Analyzing Amazon Customer Dataset.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":10421848,"sourceType":"datasetVersion","datasetId":6459520}],"dockerImageVersionId":30822,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"source":"<a href=\"https://www.kaggle.com/code/hillaryjude/amazon-customer-reviews-analysis?scriptVersionId=217143951\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:12.887028Z","iopub.execute_input":"2025-01-11T17:00:12.887387Z","iopub.status.idle":"2025-01-11T17:00:13.295632Z","shell.execute_reply.started":"2025-01-11T17:00:12.88736Z","shell.execute_reply":"2025-01-11T17:00:13.293981Z"}},"outputs":[{"name":"stdout","text":"/kaggle/input/amazon-customer-review/Reviews.csv\n","output_type":"stream"}],"execution_count":2},{"cell_type":"markdown","source":"# Reading the data","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:13.29736Z","iopub.execute_input":"2025-01-11T17:00:13.298152Z","iopub.status.idle":"2025-01-11T17:00:14.256889Z","shell.execute_reply.started":"2025-01-11T17:00:13.298114Z","shell.execute_reply":"2025-01-11T17:00:14.25565Z"}},"outputs":[],"execution_count":3},{"cell_type":"code","source":"# extracting the data\ndf = pd.read_csv('/kaggle/input/amazon-customer-review/Reviews.csv')\ndf.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:14.259387Z","iopub.execute_input":"2025-01-11T17:00:14.259891Z","iopub.status.idle":"2025-01-11T17:00:21.788321Z","shell.execute_reply.started":"2025-01-11T17:00:14.259855Z","shell.execute_reply":"2025-01-11T17:00:21.787196Z"}},"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"   Id   ProductId          UserId                      ProfileName  \\\n0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n3   4  B000UA0QIQ  A395BORC6FGVXV                             Karl   \n4   5  B006K2ZZ7K  A1UQRSCLF8GW1T    Michael D. Bigham \"M. Wassir\"   \n\n   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n0                     1                       1      5  1303862400   \n1                     0                       0      1  1346976000   \n2                     1                       1      4  1219017600   \n3                     3                       3      2  1307923200   \n4                     0                       0      5  1350777600   \n\n                 Summary                                               Text  \n0  Good Quality Dog Food  I have bought several of the Vitality canned d...  \n1      Not as Advertised  Product arrived labeled as Jumbo Salted Peanut...  \n2  \"Delight\" says it all  This is a confection that has been around a fe...  \n3         Cough Medicine  If you are looking for the secret ingredient i...  \n4            Great taffy  Great taffy at a great price.  There was a wid...  ","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>ProductId</th>\n      <th>UserId</th>\n      <th>ProfileName</th>\n      <th>HelpfulnessNumerator</th>\n      <th>HelpfulnessDenominator</th>\n      <th>Score</th>\n      <th>Time</th>\n      <th>Summary</th>\n      <th>Text</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>B001E4KFG0</td>\n      <td>A3SGXH7AUHU8GW</td>\n      <td>delmartian</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1303862400</td>\n      <td>Good Quality Dog Food</td>\n      <td>I have bought several of the Vitality canned d...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>B00813GRG4</td>\n      <td>A1D87F6ZCVE5NK</td>\n      <td>dll pa</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1346976000</td>\n      <td>Not as Advertised</td>\n      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>B000LQOCH0</td>\n      <td>ABXLMWJIXXAIN</td>\n      <td>Natalia Corres \"Natalia Corres\"</td>\n      <td>1</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1219017600</td>\n      <td>\"Delight\" says it all</td>\n      <td>This is a confection that has been around a fe...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>B000UA0QIQ</td>\n      <td>A395BORC6FGVXV</td>\n      <td>Karl</td>\n      <td>3</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1307923200</td>\n      <td>Cough Medicine</td>\n      <td>If you are looking for the secret ingredient i...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>B006K2ZZ7K</td>\n      <td>A1UQRSCLF8GW1T</td>\n      <td>Michael D. Bigham \"M. Wassir\"</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1350777600</td>\n      <td>Great taffy</td>\n      <td>Great taffy at a great price.  There was a wid...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":4},{"cell_type":"code","source":"df.shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:21.790337Z","iopub.execute_input":"2025-01-11T17:00:21.790763Z","iopub.status.idle":"2025-01-11T17:00:21.796941Z","shell.execute_reply.started":"2025-01-11T17:00:21.790703Z","shell.execute_reply":"2025-01-11T17:00:21.795783Z"}},"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"(568454, 10)"},"metadata":{}}],"execution_count":5},{"cell_type":"code","source":"df.columns","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:21.79882Z","iopub.execute_input":"2025-01-11T17:00:21.799378Z","iopub.status.idle":"2025-01-11T17:00:21.819543Z","shell.execute_reply.started":"2025-01-11T17:00:21.799332Z","shell.execute_reply":"2025-01-11T17:00:21.817675Z"}},"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"Index(['Id', 'ProductId', 'UserId', 'ProfileName', 'HelpfulnessNumerator',\n       'HelpfulnessDenominator', 'Score', 'Time', 'Summary', 'Text'],\n      dtype='object')"},"metadata":{}}],"execution_count":6},{"cell_type":"markdown","source":"# Processing the data","metadata":{}},{"cell_type":"raw","source":"HelpfulnessDenominator - This tells the total number of likes a product has irrespective of the ratings. Whether a person finds the product useful or not\nHelpfulnessNumerator - This tells that the product has likes and not dislikes.\n\nTherefore Numerator value cannot exceed denominator value. A good product will have numerator value close to its denominator value.","metadata":{}},{"cell_type":"code","source":"df[df['HelpfulnessDenominator']<df['HelpfulnessNumerator']]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:21.820957Z","iopub.execute_input":"2025-01-11T17:00:21.82139Z","iopub.status.idle":"2025-01-11T17:00:21.851665Z","shell.execute_reply.started":"2025-01-11T17:00:21.821346Z","shell.execute_reply":"2025-01-11T17:00:21.850603Z"}},"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"          Id   ProductId          UserId              ProfileName  \\\n44736  44737  B001EQ55RW  A2V0I904FH7ABY                      Ram   \n64421  64422  B000MIDROQ  A161DK06JJMCYF  J. E. Stephens \"Jeanne\"   \n\n       HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n44736                     3                       2      4  1212883200   \n64421                     3                       1      5  1224892800   \n\n                                            Summary  \\\n44736  Pure cocoa taste with crunchy almonds inside   \n64421             Bought This for My Son at College   \n\n                                                    Text  \n44736  It was almost a 'love at first bite' - the per...  \n64421  My son loves spaghetti so I didn't hesitate or...  ","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>ProductId</th>\n      <th>UserId</th>\n      <th>ProfileName</th>\n      <th>HelpfulnessNumerator</th>\n      <th>HelpfulnessDenominator</th>\n      <th>Score</th>\n      <th>Time</th>\n      <th>Summary</th>\n      <th>Text</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>44736</th>\n      <td>44737</td>\n      <td>B001EQ55RW</td>\n      <td>A2V0I904FH7ABY</td>\n      <td>Ram</td>\n      <td>3</td>\n      <td>2</td>\n      <td>4</td>\n      <td>1212883200</td>\n      <td>Pure cocoa taste with crunchy almonds inside</td>\n      <td>It was almost a 'love at first bite' - the per...</td>\n    </tr>\n    <tr>\n      <th>64421</th>\n      <td>64422</td>\n      <td>B000MIDROQ</td>\n      <td>A161DK06JJMCYF</td>\n      <td>J. E. Stephens \"Jeanne\"</td>\n      <td>3</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1224892800</td>\n      <td>Bought This for My Son at College</td>\n      <td>My son loves spaghetti so I didn't hesitate or...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":7},{"cell_type":"code","source":"# only these two products has its numerator value greater than its denominator. \n# Rather than removing them, I prefer to ignore them by filtering them out as an invalid row","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:21.852799Z","iopub.execute_input":"2025-01-11T17:00:21.853181Z","iopub.status.idle":"2025-01-11T17:00:21.857845Z","shell.execute_reply.started":"2025-01-11T17:00:21.853144Z","shell.execute_reply":"2025-01-11T17:00:21.856698Z"}},"outputs":[],"execution_count":8},{"cell_type":"code","source":"valid_data = df[df['HelpfulnessDenominator']>=df['HelpfulnessNumerator']]\nvalid_data.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:21.861393Z","iopub.execute_input":"2025-01-11T17:00:21.86176Z","iopub.status.idle":"2025-01-11T17:00:21.942791Z","shell.execute_reply.started":"2025-01-11T17:00:21.861711Z","shell.execute_reply":"2025-01-11T17:00:21.941772Z"}},"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"   Id   ProductId          UserId                      ProfileName  \\\n0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n3   4  B000UA0QIQ  A395BORC6FGVXV                             Karl   \n4   5  B006K2ZZ7K  A1UQRSCLF8GW1T    Michael D. Bigham \"M. Wassir\"   \n\n   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n0                     1                       1      5  1303862400   \n1                     0                       0      1  1346976000   \n2                     1                       1      4  1219017600   \n3                     3                       3      2  1307923200   \n4                     0                       0      5  1350777600   \n\n                 Summary                                               Text  \n0  Good Quality Dog Food  I have bought several of the Vitality canned d...  \n1      Not as Advertised  Product arrived labeled as Jumbo Salted Peanut...  \n2  \"Delight\" says it all  This is a confection that has been around a fe...  \n3         Cough Medicine  If you are looking for the secret ingredient i...  \n4            Great taffy  Great taffy at a great price.  There was a wid...  ","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>ProductId</th>\n      <th>UserId</th>\n      <th>ProfileName</th>\n      <th>HelpfulnessNumerator</th>\n      <th>HelpfulnessDenominator</th>\n      <th>Score</th>\n      <th>Time</th>\n      <th>Summary</th>\n      <th>Text</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>B001E4KFG0</td>\n      <td>A3SGXH7AUHU8GW</td>\n      <td>delmartian</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1303862400</td>\n      <td>Good Quality Dog Food</td>\n      <td>I have bought several of the Vitality canned d...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>B00813GRG4</td>\n      <td>A1D87F6ZCVE5NK</td>\n      <td>dll pa</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1346976000</td>\n      <td>Not as Advertised</td>\n      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>B000LQOCH0</td>\n      <td>ABXLMWJIXXAIN</td>\n      <td>Natalia Corres \"Natalia Corres\"</td>\n      <td>1</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1219017600</td>\n      <td>\"Delight\" says it all</td>\n      <td>This is a confection that has been around a fe...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>B000UA0QIQ</td>\n      <td>A395BORC6FGVXV</td>\n      <td>Karl</td>\n      <td>3</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1307923200</td>\n      <td>Cough Medicine</td>\n      <td>If you are looking for the secret ingredient i...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>B006K2ZZ7K</td>\n      <td>A1UQRSCLF8GW1T</td>\n      <td>Michael D. Bigham \"M. Wassir\"</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1350777600</td>\n      <td>Great taffy</td>\n      <td>Great taffy at a great price.  There was a wid...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":9},{"cell_type":"markdown","source":"## Removing duplicates","metadata":{}},{"cell_type":"code","source":"# One productid cannot review the same product at the same time with same description more than ones. Let's see if such kind exists.\n\nvalid_data.duplicated(subset = ['ProductId','UserId','Time','Text']).sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:21.944446Z","iopub.execute_input":"2025-01-11T17:00:21.944813Z","iopub.status.idle":"2025-01-11T17:00:23.006968Z","shell.execute_reply.started":"2025-01-11T17:00:21.944775Z","shell.execute_reply":"2025-01-11T17:00:23.005712Z"}},"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"1205"},"metadata":{}}],"execution_count":10},{"cell_type":"code","source":"valid_data[valid_data.duplicated(subset = ['ProductId','UserId','Time','Text'])]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:23.007884Z","iopub.execute_input":"2025-01-11T17:00:23.008162Z","iopub.status.idle":"2025-01-11T17:00:24.104323Z","shell.execute_reply.started":"2025-01-11T17:00:23.008138Z","shell.execute_reply":"2025-01-11T17:00:24.103133Z"}},"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"            Id   ProductId          UserId                   ProfileName  \\\n574        575  B000G6RYNE  A3PJZ8TU8FDQ1K                  Jared Castle   \n2336      2337  B0001FQVCK   A5D06XJHDXK75                         C. Po   \n2647      2648  B0016FY6H6  A2NLZ3M0OJV9NX                   Mark Bodzin   \n2653      2654  B0016FY6H6  A3I4PCBRENJNG2                       L. Cain   \n2946      2947  B0002TJAZK  A2ISKAWUPGGOLZ                 M. S. Handley   \n...        ...         ...             ...                           ...   \n562273  562274  B004HOSGWE  A1TMAVN4CEM8U8                        Gunner   \n567958  567959  B0030VJ8YU   A2MF0C4E7GYCI                       VW \"VW\"   \n568008  568009  B0030VJ8YU   ATIHDHZYNQ0EI  Kristen O'donnell \"twinsmom\"   \n568017  568018  B0030VJ8YU  A20P8VC55KPPCT                        FL Mom   \n568062  568063  B0030VJ8YU   ATIHDHZYNQ0EI  Kristen O'donnell \"twinsmom\"   \n\n        HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n574                        2                       2      5  1231718400   \n2336                       1                       1      5  1190592000   \n2647                       0                       0      2  1313107200   \n2653                       0                       0      4  1300579200   \n2946                       0                       1      1  1310774400   \n...                      ...                     ...    ...         ...   \n562273                     1                       1      5  1336348800   \n567958                     0                       0      5  1305331200   \n568008                     3                       3      5  1270425600   \n568017                     2                       2      5  1274486400   \n568062                     2                       3      5  1270425600   \n\n                                                  Summary  \\\n574           One bite and you'll become a \"chippoisseur\"   \n2336    Baci's are pure heaven - great gift, stocking ...   \n2647                           way too weak for my tastes   \n2653           quality lightly sweetened green tea powder   \n2946                                      Kitty Junk Food   \n...                                                   ...   \n562273                             My 7th favorite flavor   \n567958                                  My son loves this   \n568008                                 Organic and Tasty!   \n568017                           These pouches are great!   \n568062                                 Organic and tasty!   \n\n                                                     Text  \n574     I'm addicted to salty and tangy flavors, so wh...  \n2336    My family has been in love with Baci's ever si...  \n2647    What can i say, I love iced tea. I drink a lot...  \n2653    lots of uses for this green tea...<br /><br />...  \n2946    We have five cats - one an elderly cat of 15 y...  \n...                                                   ...  \n562273  Diamond Almonds<br />Almonds are a good source...  \n567958  My son loves this food.  He is 16 months now a...  \n568008  I have 12 month olds and no time to write a gr...  \n568017  These pouches are so easy and convenient. They...  \n568062  I have 12 month olds and no time to write a gr...  \n\n[1205 rows x 10 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>Id</th>\n      <th>ProductId</th>\n      <th>UserId</th>\n      <th>ProfileName</th>\n      <th>HelpfulnessNumerator</th>\n      <th>HelpfulnessDenominator</th>\n      <th>Score</th>\n      <th>Time</th>\n      <th>Summary</th>\n      <th>Text</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>574</th>\n      <td>575</td>\n      <td>B000G6RYNE</td>\n      <td>A3PJZ8TU8FDQ1K</td>\n      <td>Jared Castle</td>\n      <td>2</td>\n      <td>2</td>\n      <td>5</td>\n      <td>1231718400</td>\n      <td>One bite and you'll become a \"chippoisseur\"</td>\n      <td>I'm addicted to salty and tangy flavors, so wh...</td>\n    </tr>\n    <tr>\n      <th>2336</th>\n      <td>2337</td>\n      <td>B0001FQVCK</td>\n      <td>A5D06XJHDXK75</td>\n      <td>C. Po</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1190592000</td>\n      <td>Baci's are pure heaven - great gift, stocking ...</td>\n      <td>My family has been in love with Baci's ever si...</td>\n    </tr>\n    <tr>\n      <th>2647</th>\n      <td>2648</td>\n      <td>B0016FY6H6</td>\n      <td>A2NLZ3M0OJV9NX</td>\n      <td>Mark Bodzin</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1313107200</td>\n      <td>way too weak for my tastes</td>\n      <td>What can i say, I love iced tea. I drink a lot...</td>\n    </tr>\n    <tr>\n      <th>2653</th>\n      <td>2654</td>\n      <td>B0016FY6H6</td>\n      <td>A3I4PCBRENJNG2</td>\n      <td>L. Cain</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1300579200</td>\n      <td>quality lightly sweetened green tea powder</td>\n      <td>lots of uses for this green tea...&lt;br /&gt;&lt;br /&gt;...</td>\n    </tr>\n    <tr>\n      <th>2946</th>\n      <td>2947</td>\n      <td>B0002TJAZK</td>\n      <td>A2ISKAWUPGGOLZ</td>\n      <td>M. S. Handley</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1310774400</td>\n      <td>Kitty Junk Food</td>\n      <td>We have five cats - one an elderly cat of 15 y...</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    </tr>\n    <tr>\n      <th>562273</th>\n      <td>562274</td>\n      <td>B004HOSGWE</td>\n      <td>A1TMAVN4CEM8U8</td>\n      <td>Gunner</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1336348800</td>\n      <td>My 7th favorite flavor</td>\n      <td>Diamond Almonds&lt;br /&gt;Almonds are a good source...</td>\n    </tr>\n    <tr>\n      <th>567958</th>\n      <td>567959</td>\n      <td>B0030VJ8YU</td>\n      <td>A2MF0C4E7GYCI</td>\n      <td>VW \"VW\"</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1305331200</td>\n      <td>My son loves this</td>\n      <td>My son loves this food.  He is 16 months now a...</td>\n    </tr>\n    <tr>\n      <th>568008</th>\n      <td>568009</td>\n      <td>B0030VJ8YU</td>\n      <td>ATIHDHZYNQ0EI</td>\n      <td>Kristen O'donnell \"twinsmom\"</td>\n      <td>3</td>\n      <td>3</td>\n      <td>5</td>\n      <td>1270425600</td>\n      <td>Organic and Tasty!</td>\n      <td>I have 12 month olds and no time to write a gr...</td>\n    </tr>\n    <tr>\n      <th>568017</th>\n      <td>568018</td>\n      <td>B0030VJ8YU</td>\n      <td>A20P8VC55KPPCT</td>\n      <td>FL Mom</td>\n      <td>2</td>\n      <td>2</td>\n      <td>5</td>\n      <td>1274486400</td>\n      <td>These pouches are great!</td>\n      <td>These pouches are so easy and convenient. They...</td>\n    </tr>\n    <tr>\n      <th>568062</th>\n      <td>568063</td>\n      <td>B0030VJ8YU</td>\n      <td>ATIHDHZYNQ0EI</td>\n      <td>Kristen O'donnell \"twinsmom\"</td>\n      <td>2</td>\n      <td>3</td>\n      <td>5</td>\n      <td>1270425600</td>\n      <td>Organic and tasty!</td>\n      <td>I have 12 month olds and no time to write a gr...</td>\n    </tr>\n  </tbody>\n</table>\n<p>1205 rows × 10 columns</p>\n</div>"},"metadata":{}}],"execution_count":11},{"cell_type":"code","source":"clean_data = valid_data.drop_duplicates(subset = ['ProductId','UserId','Time','Text'])\nclean_data.shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:24.105317Z","iopub.execute_input":"2025-01-11T17:00:24.105612Z","iopub.status.idle":"2025-01-11T17:00:25.212124Z","shell.execute_reply.started":"2025-01-11T17:00:24.105587Z","shell.execute_reply":"2025-01-11T17:00:25.210781Z"}},"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"(567247, 10)"},"metadata":{}}],"execution_count":12},{"cell_type":"markdown","source":"## Analysing the data types","metadata":{}},{"cell_type":"code","source":"clean_data.info()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:25.21341Z","iopub.execute_input":"2025-01-11T17:00:25.213925Z","iopub.status.idle":"2025-01-11T17:00:25.433858Z","shell.execute_reply.started":"2025-01-11T17:00:25.213881Z","shell.execute_reply":"2025-01-11T17:00:25.432354Z"}},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nIndex: 567247 entries, 0 to 568453\nData columns (total 10 columns):\n #   Column                  Non-Null Count   Dtype \n---  ------                  --------------   ----- \n 0   Id                      567247 non-null  int64 \n 1   ProductId               567247 non-null  object\n 2   UserId                  567247 non-null  object\n 3   ProfileName             567221 non-null  object\n 4   HelpfulnessNumerator    567247 non-null  int64 \n 5   HelpfulnessDenominator  567247 non-null  int64 \n 6   Score                   567247 non-null  int64 \n 7   Time                    567247 non-null  int64 \n 8   Summary                 567220 non-null  object\n 9   Text                    567247 non-null  object\ndtypes: int64(5), object(5)\nmemory usage: 47.6+ MB\n","output_type":"stream"}],"execution_count":13},{"cell_type":"code","source":"# The time column is in string data type","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:25.435089Z","iopub.execute_input":"2025-01-11T17:00:25.435507Z","iopub.status.idle":"2025-01-11T17:00:25.441317Z","shell.execute_reply.started":"2025-01-11T17:00:25.435466Z","shell.execute_reply":"2025-01-11T17:00:25.43984Z"}},"outputs":[],"execution_count":14},{"cell_type":"code","source":"pd.to_datetime(clean_data['Time'])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:25.442794Z","iopub.execute_input":"2025-01-11T17:00:25.443135Z","iopub.status.idle":"2025-01-11T17:00:25.682209Z","shell.execute_reply.started":"2025-01-11T17:00:25.443108Z","shell.execute_reply":"2025-01-11T17:00:25.681077Z"}},"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"0        1970-01-01 00:00:01.303862400\n1        1970-01-01 00:00:01.346976000\n2        1970-01-01 00:00:01.219017600\n3        1970-01-01 00:00:01.307923200\n4        1970-01-01 00:00:01.350777600\n                      ...             \n568449   1970-01-01 00:00:01.299628800\n568450   1970-01-01 00:00:01.331251200\n568451   1970-01-01 00:00:01.329782400\n568452   1970-01-01 00:00:01.331596800\n568453   1970-01-01 00:00:01.338422400\nName: Time, Length: 567247, dtype: datetime64[ns]"},"metadata":{}}],"execution_count":15},{"cell_type":"raw","source":"In the context of pandas (a popular data manipulation library in Python), Unix epoch time refers to the number of seconds that have elapsed since 00:00:00 UTC on 1 January 1970, excluding leap seconds. This moment in time is known as the Unix epoch.\n\nThe timestamp 1970-01-01 00:00:01.303862400 represents the date and time format that includes both date and fractional seconds.\n","metadata":{}},{"cell_type":"code","source":"clean_data['time'] = pd.to_datetime(clean_data['Time'], unit = 's')\nclean_data.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:25.683472Z","iopub.execute_input":"2025-01-11T17:00:25.683876Z","iopub.status.idle":"2025-01-11T17:00:25.760097Z","shell.execute_reply.started":"2025-01-11T17:00:25.683846Z","shell.execute_reply":"2025-01-11T17:00:25.758845Z"}},"outputs":[{"name":"stderr","text":"<ipython-input-16-7bc86191a702>:1: 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  clean_data['time'] = pd.to_datetime(clean_data['Time'], unit = 's')\n","output_type":"stream"},{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"   Id   ProductId          UserId                      ProfileName  \\\n0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n3   4  B000UA0QIQ  A395BORC6FGVXV                             Karl   \n4   5  B006K2ZZ7K  A1UQRSCLF8GW1T    Michael D. Bigham \"M. Wassir\"   \n\n   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n0                     1                       1      5  1303862400   \n1                     0                       0      1  1346976000   \n2                     1                       1      4  1219017600   \n3                     3                       3      2  1307923200   \n4                     0                       0      5  1350777600   \n\n                 Summary                                               Text  \\\n0  Good Quality Dog Food  I have bought several of the Vitality canned d...   \n1      Not as Advertised  Product arrived labeled as Jumbo Salted Peanut...   \n2  \"Delight\" says it all  This is a confection that has been around a fe...   \n3         Cough Medicine  If you are looking for the secret ingredient i...   \n4            Great taffy  Great taffy at a great price.  There was a wid...   \n\n        time  \n0 2011-04-27  \n1 2012-09-07  \n2 2008-08-18  \n3 2011-06-13  \n4 2012-10-21  ","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>Id</th>\n      <th>ProductId</th>\n      <th>UserId</th>\n      <th>ProfileName</th>\n      <th>HelpfulnessNumerator</th>\n      <th>HelpfulnessDenominator</th>\n      <th>Score</th>\n      <th>Time</th>\n      <th>Summary</th>\n      <th>Text</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>B001E4KFG0</td>\n      <td>A3SGXH7AUHU8GW</td>\n      <td>delmartian</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1303862400</td>\n      <td>Good Quality Dog Food</td>\n      <td>I have bought several of the Vitality canned d...</td>\n      <td>2011-04-27</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>B00813GRG4</td>\n      <td>A1D87F6ZCVE5NK</td>\n      <td>dll pa</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1346976000</td>\n      <td>Not as Advertised</td>\n      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n      <td>2012-09-07</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>B000LQOCH0</td>\n      <td>ABXLMWJIXXAIN</td>\n      <td>Natalia Corres \"Natalia Corres\"</td>\n      <td>1</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1219017600</td>\n      <td>\"Delight\" says it all</td>\n      <td>This is a confection that has been around a fe...</td>\n      <td>2008-08-18</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>B000UA0QIQ</td>\n      <td>A395BORC6FGVXV</td>\n      <td>Karl</td>\n      <td>3</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1307923200</td>\n      <td>Cough Medicine</td>\n      <td>If you are looking for the secret ingredient i...</td>\n      <td>2011-06-13</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>B006K2ZZ7K</td>\n      <td>A1UQRSCLF8GW1T</td>\n      <td>Michael D. Bigham \"M. Wassir\"</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1350777600</td>\n      <td>Great taffy</td>\n      <td>Great taffy at a great price.  There was a wid...</td>\n      <td>2012-10-21</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":16},{"cell_type":"markdown","source":"# Identifying active buyers to make them buy products frequently","metadata":{}},{"cell_type":"code","source":"#Using UserId as my unique key to find number of unique users on amazon\nclean_data['UserId'].nunique()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:25.761436Z","iopub.execute_input":"2025-01-11T17:00:25.761825Z","iopub.status.idle":"2025-01-11T17:00:25.899269Z","shell.execute_reply.started":"2025-01-11T17:00:25.761729Z","shell.execute_reply":"2025-01-11T17:00:25.898261Z"}},"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"256059"},"metadata":{}}],"execution_count":17},{"cell_type":"code","source":"freq_buyers = clean_data.groupby(by = clean_data['UserId']).agg({'ProductId':'count','Score':'mean','Text':'count'}).sort_values(by = 'ProductId', ascending = False)\nfreq_buyers","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:25.90021Z","iopub.execute_input":"2025-01-11T17:00:25.900621Z","iopub.status.idle":"2025-01-11T17:00:26.757448Z","shell.execute_reply.started":"2025-01-11T17:00:25.900587Z","shell.execute_reply":"2025-01-11T17:00:26.756325Z"}},"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"                ProductId     Score  Text\nUserId                                   \nA3OXHLG6DIBRW8        442  4.529412   442\nA1YUL9PCJR3JTY        412  4.490291   412\nAY12DBB0U420B         389  4.647815   389\nA281NPSIMI1C2R        365  4.841096   365\nA1Z54EM24Y40LL        256  4.453125   256\n...                   ...       ...   ...\nA2HRM5D802WYYZ          1  5.000000     1\nA2HRN5PLORHK6E          1  5.000000     1\nA2HRNC10AGMVPC          1  5.000000     1\nA2HROKQO0GA5AF          1  3.000000     1\nAZZZOVIBXHGDR           1  2.000000     1\n\n[256059 rows x 3 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>ProductId</th>\n      <th>Score</th>\n      <th>Text</th>\n    </tr>\n    <tr>\n      <th>UserId</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>A3OXHLG6DIBRW8</th>\n      <td>442</td>\n      <td>4.529412</td>\n      <td>442</td>\n    </tr>\n    <tr>\n      <th>A1YUL9PCJR3JTY</th>\n      <td>412</td>\n      <td>4.490291</td>\n      <td>412</td>\n    </tr>\n    <tr>\n      <th>AY12DBB0U420B</th>\n      <td>389</td>\n      <td>4.647815</td>\n      <td>389</td>\n    </tr>\n    <tr>\n      <th>A281NPSIMI1C2R</th>\n      <td>365</td>\n      <td>4.841096</td>\n      <td>365</td>\n    </tr>\n    <tr>\n      <th>A1Z54EM24Y40LL</th>\n      <td>256</td>\n      <td>4.453125</td>\n      <td>256</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>A2HRM5D802WYYZ</th>\n      <td>1</td>\n      <td>5.000000</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>A2HRN5PLORHK6E</th>\n      <td>1</td>\n      <td>5.000000</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>A2HRNC10AGMVPC</th>\n      <td>1</td>\n      <td>5.000000</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>A2HROKQO0GA5AF</th>\n      <td>1</td>\n      <td>3.000000</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>AZZZOVIBXHGDR</th>\n      <td>1</td>\n      <td>2.000000</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>256059 rows × 3 columns</p>\n</div>"},"metadata":{}}],"execution_count":18},{"cell_type":"code","source":"freq_buyers.columns","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:26.758807Z","iopub.execute_input":"2025-01-11T17:00:26.759228Z","iopub.status.idle":"2025-01-11T17:00:26.765985Z","shell.execute_reply.started":"2025-01-11T17:00:26.759187Z","shell.execute_reply":"2025-01-11T17:00:26.764865Z"}},"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"Index(['ProductId', 'Score', 'Text'], dtype='object')"},"metadata":{}}],"execution_count":19},{"cell_type":"code","source":"freq_buyers.columns = ['Total_Products', 'Average_Rating', 'Total_Reviews']","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:26.767147Z","iopub.execute_input":"2025-01-11T17:00:26.767529Z","iopub.status.idle":"2025-01-11T17:00:26.783862Z","shell.execute_reply.started":"2025-01-11T17:00:26.767492Z","shell.execute_reply":"2025-01-11T17:00:26.782809Z"}},"outputs":[],"execution_count":20},{"cell_type":"code","source":"freq_buyers.reset_index(inplace = True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:26.785155Z","iopub.execute_input":"2025-01-11T17:00:26.785554Z","iopub.status.idle":"2025-01-11T17:00:26.815115Z","shell.execute_reply.started":"2025-01-11T17:00:26.785513Z","shell.execute_reply":"2025-01-11T17:00:26.813682Z"}},"outputs":[],"execution_count":21},{"cell_type":"code","source":"freq_buyers.index = freq_buyers.index + 1","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:26.816267Z","iopub.execute_input":"2025-01-11T17:00:26.816716Z","iopub.status.idle":"2025-01-11T17:00:26.821394Z","shell.execute_reply.started":"2025-01-11T17:00:26.816675Z","shell.execute_reply":"2025-01-11T17:00:26.820113Z"}},"outputs":[],"execution_count":22},{"cell_type":"code","source":"top_10_buyers = freq_buyers.head(10)\ntop_10_buyers","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:26.822503Z","iopub.execute_input":"2025-01-11T17:00:26.82285Z","iopub.status.idle":"2025-01-11T17:00:26.849199Z","shell.execute_reply.started":"2025-01-11T17:00:26.822822Z","shell.execute_reply":"2025-01-11T17:00:26.847928Z"}},"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":"            UserId  Total_Products  Average_Rating  Total_Reviews\n1   A3OXHLG6DIBRW8             442        4.529412            442\n2   A1YUL9PCJR3JTY             412        4.490291            412\n3    AY12DBB0U420B             389        4.647815            389\n4   A281NPSIMI1C2R             365        4.841096            365\n5   A1Z54EM24Y40LL             256        4.453125            256\n6   A2MUGFV2TDQ47K             201        3.751244            201\n7   A1TMAVN4CEM8U8             181        4.812155            181\n8    AQQLWCMRNDFGI             176        3.954545            176\n9   A2SZLNSI5KOQJT             175        3.834286            175\n10   AZV26LP92E6WU             167        4.886228            167","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>UserId</th>\n      <th>Total_Products</th>\n      <th>Average_Rating</th>\n      <th>Total_Reviews</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>A3OXHLG6DIBRW8</td>\n      <td>442</td>\n      <td>4.529412</td>\n      <td>442</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>A1YUL9PCJR3JTY</td>\n      <td>412</td>\n      <td>4.490291</td>\n      <td>412</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>AY12DBB0U420B</td>\n      <td>389</td>\n      <td>4.647815</td>\n      <td>389</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>A281NPSIMI1C2R</td>\n      <td>365</td>\n      <td>4.841096</td>\n      <td>365</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>A1Z54EM24Y40LL</td>\n      <td>256</td>\n      <td>4.453125</td>\n      <td>256</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>A2MUGFV2TDQ47K</td>\n      <td>201</td>\n      <td>3.751244</td>\n      <td>201</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>A1TMAVN4CEM8U8</td>\n      <td>181</td>\n      <td>4.812155</td>\n      <td>181</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>AQQLWCMRNDFGI</td>\n      <td>176</td>\n      <td>3.954545</td>\n      <td>176</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>A2SZLNSI5KOQJT</td>\n      <td>175</td>\n      <td>3.834286</td>\n      <td>175</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>AZV26LP92E6WU</td>\n      <td>167</td>\n      <td>4.886228</td>\n      <td>167</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":23},{"cell_type":"code","source":"plt.bar(top_10_buyers['UserId'], top_10_buyers['Total_Products'])\nplt.xticks(rotation = 90)\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:26.853473Z","iopub.execute_input":"2025-01-11T17:00:26.853911Z","iopub.status.idle":"2025-01-11T17:00:27.079969Z","shell.execute_reply.started":"2025-01-11T17:00:26.853864Z","shell.execute_reply":"2025-01-11T17:00:27.078613Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":24},{"cell_type":"markdown","source":"# Finding products with good ratings","metadata":{}},{"cell_type":"code","source":"highest_rated = clean_data['ProductId'].value_counts().to_frame()\nhighest_rated","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:27.081805Z","iopub.execute_input":"2025-01-11T17:00:27.082134Z","iopub.status.idle":"2025-01-11T17:00:27.178192Z","shell.execute_reply.started":"2025-01-11T17:00:27.082102Z","shell.execute_reply":"2025-01-11T17:00:27.177054Z"}},"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"            count\nProductId        \nB007JFMH8M    913\nB002QWHJOU    630\nB002QWP89S    630\nB002QWP8H0    630\nB0026RQTGE    630\n...           ...\nB0058FN5CM      1\nB0047435DO      1\nB004DSPTTM      1\nB008C9QWU8      1\nB001LR2CU2      1\n\n[74258 rows x 1 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>count</th>\n    </tr>\n    <tr>\n      <th>ProductId</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>B007JFMH8M</th>\n      <td>913</td>\n    </tr>\n    <tr>\n      <th>B002QWHJOU</th>\n      <td>630</td>\n    </tr>\n    <tr>\n      <th>B002QWP89S</th>\n      <td>630</td>\n    </tr>\n    <tr>\n      <th>B002QWP8H0</th>\n      <td>630</td>\n    </tr>\n    <tr>\n      <th>B0026RQTGE</th>\n      <td>630</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>B0058FN5CM</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>B0047435DO</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>B004DSPTTM</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>B008C9QWU8</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>B001LR2CU2</th>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>74258 rows × 1 columns</p>\n</div>"},"metadata":{}}],"execution_count":25},{"cell_type":"code","source":"# filtering products that have sold more than 600 items\nhigh_prod = highest_rated[highest_rated['count']>550].index\nhigh_prod","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:27.179345Z","iopub.execute_input":"2025-01-11T17:00:27.17972Z","iopub.status.idle":"2025-01-11T17:00:27.188601Z","shell.execute_reply.started":"2025-01-11T17:00:27.179662Z","shell.execute_reply":"2025-01-11T17:00:27.187572Z"}},"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"Index(['B007JFMH8M', 'B002QWHJOU', 'B002QWP89S', 'B002QWP8H0', 'B0026RQTGE',\n       'B003B3OOPA', 'B001EO5Q64', 'B001RVFEP2', 'B0013NUGDE', 'B006HYLW32',\n       'B000VK8AVK', 'B0026KNQSA', 'B007M832YY', 'B007M83302', 'B0026KPDG8',\n       'B001RVFERK', 'B000KV61FC', 'B000KV7ZGQ'],\n      dtype='object', name='ProductId')"},"metadata":{}}],"execution_count":26},{"cell_type":"code","source":"freq_prod = clean_data[clean_data['ProductId'].isin(high_prod)]\nfreq_prod","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:27.189686Z","iopub.execute_input":"2025-01-11T17:00:27.190086Z","iopub.status.idle":"2025-01-11T17:00:27.249046Z","shell.execute_reply.started":"2025-01-11T17:00:27.190048Z","shell.execute_reply":"2025-01-11T17:00:27.247758Z"}},"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"            Id   ProductId          UserId             ProfileName  \\\n20982    20983  B002QWP89S  A21U4DR8M6I9QN  K. M Merrill \"justine\"   \n20983    20984  B002QWP89S  A17TDUBB4Z1PEC             jaded_green   \n20984    20985  B002QWP89S   ABQH3WAWMSMBH             tenisbrat87   \n20985    20986  B002QWP89S   AVTY5M74VA1BJ              tarotqueen   \n20986    20987  B002QWP89S  A13TNN54ZEAUB1                 dcz2221   \n...        ...         ...             ...                     ...   \n563878  563879  B007JFMH8M  A366PSH7KFLRPB            TheRosySnail   \n563879  563880  B007JFMH8M  A2KV6EYQPKJRR5                  Kelley   \n563880  563881  B007JFMH8M  A3O7REI0OSV89M                    Esme   \n563881  563882  B007JFMH8M   A9JS5GQQ6GIQT                    Syne   \n563882  563883  B007JFMH8M   AMAVEZAGCH52H                 Tangela   \n\n        HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n20982                      1                       1      5  1318896000   \n20983                      1                       1      5  1318550400   \n20984                      1                       1      5  1317168000   \n20985                      1                       1      5  1316822400   \n20986                      1                       1      5  1316736000   \n...                      ...                     ...    ...         ...   \n563878                     0                       0      5  1343520000   \n563879                     0                       0      5  1343433600   \n563880                     0                       0      4  1343433600   \n563881                     0                       0      5  1343433600   \n563882                     0                       0      5  1343433600   \n\n                                                Summary  \\\n20982   addictive! but works for night coughing in dogs   \n20983                       genuine Greenies best price   \n20984                    Perfect for our little doggies   \n20985                                dogs love greenies   \n20986                                   Greenies review   \n...                                                 ...   \n563878                                           Yummy!   \n563879                        Quaker Soft Baked Cookies   \n563880                                       Delicious!   \n563881                                            yummy   \n563882                         Quaker Soft Baked Cookie   \n\n                                                     Text       time  \n20982   my 12 year old sheltie has chronic brochotitis... 2011-10-18  \n20983   These are genuine Greenies product, not a knoc... 2011-10-14  \n20984   Our dogs love Greenies, but of course, which d... 2011-09-28  \n20985   What can I say, dogs love greenies. They begg ... 2011-09-24  \n20986   This review is for a box of Greenies Lite for ... 2011-09-23  \n...                                                   ...        ...  \n563878  I loved these cookies and so did my kids. You ... 2012-07-29  \n563879  This is a great tasting cookie. It is very sof... 2012-07-28  \n563880  These are great for a quick snack! They are sa... 2012-07-28  \n563881  I love the Quaker soft baked cookies.  The rea... 2012-07-28  \n563882  This cookie is really good and works really we... 2012-07-28  \n\n[10793 rows x 11 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>Id</th>\n      <th>ProductId</th>\n      <th>UserId</th>\n      <th>ProfileName</th>\n      <th>HelpfulnessNumerator</th>\n      <th>HelpfulnessDenominator</th>\n      <th>Score</th>\n      <th>Time</th>\n      <th>Summary</th>\n      <th>Text</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>20982</th>\n      <td>20983</td>\n      <td>B002QWP89S</td>\n      <td>A21U4DR8M6I9QN</td>\n      <td>K. M Merrill \"justine\"</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1318896000</td>\n      <td>addictive! but works for night coughing in dogs</td>\n      <td>my 12 year old sheltie has chronic brochotitis...</td>\n      <td>2011-10-18</td>\n    </tr>\n    <tr>\n      <th>20983</th>\n      <td>20984</td>\n      <td>B002QWP89S</td>\n      <td>A17TDUBB4Z1PEC</td>\n      <td>jaded_green</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1318550400</td>\n      <td>genuine Greenies best price</td>\n      <td>These are genuine Greenies product, not a knoc...</td>\n      <td>2011-10-14</td>\n    </tr>\n    <tr>\n      <th>20984</th>\n      <td>20985</td>\n      <td>B002QWP89S</td>\n      <td>ABQH3WAWMSMBH</td>\n      <td>tenisbrat87</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1317168000</td>\n      <td>Perfect for our little doggies</td>\n      <td>Our dogs love Greenies, but of course, which d...</td>\n      <td>2011-09-28</td>\n    </tr>\n    <tr>\n      <th>20985</th>\n      <td>20986</td>\n      <td>B002QWP89S</td>\n      <td>AVTY5M74VA1BJ</td>\n      <td>tarotqueen</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1316822400</td>\n      <td>dogs love greenies</td>\n      <td>What can I say, dogs love greenies. They begg ...</td>\n      <td>2011-09-24</td>\n    </tr>\n    <tr>\n      <th>20986</th>\n      <td>20987</td>\n      <td>B002QWP89S</td>\n      <td>A13TNN54ZEAUB1</td>\n      <td>dcz2221</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1316736000</td>\n      <td>Greenies review</td>\n      <td>This review is for a box of Greenies Lite for ...</td>\n      <td>2011-09-23</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    </tr>\n    <tr>\n      <th>563878</th>\n      <td>563879</td>\n      <td>B007JFMH8M</td>\n      <td>A366PSH7KFLRPB</td>\n      <td>TheRosySnail</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1343520000</td>\n      <td>Yummy!</td>\n      <td>I loved these cookies and so did my kids. You ...</td>\n      <td>2012-07-29</td>\n    </tr>\n    <tr>\n      <th>563879</th>\n      <td>563880</td>\n      <td>B007JFMH8M</td>\n      <td>A2KV6EYQPKJRR5</td>\n      <td>Kelley</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1343433600</td>\n      <td>Quaker Soft Baked Cookies</td>\n      <td>This is a great tasting cookie. It is very sof...</td>\n      <td>2012-07-28</td>\n    </tr>\n    <tr>\n      <th>563880</th>\n      <td>563881</td>\n      <td>B007JFMH8M</td>\n      <td>A3O7REI0OSV89M</td>\n      <td>Esme</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1343433600</td>\n      <td>Delicious!</td>\n      <td>These are great for a quick snack! They are sa...</td>\n      <td>2012-07-28</td>\n    </tr>\n    <tr>\n      <th>563881</th>\n      <td>563882</td>\n      <td>B007JFMH8M</td>\n      <td>A9JS5GQQ6GIQT</td>\n      <td>Syne</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1343433600</td>\n      <td>yummy</td>\n      <td>I love the Quaker soft baked cookies.  The rea...</td>\n      <td>2012-07-28</td>\n    </tr>\n    <tr>\n      <th>563882</th>\n      <td>563883</td>\n      <td>B007JFMH8M</td>\n      <td>AMAVEZAGCH52H</td>\n      <td>Tangela</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1343433600</td>\n      <td>Quaker Soft Baked Cookie</td>\n      <td>This cookie is really good and works really we...</td>\n      <td>2012-07-28</td>\n    </tr>\n  </tbody>\n</table>\n<p>10793 rows × 11 columns</p>\n</div>"},"metadata":{}}],"execution_count":27},{"cell_type":"code","source":"sns.countplot(data = freq_prod, y = 'ProductId')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:27.250453Z","iopub.execute_input":"2025-01-11T17:00:27.250931Z","iopub.status.idle":"2025-01-11T17:00:27.552295Z","shell.execute_reply.started":"2025-01-11T17:00:27.250888Z","shell.execute_reply":"2025-01-11T17:00:27.550919Z"}},"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"<Axes: xlabel='count', ylabel='ProductId'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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\n"},"metadata":{}}],"execution_count":28},{"cell_type":"code","source":"plt.figure(figsize = (12.5,7))\nsns.countplot(data = freq_prod, y = 'ProductId', hue = 'Score')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:27.553504Z","iopub.execute_input":"2025-01-11T17:00:27.553962Z","iopub.status.idle":"2025-01-11T17:00:28.052963Z","shell.execute_reply.started":"2025-01-11T17:00:27.553918Z","shell.execute_reply":"2025-01-11T17:00:28.051887Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1250x700 with 1 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Analyzing the trend between frequent and non-frequent users","metadata":{}},{"cell_type":"raw","source":"People who buy products more are considered as frequent users in our use case","metadata":{}},{"cell_type":"code","source":"users = clean_data['UserId'].value_counts()\nusers","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:28.054224Z","iopub.execute_input":"2025-01-11T17:00:28.054657Z","iopub.status.idle":"2025-01-11T17:00:28.418978Z","shell.execute_reply.started":"2025-01-11T17:00:28.054623Z","shell.execute_reply":"2025-01-11T17:00:28.417965Z"}},"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"UserId\nA3OXHLG6DIBRW8    442\nA1YUL9PCJR3JTY    412\nAY12DBB0U420B     389\nA281NPSIMI1C2R    365\nA1Z54EM24Y40LL    256\n                 ... \nAPKQBK5EAZ6BV       1\nA31ZSRLG55BLRH      1\nA2YQH34KFB65BR      1\nA14AMMHQ1EA52K      1\nA3LGQPJCZVL9UC      1\nName: count, Length: 256059, dtype: int64"},"metadata":{}}],"execution_count":30},{"cell_type":"code","source":"clean_data['user_type'] = clean_data['UserId'].apply(lambda x: \"Frequent\" if users[x]>50 else \"Non-frequent\")\nclean_data","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:28.41987Z","iopub.execute_input":"2025-01-11T17:00:28.420126Z","iopub.status.idle":"2025-01-11T17:00:30.265086Z","shell.execute_reply.started":"2025-01-11T17:00:28.420105Z","shell.execute_reply":"2025-01-11T17:00:30.263869Z"}},"outputs":[{"name":"stderr","text":"<ipython-input-31-24ddd2e46b06>:1: 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  clean_data['user_type'] = clean_data['UserId'].apply(lambda x: \"Frequent\" if users[x]>50 else \"Non-frequent\")\n","output_type":"stream"},{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"            Id   ProductId          UserId                      ProfileName  \\\n0            1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n1            2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n2            3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n3            4  B000UA0QIQ  A395BORC6FGVXV                             Karl   \n4            5  B006K2ZZ7K  A1UQRSCLF8GW1T    Michael D. Bigham \"M. Wassir\"   \n...        ...         ...             ...                              ...   \n568449  568450  B001EO7N10  A28KG5XORO54AY                 Lettie D. Carter   \n568450  568451  B003S1WTCU  A3I8AFVPEE8KI5                        R. Sawyer   \n568451  568452  B004I613EE  A121AA1GQV751Z                    pksd \"pk_007\"   \n568452  568453  B004I613EE   A3IBEVCTXKNOH          Kathy A. Welch \"katwel\"   \n568453  568454  B001LR2CU2  A3LGQPJCZVL9UC                         srfell17   \n\n        HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n0                          1                       1      5  1303862400   \n1                          0                       0      1  1346976000   \n2                          1                       1      4  1219017600   \n3                          3                       3      2  1307923200   \n4                          0                       0      5  1350777600   \n...                      ...                     ...    ...         ...   \n568449                     0                       0      5  1299628800   \n568450                     0                       0      2  1331251200   \n568451                     2                       2      5  1329782400   \n568452                     1                       1      5  1331596800   \n568453                     0                       0      5  1338422400   \n\n                                   Summary  \\\n0                    Good Quality Dog Food   \n1                        Not as Advertised   \n2                    \"Delight\" says it all   \n3                           Cough Medicine   \n4                              Great taffy   \n...                                    ...   \n568449                 Will not do without   \n568450                        disappointed   \n568451            Perfect for our maltipoo   \n568452  Favorite Training and reward treat   \n568453                         Great Honey   \n\n                                                     Text       time  \\\n0       I have bought several of the Vitality canned d... 2011-04-27   \n1       Product arrived labeled as Jumbo Salted Peanut... 2012-09-07   \n2       This is a confection that has been around a fe... 2008-08-18   \n3       If you are looking for the secret ingredient i... 2011-06-13   \n4       Great taffy at a great price.  There was a wid... 2012-10-21   \n...                                                   ...        ...   \n568449  Great for sesame chicken..this is a good if no... 2011-03-09   \n568450  I'm disappointed with the flavor. The chocolat... 2012-03-09   \n568451  These stars are small, so you can give 10-15 o... 2012-02-21   \n568452  These are the BEST treats for training and rew... 2012-03-13   \n568453  I am very satisfied ,product is as advertised,... 2012-05-31   \n\n           user_type  \n0       Non-frequent  \n1       Non-frequent  \n2       Non-frequent  \n3       Non-frequent  \n4       Non-frequent  \n...              ...  \n568449  Non-frequent  \n568450  Non-frequent  \n568451  Non-frequent  \n568452  Non-frequent  \n568453  Non-frequent  \n\n[567247 rows x 12 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>Id</th>\n      <th>ProductId</th>\n      <th>UserId</th>\n      <th>ProfileName</th>\n      <th>HelpfulnessNumerator</th>\n      <th>HelpfulnessDenominator</th>\n      <th>Score</th>\n      <th>Time</th>\n      <th>Summary</th>\n      <th>Text</th>\n      <th>time</th>\n      <th>user_type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>B001E4KFG0</td>\n      <td>A3SGXH7AUHU8GW</td>\n      <td>delmartian</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1303862400</td>\n      <td>Good Quality Dog Food</td>\n      <td>I have bought several of the Vitality canned d...</td>\n      <td>2011-04-27</td>\n      <td>Non-frequent</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>B00813GRG4</td>\n      <td>A1D87F6ZCVE5NK</td>\n      <td>dll pa</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1346976000</td>\n      <td>Not as Advertised</td>\n      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n      <td>2012-09-07</td>\n      <td>Non-frequent</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>B000LQOCH0</td>\n      <td>ABXLMWJIXXAIN</td>\n      <td>Natalia Corres \"Natalia Corres\"</td>\n      <td>1</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1219017600</td>\n      <td>\"Delight\" says it all</td>\n      <td>This is a confection that has been around a fe...</td>\n      <td>2008-08-18</td>\n      <td>Non-frequent</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>B000UA0QIQ</td>\n      <td>A395BORC6FGVXV</td>\n      <td>Karl</td>\n      <td>3</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1307923200</td>\n      <td>Cough Medicine</td>\n      <td>If you are looking for the secret ingredient i...</td>\n      <td>2011-06-13</td>\n      <td>Non-frequent</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>B006K2ZZ7K</td>\n      <td>A1UQRSCLF8GW1T</td>\n      <td>Michael D. Bigham \"M. Wassir\"</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1350777600</td>\n      <td>Great taffy</td>\n      <td>Great taffy at a great price.  There was a wid...</td>\n      <td>2012-10-21</td>\n      <td>Non-frequent</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    </tr>\n    <tr>\n      <th>568449</th>\n      <td>568450</td>\n      <td>B001EO7N10</td>\n      <td>A28KG5XORO54AY</td>\n      <td>Lettie D. Carter</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1299628800</td>\n      <td>Will not do without</td>\n      <td>Great for sesame chicken..this is a good if no...</td>\n      <td>2011-03-09</td>\n      <td>Non-frequent</td>\n    </tr>\n    <tr>\n      <th>568450</th>\n      <td>568451</td>\n      <td>B003S1WTCU</td>\n      <td>A3I8AFVPEE8KI5</td>\n      <td>R. Sawyer</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1331251200</td>\n      <td>disappointed</td>\n      <td>I'm disappointed with the flavor. The chocolat...</td>\n      <td>2012-03-09</td>\n      <td>Non-frequent</td>\n    </tr>\n    <tr>\n      <th>568451</th>\n      <td>568452</td>\n      <td>B004I613EE</td>\n      <td>A121AA1GQV751Z</td>\n      <td>pksd \"pk_007\"</td>\n      <td>2</td>\n      <td>2</td>\n      <td>5</td>\n      <td>1329782400</td>\n      <td>Perfect for our maltipoo</td>\n      <td>These stars are small, so you can give 10-15 o...</td>\n      <td>2012-02-21</td>\n      <td>Non-frequent</td>\n    </tr>\n    <tr>\n      <th>568452</th>\n      <td>568453</td>\n      <td>B004I613EE</td>\n      <td>A3IBEVCTXKNOH</td>\n      <td>Kathy A. Welch \"katwel\"</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1331596800</td>\n      <td>Favorite Training and reward treat</td>\n      <td>These are the BEST treats for training and rew...</td>\n      <td>2012-03-13</td>\n      <td>Non-frequent</td>\n    </tr>\n    <tr>\n      <th>568453</th>\n      <td>568454</td>\n      <td>B001LR2CU2</td>\n      <td>A3LGQPJCZVL9UC</td>\n      <td>srfell17</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1338422400</td>\n      <td>Great Honey</td>\n      <td>I am very satisfied ,product is as advertised,...</td>\n      <td>2012-05-31</td>\n      <td>Non-frequent</td>\n    </tr>\n  </tbody>\n</table>\n<p>567247 rows × 12 columns</p>\n</div>"},"metadata":{}}],"execution_count":31},{"cell_type":"code","source":"Freq_users = clean_data[clean_data['user_type']== 'Frequent']\nFreq_users","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:30.266097Z","iopub.execute_input":"2025-01-11T17:00:30.266394Z","iopub.status.idle":"2025-01-11T17:00:30.333884Z","shell.execute_reply.started":"2025-01-11T17:00:30.266368Z","shell.execute_reply":"2025-01-11T17:00:30.332716Z"}},"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"            Id   ProductId          UserId  \\\n14          15  B001GVISJM  A2MUGFV2TDQ47K   \n44          45  B001EO5QW8  A2G7B7FKP2O2PU   \n46          47  B001EO5QW8   AQLL2R1PPR46X   \n109        110  B001REEG6C   AY12DBB0U420B   \n141        142  B001GVISJW  A2YIO225BTKVPU   \n...        ...         ...             ...   \n568261  568262  B00374ZKQ0  A2GEZJHBV92EVR   \n568317  568318  B0013Z0PTW  A1ODOGXEYECQQ8   \n568331  568332  B001BOAOLY  A36MP37DITBU6F   \n568361  568362  B000LKVRQA  A1YUL9PCJR3JTY   \n568367  568368  B000LKVRQA   AYB4ELCS5AM8P   \n\n                                             ProfileName  \\\n14                                   Lynrie \"Oh HELL no\"   \n44                                            D. Leschke   \n46                                         grumpyrainbow   \n109                                        Gary Peterson   \n141     Angel_Boy \"Treat the people around you the wa...   \n...                                                  ...   \n568261                                      History buff   \n568317                                            Nuknuk   \n568331                  Enchanted  In  Dixie \"Enchanted\"   \n568361                   O. Brown \"Ms. O. Khannah-Brown\"   \n568367                               John B. Goode \"JBG\"   \n\n        HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n14                         4                       5      5  1268352000   \n44                         0                       0      5  1209686400   \n46                         0                       0      5  1192752000   \n109                        0                       0      5  1316390400   \n141                        0                       0      4  1301788800   \n...                      ...                     ...    ...         ...   \n568261                     0                       0      5  1345075200   \n568317                     0                       0      4  1226880000   \n568331                     6                       6      4  1244419200   \n568361                     1                       1      5  1282608000   \n568367                     0                       0      5  1313539200   \n\n                                                  Summary  \\\n14                           Strawberry Twizzlers - Yummy   \n44                            Great taste and convenience   \n46                                                   good   \n109                          My Idea of a Good Diet Food.   \n141                                         Good Licorice   \n...                                                   ...   \n568261                    Very good alternative to sugar.   \n568317                          Good Tasting Granola Bars   \n568331  GREAT ORGANIC Honey flavor Hard candy. Great c...   \n568361             An Earl Grey Tea That Won't Disappoint   \n568367                                         Excellent!   \n\n                                                     Text       time user_type  \n14      The Strawberry Twizzlers are my guilty pleasur... 2010-03-12  Frequent  \n44      We really like the McCann's steel cut oats but... 2008-05-02  Frequent  \n46      Good oatmeal.  I like the apple cinnamon the b... 2007-10-19  Frequent  \n109     I'm presently on a diet and I was at my Fresh ... 2011-09-19  Frequent  \n141     Twizzlers brand licorice is much better than t... 2011-04-03  Frequent  \n...                                                   ...        ...       ...  \n568261  I have tried many of the no sugar sweeteners o... 2012-08-16  Frequent  \n568317  Most health bars taste awful.  Fiber One Grano... 2008-11-17  Frequent  \n568331  These are delicious organic hard candies. I on... 2009-06-08  Frequent  \n568361  *****<br />St. Dalfour's wonderful Certified O... 2010-08-24  Frequent  \n568367  Quite simply one of the best Earl Grey's I've ... 2011-08-17  Frequent  \n\n[21702 rows x 12 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>Id</th>\n      <th>ProductId</th>\n      <th>UserId</th>\n      <th>ProfileName</th>\n      <th>HelpfulnessNumerator</th>\n      <th>HelpfulnessDenominator</th>\n      <th>Score</th>\n      <th>Time</th>\n      <th>Summary</th>\n      <th>Text</th>\n      <th>time</th>\n      <th>user_type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>14</th>\n      <td>15</td>\n      <td>B001GVISJM</td>\n      <td>A2MUGFV2TDQ47K</td>\n      <td>Lynrie \"Oh HELL no\"</td>\n      <td>4</td>\n      <td>5</td>\n      <td>5</td>\n      <td>1268352000</td>\n      <td>Strawberry Twizzlers - Yummy</td>\n      <td>The Strawberry Twizzlers are my guilty pleasur...</td>\n      <td>2010-03-12</td>\n      <td>Frequent</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>45</td>\n      <td>B001EO5QW8</td>\n      <td>A2G7B7FKP2O2PU</td>\n      <td>D. Leschke</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1209686400</td>\n      <td>Great taste and convenience</td>\n      <td>We really like the McCann's steel cut oats but...</td>\n      <td>2008-05-02</td>\n      <td>Frequent</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>47</td>\n      <td>B001EO5QW8</td>\n      <td>AQLL2R1PPR46X</td>\n      <td>grumpyrainbow</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1192752000</td>\n      <td>good</td>\n      <td>Good oatmeal.  I like the apple cinnamon the b...</td>\n      <td>2007-10-19</td>\n      <td>Frequent</td>\n    </tr>\n    <tr>\n      <th>109</th>\n      <td>110</td>\n      <td>B001REEG6C</td>\n      <td>AY12DBB0U420B</td>\n      <td>Gary Peterson</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1316390400</td>\n      <td>My Idea of a Good Diet Food.</td>\n      <td>I'm presently on a diet and I was at my Fresh ...</td>\n      <td>2011-09-19</td>\n      <td>Frequent</td>\n    </tr>\n    <tr>\n      <th>141</th>\n      <td>142</td>\n      <td>B001GVISJW</td>\n      <td>A2YIO225BTKVPU</td>\n      <td>Angel_Boy \"Treat the people around you the wa...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1301788800</td>\n      <td>Good Licorice</td>\n      <td>Twizzlers brand licorice is much better than t...</td>\n      <td>2011-04-03</td>\n      <td>Frequent</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    </tr>\n    <tr>\n      <th>568261</th>\n      <td>568262</td>\n      <td>B00374ZKQ0</td>\n      <td>A2GEZJHBV92EVR</td>\n      <td>History buff</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1345075200</td>\n      <td>Very good alternative to sugar.</td>\n      <td>I have tried many of the no sugar sweeteners o...</td>\n      <td>2012-08-16</td>\n      <td>Frequent</td>\n    </tr>\n    <tr>\n      <th>568317</th>\n      <td>568318</td>\n      <td>B0013Z0PTW</td>\n      <td>A1ODOGXEYECQQ8</td>\n      <td>Nuknuk</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1226880000</td>\n      <td>Good Tasting Granola Bars</td>\n      <td>Most health bars taste awful.  Fiber One Grano...</td>\n      <td>2008-11-17</td>\n      <td>Frequent</td>\n    </tr>\n    <tr>\n      <th>568331</th>\n      <td>568332</td>\n      <td>B001BOAOLY</td>\n      <td>A36MP37DITBU6F</td>\n      <td>Enchanted  In  Dixie \"Enchanted\"</td>\n      <td>6</td>\n      <td>6</td>\n      <td>4</td>\n      <td>1244419200</td>\n      <td>GREAT ORGANIC Honey flavor Hard candy. Great c...</td>\n      <td>These are delicious organic hard candies. I on...</td>\n      <td>2009-06-08</td>\n      <td>Frequent</td>\n    </tr>\n    <tr>\n      <th>568361</th>\n      <td>568362</td>\n      <td>B000LKVRQA</td>\n      <td>A1YUL9PCJR3JTY</td>\n      <td>O. Brown \"Ms. O. Khannah-Brown\"</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1282608000</td>\n      <td>An Earl Grey Tea That Won't Disappoint</td>\n      <td>*****&lt;br /&gt;St. Dalfour's wonderful Certified O...</td>\n      <td>2010-08-24</td>\n      <td>Frequent</td>\n    </tr>\n    <tr>\n      <th>568367</th>\n      <td>568368</td>\n      <td>B000LKVRQA</td>\n      <td>AYB4ELCS5AM8P</td>\n      <td>John B. Goode \"JBG\"</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1313539200</td>\n      <td>Excellent!</td>\n      <td>Quite simply one of the best Earl Grey's I've ...</td>\n      <td>2011-08-17</td>\n      <td>Frequent</td>\n    </tr>\n  </tbody>\n</table>\n<p>21702 rows × 12 columns</p>\n</div>"},"metadata":{}}],"execution_count":32},{"cell_type":"code","source":"# Now lets find the non-frequent users \n\nNon_freq_users = clean_data[clean_data['user_type'] == 'Non-frequent']\nNon_freq_users","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:30.335186Z","iopub.execute_input":"2025-01-11T17:00:30.335545Z","iopub.status.idle":"2025-01-11T17:00:30.49073Z","shell.execute_reply.started":"2025-01-11T17:00:30.335511Z","shell.execute_reply":"2025-01-11T17:00:30.48951Z"}},"outputs":[{"execution_count":33,"output_type":"execute_result","data":{"text/plain":"            Id   ProductId          UserId                      ProfileName  \\\n0            1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n1            2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n2            3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n3            4  B000UA0QIQ  A395BORC6FGVXV                             Karl   \n4            5  B006K2ZZ7K  A1UQRSCLF8GW1T    Michael D. Bigham \"M. Wassir\"   \n...        ...         ...             ...                              ...   \n568449  568450  B001EO7N10  A28KG5XORO54AY                 Lettie D. Carter   \n568450  568451  B003S1WTCU  A3I8AFVPEE8KI5                        R. Sawyer   \n568451  568452  B004I613EE  A121AA1GQV751Z                    pksd \"pk_007\"   \n568452  568453  B004I613EE   A3IBEVCTXKNOH          Kathy A. Welch \"katwel\"   \n568453  568454  B001LR2CU2  A3LGQPJCZVL9UC                         srfell17   \n\n        HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n0                          1                       1      5  1303862400   \n1                          0                       0      1  1346976000   \n2                          1                       1      4  1219017600   \n3                          3                       3      2  1307923200   \n4                          0                       0      5  1350777600   \n...                      ...                     ...    ...         ...   \n568449                     0                       0      5  1299628800   \n568450                     0                       0      2  1331251200   \n568451                     2                       2      5  1329782400   \n568452                     1                       1      5  1331596800   \n568453                     0                       0      5  1338422400   \n\n                                   Summary  \\\n0                    Good Quality Dog Food   \n1                        Not as Advertised   \n2                    \"Delight\" says it all   \n3                           Cough Medicine   \n4                              Great taffy   \n...                                    ...   \n568449                 Will not do without   \n568450                        disappointed   \n568451            Perfect for our maltipoo   \n568452  Favorite Training and reward treat   \n568453                         Great Honey   \n\n                                                     Text       time  \\\n0       I have bought several of the Vitality canned d... 2011-04-27   \n1       Product arrived labeled as Jumbo Salted Peanut... 2012-09-07   \n2       This is a confection that has been around a fe... 2008-08-18   \n3       If you are looking for the secret ingredient i... 2011-06-13   \n4       Great taffy at a great price.  There was a wid... 2012-10-21   \n...                                                   ...        ...   \n568449  Great for sesame chicken..this is a good if no... 2011-03-09   \n568450  I'm disappointed with the flavor. The chocolat... 2012-03-09   \n568451  These stars are small, so you can give 10-15 o... 2012-02-21   \n568452  These are the BEST treats for training and rew... 2012-03-13   \n568453  I am very satisfied ,product is as advertised,... 2012-05-31   \n\n           user_type  \n0       Non-frequent  \n1       Non-frequent  \n2       Non-frequent  \n3       Non-frequent  \n4       Non-frequent  \n...              ...  \n568449  Non-frequent  \n568450  Non-frequent  \n568451  Non-frequent  \n568452  Non-frequent  \n568453  Non-frequent  \n\n[545545 rows x 12 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>Id</th>\n      <th>ProductId</th>\n      <th>UserId</th>\n      <th>ProfileName</th>\n      <th>HelpfulnessNumerator</th>\n      <th>HelpfulnessDenominator</th>\n      <th>Score</th>\n      <th>Time</th>\n      <th>Summary</th>\n      <th>Text</th>\n      <th>time</th>\n      <th>user_type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>B001E4KFG0</td>\n      <td>A3SGXH7AUHU8GW</td>\n      <td>delmartian</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1303862400</td>\n      <td>Good Quality Dog Food</td>\n      <td>I have bought several of the Vitality canned d...</td>\n      <td>2011-04-27</td>\n      <td>Non-frequent</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>B00813GRG4</td>\n      <td>A1D87F6ZCVE5NK</td>\n      <td>dll pa</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1346976000</td>\n      <td>Not as Advertised</td>\n      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n      <td>2012-09-07</td>\n      <td>Non-frequent</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>B000LQOCH0</td>\n      <td>ABXLMWJIXXAIN</td>\n      <td>Natalia Corres \"Natalia Corres\"</td>\n      <td>1</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1219017600</td>\n      <td>\"Delight\" says it all</td>\n      <td>This is a confection that has been around a fe...</td>\n      <td>2008-08-18</td>\n      <td>Non-frequent</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>B000UA0QIQ</td>\n      <td>A395BORC6FGVXV</td>\n      <td>Karl</td>\n      <td>3</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1307923200</td>\n      <td>Cough Medicine</td>\n      <td>If you are looking for the secret ingredient i...</td>\n      <td>2011-06-13</td>\n      <td>Non-frequent</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>B006K2ZZ7K</td>\n      <td>A1UQRSCLF8GW1T</td>\n      <td>Michael D. Bigham \"M. Wassir\"</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1350777600</td>\n      <td>Great taffy</td>\n      <td>Great taffy at a great price.  There was a wid...</td>\n      <td>2012-10-21</td>\n      <td>Non-frequent</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    </tr>\n    <tr>\n      <th>568449</th>\n      <td>568450</td>\n      <td>B001EO7N10</td>\n      <td>A28KG5XORO54AY</td>\n      <td>Lettie D. Carter</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1299628800</td>\n      <td>Will not do without</td>\n      <td>Great for sesame chicken..this is a good if no...</td>\n      <td>2011-03-09</td>\n      <td>Non-frequent</td>\n    </tr>\n    <tr>\n      <th>568450</th>\n      <td>568451</td>\n      <td>B003S1WTCU</td>\n      <td>A3I8AFVPEE8KI5</td>\n      <td>R. Sawyer</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1331251200</td>\n      <td>disappointed</td>\n      <td>I'm disappointed with the flavor. The chocolat...</td>\n      <td>2012-03-09</td>\n      <td>Non-frequent</td>\n    </tr>\n    <tr>\n      <th>568451</th>\n      <td>568452</td>\n      <td>B004I613EE</td>\n      <td>A121AA1GQV751Z</td>\n      <td>pksd \"pk_007\"</td>\n      <td>2</td>\n      <td>2</td>\n      <td>5</td>\n      <td>1329782400</td>\n      <td>Perfect for our maltipoo</td>\n      <td>These stars are small, so you can give 10-15 o...</td>\n      <td>2012-02-21</td>\n      <td>Non-frequent</td>\n    </tr>\n    <tr>\n      <th>568452</th>\n      <td>568453</td>\n      <td>B004I613EE</td>\n      <td>A3IBEVCTXKNOH</td>\n      <td>Kathy A. Welch \"katwel\"</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1331596800</td>\n      <td>Favorite Training and reward treat</td>\n      <td>These are the BEST treats for training and rew...</td>\n      <td>2012-03-13</td>\n      <td>Non-frequent</td>\n    </tr>\n    <tr>\n      <th>568453</th>\n      <td>568454</td>\n      <td>B001LR2CU2</td>\n      <td>A3LGQPJCZVL9UC</td>\n      <td>srfell17</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1338422400</td>\n      <td>Great Honey</td>\n      <td>I am very satisfied ,product is as advertised,...</td>\n      <td>2012-05-31</td>\n      <td>Non-frequent</td>\n    </tr>\n  </tbody>\n</table>\n<p>545545 rows × 12 columns</p>\n</div>"},"metadata":{}}],"execution_count":33},{"cell_type":"markdown","source":"# Visualizing how frequent users rate product vs non-frequent users product rating","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize = (12,6))\n\nplt.subplot(1,2,1)\nFreq_users['Score'].value_counts().plot(kind = 'bar')\nplt.title('Frequent users ratings')\nplt.xticks(rotation = 0)\n\nplt.subplot(1,2,2)\nNon_freq_users['Score'].value_counts().plot(kind = 'bar')\nplt.title('Non-frequent users ratings')\nplt.xticks(rotation = 0)\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:00:30.491954Z","iopub.execute_input":"2025-01-11T17:00:30.492254Z","iopub.status.idle":"2025-01-11T17:00:30.871862Z","shell.execute_reply.started":"2025-01-11T17:00:30.492228Z","shell.execute_reply":"2025-01-11T17:00:30.870705Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x600 with 2 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":34},{"cell_type":"markdown","source":"We can infer from this graph that \n1. The majority of ratings are 5 stars, indicating that frequent users tend to rate products highly.\n2. There is a significant drop in the number of 4-star ratings, with even fewer 3, 2, and 1-star ratings.\n3. This suggests that frequent users generally have positive experiences with products and are more inclined to give high ratings.","metadata":{}},{"cell_type":"markdown","source":"Non-Frequent Users:\n\n1. Similar to frequent users, the majority of ratings are 5 stars.\n2. There is a slightly higher tendency for non-frequent users to give 1-star ratings compared to frequent users.","metadata":{}},{"cell_type":"markdown","source":"# Analyzing the expressiveness of buyers between frequent and not frequent users","metadata":{}},{"cell_type":"code","source":"def length_of_text(text):\n    return len(text.split())\n\nclean_data['Text_length'] = clean_data['Text'].apply(length_of_text)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:10:38.065397Z","iopub.execute_input":"2025-01-11T17:10:38.065806Z","iopub.status.idle":"2025-01-11T17:10:41.00576Z","shell.execute_reply.started":"2025-01-11T17:10:38.065736Z","shell.execute_reply":"2025-01-11T17:10:41.004498Z"}},"outputs":[{"name":"stderr","text":"<ipython-input-43-4384887b52ce>:4: 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  clean_data['Text_length'] = clean_data['Text'].apply(length_of_text)\n","output_type":"stream"}],"execution_count":43},{"cell_type":"code","source":"clean_data","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:11:10.115448Z","iopub.execute_input":"2025-01-11T17:11:10.115868Z","iopub.status.idle":"2025-01-11T17:11:10.134813Z","shell.execute_reply.started":"2025-01-11T17:11:10.115828Z","shell.execute_reply":"2025-01-11T17:11:10.133515Z"}},"outputs":[{"execution_count":44,"output_type":"execute_result","data":{"text/plain":"            Id   ProductId          UserId                      ProfileName  \\\n0            1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n1            2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n2            3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n3            4  B000UA0QIQ  A395BORC6FGVXV                             Karl   \n4            5  B006K2ZZ7K  A1UQRSCLF8GW1T    Michael D. Bigham \"M. Wassir\"   \n...        ...         ...             ...                              ...   \n568449  568450  B001EO7N10  A28KG5XORO54AY                 Lettie D. Carter   \n568450  568451  B003S1WTCU  A3I8AFVPEE8KI5                        R. Sawyer   \n568451  568452  B004I613EE  A121AA1GQV751Z                    pksd \"pk_007\"   \n568452  568453  B004I613EE   A3IBEVCTXKNOH          Kathy A. Welch \"katwel\"   \n568453  568454  B001LR2CU2  A3LGQPJCZVL9UC                         srfell17   \n\n        HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n0                          1                       1      5  1303862400   \n1                          0                       0      1  1346976000   \n2                          1                       1      4  1219017600   \n3                          3                       3      2  1307923200   \n4                          0                       0      5  1350777600   \n...                      ...                     ...    ...         ...   \n568449                     0                       0      5  1299628800   \n568450                     0                       0      2  1331251200   \n568451                     2                       2      5  1329782400   \n568452                     1                       1      5  1331596800   \n568453                     0                       0      5  1338422400   \n\n                                   Summary  \\\n0                    Good Quality Dog Food   \n1                        Not as Advertised   \n2                    \"Delight\" says it all   \n3                           Cough Medicine   \n4                              Great taffy   \n...                                    ...   \n568449                 Will not do without   \n568450                        disappointed   \n568451            Perfect for our maltipoo   \n568452  Favorite Training and reward treat   \n568453                         Great Honey   \n\n                                                     Text       time  \\\n0       I have bought several of the Vitality canned d... 2011-04-27   \n1       Product arrived labeled as Jumbo Salted Peanut... 2012-09-07   \n2       This is a confection that has been around a fe... 2008-08-18   \n3       If you are looking for the secret ingredient i... 2011-06-13   \n4       Great taffy at a great price.  There was a wid... 2012-10-21   \n...                                                   ...        ...   \n568449  Great for sesame chicken..this is a good if no... 2011-03-09   \n568450  I'm disappointed with the flavor. The chocolat... 2012-03-09   \n568451  These stars are small, so you can give 10-15 o... 2012-02-21   \n568452  These are the BEST treats for training and rew... 2012-03-13   \n568453  I am very satisfied ,product is as advertised,... 2012-05-31   \n\n           user_type  Text_length  \n0       Non-frequent           48  \n1       Non-frequent           31  \n2       Non-frequent           94  \n3       Non-frequent           41  \n4       Non-frequent           27  \n...              ...          ...  \n568449  Non-frequent           26  \n568450  Non-frequent           46  \n568451  Non-frequent           66  \n568452  Non-frequent           35  \n568453  Non-frequent           21  \n\n[567247 rows x 13 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>Id</th>\n      <th>ProductId</th>\n      <th>UserId</th>\n      <th>ProfileName</th>\n      <th>HelpfulnessNumerator</th>\n      <th>HelpfulnessDenominator</th>\n      <th>Score</th>\n      <th>Time</th>\n      <th>Summary</th>\n      <th>Text</th>\n      <th>time</th>\n      <th>user_type</th>\n      <th>Text_length</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>B001E4KFG0</td>\n      <td>A3SGXH7AUHU8GW</td>\n      <td>delmartian</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1303862400</td>\n      <td>Good Quality Dog Food</td>\n      <td>I have bought several of the Vitality canned d...</td>\n      <td>2011-04-27</td>\n      <td>Non-frequent</td>\n      <td>48</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>B00813GRG4</td>\n      <td>A1D87F6ZCVE5NK</td>\n      <td>dll pa</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1346976000</td>\n      <td>Not as Advertised</td>\n      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n      <td>2012-09-07</td>\n      <td>Non-frequent</td>\n      <td>31</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>B000LQOCH0</td>\n      <td>ABXLMWJIXXAIN</td>\n      <td>Natalia Corres \"Natalia Corres\"</td>\n      <td>1</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1219017600</td>\n      <td>\"Delight\" says it all</td>\n      <td>This is a confection that has been around a fe...</td>\n      <td>2008-08-18</td>\n      <td>Non-frequent</td>\n      <td>94</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>B000UA0QIQ</td>\n      <td>A395BORC6FGVXV</td>\n      <td>Karl</td>\n      <td>3</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1307923200</td>\n      <td>Cough Medicine</td>\n      <td>If you are looking for the secret ingredient i...</td>\n      <td>2011-06-13</td>\n      <td>Non-frequent</td>\n      <td>41</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>B006K2ZZ7K</td>\n      <td>A1UQRSCLF8GW1T</td>\n      <td>Michael D. Bigham \"M. Wassir\"</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1350777600</td>\n      <td>Great taffy</td>\n      <td>Great taffy at a great price.  There was a wid...</td>\n      <td>2012-10-21</td>\n      <td>Non-frequent</td>\n      <td>27</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    </tr>\n    <tr>\n      <th>568449</th>\n      <td>568450</td>\n      <td>B001EO7N10</td>\n      <td>A28KG5XORO54AY</td>\n      <td>Lettie D. Carter</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1299628800</td>\n      <td>Will not do without</td>\n      <td>Great for sesame chicken..this is a good if no...</td>\n      <td>2011-03-09</td>\n      <td>Non-frequent</td>\n      <td>26</td>\n    </tr>\n    <tr>\n      <th>568450</th>\n      <td>568451</td>\n      <td>B003S1WTCU</td>\n      <td>A3I8AFVPEE8KI5</td>\n      <td>R. Sawyer</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1331251200</td>\n      <td>disappointed</td>\n      <td>I'm disappointed with the flavor. The chocolat...</td>\n      <td>2012-03-09</td>\n      <td>Non-frequent</td>\n      <td>46</td>\n    </tr>\n    <tr>\n      <th>568451</th>\n      <td>568452</td>\n      <td>B004I613EE</td>\n      <td>A121AA1GQV751Z</td>\n      <td>pksd \"pk_007\"</td>\n      <td>2</td>\n      <td>2</td>\n      <td>5</td>\n      <td>1329782400</td>\n      <td>Perfect for our maltipoo</td>\n      <td>These stars are small, so you can give 10-15 o...</td>\n      <td>2012-02-21</td>\n      <td>Non-frequent</td>\n      <td>66</td>\n    </tr>\n    <tr>\n      <th>568452</th>\n      <td>568453</td>\n      <td>B004I613EE</td>\n      <td>A3IBEVCTXKNOH</td>\n      <td>Kathy A. Welch \"katwel\"</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1331596800</td>\n      <td>Favorite Training and reward treat</td>\n      <td>These are the BEST treats for training and rew...</td>\n      <td>2012-03-13</td>\n      <td>Non-frequent</td>\n      <td>35</td>\n    </tr>\n    <tr>\n      <th>568453</th>\n      <td>568454</td>\n      <td>B001LR2CU2</td>\n      <td>A3LGQPJCZVL9UC</td>\n      <td>srfell17</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1338422400</td>\n      <td>Great Honey</td>\n      <td>I am very satisfied ,product is as advertised,...</td>\n      <td>2012-05-31</td>\n      <td>Non-frequent</td>\n      <td>21</td>\n    </tr>\n  </tbody>\n</table>\n<p>567247 rows × 13 columns</p>\n</div>"},"metadata":{}}],"execution_count":44},{"cell_type":"code","source":"freq_data = clean_data[clean_data['user_type'] == 'Frequent']\nnon_freq_data = clean_data[clean_data['user_type'] == 'Non-frequent']","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:19:39.530009Z","iopub.execute_input":"2025-01-11T17:19:39.530376Z","iopub.status.idle":"2025-01-11T17:19:39.706054Z","shell.execute_reply.started":"2025-01-11T17:19:39.530349Z","shell.execute_reply":"2025-01-11T17:19:39.704915Z"}},"outputs":[],"execution_count":48},{"cell_type":"code","source":"non_freq_data.head(3)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:20:00.996318Z","iopub.execute_input":"2025-01-11T17:20:00.99667Z","iopub.status.idle":"2025-01-11T17:20:01.014301Z","shell.execute_reply.started":"2025-01-11T17:20:00.996641Z","shell.execute_reply":"2025-01-11T17:20:01.012707Z"}},"outputs":[{"execution_count":50,"output_type":"execute_result","data":{"text/plain":"   Id   ProductId          UserId                      ProfileName  \\\n0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n\n   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n0                     1                       1      5  1303862400   \n1                     0                       0      1  1346976000   \n2                     1                       1      4  1219017600   \n\n                 Summary                                               Text  \\\n0  Good Quality Dog Food  I have bought several of the Vitality canned d...   \n1      Not as Advertised  Product arrived labeled as Jumbo Salted Peanut...   \n2  \"Delight\" says it all  This is a confection that has been around a fe...   \n\n        time     user_type  Text_length  \n0 2011-04-27  Non-frequent           48  \n1 2012-09-07  Non-frequent           31  \n2 2008-08-18  Non-frequent           94  ","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>ProductId</th>\n      <th>UserId</th>\n      <th>ProfileName</th>\n      <th>HelpfulnessNumerator</th>\n      <th>HelpfulnessDenominator</th>\n      <th>Score</th>\n      <th>Time</th>\n      <th>Summary</th>\n      <th>Text</th>\n      <th>time</th>\n      <th>user_type</th>\n      <th>Text_length</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>B001E4KFG0</td>\n      <td>A3SGXH7AUHU8GW</td>\n      <td>delmartian</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1303862400</td>\n      <td>Good Quality Dog Food</td>\n      <td>I have bought several of the Vitality canned d...</td>\n      <td>2011-04-27</td>\n      <td>Non-frequent</td>\n      <td>48</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>B00813GRG4</td>\n      <td>A1D87F6ZCVE5NK</td>\n      <td>dll pa</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1346976000</td>\n      <td>Not as Advertised</td>\n      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n      <td>2012-09-07</td>\n      <td>Non-frequent</td>\n      <td>31</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>B000LQOCH0</td>\n      <td>ABXLMWJIXXAIN</td>\n      <td>Natalia Corres \"Natalia Corres\"</td>\n      <td>1</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1219017600</td>\n      <td>\"Delight\" says it all</td>\n      <td>This is a confection that has been around a fe...</td>\n      <td>2008-08-18</td>\n      <td>Non-frequent</td>\n      <td>94</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":50},{"cell_type":"code","source":"freq_data.head(3)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:20:26.903784Z","iopub.execute_input":"2025-01-11T17:20:26.904506Z","iopub.status.idle":"2025-01-11T17:20:26.925589Z","shell.execute_reply.started":"2025-01-11T17:20:26.904404Z","shell.execute_reply":"2025-01-11T17:20:26.924363Z"}},"outputs":[{"execution_count":53,"output_type":"execute_result","data":{"text/plain":"    Id   ProductId          UserId          ProfileName  HelpfulnessNumerator  \\\n14  15  B001GVISJM  A2MUGFV2TDQ47K  Lynrie \"Oh HELL no\"                     4   \n44  45  B001EO5QW8  A2G7B7FKP2O2PU           D. Leschke                     0   \n46  47  B001EO5QW8   AQLL2R1PPR46X        grumpyrainbow                     0   \n\n    HelpfulnessDenominator  Score        Time                       Summary  \\\n14                       5      5  1268352000  Strawberry Twizzlers - Yummy   \n44                       0      5  1209686400   Great taste and convenience   \n46                       0      5  1192752000                          good   \n\n                                                 Text       time user_type  \\\n14  The Strawberry Twizzlers are my guilty pleasur... 2010-03-12  Frequent   \n44  We really like the McCann's steel cut oats but... 2008-05-02  Frequent   \n46  Good oatmeal.  I like the apple cinnamon the b... 2007-10-19  Frequent   \n\n    Text_length  \n14           22  \n44           44  \n46           47  ","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>ProductId</th>\n      <th>UserId</th>\n      <th>ProfileName</th>\n      <th>HelpfulnessNumerator</th>\n      <th>HelpfulnessDenominator</th>\n      <th>Score</th>\n      <th>Time</th>\n      <th>Summary</th>\n      <th>Text</th>\n      <th>time</th>\n      <th>user_type</th>\n      <th>Text_length</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>14</th>\n      <td>15</td>\n      <td>B001GVISJM</td>\n      <td>A2MUGFV2TDQ47K</td>\n      <td>Lynrie \"Oh HELL no\"</td>\n      <td>4</td>\n      <td>5</td>\n      <td>5</td>\n      <td>1268352000</td>\n      <td>Strawberry Twizzlers - Yummy</td>\n      <td>The Strawberry Twizzlers are my guilty pleasur...</td>\n      <td>2010-03-12</td>\n      <td>Frequent</td>\n      <td>22</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>45</td>\n      <td>B001EO5QW8</td>\n      <td>A2G7B7FKP2O2PU</td>\n      <td>D. Leschke</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1209686400</td>\n      <td>Great taste and convenience</td>\n      <td>We really like the McCann's steel cut oats but...</td>\n      <td>2008-05-02</td>\n      <td>Frequent</td>\n      <td>44</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>47</td>\n      <td>B001EO5QW8</td>\n      <td>AQLL2R1PPR46X</td>\n      <td>grumpyrainbow</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1192752000</td>\n      <td>good</td>\n      <td>Good oatmeal.  I like the apple cinnamon the b...</td>\n      <td>2007-10-19</td>\n      <td>Frequent</td>\n      <td>47</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":53},{"cell_type":"code","source":"plt.figure(figsize = (10,7))\n\nplt.subplot(121)\nplt.boxplot(data = freq_data, x = freq_data['Text_length'])\nplt.xlabel(\"Review text length of frequent buyers\")\nplt.title(\"Frequent Buyers\")\nplt.ylim(top = 1500)\n\nplt.subplot(122)\nplt.boxplot(data = non_freq_data, x = non_freq_data['Text_length'])\nplt.xlabel(\"Review text length of non-frequent buyers\")\nplt.title(\"Less frequent Buyers\")\nplt.ylim(top = 1500)\n\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T17:27:49.746041Z","iopub.execute_input":"2025-01-11T17:27:49.746374Z","iopub.status.idle":"2025-01-11T17:27:50.134378Z","shell.execute_reply.started":"2025-01-11T17:27:49.746346Z","shell.execute_reply":"2025-01-11T17:27:50.133293Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x700 with 2 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\n"},"metadata":{}}],"execution_count":62},{"cell_type":"markdown","source":"Frequent Buyers: Show a more spread-out distribution, indicating a tendency to write longer reviews. They also have a higher median review length.\n\nLess Frequent Buyers: Have a more condensed distribution, writing shorter reviews on average. They also have a lower median review length.\n","metadata":{}},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}