td>\n      <td>LisaRoxanne</td>\n      <td>Brooklyn</td>\n      <td>Clinton Hill</td>\n      <td>40.68514</td>\n      <td>-73.95976</td>\n      <td>Entire home/apt</td>\n      <td>89</td>\n      <td>1</td>\n      <td>270</td>\n      <td>2019-07-05</td>\n      <td>4.64</td>\n      <td>1</td>\n      <td>194</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5022</td>\n      <td>Entire Apt: Spacious Studio/Loft by central park</td>\n      <td>7192</td>\n      <td>Laura</td>\n      <td>Manhattan</td>\n      <td>East Harlem</td>\n      <td>40.79851</td>\n      <td>-73.94399</td>\n      <td>Entire home/apt</td>\n      <td>80</td>\n      <td>10</td>\n      <td>9</td>\n      <td>2018-11-19</td>\n      <td>0.10</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":3},{"cell_type":"code","source":"df = df.rename(columns={'host_id': 'product_id'})\ndf","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T06:29:35.983232Z","iopub.execute_input":"2024-12-18T06:29:35.983677Z","iopub.status.idle":"2024-12-18T06:29:36.011134Z","shell.execute_reply.started":"2024-12-18T06:29:35.983634Z","shell.execute_reply":"2024-12-18T06:29:36.009985Z"}},"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"       order_id                                               name  \\\n0          2539                 Clean & quiet apt home by the park   \n1          2595                              Skylit Midtown Castle   \n2          3647                THE VILLAGE OF HARLEM....NEW YORK !   \n3          3831                    Cozy Entire Floor of Brownstone   \n4          5022   Entire Apt: Spacious Studio/Loft by central park   \n...         ...                                                ...   \n48890  36484665    Charming one bedroom - newly renovated rowhouse   \n48891  36485057      Affordable room in Bushwick/East Williamsburg   \n48892  36485431            Sunny Studio at Historical Neighborhood   \n48893  36485609               43rd St. Time Square-cozy single bed   \n48894  36487245  Trendy duplex in the very heart of Hell's Kitchen   \n\n       product_id      host_name neighbourhood_group       neighbourhood  \\\n0            2787           John            Brooklyn          Kensington   \n1            2845       Jennifer           Manhattan             Midtown   \n2            4632      Elisabeth           Manhattan              Harlem   \n3            4869    LisaRoxanne            Brooklyn        Clinton Hill   \n4            7192          Laura           Manhattan         East Harlem   \n...           ...            ...                 ...                 ...   \n48890     8232441        Sabrina            Brooklyn  Bedford-Stuyvesant   \n48891     6570630        Marisol            Brooklyn            Bushwick   \n48892    23492952  Ilgar & Aysel           Manhattan              Harlem   \n48893    30985759            Taz           Manhattan      Hell's Kitchen   \n48894    68119814     Christophe           Manhattan      Hell's Kitchen   \n\n       latitude  longitude        room_type  price  minimum_nights  \\\n0      40.64749  -73.97237     Private room    149               1   \n1      40.75362  -73.98377  Entire home/apt    225               1   \n2      40.80902  -73.94190     Private room    150               3   \n3      40.68514  -73.95976  Entire home/apt     89               1   \n4      40.79851  -73.94399  Entire home/apt     80              10   \n...         ...        ...              ...    ...             ...   \n48890  40.67853  -73.94995     Private room     70               2   \n48891  40.70184  -73.93317     Private room     40               4   \n48892  40.81475  -73.94867  Entire home/apt    115              10   \n48893  40.75751  -73.99112      Shared room     55               1   \n48894  40.76404  -73.98933     Private room     90               7   \n\n       number_of_reviews last_review  reviews_per_month  \\\n0                      9  2018-10-19               0.21   \n1                     45  2019-05-21               0.38   \n2                      0         NaN                NaN   \n3                    270  2019-07-05               4.64   \n4                      9  2018-11-19               0.10   \n...                  ...         ...                ...   \n48890                  0         NaN                NaN   \n48891                  0         NaN                NaN   \n48892                  0         NaN                NaN   \n48893                  0         NaN                NaN   \n48894                  0         NaN                NaN   \n\n       calculated_host_listings_count  availability_365  \n0                                   6               365  \n1                                   2               355  \n2                                   1               365  \n3                                   1               194  \n4                                   1                 0  \n...                               ...               ...  \n48890                               2                 9  \n48891                               2                36  \n48892                               1                27  \n48893                               6                 2  \n48894                               1                23  \n\n[48895 rows x 16 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>order_id</th>\n      <th>name</th>\n      <th>product_id</th>\n      <th>host_name</th>\n      <th>neighbourhood_group</th>\n      <th>neighbourhood</th>\n      <th>latitude</th>\n      <th>longitude</th>\n      <th>room_type</th>\n      <th>price</th>\n      <th>minimum_nights</th>\n      <th>number_of_reviews</th>\n      <th>last_review</th>\n      <th>reviews_per_month</th>\n      <th>calculated_host_listings_count</th>\n      <th>availability_365</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2539</td>\n      <td>Clean &amp; quiet apt home by the park</td>\n      <td>2787</td>\n      <td>John</td>\n      <td>Brooklyn</td>\n      <td>Kensington</td>\n      <td>40.64749</td>\n      <td>-73.97237</td>\n      <td>Private room</td>\n      <td>149</td>\n      <td>1</td>\n      <td>9</td>\n      <td>2018-10-19</td>\n      <td>0.21</td>\n      <td>6</td>\n      <td>365</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2595</td>\n      <td>Skylit Midtown Castle</td>\n      <td>2845</td>\n      <td>Jennifer</td>\n      <td>Manhattan</td>\n      <td>Midtown</td>\n      <td>40.75362</td>\n      <td>-73.98377</td>\n      <td>Entire home/apt</td>\n      <td>225</td>\n      <td>1</td>\n      <td>45</td>\n      <td>2019-05-21</td>\n      <td>0.38</td>\n      <td>2</td>\n      <td>355</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3647</td>\n      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n      <td>4632</td>\n      <td>Elisabeth</td>\n      <td>Manhattan</td>\n      <td>Harlem</td>\n      <td>40.80902</td>\n      <td>-73.94190</td>\n      <td>Private room</td>\n      <td>150</td>\n      <td>3</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>365</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3831</td>\n      <td>Cozy Entire Floor of Brownstone</td>\n      <td>4869</td>\n      <td>LisaRoxanne</td>\n      <td>Brooklyn</td>\n      <td>Clinton Hill</td>\n      <td>40.68514</td>\n      <td>-73.95976</td>\n      <td>Entire home/apt</td>\n      <td>89</td>\n      <td>1</td>\n      <td>270</td>\n      <td>2019-07-05</td>\n      <td>4.64</td>\n      <td>1</td>\n      <td>194</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5022</td>\n      <td>Entire Apt: Spacious Studio/Loft by central park</td>\n      <td>7192</td>\n      <td>Laura</td>\n      <td>Manhattan</td>\n      <td>East Harlem</td>\n      <td>40.79851</td>\n      <td>-73.94399</td>\n      <td>Entire home/apt</td>\n      <td>80</td>\n      <td>10</td>\n      <td>9</td>\n      <td>2018-11-19</td>\n      <td>0.10</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>48890</th>\n      <td>36484665</td>\n      <td>Charming one bedroom - newly renovated rowhouse</td>\n      <td>8232441</td>\n      <td>Sabrina</td>\n      <td>Brooklyn</td>\n      <td>Bedford-Stuyvesant</td>\n      <td>40.67853</td>\n      <td>-73.94995</td>\n      <td>Private room</td>\n      <td>70</td>\n      <td>2</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>48891</th>\n      <td>36485057</td>\n      <td>Affordable room in Bushwick/East Williamsburg</td>\n      <td>6570630</td>\n      <td>Marisol</td>\n      <td>Brooklyn</td>\n      <td>Bushwick</td>\n      <td>40.70184</td>\n      <td>-73.93317</td>\n      <td>Private room</td>\n      <td>40</td>\n      <td>4</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2</td>\n      <td>36</td>\n    </tr>\n    <tr>\n      <th>48892</th>\n      <td>36485431</td>\n      <td>Sunny Studio at Historical Neighborhood</td>\n      <td>23492952</td>\n      <td>Ilgar &amp; Aysel</td>\n      <td>Manhattan</td>\n      <td>Harlem</td>\n      <td>40.81475</td>\n      <td>-73.94867</td>\n      <td>Entire home/apt</td>\n      <td>115</td>\n      <td>10</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>27</td>\n    </tr>\n    <tr>\n      <th>48893</th>\n      <td>36485609</td>\n      <td>43rd St. Time Square-cozy single bed</td>\n      <td>30985759</td>\n      <td>Taz</td>\n      <td>Manhattan</td>\n      <td>Hell's Kitchen</td>\n      <td>40.75751</td>\n      <td>-73.99112</td>\n      <td>Shared room</td>\n      <td>55</td>\n      <td>1</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>6</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>48894</th>\n      <td>36487245</td>\n      <td>Trendy duplex in the very heart of Hell's Kitchen</td>\n      <td>68119814</td>\n      <td>Christophe</td>\n      <td>Manhattan</td>\n      <td>Hell's Kitchen</td>\n      <td>40.76404</td>\n      <td>-73.98933</td>\n      <td>Private room</td>\n      <td>90</td>\n      <td>7</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>23</td>\n    </tr>\n  </tbody>\n</table>\n<p>48895 rows × 16 columns</p>\n</div>"},"metadata":{}}],"execution_count":6},{"cell_type":"code","source":"df = df.rename(columns={'id': 'order_id'})\ndf.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T06:29:40.735411Z","iopub.execute_input":"2024-12-18T06:29:40.736280Z","iopub.status.idle":"2024-12-18T06:29:40.759168Z","shell.execute_reply.started":"2024-12-18T06:29:40.736242Z","shell.execute_reply":"2024-12-18T06:29:40.758073Z"}},"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"   order_id                                              name  product_id  \\\n0      2539                Clean & quiet apt home by the park        2787   \n1      2595                             Skylit Midtown Castle        2845   \n2      3647               THE VILLAGE OF HARLEM....NEW YORK !        4632   \n3      3831                   Cozy Entire Floor of Brownstone        4869   \n4      5022  Entire Apt: Spacious Studio/Loft by central park        7192   \n\n     host_name neighbourhood_group neighbourhood  latitude  longitude  \\\n0         John            Brooklyn    Kensington  40.64749  -73.97237   \n1     Jennifer           Manhattan       Midtown  40.75362  -73.98377   \n2    Elisabeth           Manhattan        Harlem  40.80902  -73.94190   \n3  LisaRoxanne            Brooklyn  Clinton Hill  40.68514  -73.95976   \n4        Laura           Manhattan   East Harlem  40.79851  -73.94399   \n\n         room_type  price  minimum_nights  number_of_reviews last_review  \\\n0     Private room    149               1                  9  2018-10-19   \n1  Entire home/apt    225               1                 45  2019-05-21   \n2     Private room    150               3                  0         NaN   \n3  Entire home/apt     89               1                270  2019-07-05   \n4  Entire home/apt     80              10                  9  2018-11-19   \n\n   reviews_per_month  calculated_host_listings_count  availability_365  \n0               0.21                               6               365  \n1               0.38                               2               355  \n2                NaN                               1               365  \n3               4.64                               1               194  \n4               0.10                               1                 0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>order_id</th>\n      <th>name</th>\n      <th>product_id</th>\n      <th>host_name</th>\n      <th>neighbourhood_group</th>\n      <th>neighbourhood</th>\n      <th>latitude</th>\n      <th>longitude</th>\n      <th>room_type</th>\n      <th>price</th>\n      <th>minimum_nights</th>\n      <th>number_of_reviews</th>\n      <th>last_review</th>\n      <th>reviews_per_month</th>\n      <th>calculated_host_listings_count</th>\n      <th>availability_365</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2539</td>\n      <td>Clean &amp; quiet apt home by the park</td>\n      <td>2787</td>\n      <td>John</td>\n      <td>Brooklyn</td>\n      <td>Kensington</td>\n      <td>40.64749</td>\n      <td>-73.97237</td>\n      <td>Private room</td>\n      <td>149</td>\n      <td>1</td>\n      <td>9</td>\n      <td>2018-10-19</td>\n      <td>0.21</td>\n      <td>6</td>\n      <td>365</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2595</td>\n      <td>Skylit Midtown Castle</td>\n      <td>2845</td>\n      <td>Jennifer</td>\n      <td>Manhattan</td>\n      <td>Midtown</td>\n      <td>40.75362</td>\n      <td>-73.98377</td>\n      <td>Entire home/apt</td>\n      <td>225</td>\n      <td>1</td>\n      <td>45</td>\n      <td>2019-05-21</td>\n      <td>0.38</td>\n      <td>2</td>\n      <td>355</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3647</td>\n      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n      <td>4632</td>\n      <td>Elisabeth</td>\n      <td>Manhattan</td>\n      <td>Harlem</td>\n      <td>40.80902</td>\n      <td>-73.94190</td>\n      <td>Private room</td>\n      <td>150</td>\n      <td>3</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>365</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3831</td>\n      <td>Cozy Entire Floor of Brownstone</td>\n      <td>4869</td>\n      <td>LisaRoxanne</td>\n      <td>Brooklyn</td>\n      <td>Clinton Hill</td>\n      <td>40.68514</td>\n      <td>-73.95976</td>\n      <td>Entire home/apt</td>\n      <td>89</td>\n      <td>1</td>\n      <td>270</td>\n      <td>2019-07-05</td>\n      <td>4.64</td>\n      <td>1</td>\n      <td>194</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5022</td>\n      <td>Entire Apt: Spacious Studio/Loft by central park</td>\n      <td>7192</td>\n      <td>Laura</td>\n      <td>Manhattan</td>\n      <td>East Harlem</td>\n      <td>40.79851</td>\n      <td>-73.94399</td>\n      <td>Entire home/apt</td>\n      <td>80</td>\n      <td>10</td>\n      <td>9</td>\n      <td>2018-11-19</td>\n      <td>0.10</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":7},{"cell_type":"code","source":"df = df.rename(columns={'minimum_nights': 'add_to_cart_order'})\ndf.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T06:29:43.979230Z","iopub.execute_input":"2024-12-18T06:29:43.979651Z","iopub.status.idle":"2024-12-18T06:29:44.004212Z","shell.execute_reply.started":"2024-12-18T06:29:43.979605Z","shell.execute_reply":"2024-12-18T06:29:44.002946Z"}},"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"   order_id                                              name  product_id  \\\n0      2539                Clean & quiet apt home by the park        2787   \n1      2595                             Skylit Midtown Castle        2845   \n2      3647               THE VILLAGE OF HARLEM....NEW YORK !        4632   \n3      3831                   Cozy Entire Floor of Brownstone        4869   \n4      5022  Entire Apt: Spacious Studio/Loft by central park        7192   \n\n     host_name neighbourhood_group neighbourhood  latitude  longitude  \\\n0         John            Brooklyn    Kensington  40.64749  -73.97237   \n1     Jennifer           Manhattan       Midtown  40.75362  -73.98377   \n2    Elisabeth           Manhattan        Harlem  40.80902  -73.94190   \n3  LisaRoxanne            Brooklyn  Clinton Hill  40.68514  -73.95976   \n4        Laura           Manhattan   East Harlem  40.79851  -73.94399   \n\n         room_type  price  add_to_cart_order  number_of_reviews last_review  \\\n0     Private room    149                  1                  9  2018-10-19   \n1  Entire home/apt    225                  1                 45  2019-05-21   \n2     Private room    150                  3                  0         NaN   \n3  Entire home/apt     89                  1                270  2019-07-05   \n4  Entire home/apt     80                 10                  9  2018-11-19   \n\n   reviews_per_month  calculated_host_listings_count  availability_365  \n0               0.21                               6               365  \n1               0.38                               2               355  \n2                NaN                               1               365  \n3               4.64                               1               194  \n4               0.10                               1                 0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>order_id</th>\n      <th>name</th>\n      <th>product_id</th>\n      <th>host_name</th>\n      <th>neighbourhood_group</th>\n      <th>neighbourhood</th>\n      <th>latitude</th>\n      <th>longitude</th>\n      <th>room_type</th>\n      <th>price</th>\n      <th>add_to_cart_order</th>\n      <th>number_of_reviews</th>\n      <th>last_review</th>\n      <th>reviews_per_month</th>\n      <th>calculated_host_listings_count</th>\n      <th>availability_365</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2539</td>\n      <td>Clean &amp; quiet apt home by the park</td>\n      <td>2787</td>\n      <td>John</td>\n      <td>Brooklyn</td>\n      <td>Kensington</td>\n      <td>40.64749</td>\n      <td>-73.97237</td>\n      <td>Private room</td>\n      <td>149</td>\n      <td>1</td>\n      <td>9</td>\n      <td>2018-10-19</td>\n      <td>0.21</td>\n      <td>6</td>\n      <td>365</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2595</td>\n      <td>Skylit Midtown Castle</td>\n      <td>2845</td>\n      <td>Jennifer</td>\n      <td>Manhattan</td>\n      <td>Midtown</td>\n      <td>40.75362</td>\n      <td>-73.98377</td>\n      <td>Entire home/apt</td>\n      <td>225</td>\n      <td>1</td>\n      <td>45</td>\n      <td>2019-05-21</td>\n      <td>0.38</td>\n      <td>2</td>\n      <td>355</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3647</td>\n      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n      <td>4632</td>\n      <td>Elisabeth</td>\n      <td>Manhattan</td>\n      <td>Harlem</td>\n      <td>40.80902</td>\n      <td>-73.94190</td>\n      <td>Private room</td>\n      <td>150</td>\n      <td>3</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>365</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3831</td>\n      <td>Cozy Entire Floor of Brownstone</td>\n      <td>4869</td>\n      <td>LisaRoxanne</td>\n      <td>Brooklyn</td>\n      <td>Clinton Hill</td>\n      <td>40.68514</td>\n      <td>-73.95976</td>\n      <td>Entire home/apt</td>\n      <td>89</td>\n      <td>1</td>\n      <td>270</td>\n      <td>2019-07-05</td>\n      <td>4.64</td>\n      <td>1</td>\n      <td>194</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5022</td>\n      <td>Entire Apt: Spacious Studio/Loft by central park</td>\n      <td>7192</td>\n      <td>Laura</td>\n      <td>Manhattan</td>\n      <td>East Harlem</td>\n      <td>40.79851</td>\n      <td>-73.94399</td>\n      <td>Entire home/apt</td>\n      <td>80</td>\n      <td>10</td>\n      <td>9</td>\n      <td>2018-11-19</td>\n      <td>0.10</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":8},{"cell_type":"code","source":"df = df.rename(columns={'number_of_reviews': 'reordered'})\ndf.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T06:29:46.797342Z","iopub.execute_input":"2024-12-18T06:29:46.798620Z","iopub.status.idle":"2024-12-18T06:29:46.823865Z","shell.execute_reply.started":"2024-12-18T06:29:46.798560Z","shell.execute_reply":"2024-12-18T06:29:46.822598Z"}},"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"   order_id                                              name  product_id  \\\n0      2539                Clean & quiet apt home by the park        2787   \n1      2595                             Skylit Midtown Castle        2845   \n2      3647               THE VILLAGE OF HARLEM....NEW YORK !        4632   \n3      3831                   Cozy Entire Floor of Brownstone        4869   \n4      5022  Entire Apt: Spacious Studio/Loft by central park        7192   \n\n     host_name neighbourhood_group neighbourhood  latitude  longitude  \\\n0         John            Brooklyn    Kensington  40.64749  -73.97237   \n1     Jennifer           Manhattan       Midtown  40.75362  -73.98377   \n2    Elisabeth           Manhattan        Harlem  40.80902  -73.94190   \n3  LisaRoxanne            Brooklyn  Clinton Hill  40.68514  -73.95976   \n4        Laura           Manhattan   East Harlem  40.79851  -73.94399   \n\n         room_type  price  add_to_cart_order  reordered last_review  \\\n0     Private room    149                  1          9  2018-10-19   \n1  Entire home/apt    225                  1         45  2019-05-21   \n2     Private room    150                  3          0         NaN   \n3  Entire home/apt     89                  1        270  2019-07-05   \n4  Entire home/apt     80                 10          9  2018-11-19   \n\n   reviews_per_month  calculated_host_listings_count  availability_365  \n0               0.21                               6               365  \n1               0.38                               2               355  \n2                NaN                               1               365  \n3               4.64                               1               194  \n4               0.10                               1                 0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>order_id</th>\n      <th>name</th>\n      <th>product_id</th>\n      <th>host_name</th>\n      <th>neighbourhood_group</th>\n      <th>neighbourhood</th>\n      <th>latitude</th>\n      <th>longitude</th>\n      <th>room_type</th>\n      <th>price</th>\n      <th>add_to_cart_order</th>\n      <th>reordered</th>\n      <th>last_review</th>\n      <th>reviews_per_month</th>\n      <th>calculated_host_listings_count</th>\n      <th>availability_365</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2539</td>\n      <td>Clean &amp; quiet apt home by the park</td>\n      <td>2787</td>\n      <td>John</td>\n      <td>Brooklyn</td>\n      <td>Kensington</td>\n      <td>40.64749</td>\n      <td>-73.97237</td>\n      <td>Private room</td>\n      <td>149</td>\n      <td>1</td>\n      <td>9</td>\n      <td>2018-10-19</td>\n      <td>0.21</td>\n      <td>6</td>\n      <td>365</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2595</td>\n      <td>Skylit Midtown Castle</td>\n      <td>2845</td>\n      <td>Jennifer</td>\n      <td>Manhattan</td>\n      <td>Midtown</td>\n      <td>40.75362</td>\n      <td>-73.98377</td>\n      <td>Entire home/apt</td>\n      <td>225</td>\n      <td>1</td>\n      <td>45</td>\n      <td>2019-05-21</td>\n      <td>0.38</td>\n      <td>2</td>\n      <td>355</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3647</td>\n      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n      <td>4632</td>\n      <td>Elisabeth</td>\n      <td>Manhattan</td>\n      <td>Harlem</td>\n      <td>40.80902</td>\n      <td>-73.94190</td>\n      <td>Private room</td>\n      <td>150</td>\n      <td>3</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>365</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3831</td>\n      <td>Cozy Entire Floor of Brownstone</td>\n      <td>4869</td>\n      <td>LisaRoxanne</td>\n      <td>Brooklyn</td>\n      <td>Clinton Hill</td>\n      <td>40.68514</td>\n      <td>-73.95976</td>\n      <td>Entire home/apt</td>\n      <td>89</td>\n      <td>1</td>\n      <td>270</td>\n      <td>2019-07-05</td>\n      <td>4.64</td>\n      <td>1</td>\n      <td>194</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5022</td>\n      <td>Entire Apt: Spacious Studio/Loft by central park</td>\n      <td>7192</td>\n      <td>Laura</td>\n      <td>Manhattan</td>\n      <td>East Harlem</td>\n      <td>40.79851</td>\n      <td>-73.94399</td>\n      <td>Entire home/apt</td>\n      <td>80</td>\n      <td>10</td>\n      <td>9</td>\n      <td>2018-11-19</td>\n      <td>0.10</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":9},{"cell_type":"code","source":"#每個使用者的購物紀錄\ndf1 = df[['order_id','product_id','add_to_cart_order','reordered']]\norders=df1\narticles = orders\narticles","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T06:29:50.308690Z","iopub.execute_input":"2024-12-18T06:29:50.309517Z","iopub.status.idle":"2024-12-18T06:29:50.325391Z","shell.execute_reply.started":"2024-12-18T06:29:50.309438Z","shell.execute_reply":"2024-12-18T06:29:50.324372Z"}},"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"       order_id  product_id  add_to_cart_order  reordered\n0          2539        2787                  1          9\n1          2595        2845                  1         45\n2          3647        4632                  3          0\n3          3831        4869                  1        270\n4          5022        7192                 10          9\n...         ...         ...                ...        ...\n48890  36484665     8232441                  2          0\n48891  36485057     6570630                  4          0\n48892  36485431    23492952                 10          0\n48893  36485609    30985759                  1          0\n48894  36487245    68119814                  7          0\n\n[48895 rows x 4 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>order_id</th>\n      <th>product_id</th>\n      <th>add_to_cart_order</th>\n      <th>reordered</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2539</td>\n      <td>2787</td>\n      <td>1</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2595</td>\n      <td>2845</td>\n      <td>1</td>\n      <td>45</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3647</td>\n      <td>4632</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3831</td>\n      <td>4869</td>\n      <td>1</td>\n      <td>270</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5022</td>\n      <td>7192</td>\n      <td>10</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>48890</th>\n      <td>36484665</td>\n      <td>8232441</td>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>48891</th>\n      <td>36485057</td>\n      <td>6570630</td>\n      <td>4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>48892</th>\n      <td>36485431</td>\n      <td>23492952</td>\n      <td>10</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>48893</th>\n      <td>36485609</td>\n      <td>30985759</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>48894</th>\n      <td>36487245</td>\n      <td>68119814</td>\n      <td>7</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>48895 rows × 4 columns</p>\n</div>"},"metadata":{}}],"execution_count":10},{"cell_type":"code","source":"aisles1 = pd.read_csv(\"../input/new-york-city-airbnb-open-data/AB_NYC_2019.csv\")\naisles1.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T06:29:54.107083Z","iopub.execute_input":"2024-12-18T06:29:54.107469Z","iopub.status.idle":"2024-12-18T06:29:54.342907Z","shell.execute_reply.started":"2024-12-18T06:29:54.107435Z","shell.execute_reply":"2024-12-18T06:29:54.341701Z"}},"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"     id                                              name  host_id  \\\n0  2539                Clean & quiet apt home by the park     2787   \n1  2595                             Skylit Midtown Castle     2845   \n2  3647               THE VILLAGE OF HARLEM....NEW YORK !     4632   \n3  3831                   Cozy Entire Floor of Brownstone     4869   \n4  5022  Entire Apt: Spacious Studio/Loft by central park     7192   \n\n     host_name neighbourhood_group neighbourhood  latitude  longitude  \\\n0         John            Brooklyn    Kensington  40.64749  -73.97237   \n1     Jennifer           Manhattan       Midtown  40.75362  -73.98377   \n2    Elisabeth           Manhattan        Harlem  40.80902  -73.94190   \n3  LisaRoxanne            Brooklyn  Clinton Hill  40.68514  -73.95976   \n4        Laura           Manhattan   East Harlem  40.79851  -73.94399   \n\n         room_type  price  minimum_nights  number_of_reviews last_review  \\\n0     Private room    149               1                  9  2018-10-19   \n1  Entire home/apt    225               1                 45  2019-05-21   \n2     Private room    150               3                  0         NaN   \n3  Entire home/apt     89               1                270  2019-07-05   \n4  Entire home/apt     80              10                  9  2018-11-19   \n\n   reviews_per_month  calculated_host_listings_count  availability_365  \n0               0.21                               6               365  \n1               0.38                               2               355  \n2                NaN                               1               365  \n3               4.64                               1               194  \n4               0.10                               1                 0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>name</th>\n      <th>host_id</th>\n      <th>host_name</th>\n      <th>neighbourhood_group</th>\n      <th>neighbourhood</th>\n      <th>latitude</th>\n      <th>longitude</th>\n      <th>room_type</th>\n      <th>price</th>\n      <th>minimum_nights</th>\n      <th>number_of_reviews</th>\n      <th>last_review</th>\n      <th>reviews_per_month</th>\n      <th>calculated_host_listings_count</th>\n      <th>availability_365</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2539</td>\n      <td>Clean &amp; quiet apt home by the park</td>\n      <td>2787</td>\n      <td>John</td>\n      <td>Brooklyn</td>\n      <td>Kensington</td>\n      <td>40.64749</td>\n      <td>-73.97237</td>\n      <td>Private room</td>\n      <td>149</td>\n      <td>1</td>\n      <td>9</td>\n      <td>2018-10-19</td>\n      <td>0.21</td>\n      <td>6</td>\n      <td>365</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2595</td>\n      <td>Skylit Midtown Castle</td>\n      <td>2845</td>\n      <td>Jennifer</td>\n      <td>Manhattan</td>\n      <td>Midtown</td>\n      <td>40.75362</td>\n      <td>-73.98377</td>\n      <td>Entire home/apt</td>\n      <td>225</td>\n      <td>1</td>\n      <td>45</td>\n      <td>2019-05-21</td>\n      <td>0.38</td>\n      <td>2</td>\n      <td>355</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3647</td>\n      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n      <td>4632</td>\n      <td>Elisabeth</td>\n      <td>Manhattan</td>\n      <td>Harlem</td>\n      <td>40.80902</td>\n      <td>-73.94190</td>\n      <td>Private room</td>\n      <td>150</td>\n      <td>3</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>365</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3831</td>\n      <td>Cozy Entire Floor of Brownstone</td>\n      <td>4869</td>\n      <td>LisaRoxanne</td>\n      <td>Brooklyn</td>\n      <td>Clinton Hill</td>\n      <td>40.68514</td>\n      <td>-73.95976</td>\n      <td>Entire home/apt</td>\n      <td>89</td>\n      <td>1</td>\n      <td>270</td>\n      <td>2019-07-05</td>\n      <td>4.64</td>\n      <td>1</td>\n      <td>194</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5022</td>\n      <td>Entire Apt: Spacious Studio/Loft by central park</td>\n      <td>7192</td>\n      <td>Laura</td>\n      <td>Manhattan</td>\n      <td>East Harlem</td>\n      <td>40.79851</td>\n      <td>-73.94399</td>\n      <td>Entire home/apt</td>\n      <td>80</td>\n      <td>10</td>\n      <td>9</td>\n      <td>2018-11-19</td>\n      <td>0.10</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":11},{"cell_type":"code","source":"aisles = aisles1[['host_id', 'host_name',]]\naisles = aisles.rename(columns={'host_id': 'order_id','host_name': 'aisle_name'})\naisles","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T06:29:58.267459Z","iopub.execute_input":"2024-12-18T06:29:58.267874Z","iopub.status.idle":"2024-12-18T06:29:58.285258Z","shell.execute_reply.started":"2024-12-18T06:29:58.267832Z","shell.execute_reply":"2024-12-18T06:29:58.283916Z"}},"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"       order_id     aisle_name\n0          2787           John\n1          2845       Jennifer\n2          4632      Elisabeth\n3          4869    LisaRoxanne\n4          7192          Laura\n...         ...            ...\n48890   8232441        Sabrina\n48891   6570630        Marisol\n48892  23492952  Ilgar & Aysel\n48893  30985759            Taz\n48894  68119814     Christophe\n\n[48895 rows x 2 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>order_id</th>\n      <th>aisle_name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2787</td>\n      <td>John</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2845</td>\n      <td>Jennifer</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4632</td>\n      <td>Elisabeth</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4869</td>\n      <td>LisaRoxanne</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>7192</td>\n      <td>Laura</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>48890</th>\n      <td>8232441</td>\n      <td>Sabrina</td>\n    </tr>\n    <tr>\n      <th>48891</th>\n      <td>6570630</td>\n      <td>Marisol</td>\n    </tr>\n    <tr>\n      <th>48892</th>\n      <td>23492952</td>\n      <td>Ilgar &amp; Aysel</td>\n    </tr>\n    <tr>\n      <th>48893</th>\n      <td>30985759</td>\n      <td>Taz</td>\n    </tr>\n    <tr>\n      <th>48894</th>\n      <td>68119814</td>\n      <td>Christophe</td>\n    </tr>\n  </tbody>\n</table>\n<p>48895 rows × 2 columns</p>\n</div>"},"metadata":{}}],"execution_count":12},{"cell_type":"code","source":"_mt = pd.merge(df,orders, on = ['product_id','product_id'])\n_mt = _mt.rename(columns={'order_id_x': 'order_id','add_to_cart_order_x': 'add_to_cart_order','reordered_x': 'reordered'})\n_mt.head(2)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T06:30:03.140345Z","iopub.execute_input":"2024-12-18T06:30:03.140813Z","iopub.status.idle":"2024-12-18T06:30:03.443107Z","shell.execute_reply.started":"2024-12-18T06:30:03.140776Z","shell.execute_reply":"2024-12-18T06:30:03.441791Z"}},"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"   order_id                                name  product_id host_name  \\\n0      2539  Clean & quiet apt home by the park        2787      John   \n1      2539  Clean & quiet apt home by the park        2787      John   \n\n  neighbourhood_group neighbourhood  latitude  longitude     room_type  price  \\\n0            Brooklyn    Kensington  40.64749  -73.97237  Private room    149   \n1            Brooklyn    Kensington  40.64749  -73.97237  Private room    149   \n\n   add_to_cart_order  reordered last_review  reviews_per_month  \\\n0                  1          9  2018-10-19               0.21   \n1                  1          9  2018-10-19               0.21   \n\n   calculated_host_listings_count  availability_365  order_id_y  \\\n0                               6               365        2539   \n1                               6               365     7937553   \n\n   add_to_cart_order_y  reordered_y  \n0                    1            9  \n1                    1           21  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>order_id</th>\n      <th>name</th>\n      <th>product_id</th>\n      <th>host_name</th>\n      <th>neighbourhood_group</th>\n      <th>neighbourhood</th>\n      <th>latitude</th>\n      <th>longitude</th>\n      <th>room_type</th>\n      <th>price</th>\n      <th>add_to_cart_order</th>\n      <th>reordered</th>\n      <th>last_review</th>\n      <th>reviews_per_month</th>\n      <th>calculated_host_listings_count</th>\n      <th>availability_365</th>\n      <th>order_id_y</th>\n      <th>add_to_cart_order_y</th>\n      <th>reordered_y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2539</td>\n      <td>Clean &amp; quiet apt home by the park</td>\n      <td>2787</td>\n      <td>John</td>\n      <td>Brooklyn</td>\n      <td>Kensington</td>\n      <td>40.64749</td>\n      <td>-73.97237</td>\n      <td>Private room</td>\n      <td>149</td>\n      <td>1</td>\n      <td>9</td>\n      <td>2018-10-19</td>\n      <td>0.21</td>\n      <td>6</td>\n      <td>365</td>\n      <td>2539</td>\n      <td>1</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2539</td>\n      <td>Clean &amp; quiet apt home by the park</td>\n      <td>2787</td>\n      <td>John</td>\n      <td>Brooklyn</td>\n      <td>Kensington</td>\n      <td>40.64749</td>\n      <td>-73.97237</td>\n      <td>Private room</td>\n      <td>149</td>\n      <td>1</td>\n      <td>9</td>\n      <td>2018-10-19</td>\n      <td>0.21</td>\n      <td>6</td>\n      <td>365</td>\n      <td>7937553</td>\n      <td>1</td>\n      <td>21</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":13},{"cell_type":"code","source":"_mt = pd.merge(_mt,orders,on=['order_id','order_id'])\n_mt.head(3)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T06:30:06.617180Z","iopub.execute_input":"2024-12-18T06:30:06.617673Z","iopub.status.idle":"2024-12-18T06:30:06.735128Z","shell.execute_reply.started":"2024-12-18T06:30:06.617632Z","shell.execute_reply":"2024-12-18T06:30:06.733922Z"}},"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"   order_id                                name  product_id_x host_name  \\\n0      2539  Clean & quiet apt home by the park          2787      John   \n1      2539  Clean & quiet apt home by the park          2787      John   \n2      2539  Clean & quiet apt home by the park          2787      John   \n\n  neighbourhood_group neighbourhood  latitude  longitude     room_type  price  \\\n0            Brooklyn    Kensington  40.64749  -73.97237  Private room    149   \n1            Brooklyn    Kensington  40.64749  -73.97237  Private room    149   \n2            Brooklyn    Kensington  40.64749  -73.97237  Private room    149   \n\n   ...  last_review  reviews_per_month calculated_host_listings_count  \\\n0  ...   2018-10-19               0.21                              6   \n1  ...   2018-10-19               0.21                              6   \n2  ...   2018-10-19               0.21                              6   \n\n   availability_365  order_id_y  add_to_cart_order_y  reordered_y  \\\n0               365        2539                    1            9   \n1               365     7937553                    1           21   \n2               365    10160215                    1           17   \n\n   product_id_y  add_to_cart_order_y  reordered_y  \n0          2787                    1            9  \n1          2787                    1            9  \n2          2787                    1            9  \n\n[3 rows x 22 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>order_id</th>\n      <th>name</th>\n      <th>product_id_x</th>\n      <th>host_name</th>\n      <th>neighbourhood_group</th>\n      <th>neighbourhood</th>\n      <th>latitude</th>\n      <th>longitude</th>\n      <th>room_type</th>\n      <th>price</th>\n      <th>...</th>\n      <th>last_review</th>\n      <th>reviews_per_month</th>\n      <th>calculated_host_listings_count</th>\n      <th>availability_365</th>\n      <th>order_id_y</th>\n      <th>add_to_cart_order_y</th>\n      <th>reordered_y</th>\n      <th>product_id_y</th>\n      <th>add_to_cart_order_y</th>\n      <th>reordered_y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2539</td>\n      <td>Clean &amp; quiet apt home by the park</td>\n      <td>2787</td>\n      <td>John</td>\n      <td>Brooklyn</td>\n      <td>Kensington</td>\n      <td>40.64749</td>\n      <td>-73.97237</td>\n      <td>Private room</td>\n      <td>149</td>\n      <td>...</td>\n      <td>2018-10-19</td>\n      <td>0.21</td>\n      <td>6</td>\n      <td>365</td>\n      <td>2539</td>\n      <td>1</td>\n      <td>9</td>\n      <td>2787</td>\n      <td>1</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2539</td>\n      <td>Clean &amp; quiet apt home by the park</td>\n      <td>2787</td>\n      <td>John</td>\n      <td>Brooklyn</td>\n      <td>Kensington</td>\n      <td>40.64749</td>\n      <td>-73.97237</td>\n      <td>Private room</td>\n      <td>149</td>\n      <td>...</td>\n      <td>2018-10-19</td>\n      <td>0.21</td>\n      <td>6</td>\n      <td>365</td>\n      <td>7937553</td>\n      <td>1</td>\n      <td>21</td>\n      <td>2787</td>\n      <td>1</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2539</td>\n      <td>Clean &amp; quiet apt home by the park</td>\n      <td>2787</td>\n      <td>John</td>\n      <td>Brooklyn</td>\n      <td>Kensington</td>\n      <td>40.64749</td>\n      <td>-73.97237</td>\n      <td>Private room</td>\n      <td>149</td>\n      <td>...</td>\n      <td>2018-10-19</td>\n      <td>0.21</td>\n      <td>6</td>\n      <td>365</td>\n      <td>10160215</td>\n      <td>1</td>\n      <td>17</td>\n      <td>2787</td>\n      <td>1</td>\n      <td>9</td>\n    </tr>\n  </tbody>\n</table>\n<p>3 rows × 22 columns</p>\n</div>"},"metadata":{}}],"execution_count":14},{"cell_type":"code","source":"mt = pd.merge(_mt,aisles,on=['order_id','order_id'])\nmt.head(3)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T06:30:10.074039Z","iopub.execute_input":"2024-12-18T06:30:10.074416Z","iopub.status.idle":"2024-12-18T06:30:10.134822Z","shell.execute_reply.started":"2024-12-18T06:30:10.074379Z","shell.execute_reply":"2024-12-18T06:30:10.133582Z"}},"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"   order_id                                 name  product_id_x  host_name  \\\n0      3647  THE VILLAGE OF HARLEM....NEW YORK !          4632  Elisabeth   \n1      3647  THE VILLAGE OF HARLEM....NEW YORK !          4632  Elisabeth   \n2      7322                      Chelsea Perfect         18946       Doti   \n\n  neighbourhood_group neighbourhood  latitude  longitude     room_type  price  \\\n0           Manhattan        Harlem  40.80902  -73.94190  Private room    150   \n1           Manhattan        Harlem  40.80902  -73.94190  Private room    150   \n2           Manhattan       Chelsea  40.74192  -73.99501  Private room    140   \n\n   ...  reviews_per_month  calculated_host_listings_count availability_365  \\\n0  ...                NaN                               1              365   \n1  ...                NaN                               1              365   \n2  ...               2.12                               1               12   \n\n   order_id_y  add_to_cart_order_y  reordered_y  product_id_y  \\\n0        3647                    3            0          4632   \n1        3647                    3            0          4632   \n2        7322                    1          260         18946   \n\n   add_to_cart_order_y  reordered_y  aisle_name  \n0                    3            0      Rafael  \n1                    3            0      Rafael  \n2                    1          260       Chris  \n\n[3 rows x 23 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>order_id</th>\n      <th>name</th>\n      <th>product_id_x</th>\n      <th>host_name</th>\n      <th>neighbourhood_group</th>\n      <th>neighbourhood</th>\n      <th>latitude</th>\n      <th>longitude</th>\n      <th>room_type</th>\n      <th>price</th>\n      <th>...</th>\n      <th>reviews_per_month</th>\n      <th>calculated_host_listings_count</th>\n      <th>availability_365</th>\n      <th>order_id_y</th>\n      <th>add_to_cart_order_y</th>\n      <th>reordered_y</th>\n      <th>product_id_y</th>\n      <th>add_to_cart_order_y</th>\n      <th>reordered_y</th>\n      <th>aisle_name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3647</td>\n      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n      <td>4632</td>\n      <td>Elisabeth</td>\n      <td>Manhattan</td>\n      <td>Harlem</td>\n      <td>40.80902</td>\n      <td>-73.94190</td>\n      <td>Private room</td>\n      <td>150</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>365</td>\n      <td>3647</td>\n      <td>3</td>\n      <td>0</td>\n      <td>4632</td>\n      <td>3</td>\n      <td>0</td>\n      <td>Rafael</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3647</td>\n      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n      <td>4632</td>\n      <td>Elisabeth</td>\n      <td>Manhattan</td>\n      <td>Harlem</td>\n      <td>40.80902</td>\n      <td>-73.94190</td>\n      <td>Private room</td>\n      <td>150</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>365</td>\n      <td>3647</td>\n      <td>3</td>\n      <td>0</td>\n      <td>4632</td>\n      <td>3</td>\n      <td>0</td>\n      <td>Rafael</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>7322</td>\n      <td>Chelsea Perfect</td>\n      <td>18946</td>\n      <td>Doti</td>\n      <td>Manhattan</td>\n      <td>Chelsea</td>\n      <td>40.74192</td>\n      <td>-73.99501</td>\n      <td>Private room</td>\n      <td>140</td>\n      <td>...</td>\n      <td>2.12</td>\n      <td>1</td>\n      <td>12</td>\n      <td>7322</td>\n      <td>1</td>\n      <td>260</td>\n      <td>18946</td>\n      <td>1</td>\n      <td>260</td>\n      <td>Chris</td>\n    </tr>\n  </tbody>\n</table>\n<p>3 rows × 23 columns</p>\n</div>"},"metadata":{}}],"execution_count":15},{"cell_type":"code","source":"mt = mt.rename(columns={'order_id': 't_dat','order_id_y': 'customer_id', 'product_id_x': 'article_id', 'add_to_cart_order_y': 'frequency'})\nGraphTravel_HM = mt\nGraphTravel_HM = GraphTravel_HM[['t_dat','customer_id','article_id','frequency']] \nGraphTravel_HM","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T06:31:00.364636Z","iopub.execute_input":"2024-12-18T06:31:00.365029Z","iopub.status.idle":"2024-12-18T06:31:00.380386Z","shell.execute_reply.started":"2024-12-18T06:31:00.364992Z","shell.execute_reply":"2024-12-18T06:31:00.379012Z"}},"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"        t_dat  customer_id  article_id  frequency  frequency\n0        3647         3647        4632          3          3\n1        3647         3647        4632          3          3\n2        7322         7322       18946          1          1\n3       31994        31994      137814          2          2\n4       31994        42580      137814          2          2\n..        ...          ...         ...        ...        ...\n178  29941633     29941633     3218399         10         10\n179  31929849     31929849     7958766          2          2\n180  33945670     33945670    43332874          7          7\n181  33945670     33945670    43332874          7          7\n182  35914632     35914632   269987340          3          3\n\n[183 rows x 5 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>t_dat</th>\n      <th>customer_id</th>\n      <th>article_id</th>\n      <th>frequency</th>\n      <th>frequency</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3647</td>\n      <td>3647</td>\n      <td>4632</td>\n      <td>3</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3647</td>\n      <td>3647</td>\n      <td>4632</td>\n      <td>3</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>7322</td>\n      <td>7322</td>\n      <td>18946</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>31994</td>\n      <td>31994</td>\n      <td>137814</td>\n      <td>2</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>31994</td>\n      <td>42580</td>\n      <td>137814</td>\n      <td>2</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>178</th>\n      <td>29941633</td>\n      <td>29941633</td>\n      <td>3218399</td>\n      <td>10</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>179</th>\n      <td>31929849</td>\n      <td>31929849</td>\n      <td>7958766</td>\n      <td>2</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>180</th>\n      <td>33945670</td>\n      <td>33945670</td>\n      <td>43332874</td>\n      <td>7</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>181</th>\n      <td>33945670</td>\n      <td>33945670</td>\n      <td>43332874</td>\n      <td>7</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>182</th>\n      <td>35914632</td>\n      <td>35914632</td>\n      <td>269987340</td>\n      <td>3</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n<p>183 rows × 5 columns</p>\n</div>"},"metadata":{}}],"execution_count":17},{"cell_type":"code","source":"sns.distplot(GraphTravel_HM['frequency'], kde=True, bins=30)\n\nplt.title('Distribution of frequency')\nplt.xlabel('Frequency')\nplt.ylabel('Density')\n\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T06:31:04.524609Z","iopub.execute_input":"2024-12-18T06:31:04.525006Z","iopub.status.idle":"2024-12-18T06:31:05.076786Z","shell.execute_reply.started":"2024-12-18T06:31:04.524974Z","shell.execute_reply":"2024-12-18T06:31:05.075586Z"}},"outputs":[{"name":"stderr","text":"/tmp/ipykernel_24/521335795.py:1: UserWarning: \n\n`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n\nPlease adapt your code to use either `displot` (a figure-level function with\nsimilar flexibility) or `histplot` (an axes-level function for histograms).\n\nFor a guide to updating your code to use the new functions, please see\nhttps://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n\n  sns.distplot(GraphTravel_HM['frequency'], kde=True, bins=30)\n/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}],"execution_count":18},{"cell_type":"code","source":"#產品的名稱跟哪一個銷售區域\nproducts = pd.read_csv(\"../input/new-york-city-airbnb-open-data/AB_NYC_2019.csv\")\narticles = products\narticles.head(3)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T06:31:09.258213Z","iopub.execute_input":"2024-12-18T06:31:09.258690Z","iopub.status.idle":"2024-12-18T06:31:09.454096Z","shell.execute_reply.started":"2024-12-18T06:31:09.258648Z","shell.execute_reply":"2024-12-18T06:31:09.452846Z"}},"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"     id                                 name  host_id  host_name  \\\n0  2539   Clean & quiet apt home by the park     2787       John   \n1  2595                Skylit Midtown Castle     2845   Jennifer   \n2  3647  THE VILLAGE OF HARLEM....NEW YORK !     4632  Elisabeth   \n\n  neighbourhood_group neighbourhood  latitude  longitude        room_type  \\\n0            Brooklyn    Kensington  40.64749  -73.97237     Private room   \n1           Manhattan       Midtown  40.75362  -73.98377  Entire home/apt   \n2           Manhattan        Harlem  40.80902  -73.94190     Private room   \n\n   price  minimum_nights  number_of_reviews last_review  reviews_per_month  \\\n0    149               1                  9  2018-10-19               0.21   \n1    225               1                 45  2019-05-21               0.38   \n2    150               3                  0         NaN                NaN   \n\n   calculated_host_listings_count  availability_365  \n0                               6               365  \n1                               2               355  \n2                               1               365  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>name</th>\n      <th>host_id</th>\n      <th>host_name</th>\n      <th>neighbourhood_group</th>\n      <th>neighbourhood</th>\n      <th>latitude</th>\n      <th>longitude</th>\n      <th>room_type</th>\n      <th>price</th>\n      <th>minimum_nights</th>\n      <th>number_of_reviews</th>\n      <th>last_review</th>\n      <th>reviews_per_month</th>\n      <th>calculated_host_listings_count</th>\n      <th>availability_365</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2539</td>\n      <td>Clean &amp; quiet apt home by the park</td>\n      <td>2787</td>\n      <td>John</td>\n      <td>Brooklyn</td>\n      <td>Kensington</td>\n      <td>40.64749</td>\n      <td>-73.97237</td>\n      <td>Private room</td>\n      <td>149</td>\n      <td>1</td>\n      <td>9</td>\n      <td>2018-10-19</td>\n      <td>0.21</td>\n      <td>6</td>\n      <td>365</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2595</td>\n      <td>Skylit Midtown Castle</td>\n      <td>2845</td>\n      <td>Jennifer</td>\n      <td>Manhattan</td>\n      <td>Midtown</td>\n      <td>40.75362</td>\n      <td>-73.98377</td>\n      <td>Entire home/apt</td>\n      <td>225</td>\n      <td>1</td>\n      <td>45</td>\n      <td>2019-05-21</td>\n      <td>0.38</td>\n      <td>2</td>\n      <td>355</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3647</td>\n      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n      <td>4632</td>\n      <td>Elisabeth</td>\n      <td>Manhattan</td>\n      <td>Harlem</td>\n      <td>40.80902</td>\n      <td>-73.94190</td>\n      <td>Private room</td>\n      <td>150</td>\n      <td>3</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>365</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":19},{"cell_type":"code","source":"articles = articles.rename(columns={'host_id': 'article_id','host_name': 'product_name'})\narticles.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T06:31:13.683342Z","iopub.execute_input":"2024-12-18T06:31:13.683763Z","iopub.status.idle":"2024-12-18T06:31:13.704765Z","shell.execute_reply.started":"2024-12-18T06:31:13.683724Z","shell.execute_reply":"2024-12-18T06:31:13.703688Z"}},"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"     id                                              name  article_id  \\\n0  2539                Clean & quiet apt home by the park        2787   \n1  2595                             Skylit Midtown Castle        2845   \n2  3647               THE VILLAGE OF HARLEM....NEW YORK !        4632   \n3  3831                   Cozy Entire Floor of Brownstone        4869   \n4  5022  Entire Apt: Spacious Studio/Loft by central park        7192   \n\n  product_name neighbourhood_group neighbourhood  latitude  longitude  \\\n0         John            Brooklyn    Kensington  40.64749  -73.97237   \n1     Jennifer           Manhattan       Midtown  40.75362  -73.98377   \n2    Elisabeth           Manhattan        Harlem  40.80902  -73.94190   \n3  LisaRoxanne            Brooklyn  Clinton Hill  40.68514  -73.95976   \n4        Laura           Manhattan   East Harlem  40.79851  -73.94399   \n\n         room_type  price  minimum_nights  number_of_reviews last_review  \\\n0     Private room    149               1                  9  2018-10-19   \n1  Entire home/apt    225               1                 45  2019-05-21   \n2     Private room    150               3                  0         NaN   \n3  Entire home/apt     89               1                270  2019-07-05   \n4  Entire home/apt     80              10                  9  2018-11-19   \n\n   reviews_per_month  calculated_host_listings_count  availability_365  \n0               0.21                               6               365  \n1               0.38                               2               355  \n2                NaN                               1               365  \n3               4.64                               1               194  \n4               0.10                               1                 0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>name</th>\n      <th>article_id</th>\n      <th>product_name</th>\n      <th>neighbourhood_group</th>\n      <th>neighbourhood</th>\n      <th>latitude</th>\n      <th>longitude</th>\n      <th>room_type</th>\n      <th>price</th>\n      <th>minimum_nights</th>\n      <th>number_of_reviews</th>\n      <th>last_review</th>\n      <th>reviews_per_month</th>\n      <th>calculated_host_listings_count</th>\n      <th>availability_365</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2539</td>\n      <td>Clean &amp; quiet apt home by the park</td>\n      <td>2787</td>\n      <td>John</td>\n      <td>Brooklyn</td>\n      <td>Kensington</td>\n      <td>40.64749</td>\n      <td>-73.97237</td>\n      <td>Private room</td>\n      <td>149</td>\n      <td>1</td>\n      <td>9</td>\n      <td>2018-10-19</td>\n      <td>0.21</td>\n      <td>6</td>\n      <td>365</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2595</td>\n      <td>Skylit Midtown Castle</td>\n      <td>2845</td>\n      <td>Jennifer</td>\n      <td>Manhattan</td>\n      <td>Midtown</td>\n      <td>40.75362</td>\n      <td>-73.98377</td>\n      <td>Entire home/apt</td>\n      <td>225</td>\n      <td>1</td>\n      <td>45</td>\n      <td>2019-05-21</td>\n      <td>0.38</td>\n      <td>2</td>\n      <td>355</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3647</td>\n      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n      <td>4632</td>\n      <td>Elisabeth</td>\n      <td>Manhattan</td>\n      <td>Harlem</td>\n      <td>40.80902</td>\n      <td>-73.94190</td>\n      <td>Private room</td>\n      <td>150</td>\n      <td>3</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>365</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3831</td>\n      <td>Cozy Entire Floor of Brownstone</td>\n      <td>4869</td>\n      <td>LisaRoxanne</td>\n      <td>Brooklyn</td>\n      <td>Clinton Hill</td>\n      <td>40.68514</td>\n      <td>-73.95976</td>\n      <td>Entire home/apt</td>\n      <td>89</td>\n      <td>1</td>\n      <td>270</td>\n      <td>2019-07-05</td>\n      <td>4.64</td>\n      <td>1</td>\n      <td>194</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5022</td>\n      <td>Entire Apt: Spacious Studio/Loft by central park</td>\n      <td>7192</td>\n      <td>Laura</td>\n      <td>Manhattan</td>\n      <td>East Harlem</td>\n      <td>40.79851</td>\n      <td>-73.94399</td>\n      <td>Entire home/apt</td>\n      <td>80</td>\n      <td>10</td>\n      <td>9</td>\n      <td>2018-11-19</td>\n      <td>0.10</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":20},{"cell_type":"code","source":"def get_rated_items(user_id, df):\n    return set(df[df['customer_id'] == user_id]['article_id'])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T06:49:50.430239Z","iopub.execute_input":"2024-12-18T06:49:50.431277Z","iopub.status.idle":"2024-12-18T06:49:50.435992Z","shell.execute_reply.started":"2024-12-18T06:49:50.431234Z","shell.execute_reply":"2024-12-18T06:49:50.434848Z"}},"outputs":[],"execution_count":69},{"cell_type":"code","source":"# Random Walk # generate_walks(G, 2, 8, p=0.5, q=0.5)\nwalks = generate_walks(G, 2, 8, p=0.5, q=0.5)\nfiltered_walks = [walk for walk in walks if len(walk) >= 5]\n\n# to String  (for Word2Vec input)\nwalks = [[str(node) for node in walk] for walk in walks]\n\n# Word2Vec train\nmodel = Word2Vec(walks, vector_size=128, window=5, min_count=0,  hs=1, sg=1, workers=4, epochs=10)\n\n# node embedding extract\nembeddings = {node_id: model.wv[node_id] for node_id in model.wv.index_to_key}","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T06:59:13.664979Z","iopub.execute_input":"2024-12-18T06:59:13.665435Z","iopub.status.idle":"2024-12-18T06:59:13.884033Z","shell.execute_reply.started":"2024-12-18T06:59:13.665395Z","shell.execute_reply":"2024-12-18T06:59:13.882837Z"}},"outputs":[],"execution_count":102},{"cell_type":"code","source":"# biased random walk  \ndef biased_random_walk(G, start_node, walk_length, p=1, q=1):\n    walk = [start_node]\n\n    while len(walk) < walk_length:\n        cur_node = walk[-1]\n        cur_neighbors = list(G.neighbors(cur_node))\n\n        if len(cur_neighbors) > 0:\n            if len(walk) == 1:\n                walk.append(random.choice(cur_neighbors))\n            else:\n                prev_node = walk[-2]\n\n                probability = []\n                for neighbor in cur_neighbors:\n                    if neighbor == prev_node:\n                        # Return parameter \n                        probability.append(1/p)\n                    elif G.has_edge(neighbor, prev_node):\n                        # Stay parameter \n                        probability.append(1)\n                    else:\n                        # In-out parameter \n                        probability.append(1/q)\n\n                probability = np.array(probability)\n                probability = probability / probability.sum()  # normalize\n\n                next_node = np.random.choice(cur_neighbors, p=probability)\n                walk.append(next_node)\n        else:\n            break\n\n    return walk","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T06:59:03.932794Z","iopub.execute_input":"2024-12-18T06:59:03.933254Z","iopub.status.idle":"2024-12-18T06:59:03.942583Z","shell.execute_reply.started":"2024-12-18T06:59:03.933217Z","shell.execute_reply":"2024-12-18T06:59:03.941084Z"}},"outputs":[],"execution_count":101},{"cell_type":"code","source":"G = nx.Graph()\n\nfor index, row in GraphTravel_HM.iterrows():\n    G.add_node(row['customer_id'], type='user')\n    G.add_node(row['article_id'], type='item')\n    G.add_edge(row['customer_id'], row['article_id'], weight=row['frequency'])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T06:57:47.967733Z","iopub.execute_input":"2024-12-18T06:57:47.968200Z","iopub.status.idle":"2024-12-18T06:57:48.030101Z","shell.execute_reply.started":"2024-12-18T06:57:47.968161Z","shell.execute_reply":"2024-12-18T06:57:48.028603Z"}},"outputs":[],"execution_count":97},{"cell_type":"code","source":"def generate_walks(G, num_walks, walk_length, p=1, q=1):\n    walks = []\n    nodes = list(G.nodes())\n    for _ in range(num_walks):\n        random.shuffle(nodes)  # to ensure randomness\n        for node in nodes:\n            walk_from_node = biased_random_walk(G, node, walk_length, p, q)\n            walks.append(walk_from_node)\n    return walks","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T06:57:44.879913Z","iopub.execute_input":"2024-12-18T06:57:44.880391Z","iopub.status.idle":"2024-12-18T06:57:44.887766Z","shell.execute_reply.started":"2024-12-18T06:57:44.880351Z","shell.execute_reply":"2024-12-18T06:57:44.886184Z"}},"outputs":[],"execution_count":96},{"cell_type":"code","source":"unique_customer_ids = GraphTravel_HM['customer_id'].unique()\ncustomer_id_mapping = {id: i for i, id in enumerate(unique_customer_ids)}\nGraphTravel_HM['customer_id'] = GraphTravel_HM['customer_id'].map(customer_id_mapping)\nitem_name_mapping = dict(zip(articles['article_id'], articles['product_name'])) # prod_name","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T06:54:02.673133Z","iopub.execute_input":"2024-12-18T06:54:02.673667Z","iopub.status.idle":"2024-12-18T06:54:02.706199Z","shell.execute_reply.started":"2024-12-18T06:54:02.673626Z","shell.execute_reply":"2024-12-18T06:54:02.704982Z"}},"outputs":[{"name":"stderr","text":"/tmp/ipykernel_24/4090393013.py:3: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  GraphTravel_HM['customer_id'] = GraphTravel_HM['customer_id'].map(customer_id_mapping)\n","output_type":"stream"}],"execution_count":86},{"cell_type":"code","source":"def get_rated_items(user_id, df):\n    return set(df[df['customer_id'] == user_id]['article_id'])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T06:54:06.146629Z","iopub.execute_input":"2024-12-18T06:54:06.147036Z","iopub.status.idle":"2024-12-18T06:54:06.153463Z","shell.execute_reply.started":"2024-12-18T06:54:06.147004Z","shell.execute_reply":"2024-12-18T06:54:06.152030Z"}},"outputs":[],"execution_count":87},{"cell_type":"code","source":"def get_user_embedding(user_id, embeddings):\n    return embeddings[str(user_id)]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T06:54:08.521746Z","iopub.execute_input":"2024-12-18T06:54:08.522205Z","iopub.status.idle":"2024-12-18T06:54:08.528346Z","shell.execute_reply.started":"2024-12-18T06:54:08.522167Z","shell.execute_reply":"2024-12-18T06:54:08.526940Z"}},"outputs":[],"execution_count":88},{"cell_type":"code","source":"def calculate_similarities(user_id, df, embeddings):\n    rated_items = get_rated_items(user_id, df)\n    user_embedding = get_user_embedding(user_id, embeddings)\n\n    item_similarities = []\n    for item_id in set(df['article_id']):\n        if item_id not in rated_items:  \n            item_embedding = embeddings[str(item_id)]\n            similarity = cosine_similarity([user_embedding], [item_embedding])[0][0]\n            item_similarities.append((item_id, similarity))\n\n    return item_similarities","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T06:54:12.062586Z","iopub.execute_input":"2024-12-18T06:54:12.063024Z","iopub.status.idle":"2024-12-18T06:54:12.069227Z","shell.execute_reply.started":"2024-12-18T06:54:12.062988Z","shell.execute_reply":"2024-12-18T06:54:12.068158Z"}},"outputs":[],"execution_count":89},{"cell_type":"code","source":"def recommend_items(user_id, df, embeddings, item_name_mapping, num_items=5):\n    rated_items = get_rated_items(user_id, df)\n    \n    print(f\"User {user_id} has purchased:\")\n    show_images([(item_id, 0) for item_id in list(rated_items)[:5]], item_name_mapping, min(len(rated_items), 5))\n    \n    item_similarities = calculate_similarities(user_id, df, embeddings)\n\n    recommended_items = sorted(item_similarities, key=lambda x: x[1], reverse=True)[:num_items]\n\n    print(f\"\\nRecommended items for user {user_id}:\")\n    show_images(recommended_items, item_name_mapping, num_items, show_similarity=True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T06:55:39.891777Z","iopub.execute_input":"2024-12-18T06:55:39.892292Z","iopub.status.idle":"2024-12-18T06:55:39.899979Z","shell.execute_reply.started":"2024-12-18T06:55:39.892251Z","shell.execute_reply":"2024-12-18T06:55:39.898410Z"}},"outputs":[],"execution_count":92},{"cell_type":"code","source":"# costomer 45's top 5  \nrecommend_items( 45, GraphTravel_HM, embeddings, item_name_mapping, num_items=5)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T07:03:09.497425Z","iopub.execute_input":"2024-12-18T07:03:09.497839Z","iopub.status.idle":"2024-12-18T07:03:10.499146Z","shell.execute_reply.started":"2024-12-18T07:03:09.497804Z","shell.execute_reply":"2024-12-18T07:03:10.498033Z"}},"outputs":[{"name":"stdout","text":"User 45 has purchased:\n\nRecommended items for user 45:\n- Item 4632: Elisabeth with similarity score: 0.6664034724235535\nImage for item 4632 not found.\n- Item 38204730: Kathy with similarity score: 0.656387448310852\nImage for item 38204730 not found.\n- Item 12485770: Raanan with similarity score: 0.6353153586387634\nImage for item 12485770 not found.\n- Item 160495098: Miller with similarity score: 0.6127380132675171\nImage for item 160495098 not found.\n- Item 69545272: Ben with similarity score: 0.6115382313728333\nImage for item 69545272 not found.\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 2000x1000 with 5 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costomer 45's top 5  \nrecommend_items( 100, GraphTravel_HM, embeddings, item_name_mapping, num_items=5)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T07:05:53.664822Z","iopub.execute_input":"2024-12-18T07:05:53.665271Z","iopub.status.idle":"2024-12-18T07:05:54.960831Z","shell.execute_reply.started":"2024-12-18T07:05:53.665232Z","shell.execute_reply":"2024-12-18T07:05:54.959705Z"}},"outputs":[{"name":"stdout","text":"User 100 has purchased:\n\nRecommended items for user 100:\n- Item 161268152: Maytal And Ken with similarity score: 0.872789740562439\nImage for item 161268152 not found.\n- Item 7360975: Calvin with similarity score: 0.8194672465324402\nImage for item 7360975 not found.\n- Item 1675255: Jill with similarity score: 0.7791862487792969\nImage for item 1675255 not found.\n- Item 1349340: Noah with similarity score: 0.7336462140083313\nImage for item 1349340 not found.\n- Item 26079021: Michael with similarity score: 0.6948009729385376\nImage for item 26079021 not found.\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 2000x1000 with 5 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costomer 45's top 5  \nrecommend_items( 50, GraphTravel_HM, embeddings, item_name_mapping, num_items=5)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T07:13:36.001392Z","iopub.execute_input":"2024-12-18T07:13:36.001824Z","iopub.status.idle":"2024-12-18T07:13:37.077700Z","shell.execute_reply.started":"2024-12-18T07:13:36.001788Z","shell.execute_reply":"2024-12-18T07:13:37.076442Z"}},"outputs":[{"name":"stdout","text":"User 50 has purchased:\n\nRecommended items for user 50:\n- Item 269987340: Nicolas with similarity score: 0.8614672422409058\nImage for item 269987340 not found.\n- Item 144197377: Riley with similarity score: 0.8176286220550537\nImage for item 144197377 not found.\n- Item 3000089: Kimberly with similarity score: 0.8072959184646606\nImage for item 3000089 not found.\n- Item 19285185: Joel with similarity score: 0.7488809823989868\nImage for item 19285185 not found.\n- Item 7815949: Dee with similarity score: 0.6601086854934692\nImage for item 7815949 not found.\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 2000x1000 with 5 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costomer 45's top 5  \nrecommend_items( 10, GraphTravel_HM, embeddings, item_name_mapping, num_items=5)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T07:16:55.993475Z","iopub.execute_input":"2024-12-18T07:16:55.994014Z","iopub.status.idle":"2024-12-18T07:16:57.040829Z","shell.execute_reply.started":"2024-12-18T07:16:55.993963Z","shell.execute_reply":"2024-12-18T07:16:57.039735Z"}},"outputs":[{"name":"stdout","text":"User 10 has purchased:\n\nRecommended items for user 10:\n- Item 160495098: Miller with similarity score: 0.7792606353759766\nImage for item 160495098 not found.\n- Item 69545272: Ben with similarity score: 0.6935999393463135\nImage for item 69545272 not found.\n- Item 2979607: Miriam with similarity score: 0.6602954864501953\nImage for item 2979607 not found.\n- Item 12485770: Raanan with similarity score: 0.6590560674667358\nImage for item 12485770 not found.\n- Item 49964890: Hanson with similarity score: 0.617601752281189\nImage for item 49964890 not found.\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 2000x1000 with 5 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costomer 45's top 5  \nrecommend_items( 20, GraphTravel_HM, embeddings, item_name_mapping, num_items=20)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T07:22:08.657683Z","iopub.execute_input":"2024-12-18T07:22:08.658186Z","iopub.status.idle":"2024-12-18T07:22:11.334271Z","shell.execute_reply.started":"2024-12-18T07:22:08.658142Z","shell.execute_reply":"2024-12-18T07:22:11.333239Z"}},"outputs":[{"name":"stdout","text":"User 20 has purchased:\n\nRecommended items for user 20:\n- Item 69545272: Ben with similarity score: 0.8407365083694458\nImage for item 69545272 not found.\n- Item 12485770: Raanan with similarity score: 0.8302960991859436\nImage for item 12485770 not found.\n- Item 137814: Waldemar with similarity score: 0.8083434104919434\nImage for item 137814 not found.\n- Item 12798614: Jessica with similarity score: 0.7719731330871582\nImage for item 12798614 not found.\n- Item 160495098: Miller with similarity score: 0.7306702733039856\nImage for item 160495098 not found.\n- Item 2979607: Miriam with similarity score: 0.6680232286453247\nImage for item 2979607 not found.\n- Item 119523037: Anna with similarity score: 0.6530411243438721\nImage for item 119523037 not found.\n- Item 295128: Carol Gloria with similarity score: 0.5959587693214417\nImage for item 295128 not found.\n- Item 9820942: Erica And Jorel with similarity score: 0.5793652534484863\nImage for item 9820942 not found.\n- Item 12879538: Gregory with similarity score: 0.5666768550872803\nImage for item 12879538 not found.\n- Item 18946: Doti with similarity score: 0.4900018870830536\nImage for item 18946 not found.\n- Item 161268152: Maytal And Ken with similarity score: 0.4857133626937866\nImage for item 161268152 not found.\n- Item 1289635: Alan with similarity score: 0.47438645362854004\nImage for item 1289635 not found.\n- Item 1349340: Noah with similarity score: 0.45737096667289734\nImage for item 1349340 not found.\n- Item 14061862: Jay with similarity score: 0.43685564398765564\nImage for item 14061862 not found.\n- Item 5309160: Elizabeth And Roberto with similarity score: 0.4303118586540222\nImage for item 5309160 not found.\n- Item 19285185: Joel with similarity score: 0.41470056772232056\nImage for item 19285185 not found.\n- Item 38204730: Kathy with similarity score: 0.4091659188270569\nImage for item 38204730 not found.\n- Item 66513544: Michele with similarity score: 0.3897312879562378\nImage for item 66513544 not found.\n- Item 4632: Elisabeth with similarity score: 0.3448615074157715\nImage for item 4632 not found.\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 2000x1000 with 20 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show_images(items, item_name_mapping, num_items, show_similarity=False):\n    f, ax = plt.subplots(1, num_items, figsize=(20,10))\n    if num_items == 1:\n        ax = [ax]\n    for i, item in enumerate(items):\n        item_id, similarity = item\n        print(f\"- Item {item_id}: {item_name_mapping[item_id]}\", end='')\n        if show_similarity:\n            print(f\" with similarity score: {similarity}\")\n        else:\n            print()\n        img_path = f\"../input/h-and-m-personalized-fashion-recommendations/images/085/0850792002.jpg\"\n        try:\n            img = mpimg.imread(img_path)\n            ax[i].imshow(img)\n            ax[i].set_title(f'Item {item_id}')\n            ax[i].set_xticks([], [])\n            ax[i].set_yticks([], [])\n            ax[i].grid(False)\n        except FileNotFoundError:\n            print(f\"Image for item {item_id} not found.\")\n    plt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T07:03:04.407267Z","iopub.execute_input":"2024-12-18T07:03:04.407675Z","iopub.status.idle":"2024-12-18T07:03:04.415278Z","shell.execute_reply.started":"2024-12-18T07:03:04.407639Z","shell.execute_reply":"2024-12-18T07:03:04.414255Z"}},"outputs":[],"execution_count":111},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport seaborn as sns\nfrom matplotlib import pyplot as plt\nimport networkx as nx\nfrom gensim.models import Word2Vec\nfrom sklearn.metrics.pairwise import cosine_similarity\nimport matplotlib.image as mpimg\nimport random","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T06:53:36.823257Z","iopub.execute_input":"2024-12-18T06:53:36.823742Z","iopub.status.idle":"2024-12-18T06:53:45.800038Z","shell.execute_reply.started":"2024-12-18T06:53:36.823704Z","shell.execute_reply":"2024-12-18T06:53:45.798926Z"}},"outputs":[],"execution_count":83}]}