notebook_6_data_modeling_with_random_forest_classifier.ipynb

{"cells":[{"attachments":{},"cell_type":"markdown","metadata":{"id":"mzN7ShGenbbH"},"source":["## Data Modeling and Evaluation Steps\n","\n","At this moment, we are going to create and implement some machine learning models in order to find the one that can give us a better performance at a lower cost function to predict the sentiment analysis of medication reviews.\n","\n","These are the models tried:\n","\n","1. Random Forest Classifier\n","2. Naive Bayes Classifier\n","3. Ensemble Model\n","4. Long Short Term Memory (LSTM)\n","\n","Note: this notebook was split to make each model more readable and its steps followed concise and clear."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2BxJFjTbF7Df"},"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":6,"metadata":{"id":"rI8MucyQG5qV"},"outputs":[],"source":["# Import basic libraries\n","import pandas as pd\n","import numpy as np"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"executionInfo":{"elapsed":17,"status":"ok","timestamp":1679597042303,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"FafwwDBQFv73","outputId":"a0e5ff54-9f77-448d-fb43-1969ab38d423"},"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":9,"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":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":15,"status":"ok","timestamp":1679597042303,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"23Sax0ZrHCyY","outputId":"71266d85-6d40-4c1f-f0f9-79aff9e1fc18"},"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":10,"metadata":{},"output_type":"execute_result"}],"source":["# As we already know from our previous notebook (notebook 4), 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":273,"status":"ok","timestamp":1679597042564,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"6J1QsRyCHCns","outputId":"77655dc6-c441-4e0d-85f3-5eeb29263c56"},"outputs":[{"data":{"text/plain":["(112329,)"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["X_train.shape"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[],"source":["# Make the words in a string to pass them in the pipeline\n","X_train_strings = [' '.join(words) for words in X_train]"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1679597042565,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"rk0XG7kVL8ZC","outputId":"32ce4cb9-ecdc-4e9a-ae8a-293afb023ed9"},"outputs":[{"data":{"text/plain":["pandas.core.series.Series"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["type(X_train)"]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1679597042566,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"nRYu0qGSMQAh","outputId":"9b667ac3-2095-4114-a57f-54d93a4cb531"},"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":11,"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":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1679597042567,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"xWwZH4W4MW6A","outputId":"99855aa5-a675-4d40-a15b-64f9662444e3"},"outputs":[{"data":{"text/plain":["(112329,)"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["y_train.shape"]},{"cell_type":"code","execution_count":16,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1679597042568,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"MiJkJUnNMaEl","outputId":"8ca3f522-4084-49d4-d1a7-31ff9cfb00f1"},"outputs":[{"data":{"text/plain":["pandas.core.series.Series"]},"execution_count":16,"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":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":1334,"status":"ok","timestamp":1679597043895,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"_oYUAe0gFzUD","outputId":"96807ace-47dd-4099-ae1e-5eed988796ce"},"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":12,"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":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":60,"status":"ok","timestamp":1679597043897,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"WwWkUdQARgqC","outputId":"b644cec1-933c-42e3-95e3-c65715ebc539"},"outputs":[{"data":{"text/plain":["(53766, 7)"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["test_drug_reviews_df.shape"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":57,"status":"ok","timestamp":1679597043899,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"LJ15F9HXU6RY","outputId":"55507679-ec88-4afb-c58a-7d17adf3071f"},"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/","height":206},"executionInfo":{"elapsed":56,"status":"ok","timestamp":1679597043900,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"g9OP8It4OJI8","outputId":"59ca28a5-159f-445a-c81c-ddb7ea6353bc"},"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","    <tr>\n","      <th>5</th>\n","      <td>208087</td>\n","      <td>Zyclara</td>\n","      <td>Keratosis</td>\n","      <td>\"4 days in on first 2 weeks.  Using on arms an...</td>\n","      <td>4.0</td>\n","      <td>July 3, 2014</td>\n","      <td>13</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>215892</td>\n","      <td>Copper</td>\n","      <td>Birth Control</td>\n","      <td>\"I&amp;#039;ve had the copper coil for about 3 mon...</td>\n","      <td>6.0</td>\n","      <td>June 6, 2016</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>169852</td>\n","      <td>Amitriptyline</td>\n","      <td>Migraine Prevention</td>\n","      <td>\"This has been great for me. I&amp;#039;ve been on...</td>\n","      <td>9.0</td>\n","      <td>April 21, 2009</td>\n","      <td>32</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>23295</td>\n","      <td>Methadone</td>\n","      <td>Opiate Withdrawal</td>\n","      <td>\"Ive been on Methadone for over ten years and ...</td>\n","      <td>7.0</td>\n","      <td>October 18, 2016</td>\n","      <td>21</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>71428</td>\n","      <td>Levora</td>\n","      <td>Birth Control</td>\n","      <td>\"I was on this pill for almost two years. It d...</td>\n","      <td>2.0</td>\n","      <td>April 16, 2011</td>\n","      <td>3</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>196802</td>\n","      <td>Paroxetine</td>\n","      <td>Hot Flashes</td>\n","      <td>\"Holy Hell is exactly how I feel. I had been t...</td>\n","      <td>1.0</td>\n","      <td>February 22, 2017</td>\n","      <td>17</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>31947</td>\n","      <td>Miconazole</td>\n","      <td>Vaginal Yeast Infection</td>\n","      <td>\"Honestly its day one on the 3 day treatment. ...</td>\n","      <td>6.0</td>\n","      <td>May 7, 2015</td>\n","      <td>7</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>4907</td>\n","      <td>Belviq</td>\n","      <td>Weight Loss</td>\n","      <td>\"This is a waste of money.  Did not curb my ap...</td>\n","      <td>1.0</td>\n","      <td>September 23, 2014</td>\n","      <td>57</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>66736</td>\n","      <td>Seroquel</td>\n","      <td>Schizoaffective Disorde</td>\n","      <td>\"No problems, watch what you eat.\"</td>\n","      <td>10.0</td>\n","      <td>October 8, 2014</td>\n","      <td>19</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>97013</td>\n","      <td>Ambien</td>\n","      <td>Insomnia</td>\n","      <td>\"Ditto on rebound sleepless when discontinued....</td>\n","      <td>2.0</td>\n","      <td>January 13, 2015</td>\n","      <td>44</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>213376</td>\n","      <td>Nuvigil</td>\n","      <td>Narcolepsy</td>\n","      <td>\"A doctor in the ER prescribed me 200 mg of Pr...</td>\n","      <td>9.0</td>\n","      <td>June 30, 2010</td>\n","      <td>14</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>151674</td>\n","      <td>Chantix</td>\n","      <td>Smoking Cessation</td>\n","      <td>\"I smoked for 50+ years.  Took it for one week...</td>\n","      <td>10.0</td>\n","      <td>February 14, 2015</td>\n","      <td>26</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>33173</td>\n","      <td>Microgestin Fe 1 / 20</td>\n","      <td>Acne</td>\n","      <td>\"So I was on Ginanvi for about 3 months before...</td>\n","      <td>3.0</td>\n","      <td>June 22, 2017</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>30401</td>\n","      <td>Klonopin</td>\n","      <td>Bipolar Disorde</td>\n","      <td>\"This medication helped me  sleep, but eventua...</td>\n","      <td>6.0</td>\n","      <td>July 14, 2009</td>\n","      <td>24</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>152490</td>\n","      <td>Ciprofloxacin</td>\n","      <td>Urinary Tract Infection</td>\n","      <td>\"After just 1 dose of this ciprofloxacn, I fel...</td>\n","      <td>10.0</td>\n","      <td>June 9, 2010</td>\n","      <td>9</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>231397</td>\n","      <td>Trazodone</td>\n","      <td>Insomnia</td>\n","      <td>\"If I could give it a 0, I would absolutely do...</td>\n","      <td>1.0</td>\n","      <td>October 18, 2016</td>\n","      <td>15</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>38116</td>\n","      <td>EnteraGam</td>\n","      <td>Irritable Bowel Syndrome</td>\n","      <td>\"I am so happy with the samples provided by my...</td>\n","      <td>9.0</td>\n","      <td>January 1, 2015</td>\n","      <td>43</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>22</th>\n","      <td>102969</td>\n","      <td>Aripiprazole</td>\n","      <td>Bipolar Disorde</td>\n","      <td>\"After a few days and it &amp;quot;kicked in,&amp;quot...</td>\n","      <td>1.0</td>\n","      <td>February 10, 2010</td>\n","      <td>27</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>12626</td>\n","      <td>Cyclosporine</td>\n","      <td>Keratoconjunctivitis Sicca</td>\n","      <td>\"They didn&amp;#039;t help my dry eyes at all.\"</td>\n","      <td>1.0</td>\n","      <td>December 31, 2015</td>\n","      <td>17</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>190527</td>\n","      <td>Oxybutynin</td>\n","      <td>Hyperhidrosis</td>\n","      <td>\"This drug is pretty amazing. I&amp;#039;ve had hy...</td>\n","      <td>9.0</td>\n","      <td>May 30, 2017</td>\n","      <td>10</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>25</th>\n","      <td>229975</td>\n","      <td>Lurasidone</td>\n","      <td>Bipolar Disorde</td>\n","      <td>\"This medication should not be being prescribe...</td>\n","      <td>6.0</td>\n","      <td>February 11, 2017</td>\n","      <td>18</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>26</th>\n","      <td>173391</td>\n","      <td>Clonazepam</td>\n","      <td>Panic Disorde</td>\n","      <td>\"klonopin has definitely given me my life back!\"</td>\n","      <td>10.0</td>\n","      <td>April 19, 2008</td>\n","      <td>47</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>35608</td>\n","      <td>Ciclopirox</td>\n","      <td>Onychomycosis, Fingernail</td>\n","      <td>\"Worked quickly and well. I learned by trial t...</td>\n","      <td>8.0</td>\n","      <td>June 2, 2016</td>\n","      <td>38</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>187230</td>\n","      <td>Sodium oxybate</td>\n","      <td>Narcolepsy</td>\n","      <td>\"2.75g x 2 every night for five years. Deeper ...</td>\n","      <td>10.0</td>\n","      <td>January 10, 2013</td>\n","      <td>19</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>211675</td>\n","      <td>Lamotrigine</td>\n","      <td>Bipolar Disorde</td>\n","      <td>\"I am a 25 year old female. I was diagnosed wi...</td>\n","      <td>7.0</td>\n","      <td>November 22, 2017</td>\n","      <td>1</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","5       208087                Zyclara                     Keratosis   \n","6       215892                 Copper                 Birth Control   \n","7       169852          Amitriptyline           Migraine Prevention   \n","8        23295              Methadone             Opiate Withdrawal   \n","9        71428                 Levora                 Birth Control   \n","10      196802             Paroxetine                   Hot Flashes   \n","11       31947             Miconazole       Vaginal Yeast Infection   \n","12        4907                 Belviq                   Weight Loss   \n","13       66736               Seroquel       Schizoaffective Disorde   \n","14       97013                 Ambien                      Insomnia   \n","15      213376                Nuvigil                    Narcolepsy   \n","16      151674                Chantix             Smoking Cessation   \n","17       33173  Microgestin Fe 1 / 20                          Acne   \n","18       30401               Klonopin               Bipolar Disorde   \n","19      152490          Ciprofloxacin       Urinary Tract Infection   \n","20      231397              Trazodone                      Insomnia   \n","21       38116              EnteraGam      Irritable Bowel Syndrome   \n","22      102969           Aripiprazole               Bipolar Disorde   \n","23       12626           Cyclosporine    Keratoconjunctivitis Sicca   \n","24      190527             Oxybutynin                 Hyperhidrosis   \n","25      229975             Lurasidone               Bipolar Disorde   \n","26      173391             Clonazepam                 Panic Disorde   \n","27       35608             Ciclopirox     Onychomycosis, Fingernail   \n","28      187230         Sodium oxybate                    Narcolepsy   \n","29      211675            Lamotrigine               Bipolar Disorde   \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","5   \"4 days in on first 2 weeks.  Using on arms an...     4.0   \n","6   \"I&#039;ve had the copper coil for about 3 mon...     6.0   \n","7   \"This has been great for me. I&#039;ve been on...     9.0   \n","8   \"Ive been on Methadone for over ten years and ...     7.0   \n","9   \"I was on this pill for almost two years. It d...     2.0   \n","10  \"Holy Hell is exactly how I feel. I had been t...     1.0   \n","11  \"Honestly its day one on the 3 day treatment. ...     6.0   \n","12  \"This is a waste of money.  Did not curb my ap...     1.0   \n","13                 \"No problems, watch what you eat.\"    10.0   \n","14  \"Ditto on rebound sleepless when discontinued....     2.0   \n","15  \"A doctor in the ER prescribed me 200 mg of Pr...     9.0   \n","16  \"I smoked for 50+ years.  Took it for one week...    10.0   \n","17  \"So I was on Ginanvi for about 3 months before...     3.0   \n","18  \"This medication helped me  sleep, but eventua...     6.0   \n","19  \"After just 1 dose of this ciprofloxacn, I fel...    10.0   \n","20  \"If I could give it a 0, I would absolutely do...     1.0   \n","21  \"I am so happy with the samples provided by my...     9.0   \n","22  \"After a few days and it &quot;kicked in,&quot...     1.0   \n","23        \"They didn&#039;t help my dry eyes at all.\"     1.0   \n","24  \"This drug is pretty amazing. I&#039;ve had hy...     9.0   \n","25  \"This medication should not be being prescribe...     6.0   \n","26   \"klonopin has definitely given me my life back!\"    10.0   \n","27  \"Worked quickly and well. I learned by trial t...     8.0   \n","28  \"2.75g x 2 every night for five years. Deeper ...    10.0   \n","29  \"I am a 25 year old female. I was diagnosed wi...     7.0   \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  \n","5         July 3, 2014           13                      0  \n","6         June 6, 2016            1                      1  \n","7       April 21, 2009           32                      2  \n","8     October 18, 2016           21                      2  \n","9       April 16, 2011            3                      0  \n","10   February 22, 2017           17                      0  \n","11         May 7, 2015            7                      1  \n","12  September 23, 2014           57                      0  \n","13     October 8, 2014           19                      2  \n","14    January 13, 2015           44                      0  \n","15       June 30, 2010           14                      2  \n","16   February 14, 2015           26                      2  \n","17       June 22, 2017            1                      0  \n","18       July 14, 2009           24                      1  \n","19        June 9, 2010            9                      2  \n","20    October 18, 2016           15                      0  \n","21     January 1, 2015           43                      2  \n","22   February 10, 2010           27                      0  \n","23   December 31, 2015           17                      0  \n","24        May 30, 2017           10                      2  \n","25   February 11, 2017           18                      1  \n","26      April 19, 2008           47                      2  \n","27        June 2, 2016           38                      2  \n","28    January 10, 2013           19                      2  \n","29   November 22, 2017            1                      2  "]},"execution_count":13,"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(30)"]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":57,"status":"ok","timestamp":1679597043902,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"3BFPXjdtWd-p","outputId":"4b92f862-6e80-4da6-a801-1c300d1e5511"},"outputs":[{"data":{"text/plain":["pandas.core.frame.DataFrame"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["type(test_drug_reviews_df)"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":55,"status":"ok","timestamp":1679597043904,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"ZLYxDinWlj0Y","outputId":"def15d67-e531-42bf-8043-42fe12ed7ca6"},"outputs":[{"data":{"text/plain":["(53766, 8)"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["test_drug_reviews_df.shape"]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":51,"status":"ok","timestamp":1679597043904,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"abZlyIi4lnlF","outputId":"a23c4035-a8d0-40ba-ddc5-36d9dfa76815"},"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":14,"metadata":{},"output_type":"execute_result"}],"source":["X_test = test_drug_reviews_df['review']\n","X_test"]},{"cell_type":"code","execution_count":27,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":50,"status":"ok","timestamp":1679597043905,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"lG3uzddzmEDW","outputId":"4eee7a67-0faf-480a-bca7-5d772d383210"},"outputs":[{"data":{"text/plain":["pandas.core.series.Series"]},"execution_count":27,"metadata":{},"output_type":"execute_result"}],"source":["type(X_test)"]},{"cell_type":"code","execution_count":28,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":49,"status":"ok","timestamp":1679597043906,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"DMbYX-1GmIR7","outputId":"3a81045d-2331-46cc-cd2d-8c6999db1b43"},"outputs":[{"data":{"text/plain":["(53766,)"]},"execution_count":28,"metadata":{},"output_type":"execute_result"}],"source":["X_test.shape"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":48,"status":"ok","timestamp":1679597043907,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"1kkxJIMJWdzR","outputId":"5496583c-cb78-435e-ae70-8422e679afdc"},"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":15,"metadata":{},"output_type":"execute_result"}],"source":["y_test = test_drug_reviews_df.rating_classification\n","y_test"]},{"cell_type":"code","execution_count":22,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":47,"status":"ok","timestamp":1679597043908,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"xxJ51B7zmBiz","outputId":"8c29c20b-74e9-49cb-a7a5-62e0f7d6249d"},"outputs":[{"data":{"text/plain":["pandas.core.series.Series"]},"execution_count":22,"metadata":{},"output_type":"execute_result"}],"source":["type(y_test)"]},{"cell_type":"code","execution_count":23,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":45,"status":"ok","timestamp":1679597043908,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"zKsT497KmLDC","outputId":"8e10e4e8-90f0-4c75-bf83-0692ba2cc779"},"outputs":[{"data":{"text/plain":["(53766,)"]},"execution_count":23,"metadata":{},"output_type":"execute_result"}],"source":["y_test.shape"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"FEVGPDdXELlT"},"source":["#### Step 3.1: Create a pipeline of the *Random Forest Classifier*\n","\n","As we know, the *Random Forest Classifier* is a machine learning algorithm used for classification tasks which offers most of the time a good performance, therefore it is our first choice to be used in our trainning. "]},{"cell_type":"code","execution_count":24,"metadata":{"id":"yy5QH_Ucu9L2"},"outputs":[],"source":["# Import libraries to create the pipeline\n","from sklearn.pipeline import Pipeline\n","from sklearn.model_selection import GridSearchCV\n","from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, TfidfTransformer # For BoW and TFIDF\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.metrics import confusion_matrix\n","from sklearn.metrics import classification_report"]},{"cell_type":"code","execution_count":36,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":41915,"status":"ok","timestamp":1679597085783,"user":{"displayName":"Rafaela Queiroz F. Cordeiro","userId":"18015024781067059704"},"user_tz":420},"id":"cTbZxlbmqaqB","outputId":"064b148f-d8bb-439e-d9cf-1aeeff2e548b"},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.91      0.40      0.56     13497\n","           1       0.99      0.05      0.10      4829\n","           2       0.74      0.99      0.85     35440\n","\n","    accuracy                           0.76     53766\n","   macro avg       0.88      0.48      0.50     53766\n","weighted avg       0.81      0.76      0.71     53766\n","\n","[[ 5423     2  8072]\n"," [  304   249  4276]\n"," [  225     0 35215]]\n"]}],"source":["# Create a function to make the pipeline\n","def pipeline_rfc():\n","    # X is the feature matrix\n","    X_train = train_medication_reviews.review_word_lemm\n","    # y is the label vector\n","    y_train = train_medication_reviews.rating_classification\n","    \n","    pipeline = Pipeline([\n","        ('vect', CountVectorizer()),\n","        ('tfidf', TfidfTransformer()),\n","        ('clf', RandomForestClassifier(n_estimators=100, random_state=42, class_weight={0: 2, 1: 1, 2: 1})) # Initialize the Random Forest Classifier\n","        ])\n","\n","    # Train the model on the training set\n","    pipeline.fit(X_train, y_train)\n","\n","    # Predict the labels for the test set\n","    X_test = test_drug_reviews_df['review']\n","    y_test = test_drug_reviews_df.rating_classification\n","    y_pred = pipeline.predict(X_test)\n","\n","    # Calculate the evaluation metrics\n","    print(classification_report(y_test, y_pred))\n","    labels = np.unique(y_pred)\n","    confusion_mat = confusion_matrix(y_test, y_pred, labels=labels)\n","    print(confusion_mat)\n","\n","pipeline_rfc()"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"lfoDLOIEwoQu"},"source":["The results of the model evaluation show the precision, recall, and F1-score for each label in the test set, as well as the macro and weighted averages of these metrics.\n","\n","From these results, we can point out the following key aspects:\n","\n","Based on the precision, recall, and F1-score, the model performed best in predicting the positive class (class 2) with an F1-score of 0.85. The model also performed relatively well in predicting the negative class (class 0) with an F1-score of 0.56. However, the model did not perform well in predicting the neutral class (class 1) with an F1-score of only 0.10.\n","\n","The confusion matrix provides more detailed information about the model's performance. \n","\n","Each row in the matrix represents the actual class, while each column represents the predicted class. The values in the matrix show the number of samples that fall into each category. Looking at the matrix, we can see that the model predicted a large number of samples as positive (class 2), which is reflected in the high recall score for that class. However, the model had a low precision score for the negative and neutral classes, indicating that many samples were incorrectly classified as positive.\n","\n","Overall, the weighted average of the F1-score was 0.71, indicating that the model's performance was moderate across all classes. It may be useful to explore further why the model performed poorly on the neutral class and whether there are ways to improve its performance."]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["Let's see if we can improve these results by applying *GridSearchCV( )* to the pipeline."]},{"cell_type":"code","execution_count":60,"metadata":{},"outputs":[],"source":["def pipeline_rfcl():\n","    # X is the feature matrix\n","    X_train = train_medication_reviews['review_word_lemm']\n","    # y is the label vector\n","    y_train = train_medication_reviews['rating_classification']\n","    \n","    pipeline = Pipeline([\n","        ('vect', CountVectorizer()),\n","        ('tfidf', TfidfTransformer()),\n","        ('clf', RandomForestClassifier()) # Initialize the Random Forest Classifier\n","        ])\n","\n","    # Define the parameter grid to search over\n","    param_grid = {\n","    'vect__max_features': [1000, 5000, 10000],\n","    'vect__max_df': [0.5, 0.75, 1.0],\n","    'tfidf__use_idf': [True, False],\n","    'clf__n_estimators': [50, 100, 200],\n","    'clf__max_depth': [5, 10, 15],\n","    'clf__class_weight': [{0: 2, 1: 1, 2: 1}, {0: 1, 1: 1, 2: 1}]\n","}\n","\n","    # Use GridSearchCV to find the best parameters\n","    grid_search = GridSearchCV(pipeline, param_grid=param_grid, cv=5)\n","    grid_search.fit(X_train, y_train)\n","\n","    # Print the best parameters and score\n","    print(\"Best parameters:\", grid_search.best_params_)\n","    print(\"Best score:\", grid_search.best_score_)\n","\n","    # Predict the labels for the test set using the best model\n","    best_model = grid_search.best_estimator_\n","    X_test = test_drug_reviews_df['review']\n","    y_test = test_drug_reviews_df['rating_classification']\n","    y_pred = best_model.predict(X_test)\n","\n","    # Calculate the evaluation metrics\n","    print(classification_report(y_test, y_pred))\n","    labels = np.unique(y_pred)\n","    confusion_mat = confusion_matrix(y_test, y_pred, labels=labels)\n","    print(confusion_mat)\n","\n","pipeline_rfcl()"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["As the GridSearchCV couldn't detect the classification number 1 (\"neutral\" reviews) with a better metric, we are going to try again another pipeline of the *Random Forest Classifier*."]},{"cell_type":"code","execution_count":25,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.84      0.69      0.76     13497\n","           1       1.00      0.13      0.24      4829\n","           2       0.82      0.97      0.89     35440\n","\n","    accuracy                           0.83     53766\n","   macro avg       0.88      0.60      0.63     53766\n","weighted avg       0.84      0.83      0.80     53766\n","\n","[[ 9279     2  4216]\n"," [  807   646  3376]\n"," [  970     1 34469]]\n"]}],"source":["# Create the pipeline\n","\n","# X is the feature matrix\n","X_train = train_medication_reviews.review_word_lemm\n","# y is the label vector\n","y_train = train_medication_reviews.rating_classification\n","\n","# Define the preprocessor\n","preprocessor_tfidf = TfidfVectorizer(min_df=2, max_features=5000, ngram_range=(1,2))\n","                               \n","# Define the pipeline \n","pipeline_rfc = Pipeline([\n","        ('preprocessing', preprocessor_tfidf),\n","        ('clf', RandomForestClassifier(n_estimators=100, random_state=42, class_weight={0: 2, 1: 1, 2: 1})) # Initialize the Random Forest Classifier\n","])\n","\n","# Train the model on the training set\n","pipeline_rfc.fit(X_train, y_train)\n","\n","# Predict the labels for the test set\n","X_test = test_drug_reviews_df['review']\n","y_test = test_drug_reviews_df.rating_classification\n","y_pred = pipeline_rfc.predict(X_test)\n","\n","# Calculate the evaluation metrics for the testing set\n","print(classification_report(y_test, y_pred))\n","labels = np.unique(y_pred)\n","confusion_mat = confusion_matrix(y_test, y_pred, labels=labels)\n","print(confusion_mat)"]},{"cell_type":"code","execution_count":26,"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 = pipeline_rfc.predict(X_train)\n","print(y_pred_train)"]},{"cell_type":"code","execution_count":27,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       1.00      1.00      1.00     27839\n","           1       1.00      1.00      1.00      9993\n","           2       1.00      1.00      1.00     74497\n","\n","    accuracy                           1.00    112329\n","   macro avg       1.00      1.00      1.00    112329\n","weighted avg       1.00      1.00      1.00    112329\n","\n","[[27830     1     8]\n"," [    7  9976    10]\n"," [    7     0 74490]]\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)"]},{"cell_type":"markdown","metadata":{"id":"qjeCKE287ULk"},"source":["#### Save the model to the disk"]},{"cell_type":"code","execution_count":50,"metadata":{"id":"Q2kkAKn17Sza"},"outputs":[],"source":["# Import pickle\n","import pickle\n","\n","# Save the trained model to a file\n","with open('rfc_model.pkl', 'wb') as f:\n","    pickle.dump(pipeline_rfc, f)"]},{"cell_type":"code","execution_count":51,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Pipeline(steps=[('vect', CountVectorizer()), ('tfidf', TfidfTransformer()),\n","                ('clf',\n","                 RandomForestClassifier(class_weight={0: 2, 1: 1, 2: 1},\n","                                        random_state=42))])\n"]}],"source":["print(pipeline_rfc)"]}],"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}