notebook_10_data_modeling_with_an_ensemble_model.ipynb

{"cells":[{"attachments":{},"cell_type":"markdown","metadata":{"id":"mzN7ShGenbbH"},"source":["## Data Modeling and Evaluation Steps (Ensemble Model)\n","\n","At this moment, we are going to create and implement the *Naive Bayes Classifier* and *Random Forest Classifier* together in an ensemble model."]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2177,"status":"ok","timestamp":1679600722477,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"2BxJFjTbF7Df","outputId":"109f6cc9-706a-49ec-e01d-2aeadad3f753"},"outputs":[],"source":["# from google.colab import drive\n","# drive.mount('/content/drive')"]},{"cell_type":"markdown","metadata":{"id":"gNYLy2VDHFPY"},"source":["#### Step 1: Retake the *X* and *y* variables from our trainning set"]},{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":27,"status":"ok","timestamp":1679600722478,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"rI8MucyQG5qV"},"outputs":[],"source":["# Import basic libraries\n","import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"executionInfo":{"elapsed":26,"status":"ok","timestamp":1679600722478,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"FafwwDBQFv73","outputId":"db61ffc4-b91b-40c4-bb32-0f118017d3c5"},"outputs":[{"data":{"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>drugName</th>\n","      <th>condition</th>\n","      <th>rating</th>\n","      <th>date</th>\n","      <th>usefulCount</th>\n","      <th>year</th>\n","      <th>review_word_lemm</th>\n","      <th>polarity</th>\n","      <th>rating_classification</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Valsartan</td>\n","      <td>Left Ventricular Dysfunction</td>\n","      <td>9.0</td>\n","      <td>2012-05-20</td>\n","      <td>27</td>\n","      <td>2012</td>\n","      <td>['no', 'side', 'effect', 'take', 'combination'...</td>\n","      <td>0.000000</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Guanfacine</td>\n","      <td>ADHD</td>\n","      <td>8.0</td>\n","      <td>2010-04-27</td>\n","      <td>192</td>\n","      <td>2010</td>\n","      <td>['son', 'halfway', 'fourth', 'week', 'intuniv'...</td>\n","      <td>0.188021</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Lybrel</td>\n","      <td>Birth Control</td>\n","      <td>5.0</td>\n","      <td>2009-12-14</td>\n","      <td>17</td>\n","      <td>2009</td>\n","      <td>['used', 'take', 'another', 'oral', 'contracep...</td>\n","      <td>0.113636</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Ortho Evra</td>\n","      <td>Birth Control</td>\n","      <td>8.0</td>\n","      <td>2015-11-03</td>\n","      <td>10</td>\n","      <td>2015</td>\n","      <td>['first', 'time', 'using', 'form', 'birth', 'c...</td>\n","      <td>0.262500</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Buprenorphine / naloxone</td>\n","      <td>Opiate Dependence</td>\n","      <td>9.0</td>\n","      <td>2016-11-27</td>\n","      <td>37</td>\n","      <td>2016</td>\n","      <td>['suboxone', 'completely', 'turned', 'life', '...</td>\n","      <td>0.163333</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>112324</th>\n","      <td>Carbamazepine</td>\n","      <td>Trigeminal Neuralgia</td>\n","      <td>1.0</td>\n","      <td>2016-01-31</td>\n","      <td>10</td>\n","      <td>2016</td>\n","      <td>['mg', 'seems', 'work', 'every', 'nd', 'day', ...</td>\n","      <td>0.000000</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>112325</th>\n","      <td>Tekturna</td>\n","      <td>High Blood Pressure</td>\n","      <td>7.0</td>\n","      <td>2010-02-07</td>\n","      <td>18</td>\n","      <td>2010</td>\n","      <td>['tekturna', 'day', 'effect', 'immediate', 'al...</td>\n","      <td>-0.087500</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>112326</th>\n","      <td>Campral</td>\n","      <td>Alcohol Dependence</td>\n","      <td>10.0</td>\n","      <td>2015-05-31</td>\n","      <td>125</td>\n","      <td>2015</td>\n","      <td>['wrote', 'first', 'report', 'midoctober', 'no...</td>\n","      <td>0.261905</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>112327</th>\n","      <td>Thyroid desiccated</td>\n","      <td>Underactive Thyroid</td>\n","      <td>10.0</td>\n","      <td>2015-09-19</td>\n","      <td>79</td>\n","      <td>2015</td>\n","      <td>['ive', 'thyroid', 'medication', 'year', 'spen...</td>\n","      <td>0.201313</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>112328</th>\n","      <td>Lubiprostone</td>\n","      <td>Constipation, Chronic</td>\n","      <td>9.0</td>\n","      <td>2014-12-13</td>\n","      <td>116</td>\n","      <td>2014</td>\n","      <td>['ive', 'chronic', 'constipation', 'adult', 'l...</td>\n","      <td>0.104167</td>\n","      <td>2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>112329 rows × 9 columns</p>\n","</div>"],"text/plain":["                        drugName                     condition  rating  \\\n","0                      Valsartan  Left Ventricular Dysfunction     9.0   \n","1                     Guanfacine                          ADHD     8.0   \n","2                         Lybrel                 Birth Control     5.0   \n","3                     Ortho Evra                 Birth Control     8.0   \n","4       Buprenorphine / naloxone             Opiate Dependence     9.0   \n","...                          ...                           ...     ...   \n","112324             Carbamazepine          Trigeminal Neuralgia     1.0   \n","112325                  Tekturna           High Blood Pressure     7.0   \n","112326                   Campral            Alcohol Dependence    10.0   \n","112327        Thyroid desiccated           Underactive Thyroid    10.0   \n","112328              Lubiprostone         Constipation, Chronic     9.0   \n","\n","              date  usefulCount  year  \\\n","0       2012-05-20           27  2012   \n","1       2010-04-27          192  2010   \n","2       2009-12-14           17  2009   \n","3       2015-11-03           10  2015   \n","4       2016-11-27           37  2016   \n","...            ...          ...   ...   \n","112324  2016-01-31           10  2016   \n","112325  2010-02-07           18  2010   \n","112326  2015-05-31          125  2015   \n","112327  2015-09-19           79  2015   \n","112328  2014-12-13          116  2014   \n","\n","                                         review_word_lemm  polarity  \\\n","0       ['no', 'side', 'effect', 'take', 'combination'...  0.000000   \n","1       ['son', 'halfway', 'fourth', 'week', 'intuniv'...  0.188021   \n","2       ['used', 'take', 'another', 'oral', 'contracep...  0.113636   \n","3       ['first', 'time', 'using', 'form', 'birth', 'c...  0.262500   \n","4       ['suboxone', 'completely', 'turned', 'life', '...  0.163333   \n","...                                                   ...       ...   \n","112324  ['mg', 'seems', 'work', 'every', 'nd', 'day', ...  0.000000   \n","112325  ['tekturna', 'day', 'effect', 'immediate', 'al... -0.087500   \n","112326  ['wrote', 'first', 'report', 'midoctober', 'no...  0.261905   \n","112327  ['ive', 'thyroid', 'medication', 'year', 'spen...  0.201313   \n","112328  ['ive', 'chronic', 'constipation', 'adult', 'l...  0.104167   \n","\n","        rating_classification  \n","0                           2  \n","1                           2  \n","2                           1  \n","3                           2  \n","4                           2  \n","...                       ...  \n","112324                      0  \n","112325                      2  \n","112326                      2  \n","112327                      2  \n","112328                      2  \n","\n","[112329 rows x 9 columns]"]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["# Load the trainning dataset\n","# train_medication_reviews = pd.read_csv('/content/drive/Othercomputers/My MacBook Pro/Sentiment-Analysis-of-Medication-Reviews-Project/medication_reviews_dataset_to_train.csv', sep=',')\n","train_medication_reviews = pd.read_csv('/Users/rafaelaqueiroz/Sentiment-Analysis-of-Medication-Reviews-Project/medication_reviews_dataset_to_train.csv', sep=',')\n","train_medication_reviews"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":25,"status":"ok","timestamp":1679600722479,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"23Sax0ZrHCyY","outputId":"d6553624-0039-4da3-fcdd-4706acb9ceee"},"outputs":[{"data":{"text/plain":["0         ['no', 'side', 'effect', 'take', 'combination'...\n","1         ['son', 'halfway', 'fourth', 'week', 'intuniv'...\n","2         ['used', 'take', 'another', 'oral', 'contracep...\n","3         ['first', 'time', 'using', 'form', 'birth', 'c...\n","4         ['suboxone', 'completely', 'turned', 'life', '...\n","                                ...                        \n","112324    ['mg', 'seems', 'work', 'every', 'nd', 'day', ...\n","112325    ['tekturna', 'day', 'effect', 'immediate', 'al...\n","112326    ['wrote', 'first', 'report', 'midoctober', 'no...\n","112327    ['ive', 'thyroid', 'medication', 'year', 'spen...\n","112328    ['ive', 'chronic', 'constipation', 'adult', 'l...\n","Name: review_word_lemm, Length: 112329, dtype: object"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["# As we already know from our previous notebook (notebook 3), our independent variable (X) is going to be the \"review_word_lemm\" variable\n","X_train = train_medication_reviews.review_word_lemm\n","X_train"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":21,"status":"ok","timestamp":1679600722479,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"6J1QsRyCHCns","outputId":"374af238-9051-48eb-b40a-5b9d95d92adb"},"outputs":[{"data":{"text/plain":["(112329,)"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["X_train.shape"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":19,"status":"ok","timestamp":1679600722480,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"rk0XG7kVL8ZC","outputId":"7fde1e07-8fb2-4842-d1c8-6c1599d22f39"},"outputs":[{"data":{"text/plain":["pandas.core.series.Series"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["type(X_train)"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":19,"status":"ok","timestamp":1679600722481,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"nRYu0qGSMQAh","outputId":"399e6641-28a8-472a-ae3c-1039364ba8f7"},"outputs":[{"data":{"text/plain":["0         2\n","1         2\n","2         1\n","3         2\n","4         2\n","         ..\n","112324    0\n","112325    2\n","112326    2\n","112327    2\n","112328    2\n","Name: rating_classification, Length: 112329, dtype: int64"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["# As we know, our target or dependent variable (y) is going to be the 'rating_classification' variable\n","y_train = train_medication_reviews.rating_classification\n","y_train"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":397,"status":"ok","timestamp":1679600722861,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"xWwZH4W4MW6A","outputId":"f9467cb8-1e3a-4652-c0db-8381d345ff1a"},"outputs":[{"data":{"text/plain":["(112329,)"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["y_train.shape"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":35,"status":"ok","timestamp":1679600722862,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"MiJkJUnNMaEl","outputId":"95aefeea-7cba-460b-f449-f815e79ecfb3"},"outputs":[{"data":{"text/plain":["pandas.core.series.Series"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["type(y_train)"]},{"cell_type":"markdown","metadata":{"id":"x80AucaFORO-"},"source":["#### Step 2: Create the *X* and *y* variables from our testing set\n","\n","Note: As this is the testing set, we are not going to apply any cleaning or processint to it. However, as we have done label encoding to the *rating* column, we would need to label encoding this column at this set as well since the model cannot predict the sentiment of the reviews from rating 1 to 10 as we are representing those numbers differently, such as: 0 - negative reviews, 1 - \"neutral\" reviews, and 2 - positive reviews."]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":30,"status":"ok","timestamp":1679600722864,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"_oYUAe0gFzUD","outputId":"32f5335a-111d-420c-cdc4-d36fc431d9d7"},"outputs":[{"data":{"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>Unnamed: 0</th>\n","      <th>drugName</th>\n","      <th>condition</th>\n","      <th>review</th>\n","      <th>rating</th>\n","      <th>date</th>\n","      <th>usefulCount</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>163740</td>\n","      <td>Mirtazapine</td>\n","      <td>Depression</td>\n","      <td>\"I&amp;#039;ve tried a few antidepressants over th...</td>\n","      <td>10.0</td>\n","      <td>February 28, 2012</td>\n","      <td>22</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>206473</td>\n","      <td>Mesalamine</td>\n","      <td>Crohn's Disease, Maintenance</td>\n","      <td>\"My son has Crohn&amp;#039;s disease and has done ...</td>\n","      <td>8.0</td>\n","      <td>May 17, 2009</td>\n","      <td>17</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>159672</td>\n","      <td>Bactrim</td>\n","      <td>Urinary Tract Infection</td>\n","      <td>\"Quick reduction of symptoms\"</td>\n","      <td>9.0</td>\n","      <td>September 29, 2017</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>39293</td>\n","      <td>Contrave</td>\n","      <td>Weight Loss</td>\n","      <td>\"Contrave combines drugs that were used for al...</td>\n","      <td>9.0</td>\n","      <td>March 5, 2017</td>\n","      <td>35</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>97768</td>\n","      <td>Cyclafem 1 / 35</td>\n","      <td>Birth Control</td>\n","      <td>\"I have been on this birth control for one cyc...</td>\n","      <td>9.0</td>\n","      <td>October 22, 2015</td>\n","      <td>4</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Unnamed: 0         drugName                     condition  \\\n","0      163740      Mirtazapine                    Depression   \n","1      206473       Mesalamine  Crohn's Disease, Maintenance   \n","2      159672          Bactrim       Urinary Tract Infection   \n","3       39293         Contrave                   Weight Loss   \n","4       97768  Cyclafem 1 / 35                 Birth Control   \n","\n","                                              review  rating  \\\n","0  \"I&#039;ve tried a few antidepressants over th...    10.0   \n","1  \"My son has Crohn&#039;s disease and has done ...     8.0   \n","2                      \"Quick reduction of symptoms\"     9.0   \n","3  \"Contrave combines drugs that were used for al...     9.0   \n","4  \"I have been on this birth control for one cyc...     9.0   \n","\n","                 date  usefulCount  \n","0   February 28, 2012           22  \n","1        May 17, 2009           17  \n","2  September 29, 2017            3  \n","3       March 5, 2017           35  \n","4    October 22, 2015            4  "]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["# Load the test dataset\n","# test_drug_reviews_df = pd.read_csv('/content/drive/MyDrive/Data-Science-Other-Materials/Data-Scientist-Bootcamp/Sentiment-Analysis-of-Drug-Reviews/drugsComTest_raw.tsv', delimiter='\\t')\n","test_drug_reviews_df = pd.read_csv('/Users/rafaelaqueiroz/Sentiment-Analysis-of-Medication-Reviews-Project/drugsComTest_raw.tsv', delimiter='\\t')\n","test_drug_reviews_df.head()"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":106,"status":"ok","timestamp":1679600723457,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"g9OP8It4OJI8","outputId":"e67d923c-5c65-4805-9aaf-278d098ec175"},"outputs":[{"data":{"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>Unnamed: 0</th>\n","      <th>drugName</th>\n","      <th>condition</th>\n","      <th>review</th>\n","      <th>rating</th>\n","      <th>date</th>\n","      <th>usefulCount</th>\n","      <th>rating_classification</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>163740</td>\n","      <td>Mirtazapine</td>\n","      <td>Depression</td>\n","      <td>\"I&amp;#039;ve tried a few antidepressants over th...</td>\n","      <td>10.0</td>\n","      <td>February 28, 2012</td>\n","      <td>22</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>206473</td>\n","      <td>Mesalamine</td>\n","      <td>Crohn's Disease, Maintenance</td>\n","      <td>\"My son has Crohn&amp;#039;s disease and has done ...</td>\n","      <td>8.0</td>\n","      <td>May 17, 2009</td>\n","      <td>17</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>159672</td>\n","      <td>Bactrim</td>\n","      <td>Urinary Tract Infection</td>\n","      <td>\"Quick reduction of symptoms\"</td>\n","      <td>9.0</td>\n","      <td>September 29, 2017</td>\n","      <td>3</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>39293</td>\n","      <td>Contrave</td>\n","      <td>Weight Loss</td>\n","      <td>\"Contrave combines drugs that were used for al...</td>\n","      <td>9.0</td>\n","      <td>March 5, 2017</td>\n","      <td>35</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>97768</td>\n","      <td>Cyclafem 1 / 35</td>\n","      <td>Birth Control</td>\n","      <td>\"I have been on this birth control for one cyc...</td>\n","      <td>9.0</td>\n","      <td>October 22, 2015</td>\n","      <td>4</td>\n","      <td>2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Unnamed: 0         drugName                     condition  \\\n","0      163740      Mirtazapine                    Depression   \n","1      206473       Mesalamine  Crohn's Disease, Maintenance   \n","2      159672          Bactrim       Urinary Tract Infection   \n","3       39293         Contrave                   Weight Loss   \n","4       97768  Cyclafem 1 / 35                 Birth Control   \n","\n","                                              review  rating  \\\n","0  \"I&#039;ve tried a few antidepressants over th...    10.0   \n","1  \"My son has Crohn&#039;s disease and has done ...     8.0   \n","2                      \"Quick reduction of symptoms\"     9.0   \n","3  \"Contrave combines drugs that were used for al...     9.0   \n","4  \"I have been on this birth control for one cyc...     9.0   \n","\n","                 date  usefulCount  rating_classification  \n","0   February 28, 2012           22                      2  \n","1        May 17, 2009           17                      2  \n","2  September 29, 2017            3                      2  \n","3       March 5, 2017           35                      2  \n","4    October 22, 2015            4                      2  "]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["# Import library to label encode the rating column\n","from sklearn.preprocessing import LabelEncoder\n","\n","# Define the bin edges and labels (0 = 'negative', 1 = 'neutral', 2 = 'positive')\n","bin_edges = [0, 4, 6, 10]  # Ratings 1-4 are negative, 5-6 are neutral, 7-10 are positive\n","bin_labels = ['negative', 'neutral', 'positive'] # The rating_classification column should now only have 0, 1, or 2 values\n","\n","# Use cut to bin the \"rating\" column and create the new column called \"rating_classification\"\n","test_drug_reviews_df['rating_classification'] = pd.cut(test_drug_reviews_df['rating'], bins=bin_edges, labels=bin_labels)\n","\n","# Instantiate LabelEncoder and fit_transform the new column\n","le = LabelEncoder()\n","test_drug_reviews_df['rating_classification'] = le.fit_transform(test_drug_reviews_df['rating_classification'])\n","test_drug_reviews_df.head(5)"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":105,"status":"ok","timestamp":1679600723458,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"3BFPXjdtWd-p","outputId":"eadb30da-13a4-40b1-951b-9ff92888f7c9"},"outputs":[{"data":{"text/plain":["pandas.core.frame.DataFrame"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["type(test_drug_reviews_df)"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":103,"status":"ok","timestamp":1679600723458,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"ZLYxDinWlj0Y","outputId":"eef1ab91-725d-4fd0-ab9b-32baac9516c2"},"outputs":[{"data":{"text/plain":["(53766, 8)"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["test_drug_reviews_df.shape"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":103,"status":"ok","timestamp":1679600723459,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"abZlyIi4lnlF","outputId":"5fa3f166-aa85-4abb-ba7a-8dea0118bc0f"},"outputs":[{"data":{"text/plain":["0        \"I&#039;ve tried a few antidepressants over th...\n","1        \"My son has Crohn&#039;s disease and has done ...\n","2                            \"Quick reduction of symptoms\"\n","3        \"Contrave combines drugs that were used for al...\n","4        \"I have been on this birth control for one cyc...\n","                               ...                        \n","53761    \"I have taken Tamoxifen for 5 years. Side effe...\n","53762    \"I&#039;ve been taking Lexapro (escitaploprgra...\n","53763    \"I&#039;m married, 34 years old and I have no ...\n","53764    \"I was prescribed Nucynta for severe neck/shou...\n","53765                                        \"It works!!!\"\n","Name: review, Length: 53766, dtype: object"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["X_test = test_drug_reviews_df['review']\n","X_test"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":102,"status":"ok","timestamp":1679600723460,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"lG3uzddzmEDW","outputId":"aa0b26c4-0386-4e11-df3c-fc12eb42648e"},"outputs":[{"data":{"text/plain":["pandas.core.series.Series"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["type(X_test)"]},{"cell_type":"code","execution_count":16,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":99,"status":"ok","timestamp":1679600723460,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"DMbYX-1GmIR7","outputId":"bfbd7d4e-d981-4c08-d8dc-c0cb5210360d"},"outputs":[{"data":{"text/plain":["(53766,)"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["X_test.shape"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":98,"status":"ok","timestamp":1679600723460,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"1kkxJIMJWdzR","outputId":"924d2eb3-8bac-4c75-d3f9-ff2afb8934e5"},"outputs":[{"data":{"text/plain":["0        2\n","1        2\n","2        2\n","3        2\n","4        2\n","        ..\n","53761    2\n","53762    2\n","53763    2\n","53764    0\n","53765    2\n","Name: rating_classification, Length: 53766, dtype: int64"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["y_test = test_drug_reviews_df.rating_classification\n","y_test"]},{"cell_type":"code","execution_count":18,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":97,"status":"ok","timestamp":1679600723461,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"xxJ51B7zmBiz","outputId":"50be8408-d582-481b-9178-a9d9689fff9e"},"outputs":[{"data":{"text/plain":["pandas.core.series.Series"]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["type(y_test)"]},{"cell_type":"code","execution_count":19,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":95,"status":"ok","timestamp":1679600723461,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"zKsT497KmLDC","outputId":"3824f891-183a-40e5-dbf9-de0160e839d9"},"outputs":[{"data":{"text/plain":["(53766,)"]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["y_test.shape"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"H2vlq0bS3-I4"},"source":["#### Step 3.2: Create the pipeline of the ensemble model with the *Naive Bayes Classifier* and *Random Forest Classifier*."]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[],"source":["# Ensemble model\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.naive_bayes import MultinomialNB\n","from sklearn.feature_extraction.text import TfidfVectorizer\n","from sklearn.pipeline import Pipeline\n","from sklearn.ensemble import VotingClassifier\n","\n","# Define the individual models with class weights\n","rf_model = RandomForestClassifier(class_weight={0: 2, 1: 1, 2: 1})\n","nb_model = MultinomialNB(class_prior=[0.3, 0.3, 0.4])\n","\n","# Define the TF-IDF vectorizer\n","tfidf_vectorizer = TfidfVectorizer(min_df=2, max_features=5000, ngram_range=(1,2))\n","\n","# Define the pipeline for each model\n","rf_pipeline = Pipeline([('tfidf', tfidf_vectorizer), ('rf', rf_model)])\n","nb_pipeline = Pipeline([('tfidf', tfidf_vectorizer), ('nb', nb_model)])\n","\n","# Define the ensemble model\n","ensemble_model = VotingClassifier(estimators=[('rf', rf_pipeline), ('nb', nb_pipeline)], voting='hard')\n","\n","# Fit the ensemble model on training data\n","ensemble_model.fit(X_train, y_train)\n","\n","# Predict on test data\n","y_pred = ensemble_model.predict(X_test)"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.69      0.79      0.74     13497\n","           1       0.27      0.32      0.29      4829\n","           2       0.88      0.81      0.85     35440\n","\n","    accuracy                           0.76     53766\n","   macro avg       0.61      0.64      0.62     53766\n","weighted avg       0.78      0.76      0.77     53766\n","\n","[[10694   769  2034]\n"," [ 1585  1543  1701]\n"," [ 3295  3410 28735]]\n","0.7620429267566864\n"]}],"source":["# Get the metrics\n","from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n","\n","# Predict on test data\n","y_pred = ensemble_model.predict(X_test)\n","print(classification_report(y_test, y_pred))\n","print(confusion_matrix(y_test, y_pred))\n","print(accuracy_score(y_test, y_pred))"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Get the confusion matrix of the testing set\n","cm = confusion_matrix(y_test, y_pred)\n","\n","# Plot the confusion matrix\n","sns.heatmap(cm, annot=True, cmap=\"Blues\", fmt=\"d\")\n","plt.title(\"Confusion Matrix\")\n","plt.xlabel(\"Predicted Labels\")\n","plt.ylabel(\"True Labels\")\n","plt.show()"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["Based on the classification report, it looks like the model performs relatively well for class 2 (positive reviews) with a precision of 0.88, recall of 0.81, and F1-score of 0.85. However, the model doesn't perform as well for class 1 (neutral reviews) with a low precision of 0.27 and F1-score of 0.29.\n","\n","Looking at the macro-average of precision, recall, and F1-score, the model performs better in class 2 than classes 0 and 1, but not so bad in regards of class 1 that is *negative* reviews. The weighted average of the metrics is also reported, which is more useful when the dataset is imbalanced.\n","\n","The confusion matrix shows the count of true positives, false positives, true negatives, and false negatives for each class. The diagonal elements represent the correctly classified instances, while the off-diagonal elements represent misclassifications.\n","\n","Finally, the accuracy score indicates the proportion of correctly classified instances out of the total number of instances. In this case, the model has an accuracy of 76%.\n","\n","Based on these results, it seems that the class imbalance is still affecting the performance of the model, especially for the neutral class. Therefore, I might need to consider other approaches to handle class imbalance, such as oversampling or undersampling, or using different evaluation metrics such as AUC-ROC or precision-recall curve."]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["[2 2 1 ... 2 2 2]\n"]}],"source":["# Predict the labels for the training set\n","X_train = train_medication_reviews.review_word_lemm\n","y_train = train_medication_reviews.rating_classification\n","y_pred_train = ensemble_model.predict(X_train)\n","print(y_pred_train)"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.79      1.00      0.88     27839\n","           1       0.47      0.79      0.59      9993\n","           2       1.00      0.81      0.90     74497\n","\n","    accuracy                           0.86    112329\n","   macro avg       0.75      0.87      0.79    112329\n","weighted avg       0.90      0.86      0.87    112329\n","\n","[[27832     1     6]\n"," [ 2126  7858     9]\n"," [ 5127  8926 60444]]\n"]}],"source":["# Calculate and print the evaluation metrics for the training set\n","print(classification_report(y_train, y_pred_train))\n","confusion_mat = confusion_matrix(y_train, y_pred_train)\n","print(confusion_mat)"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["Now, let's perform some testing on the reviews."]},{"cell_type":"code","execution_count":53,"metadata":{},"outputs":[{"data":{"text/plain":["'\"My son has Crohn&#039;s disease and has done very well on the Asacol.  He has no complaints and shows no side effects.  He has taken as many as nine tablets per day at one time.  I&#039;ve been very happy with the results, reducing his bouts of diarrhea drastically.\"'"]},"execution_count":53,"metadata":{},"output_type":"execute_result"}],"source":["X_test[1] # Positive"]},{"cell_type":"code","execution_count":54,"metadata":{},"outputs":[{"data":{"text/plain":["2"]},"execution_count":54,"metadata":{},"output_type":"execute_result"}],"source":["y_pred[1] # Right prediction"]},{"cell_type":"code","execution_count":55,"metadata":{},"outputs":[{"data":{"text/plain":["'\"I&#039;ve tried a few antidepressants over the years (citalopram, fluoxetine, amitriptyline), but none of those helped with my depression, insomnia &amp; anxiety. My doctor suggested and changed me onto 45mg mirtazapine and this medicine has saved my life. Thankfully I have had no side effects especially the most common - weight gain, I&#039;ve actually lost alot of weight. I still have suicidal thoughts but mirtazapine has saved me.\"'"]},"execution_count":55,"metadata":{},"output_type":"execute_result"}],"source":["X_test[0] # Positive"]},{"cell_type":"code","execution_count":56,"metadata":{},"outputs":[{"data":{"text/plain":["2"]},"execution_count":56,"metadata":{},"output_type":"execute_result"}],"source":["y_pred[0] # Right prediction"]},{"cell_type":"code","execution_count":57,"metadata":{},"outputs":[{"data":{"text/plain":["'\"Holy Hell is exactly how I feel. I had been taking Brisdelle for 1.5 years. The hot flashes did indeed subside - however, the side affects of this medicine coupled with the fact Noven was acquired by YET another pharmaceutical company - YOU CAN&#039;T PLACE A REP IN THE AREA, DISTRIBUTE YOUR DRUGS, AND THEN FIRE HER-AND NOT REPLACE THEREFORE there is NO medicine or support here. You dumped this drug in the Dr&#039;s hands and walked away. After calling Sebula - you act like you don&#039;t even care. You have made it impossible to obtain this. I happen to think this is illegal.  I just decided to wean myself off this and Premarin. It has been nothing short of a nightmare. If you don&#039;t need this drug- DON&#039;T START. Seriously.\"'"]},"execution_count":57,"metadata":{},"output_type":"execute_result"}],"source":["X_test[10] # Negative"]},{"cell_type":"code","execution_count":58,"metadata":{},"outputs":[{"data":{"text/plain":["0"]},"execution_count":58,"metadata":{},"output_type":"execute_result"}],"source":["y_pred[10] # Right prediction"]},{"cell_type":"code","execution_count":109,"metadata":{},"outputs":[{"data":{"text/plain":["'\"If I could give it a 0, I would absolutely do so.  Started at 50mg, and felt WIRED.  Wanted to get up and clean the house!  Bumped it to 100mg, less wired, but still wide awake all night.  Bumped to 150, with the same lack of effect.  MD informed me after this dose it becomes less effective for sleep, so why even bother.  15 years of trying different sleep medications and alternatives, and this, I can say for sure, was the LEAST effective I have ever come across.  At it&#039;s low price point, feel free to give it a try, and maybe you will be luckier?  Everyone&#039;s sleep conditions are different.  But if you get hyper after benadryl, expect the same reaction to this drug.\"'"]},"execution_count":109,"metadata":{},"output_type":"execute_result"}],"source":["X_test[20] # Between neutral and negative (more towards the 0)"]},{"cell_type":"code","execution_count":118,"metadata":{},"outputs":[{"data":{"text/plain":["2"]},"execution_count":118,"metadata":{},"output_type":"execute_result"}],"source":["y_pred[20] # Wrong "]},{"cell_type":"code","execution_count":63,"metadata":{},"outputs":[{"data":{"text/plain":["'\"Took levofloxin for pneumonia and cleared up quick. Within 2 months my achilles was sore everyday. Made an appointment with a podiatrist for Nov 17th. Achilles snapped on Nov 14th. Surgery went well to repair shredded achilles. In cast for 3 months. Since sore after 3 months on my feet and working. I nurse it everyday. Avoid this drug, if possible. Not worth the side effects.\"'"]},"execution_count":63,"metadata":{},"output_type":"execute_result"}],"source":["X_test[200] # Negative"]},{"cell_type":"code","execution_count":64,"metadata":{},"outputs":[{"data":{"text/plain":["0"]},"execution_count":64,"metadata":{},"output_type":"execute_result"}],"source":["y_pred[200] # Right"]},{"cell_type":"code","execution_count":65,"metadata":{},"outputs":[{"data":{"text/plain":["'\"Contrave combines drugs that were used for alcohol, smoking, and opioid cessation. People lose weight on it because it also helps control over-eating. I have no doubt that most obesity is caused from sugar/carb addiction, which is just as powerful as any drug. I have been taking it for five days, and the good news is, it seems to go to work immediately. I feel hungry before I want food now. I really don&#039;t care to eat; it&#039;s just to fill my stomach. Since I have only been on it a few days, I don&#039;t know if I&#039;ve lost weight (I don&#039;t have a scale), but my clothes do feel a little looser, so maybe a pound or two. I&#039;m hoping that after a few months on this medication, I will develop healthier habits that I can continue without the aid of Contrave.\"'"]},"execution_count":65,"metadata":{},"output_type":"execute_result"}],"source":["X_test[3] # Positive"]},{"cell_type":"code","execution_count":66,"metadata":{},"outputs":[{"data":{"text/plain":["2"]},"execution_count":66,"metadata":{},"output_type":"execute_result"}],"source":["y_test[3] # Right"]},{"cell_type":"code","execution_count":69,"metadata":{},"outputs":[{"data":{"text/plain":["'\"I&#039;ve had the copper coil for about 3 months now. I was really excited at the thought of not taking hormones. I&#039;m good with pain however I nearly fainted with insertion, couldn&#039;t belive how painful it was; the doctor did say it is very painful for some. Well 3 months in, my periods last 11 days and I&#039;m in pain for about 15 days with random twangs especially in the left side and I&#039;m considering whether I want to put up with the intense pain and heavy periods. I&#039;d recommend this 100% to somebody who doesn&#039;t already have heavy painful periods but right now it just isn&#039;t for me\"'"]},"execution_count":69,"metadata":{},"output_type":"execute_result"}],"source":["X_test[6] # Between negative and neutral (more towards negative)"]},{"cell_type":"code","execution_count":70,"metadata":{},"outputs":[{"data":{"text/plain":["0"]},"execution_count":70,"metadata":{},"output_type":"execute_result"}],"source":["y_pred[6] # Right prediction"]},{"cell_type":"code","execution_count":71,"metadata":{},"outputs":[{"data":{"text/plain":["'\"I was prescribed valium for tension headaches. I have severe migraines and tension headaches. They could not find out exactly why I was having them, so we have to treat the symptom. Valium actually makes me able to function through some of the worst tension headaches. Before, I was spending so much time locked in a dark room between the migraines and tension headaches. I was missing my life and the lives of my children. It is really nice to be able to have a part in their lives and my own. I am thankful daily that I can rejoin the world again.\"'"]},"execution_count":71,"metadata":{},"output_type":"execute_result"}],"source":["X_test[187] # Positive"]},{"cell_type":"code","execution_count":72,"metadata":{},"outputs":[{"data":{"text/plain":["2"]},"execution_count":72,"metadata":{},"output_type":"execute_result"}],"source":["y_pred[187] # Right"]},{"cell_type":"code","execution_count":73,"metadata":{},"outputs":[{"data":{"text/plain":["'\"After a few days and it &quot;kicked in,&quot; every noise in the house would make me jump, pictures on TV would feel as if they were 3-D and make me jump, and I literally felt like my skin was crawling, moving, and melding to things, such as blankets. My mind focused and keyed in on every piece of white noise there was and I had sweaty, night hot flashes with chills.\"'"]},"execution_count":73,"metadata":{},"output_type":"execute_result"}],"source":["X_test[22] # Negative"]},{"cell_type":"code","execution_count":74,"metadata":{},"outputs":[{"data":{"text/plain":["0"]},"execution_count":74,"metadata":{},"output_type":"execute_result"}],"source":["y_pred[22] # Right"]},{"cell_type":"code","execution_count":75,"metadata":{},"outputs":[{"data":{"text/plain":["'\"I am a 25 year old female. I was diagnosed with bipolar II disorder about 5 years ago. I have been taking 150mg of lamotrigine for over 2 years. Thus far I have experienced significant improvements in controlling my bipolar II disorder. I recently paired 100mg of sertraline to improve the lows. Also, I experience rapid cycling. I rated this drug 70% as I feel I still have a long way to go in recovery. But, the drug has definitely allowed me to be a highly functioning individual.\"'"]},"execution_count":75,"metadata":{},"output_type":"execute_result"}],"source":["X_test[29] # Positive"]},{"cell_type":"code","execution_count":76,"metadata":{},"outputs":[{"data":{"text/plain":["2"]},"execution_count":76,"metadata":{},"output_type":"execute_result"}],"source":["y_pred[29] # Right"]},{"cell_type":"code","execution_count":105,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["[0]\n"]}],"source":["review_text_1 = \"I not like this pill\"\n","predicted_rating = ensemble_model.predict([review_text_1])\n","print(predicted_rating)"]},{"cell_type":"code","execution_count":78,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["[2]\n"]}],"source":["review_text_2 = \"I like this pill\"\n","y_pred = ensemble_model.predict([review_text_2])\n","print(y_pred)"]},{"cell_type":"code","execution_count":79,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["[0]\n"]}],"source":["review_text_3 = \"This pill is horrible\"\n","y_pred = ensemble_model.predict([review_text_3])\n","print(y_pred)"]},{"cell_type":"code","execution_count":106,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["[0]\n"]}],"source":["review_text_4 = \"I got sick and sad\"\n","y_pred = ensemble_model.predict([review_text_4])\n","print(y_pred)"]},{"cell_type":"code","execution_count":107,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["[2]\n"]}],"source":["review_text_5 = \"This patch is perfect\"\n","y_pred = ensemble_model.predict([review_text_5])\n","print(y_pred)"]},{"cell_type":"markdown","metadata":{"id":"4QrTVeXBE5dX"},"source":["#### Save the model to the disk"]},{"cell_type":"code","execution_count":82,"metadata":{"executionInfo":{"elapsed":3,"status":"ok","timestamp":1679601992634,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"bnJx1IXgEf_U"},"outputs":[],"source":["# Import pickle library\n","import pickle\n","\n","# Save the trained model to a file\n","with open('ensemble_model.pkl', 'wb') as f:\n","    pickle.dump(ensemble_model, f)"]},{"cell_type":"code","execution_count":99,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["VotingClassifier(estimators=[('rf',\n","                              Pipeline(steps=[('tfidf',\n","                                               TfidfVectorizer(max_features=5000,\n","                                                               min_df=2,\n","                                                               ngram_range=(1,\n","                                                                            2))),\n","                                              ('rf',\n","                                               RandomForestClassifier(class_weight={0: 2,\n","                                                                                    1: 1,\n","                                                                                    2: 1}))])),\n","                             ('nb',\n","                              Pipeline(steps=[('tfidf',\n","                                               TfidfVectorizer(max_features=5000,\n","                                                               min_df=2,\n","                                                               ngram_range=(1,\n","                                                                            2))),\n","                                              ('nb',\n","                                               MultinomialNB(class_prior=[0.3,\n","                                                                          0.3,\n","                                                                          0.4]))]))])\n"]}],"source":["print(ensemble_model)"]},{"cell_type":"code","execution_count":116,"metadata":{"executionInfo":{"elapsed":4,"status":"ok","timestamp":1679601984480,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"vbEwMbJPE-9R"},"outputs":[],"source":["# # Load the saved model from a file\n","# with open('nbc_model.pkl', 'rb') as f:\n","#     nbc = pickle.load(f)"]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"base","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.13"},"orig_nbformat":4},"nbformat":4,"nbformat_minor":0}