code/03_Modeling.ipynb

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   "source": [
    "# NLP Classification Modeling\n",
    "\n",
    "> **NOTE** Many of the cells are commented out since if they were run they may take hours to complete.  I have left cells relating to the final selected model active, so if all cells are run as is, this notebook could take 30 minutes or longer to complete."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aa2472b-dd6c-4a72-8d57-b1256a1321fb",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "6f7ff2a9-51e2-49aa-af1e-b2bdcaa7e40e",
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from wordcloud import WordCloud\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "from sklearn.metrics import (ConfusionMatrixDisplay, classification_report, \n",
    "                             RocCurveDisplay)\n",
    "\n",
    "# this setting widens how many characters pandas will display in a column:\n",
    "pd.options.display.max_colwidth = 400"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c532b890-914c-4799-8640-0314b4c1152c",
   "metadata": {},
   "source": [
    "### Read Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b40541b2-e324-47bf-ae48-2a5aa4abc0be",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2022-12-23T01:04:21.980336Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('../data/reddit_cleaned.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "e590ec22-1625-4c58-9512-5f44c664f6f4",
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   "outputs": [
    {
     "data": {
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       "<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>subreddit</th>\n",
       "      <th>author</th>\n",
       "      <th>num_comments</th>\n",
       "      <th>selftext</th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>playstation</td>\n",
       "      <td>Blix404</td>\n",
       "      <td>8</td>\n",
       "      <td>PlayStation. Why\\n1st of PlayStation has been out since 2013 and you guys close to releasing the PS5 so why tf do you now all of a sudden start recording our party conversations.\\n2nd of I ain’t getting banned just cause some snowflake in my party chat felt the need to snitch on me. Fuck outta here with that BS.\\n\\n3rd. Xbox Doesn’t do that so why do you feel the need to do that you dickheads....</td>\n",
       "      <td>PARTY CHAT BEING RECORDED</td>\n",
       "      <td>party chat be record  why 1st of  have be out since  and you guy close to release the 5 so why tf do you now all of a sudden start record our party conversation 2nd of I ai not getting ban just cause some snowflake in my party chat feel the need to snitch on I fuck outta here with that bs 3rd  do not do that so why do you feel the need to do that you dickhead just prove to be more and more of ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>playstation</td>\n",
       "      <td>ImmaPoodle</td>\n",
       "      <td>0</td>\n",
       "      <td>So watch dogs legion is coming out soon and with interesting mechanics and ray tracing I think it might be what people thought watch dogs 2 would be... a predecessor to GTA 5, ok let's be honest watch dogs 2 was an amazing game but it lacked that feeling that GTA had as well as the already large player base.\\n\\nBut this time with already tons of preordered copies of watch dogs legion in circle...</td>\n",
       "      <td>Ubisoft vs Rockstar</td>\n",
       "      <td>ubisoft vs rockstar so watch dog legion be come out soon and with interesting mechanic and ray trace I think it might be what people think watch dog  would be a predecessor to gta  ok let us be honest watch dog  be an amazing game but it lack that feeling that gta have as well as the already large player base but this time with already ton of preordere copy of watch dog legion in circle and ro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>playstation</td>\n",
       "      <td>georgios82</td>\n",
       "      <td>6</td>\n",
       "      <td>So guys let me get this straight. If you want to temporarily invite someone to your party, then they will be automatically (permanently) added to the chat as well? Not only that but from my understanding they will also be able to see all older discussions that took place in that group? Am I missing something here? Is there a way to avoid this?</td>\n",
       "      <td>Question regarding the recent party changes</td>\n",
       "      <td>question regard the recent party change so guy let I get this straight if you want to temporarily invite someone to your party then they will be automatically permanently add to the chat as well not only that but from my understanding they will also be able to see all old discussion that take place in that group be I miss something here be there a way to avoid this</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     subreddit      author  num_comments  \\\n",
       "0  playstation     Blix404             8   \n",
       "1  playstation  ImmaPoodle             0   \n",
       "2  playstation  georgios82             6   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                          selftext  \\\n",
       "0  PlayStation. Why\\n1st of PlayStation has been out since 2013 and you guys close to releasing the PS5 so why tf do you now all of a sudden start recording our party conversations.\\n2nd of I ain’t getting banned just cause some snowflake in my party chat felt the need to snitch on me. Fuck outta here with that BS.\\n\\n3rd. Xbox Doesn’t do that so why do you feel the need to do that you dickheads....   \n",
       "1  So watch dogs legion is coming out soon and with interesting mechanics and ray tracing I think it might be what people thought watch dogs 2 would be... a predecessor to GTA 5, ok let's be honest watch dogs 2 was an amazing game but it lacked that feeling that GTA had as well as the already large player base.\\n\\nBut this time with already tons of preordered copies of watch dogs legion in circle...   \n",
       "2                                                        So guys let me get this straight. If you want to temporarily invite someone to your party, then they will be automatically (permanently) added to the chat as well? Not only that but from my understanding they will also be able to see all older discussions that took place in that group? Am I missing something here? Is there a way to avoid this?   \n",
       "\n",
       "                                         title  \\\n",
       "0                    PARTY CHAT BEING RECORDED   \n",
       "1                          Ubisoft vs Rockstar   \n",
       "2  Question regarding the recent party changes   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                              text  \n",
       "0  party chat be record  why 1st of  have be out since  and you guy close to release the 5 so why tf do you now all of a sudden start record our party conversation 2nd of I ai not getting ban just cause some snowflake in my party chat feel the need to snitch on I fuck outta here with that bs 3rd  do not do that so why do you feel the need to do that you dickhead just prove to be more and more of ...  \n",
       "1  ubisoft vs rockstar so watch dog legion be come out soon and with interesting mechanic and ray trace I think it might be what people think watch dog  would be a predecessor to gta  ok let us be honest watch dog  be an amazing game but it lack that feeling that gta have as well as the already large player base but this time with already ton of preordere copy of watch dog legion in circle and ro...  \n",
       "2                                  question regard the recent party change so guy let I get this straight if you want to temporarily invite someone to your party then they will be automatically permanently add to the chat as well not only that but from my understanding they will also be able to see all old discussion that take place in that group be I miss something here be there a way to avoid this  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06f5b114-d629-4135-adbb-4e406e093861",
   "metadata": {},
   "source": [
    "Transform the subreddit feature to binary for use in our classification modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "b08e704a-2594-4f16-a03b-ad3ac15ae7be",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2022-12-23T01:04:22.194794Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "playstation    52.37\n",
       "xbox           47.63\n",
       "Name: subreddit, dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['subreddit'].value_counts(normalize=True).mul(100).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "110cb6d1-1210-4695-99a1-2077c0a0fa42",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2022-12-23T01:04:22.205363Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['subreddit'] = df['subreddit'].map(lambda x: 1 if x == 'playstation' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "455bec4f-e8c8-4c4f-9b92-34e13feab75f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:04:22.215434Z",
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     "shell.execute_reply.started": "2022-12-23T01:04:22.215421Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    52.37\n",
       "0    47.63\n",
       "Name: subreddit, dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['subreddit'].value_counts(normalize=True).mul(100).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6f3c168-552f-4cea-8e2b-62ccc8da5c16",
   "metadata": {},
   "source": [
    "1 = Playstation<br>\n",
    "0 = Xbox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "fe187945-e92a-473d-8ccd-6faa27355eae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:04:22.223085Z",
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     "shell.execute_reply.started": "2022-12-23T01:04:22.223067Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# rename as target since that is easier for me to remember\n",
    "df.rename({'subreddit': 'target'}, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4dbeabc-f16b-4ec9-9742-47f021ef5481",
   "metadata": {},
   "source": [
    "### Configure X, y, and split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "9e2cbc42-ed93-4447-b9d2-d45716458b01",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:04:22.227286Z",
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = df['text']\n",
    "y = df['target']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=415,\n",
    "                                                    stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "80fd0a20-881f-4f93-a242-7449f394a95e",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2022-12-23T01:04:22.271576Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((13998,), (4666,), (13998,), (4666,))"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ea0a4ed-1f01-4b45-a804-fe79356367fc",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "\n",
    "Overall, I'd like to look at a few different models by first checking their scores with default settings in both CountVectorizer and TfidfVectorizer processing with the default settings, and then an additional GridSearch for each model to try and identify which classifier I would like to tune and select for production.  \n",
    "\n",
    "I'll start by establishing the baseline, and then work through various models to try and identify which one should be tuned for the best predictive power. \n",
    "\n",
    "> Note: I have commented out some of the code that requires long periods of time to run, such as the Grid Searches, in order to be able to run this entire notebook relatively quickly upon opening."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cd0b6c1-6c3d-488c-82cd-db7702167ea3",
   "metadata": {},
   "source": [
    "### Baseline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0a65e1d-1368-4a4c-9324-747aaaf500a3",
   "metadata": {},
   "source": [
    "To calculate our baseline, since we're classifying, we'll look at the majority class of the y of the data.  For our model to provide value, it will need to do better than just predicting the majority class each time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "dafd51f1-dd1f-405f-9855-9e0f7eb05451",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:04:22.275422Z",
     "iopub.status.busy": "2022-12-23T01:04:22.275331Z",
     "iopub.status.idle": "2022-12-23T01:04:22.279655Z",
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     "shell.execute_reply.started": "2022-12-23T01:04:22.275411Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    52.37\n",
       "0    47.63\n",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts(normalize=True).mul(100).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7463829b-810e-4fba-a35b-92ea72239f75",
   "metadata": {},
   "source": [
    "Compare the train and test split stratification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "6bf70ab2-9950-4899-9b84-340e385290cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:04:22.280392Z",
     "iopub.status.busy": "2022-12-23T01:04:22.280303Z",
     "iopub.status.idle": "2022-12-23T01:04:22.284404Z",
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     "shell.execute_reply.started": "2022-12-23T01:04:22.280382Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    0.523718\n",
      "0    0.476282\n",
      "Name: target, dtype: float64\n",
      "1    0.523789\n",
      "0    0.476211\n",
      "Name: target, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(y_train.value_counts(normalize=True))\n",
    "print(y_test.value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c32f8e26-c9f2-4831-ae19-be40e818f481",
   "metadata": {},
   "source": [
    "We'll need our models to perform better than 52.4% accuracy in order to be better than just always predicting the majority class (1 = playstation)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ab6a9bb-2e7b-4c67-ba9d-46f320656774",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ec43413-1d84-47b1-b13f-a68582dd12c0",
   "metadata": {},
   "source": [
    "#### Configure Decision Tree Pipelines"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00d84c67-ebe6-4782-8acd-bb1038109004",
   "metadata": {},
   "source": [
    "DecisionTreeClassifier with CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "e6119343-894e-4b3c-888a-174f8cb6abbf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:04:22.285328Z",
     "iopub.status.busy": "2022-12-23T01:04:22.285172Z",
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     "shell.execute_reply.started": "2022-12-23T01:04:22.285305Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dtc_cv = Pipeline([\n",
    "    ('cv', CountVectorizer()),\n",
    "    ('dtc', DecisionTreeClassifier())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "6180a4c1-e6de-4458-9413-162704af22bb",
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    "execution": {
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       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;cv&#x27;, CountVectorizer()), (&#x27;dtc&#x27;, DecisionTreeClassifier())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;cv&#x27;, CountVectorizer()), (&#x27;dtc&#x27;, DecisionTreeClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CountVectorizer</label><div class=\"sk-toggleable__content\"><pre>CountVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('cv', CountVectorizer()), ('dtc', DecisionTreeClassifier())])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit with default settings for CountVectorizer and DeciscionTree\n",
    "dtc_cv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "cebcac56-c2fc-4092-b1b2-24a9f5b8f81c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:04:25.370525Z",
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     "iopub.status.idle": "2022-12-23T01:04:25.960971Z",
     "shell.execute_reply": "2022-12-23T01:04:25.960662Z",
     "shell.execute_reply.started": "2022-12-23T01:04:25.370510Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9997856836690956, 0.7130304329189884)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# score with default settings for CountVectorizer and DeciscionTree\n",
    "dtc_cv.score(X_train, y_train), dtc_cv.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5a57773-a253-4c34-b8e8-b4a664af72ad",
   "metadata": {},
   "source": [
    "DecisionTree Classifier with TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "1c5ba5ed-5b53-49eb-9a5e-c5188600ea45",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:04:25.961732Z",
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     "shell.execute_reply": "2022-12-23T01:04:25.963923Z",
     "shell.execute_reply.started": "2022-12-23T01:04:25.961714Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dtc_tdf = Pipeline([\n",
    "    ('tf', TfidfVectorizer()),\n",
    "    ('dtc', DecisionTreeClassifier())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "92f847be-6496-45e5-a079-b40a4970d7f9",
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    "execution": {
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     "shell.execute_reply": "2022-12-23T01:04:30.000234Z",
     "shell.execute_reply.started": "2022-12-23T01:04:25.964905Z"
    },
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;tf&#x27;, TfidfVectorizer()), (&#x27;dtc&#x27;, DecisionTreeClassifier())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;tf&#x27;, TfidfVectorizer()), (&#x27;dtc&#x27;, DecisionTreeClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('tf', TfidfVectorizer()), ('dtc', DecisionTreeClassifier())])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit with default settings for TfidfVectorizer and DeciscionTree\n",
    "dtc_tdf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "a141842f-09b0-40d0-9940-f52c11d72504",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:04:30.001283Z",
     "iopub.status.busy": "2022-12-23T01:04:30.001195Z",
     "iopub.status.idle": "2022-12-23T01:04:30.612050Z",
     "shell.execute_reply": "2022-12-23T01:04:30.611664Z",
     "shell.execute_reply.started": "2022-12-23T01:04:30.001272Z"
    },
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9997856836690956, 0.6954564937848264)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# score with default settings for TfidfVectorizer and DeciscionTree\n",
    "dtc_tdf.score(X_train, y_train), dtc_tdf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95740bc1-96ff-420c-92ad-aef644ab85e3",
   "metadata": {},
   "source": [
    "As we can expect with DecisionTree, we're seeing the model overfit, which can tend to happen, and the model isn't generalizing to the test data very will.  In this case, it appears that the CountVectorizer is performing a little bit better than the TfidfVectorizer, at least when both have their default settings.  The CV version is about 1% more accurate."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71512af3-421d-4c9f-be6f-372e7e1a1125",
   "metadata": {},
   "source": [
    "#### GridSearch\n",
    "\n",
    "GridSearch to use coarse hyperparameter searching, instead of default parameters, in order to find and select the best model for tuning."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "485232f0-c627-45f8-bef8-d95fdab5f24e",
   "metadata": {},
   "source": [
    "GridSearching both CountVectorizer and TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "06b34b15-3869-4c4a-a04d-9ce829500a8b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:04:30.612783Z",
     "iopub.status.busy": "2022-12-23T01:04:30.612696Z",
     "iopub.status.idle": "2022-12-23T01:04:30.616384Z",
     "shell.execute_reply": "2022-12-23T01:04:30.615884Z",
     "shell.execute_reply.started": "2022-12-23T01:04:30.612772Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# set two\n",
    "cv_dtc_params = {\n",
    "    'cv__stop_words': ['english'],\n",
    "    'cv__max_df': [0.8, 0.85, 0.90],\n",
    "    'cv__min_df': [6, 10, 15],\n",
    "    'cv__ngram_range': [(1,2), (1,3)],\n",
    "    'cv__max_features': [5_000, 10_000, 15_000],\n",
    "    'dtc__criterion': ['gini', 'entropy'],\n",
    "    'dtc__max_depth': [25, 50, 100],\n",
    "    'dtc__min_samples_split': [10, 50, 100],\n",
    "    'dtc__max_features': [None, 'sqrt']\n",
    "}\n",
    "\n",
    "## set one\n",
    "# cv_dtc_params = {\n",
    "#     'cv__stop_words': [None, 'english'],\n",
    "#     'cv__max_df': [0.85, 0.95],\n",
    "#     'cv__min_df': [4, 5, 6],\n",
    "#     'cv__ngram_range': [(1,1), (1,2), (2,2)],\n",
    "#     'cv__max_features': [1_000, 5_000, 10_000],\n",
    "#     'dtc__criterion': ['gini', 'entropy'],\n",
    "#     'dtc__max_depth': [3, 10, 20],\n",
    "#     'dtc__min_samples_split': [2, 4, 10],\n",
    "#     'dtc__max_features': [None, 'sqrt', 'log2']\n",
    "# }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "92965260-d7b3-43b0-b1f0-1c63e73080b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:04:30.616942Z",
     "iopub.status.busy": "2022-12-23T01:04:30.616860Z",
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     "shell.execute_reply": "2022-12-23T01:04:30.618876Z",
     "shell.execute_reply.started": "2022-12-23T01:04:30.616932Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # configure GridSearch\n",
    "# gs_dtc_cv = GridSearchCV(dtc_cv, cv_dtc_params, n_jobs=-1, verbose=1)\n",
    "\n",
    "# # fit with training data\n",
    "# gs_dtc_cv.fit(X_train, y_train)\n",
    "\n",
    "# # output the best params after GridSearch is complete\n",
    "# gs_dtc_cv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "e9ad762b-f81d-4d2d-be2d-a678dda406c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:04:30.620028Z",
     "iopub.status.busy": "2022-12-23T01:04:30.619898Z",
     "iopub.status.idle": "2022-12-23T01:04:30.622398Z",
     "shell.execute_reply": "2022-12-23T01:04:30.622009Z",
     "shell.execute_reply.started": "2022-12-23T01:04:30.620015Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # get train and test scores from the model fit with the parameters from \n",
    "# # GridSearch\n",
    "# gs_dtc_cv.score(X_train, y_train), gs_dtc_cv.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40e6b589-3d88-4012-8043-a5e584d725dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-20T04:15:36.052818Z",
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     "shell.execute_reply": "2022-12-20T04:56:50.446636Z",
     "shell.execute_reply.started": "2022-12-20T04:15:36.052785Z"
    },
    "tags": []
   },
   "source": [
    "Decision Tree w/CountVectorizer Results\n",
    "- set two (0.8478354050578654, 0.744320617231033)\n",
    "    - 'cv__max_df': 0.85,\n",
    "    - 'cv__max_features': 10000,\n",
    "    - 'cv__min_df': 6,\n",
    "    - 'cv__ngram_range': (1, 3),\n",
    "    - 'cv__stop_words': 'english',\n",
    "    - 'dtc__criterion': 'entropy',\n",
    "    - 'dtc__max_depth': 100,\n",
    "    - 'dtc__max_features': None,\n",
    "    - 'dtc__min_samples_split': 100\n",
    "\n",
    "- set one (0.769609944277754, 0.7368195456493785)\n",
    "    - 'cv__max_df': 0.85,\n",
    "    - 'cv__max_features': 10000,\n",
    "    - 'cv__min_df': 6,\n",
    "    - 'cv__ngram_range': (1, 2),\n",
    "    - 'cv__stop_words': None,\n",
    "    - 'dtc__criterion': 'entropy',\n",
    "    - 'dtc__max_depth': 20,\n",
    "    - 'dtc__max_features': None,\n",
    "    - 'dtc__min_samples_split': 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "56b65ff6-57e1-46b9-8052-0b3bc3ef9386",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:04:30.623303Z",
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     "shell.execute_reply": "2022-12-23T01:04:30.626000Z",
     "shell.execute_reply.started": "2022-12-23T01:04:30.623289Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# set two\n",
    "tdf_dtc_params = {\n",
    "    'tf__stop_words': ['english'],\n",
    "    'tf__max_features': [4_000, 5_000, 7_500],\n",
    "    'tf__min_df': [3, 4, 10],\n",
    "    'tf__ngram_range': [(1,2), (1,3)],\n",
    "    'dtc__criterion': ['gini', 'entropy'],\n",
    "    'dtc__max_depth': [25, 50, 75],\n",
    "    'dtc__min_samples_split': [4, 5, 6],\n",
    "    'dtc__max_features': [None, 'sqrt']\n",
    "}\n",
    "\n",
    "# # set one\n",
    "# tdf_dtc_params = {\n",
    "#     'tf__stop_words': ['english'],\n",
    "#     'tf__max_features': [1_000, 5_000, 10_000],\n",
    "#     'tf__min_df': [1, 3, 5],\n",
    "#     'tf__ngram_range': [(1,2), (1,3)],\n",
    "#     'dtc__criterion': ['gini', 'entropy'],\n",
    "#     'dtc__max_depth': [3, 10, 20],\n",
    "#     'dtc__min_samples_split': [2, 5, 10],\n",
    "#     'dtc__max_features': [None, 'sqrt']\n",
    "# }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "f2232b43-62b8-462a-a95c-d174a319d9bf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:04:30.627102Z",
     "iopub.status.busy": "2022-12-23T01:04:30.627014Z",
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     "shell.execute_reply": "2022-12-23T01:04:30.628944Z",
     "shell.execute_reply.started": "2022-12-23T01:04:30.627090Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # configure GridSearch\n",
    "# gs_dtc_tf = GridSearchCV(dtc_tdf, tdf_dtc_params, n_jobs=-1, verbose=1)\n",
    "\n",
    "# # fit with training data\n",
    "# gs_dtc_tf.fit(X_train, y_train)\n",
    "\n",
    "# # output the best params after GridSearch is complete\n",
    "# gs_dtc_tf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "b7acf779-b15d-4f35-8df7-b873bef31689",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:04:30.630083Z",
     "iopub.status.busy": "2022-12-23T01:04:30.629947Z",
     "iopub.status.idle": "2022-12-23T01:04:30.631928Z",
     "shell.execute_reply": "2022-12-23T01:04:30.631563Z",
     "shell.execute_reply.started": "2022-12-23T01:04:30.630072Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # get train and test scores from the model fit with the parameters from \n",
    "# # GridSearch\n",
    "# gs_dtc_tf.score(X_train, y_train), gs_dtc_tf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2986fa2-39fc-4cd9-b9e5-e27a4cc2a292",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-20T05:02:49.171948Z",
     "iopub.status.busy": "2022-12-20T05:02:49.171547Z",
     "iopub.status.idle": "2022-12-20T05:17:38.253586Z",
     "shell.execute_reply": "2022-12-20T05:17:38.253038Z",
     "shell.execute_reply.started": "2022-12-20T05:02:49.171915Z"
    },
    "tags": []
   },
   "source": [
    "Decision Tree w/TfidfVectorizer Results\n",
    "- set two (0.9172024574939277, 0.7353193313330476)\n",
    "    - 'dtc__criterion': 'gini',\n",
    "    - 'dtc__max_depth': 75,\n",
    "    - 'dtc__max_features': None,\n",
    "    - 'dtc__min_samples_split': 5,\n",
    "    - 'tf__max_features': 7500,\n",
    "    - 'tf__min_df': 3,\n",
    "    - 'tf__ngram_range': (1, 2),\n",
    "    - 'tf__stop_words': 'english'\n",
    "\n",
    "- set one  (0.7657522503214745, 0.7297471067295328)\n",
    "    - 'dtc__criterion': 'gini',\n",
    "    - 'dtc__max_depth': 20,\n",
    "    - 'dtc__max_features': None,\n",
    "    - 'dtc__min_samples_split': 5,\n",
    "    - 'tf__max_features': 5000,\n",
    "    - 'tf__min_df': 3,\n",
    "    - 'tf__ngram_range': (1, 3),\n",
    "    - 'tf__stop_words': 'english'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "910842f2-d493-4db2-a6a3-deb2e6fea52f",
   "metadata": {},
   "source": [
    "#### Best Decision Tree\n",
    "\n",
    "The best performing Decision Tree:\n",
    "- Train: 0.845\n",
    "- Test:  0.744\n",
    "\n",
    "Params\n",
    "- 'cv__max_df': 0.85,\n",
    "- 'cv__max_features': 10000,\n",
    "- 'cv__min_df': 6,\n",
    "- 'cv__ngram_range': (1, 3),\n",
    "- 'cv__stop_words': 'english',\n",
    "- 'dtc__criterion': 'entropy',\n",
    "- 'dtc__max_depth': 100,\n",
    "- 'dtc__max_features': None,\n",
    "- 'dtc__min_samples_split': 100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ec2aca9-63a5-4387-8a9e-cc232fb9bc2d",
   "metadata": {},
   "source": [
    "These scores are certainly improved from the baseline, but not much of an improvement from the default settings with each vectorizer paired with a Decision Tree classifier. The overfit that appeared on the train score from the default settings appears to be reduced, probably in part to limiting how deep the decision tree was able to go, especially on the first set of grid search parameters.  \n",
    "\n",
    "The second grid search only improved the test scores marginally.  From these results, I think a different classifier will be more effective.  Let's move to a Random Forest classifier and see how that performs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaab7147-1fa7-41a9-91c4-bb2cc5429398",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6613a31-cb74-47a6-8ab0-2fdae460360e",
   "metadata": {},
   "source": [
    "#### Configure Random Forest Pipelines"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f402e32b-b397-489e-9c2d-44be5b873ad6",
   "metadata": {},
   "source": [
    "Random Forest with CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "13d3e180-ff59-45f3-93cc-b00415929d4d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:04:30.632650Z",
     "iopub.status.busy": "2022-12-23T01:04:30.632563Z",
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     "shell.execute_reply": "2022-12-23T01:04:30.634860Z",
     "shell.execute_reply.started": "2022-12-23T01:04:30.632639Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "rf_cv = Pipeline([\n",
    "    ('cv', CountVectorizer()),\n",
    "    ('rf', RandomForestClassifier())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "111981d8-c091-4570-81b3-f169710a8ffd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:04:30.636200Z",
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     "iopub.status.idle": "2022-12-23T01:04:41.585007Z",
     "shell.execute_reply": "2022-12-23T01:04:41.584661Z",
     "shell.execute_reply.started": "2022-12-23T01:04:30.636180Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;cv&#x27;, CountVectorizer()), (&#x27;rf&#x27;, RandomForestClassifier())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;cv&#x27;, CountVectorizer()), (&#x27;rf&#x27;, RandomForestClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CountVectorizer</label><div class=\"sk-toggleable__content\"><pre>CountVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('cv', CountVectorizer()), ('rf', RandomForestClassifier())])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit with default parameters\n",
    "rf_cv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "49bd62fe-d811-4f3e-9f4d-56a59d18af64",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:04:41.585813Z",
     "iopub.status.busy": "2022-12-23T01:04:41.585729Z",
     "iopub.status.idle": "2022-12-23T01:04:42.806794Z",
     "shell.execute_reply": "2022-12-23T01:04:42.806471Z",
     "shell.execute_reply.started": "2022-12-23T01:04:41.585802Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9997856836690956, 0.8004714959279897)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# score train and test data with default parameters\n",
    "rf_cv.score(X_train, y_train), rf_cv.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28353897-e385-4913-9aad-1bbe58bfbb51",
   "metadata": {},
   "source": [
    "Random Forest with TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "a5026b9f-4cf7-4641-8fbd-3b0a1ae9e743",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:04:42.807575Z",
     "iopub.status.busy": "2022-12-23T01:04:42.807488Z",
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     "shell.execute_reply": "2022-12-23T01:04:42.809721Z",
     "shell.execute_reply.started": "2022-12-23T01:04:42.807564Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "rf_tdf = Pipeline([\n",
    "    ('tf', TfidfVectorizer()),\n",
    "    ('rf', RandomForestClassifier())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
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      "text/html": [
       "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;tf&#x27;, TfidfVectorizer()), (&#x27;rf&#x27;, RandomForestClassifier())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;tf&#x27;, TfidfVectorizer()), (&#x27;rf&#x27;, RandomForestClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('tf', TfidfVectorizer()), ('rf', RandomForestClassifier())])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit with default parameters\n",
    "rf_tdf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "c6018f5b-c340-485d-82b9-9039c5542546",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:04:53.154322Z",
     "iopub.status.busy": "2022-12-23T01:04:53.154236Z",
     "iopub.status.idle": "2022-12-23T01:04:54.346111Z",
     "shell.execute_reply": "2022-12-23T01:04:54.345609Z",
     "shell.execute_reply.started": "2022-12-23T01:04:53.154311Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9997856836690956, 0.7918988426918131)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# score train and test data with default parameters\n",
    "rf_tdf.score(X_train, y_train), rf_tdf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a37ec120-10fd-455c-a8d2-2c7f385f80c0",
   "metadata": {},
   "source": [
    "Even though these models are overfit on the training data, like we saw on Decision Tree classifier, the Random Forest classifier is generalizing to the test data fairly well.  With default settings this is an accuracy increase of 27 percentage points over the baseline and about 5 points better than the Decision Tree classifier."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f8b5225-da26-41cd-8dbc-a83f47982fac",
   "metadata": {},
   "source": [
    "#### GridSearch\n",
    "\n",
    "GridSearch to use coarse hyperparameter searching, instead of default parameters, in order to find and select the best model for tuning."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ed2b571-03e3-4131-ba04-2761f7e6ba9c",
   "metadata": {},
   "source": [
    "For the first set of parameters in the grid search, I'll use same parameters that yielded the best results from the Decision Trees section for each vectorizer."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "befe0063-0d5b-4f9f-b34f-162f0f670e26",
   "metadata": {},
   "source": [
    "GridSearching both CountVectorizer and TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "0bff1d96-ba79-49b3-a022-6e9781e14f6c",
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# set two\n",
    "# cv_rf_params = {\n",
    "#     'cv__stop_words': ['english'],\n",
    "#     'cv__max_df': [0.85, 0.90],\n",
    "#     'cv__min_df': [6],\n",
    "#     'cv__ngram_range': [(1,2), (1,3)],\n",
    "#     'cv__max_features': [5_000, 10_000],\n",
    "#     'rf__n_estimators': [750, 1000, 1500],\n",
    "#     'rf__max_depth': [200, 500],\n",
    "#     'rf__max_samples': [0.85, 0.9],\n",
    "#     'rf__max_features': ['sqrt', 'log2']\n",
    "# }\n",
    "\n",
    "# set one\n",
    "# cv_rf_params = {\n",
    "#     'cv__stop_words': ['english'],\n",
    "#     'cv__max_df': [0.8, 0.85, 0.90],\n",
    "#     'cv__min_df': [3, 6, 9],\n",
    "#     'cv__ngram_range': [(1,2), (1,3)],\n",
    "#     'cv__max_features': [5_000, 10_000, 15_000],\n",
    "#     'rf__n_estimators': [100, 500, 750],\n",
    "#     'rf__max_depth': [50, 100, 200],\n",
    "#     'rf__max_samples': [0.8, 0.9, 1.0],\n",
    "#     'rf__max_features': ['sqrt', 'log2']\n",
    "# }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "36deae92-67bf-4c59-8a3e-027045018e68",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:04:54.349852Z",
     "iopub.status.busy": "2022-12-23T01:04:54.349773Z",
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     "shell.execute_reply": "2022-12-23T01:04:54.351623Z",
     "shell.execute_reply.started": "2022-12-23T01:04:54.349842Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # configure GridSearch\n",
    "# gs_rf_cv = GridSearchCV(rf_cv, cv_rf_params, n_jobs=-1, verbose=1)\n",
    "\n",
    "# # fit with training data\n",
    "# gs_rf_cv.fit(X_train, y_train)\n",
    "\n",
    "# # output the best params after GridSearch is complete\n",
    "# gs_rf_cv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "afe5cee4-945c-4d92-ab76-22c24c058b65",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:04:54.352758Z",
     "iopub.status.busy": "2022-12-23T01:04:54.352646Z",
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     "shell.execute_reply": "2022-12-23T01:04:54.354662Z",
     "shell.execute_reply.started": "2022-12-23T01:04:54.352739Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # get train and test scores from the model fit with the parameters from \n",
    "# # GridSearch\n",
    "# gs_rf_cv.score(X_train, y_train), gs_rf_cv.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db6aea5c-a120-477a-8a8d-768617aedfc0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-20T23:04:01.541725Z",
     "iopub.status.busy": "2022-12-20T23:04:01.541305Z",
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     "shell.execute_reply": "2022-12-20T23:47:49.725953Z",
     "shell.execute_reply.started": "2022-12-20T23:04:01.541664Z"
    },
    "tags": []
   },
   "source": [
    "Random Forest w/CountVectorizer Results\n",
    "- set two (0.9962137448206887, 0.8009001285897985)\n",
    "    - 'cv__max_df': 0.9,\n",
    "    - 'cv__max_features': 10000,\n",
    "    - 'cv__min_df': 6,\n",
    "    - 'cv__ngram_range': (1, 2),\n",
    "    - 'cv__stop_words': 'english',\n",
    "    - 'rf__max_depth': 500,\n",
    "    - 'rf__max_features': 'log2',\n",
    "    - 'rf__max_samples': 0.85,\n",
    "    - 'rf__n_estimators': 1000\n",
    "\n",
    "- set one (0.9864980711530219, 0.8015430775825118)\n",
    "    - 'cv__max_df': 0.85,\n",
    "    - 'cv__max_features': 5000,\n",
    "    - 'cv__min_df': 6,\n",
    "    - 'cv__ngram_range': (1, 2),\n",
    "    - 'cv__stop_words': 'english',\n",
    "    - 'rf__max_depth': 200,\n",
    "    - 'rf__max_features': 'log2',\n",
    "    - 'rf__max_samples': 0.9,\n",
    "    - 'rf__n_estimators': 750"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "71881902-b486-4ea7-b207-6316a8bde205",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:04:54.355859Z",
     "iopub.status.busy": "2022-12-23T01:04:54.355756Z",
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     "shell.execute_reply": "2022-12-23T01:04:54.359085Z",
     "shell.execute_reply.started": "2022-12-23T01:04:54.355848Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# set three\n",
    "tdf_rf_params = {\n",
    "    'tf__stop_words': ['english'],\n",
    "    'tf__max_features': [10_000],\n",
    "    'tf__min_df': [6],\n",
    "    'tf__ngram_range': [(1,2)],\n",
    "    'rf__n_estimators': [100, 500, 1000],\n",
    "    'rf__max_depth': [50, 100, 200],\n",
    "    'rf__max_samples': [0.8, 0.9, 1.0],\n",
    "    'rf__max_features': ['sqrt', 'log2']\n",
    "}\n",
    "\n",
    "# set one\n",
    "# tdf_rf_params = {\n",
    "#     'tf__stop_words': ['english'],\n",
    "#     'tf__max_features': [10_000, 15_000],\n",
    "#     'tf__min_df': [3, 6, 9],\n",
    "#     'tf__ngram_range': [(1,2), (1,3)]#,\n",
    "#     # 'rf__n_estimators': [100, 500, 750],\n",
    "#     # 'rf__max_depth': [50, 100, 200],\n",
    "#     # 'rf__max_samples': [0.8, 0.9, 1.0],\n",
    "#     # 'rf__max_features': ['sqrt', 'log2']\n",
    "# }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "142317fc-ca0a-4aab-a765-ff65392f7fea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:04:54.360482Z",
     "iopub.status.busy": "2022-12-23T01:04:54.360346Z",
     "iopub.status.idle": "2022-12-23T01:04:54.362543Z",
     "shell.execute_reply": "2022-12-23T01:04:54.362181Z",
     "shell.execute_reply.started": "2022-12-23T01:04:54.360470Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # configure GridSearch\n",
    "# gs_rf_tdf = GridSearchCV(rf_tdf, tdf_rf_params, n_jobs=-1, verbose=1)\n",
    "\n",
    "# # fit with training data\n",
    "# gs_rf_tdf.fit(X_train, y_train)\n",
    "\n",
    "# # output the best params after GridSearch is complete\n",
    "# gs_rf_tdf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "8f718c8a-c7f1-46d9-8081-f00cbeb716a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:04:54.363302Z",
     "iopub.status.busy": "2022-12-23T01:04:54.363206Z",
     "iopub.status.idle": "2022-12-23T01:04:54.365443Z",
     "shell.execute_reply": "2022-12-23T01:04:54.365101Z",
     "shell.execute_reply.started": "2022-12-23T01:04:54.363290Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # get train and test scores from the model fit with the parameters from \n",
    "# # GridSearch\n",
    "# gs_rf_tdf.score(X_train, y_train), gs_rf_tdf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "844e79b8-6a80-4ac3-a5d4-27ade48f3d77",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-20T16:35:28.328699Z",
     "iopub.status.busy": "2022-12-20T16:35:28.328277Z",
     "iopub.status.idle": "2022-12-20T16:46:06.320389Z",
     "shell.execute_reply": "2022-12-20T16:46:06.319904Z",
     "shell.execute_reply.started": "2022-12-20T16:35:28.328664Z"
    },
    "tags": []
   },
   "source": [
    "Random Forest w/TfidfVectorizer Results\n",
    "- set three (0.9933561937419632, 0.7931847406772397)\n",
    "     - 'rf__max_depth': 200,\n",
    "     - 'rf__max_features': 'log2',\n",
    "     - 'rf__max_samples': 1.0,\n",
    "     - 'rf__n_estimators': 1000,\n",
    "     - 'tf__max_features': 10000,\n",
    "     - 'tf__min_df': 6,\n",
    "     - 'tf__ngram_range': (1, 2),\n",
    "     - 'tf__stop_words': 'english'\n",
    "\n",
    "- set two (0.9993570510072868, 0.8002571795970853)\n",
    "     - 'tf__max_features': 10000,\n",
    "     - 'tf__min_df': 6,\n",
    "     - 'tf__ngram_range': (1, 2),\n",
    "     - 'tf__stop_words': 'english'\n",
    "    \n",
    "- set one few tf params (0.9993570510072868, 0.7916845263609087)\n",
    "     - 'tf__max_features': 10000, 'tf__min_df': 6, 'tf__stop_words': 'english'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "764715f2-32f4-471c-b519-ac4fde3c97da",
   "metadata": {},
   "source": [
    "#### Best Random Forest\n",
    "\n",
    "The best performing Random Forest:\n",
    "- Train: .986\n",
    "- Test:  .802\n",
    "\n",
    "Params\n",
    "- 'cv__max_df': 0.85,\n",
    "- 'cv__max_features': 5000,\n",
    "- 'cv__min_df': 6,\n",
    "- 'cv__ngram_range': (1, 2),\n",
    "- 'cv__stop_words': 'english',\n",
    "- 'rf__max_depth': 200,\n",
    "- 'rf__max_features': 'log2',\n",
    "- 'rf__max_samples': 0.9,\n",
    "- 'rf__n_estimators': 750\n",
    "\n",
    "The Random Forest models, appear to be the highest performing so far.  Even though these models are showing they are overfit to the training data, they are still generalizing to the test data fairly well. It is likely a front runner for choice as the production model for this project, but I will wait until I finish with the modeling before making that decision."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e94f511-0f3f-4734-84dd-6615218d5b25",
   "metadata": {},
   "source": [
    "### k Nearest Neighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eea95e7a-a162-46d1-b586-74d11ecf1b54",
   "metadata": {},
   "source": [
    "Now that I've evaluated two different tree-based models, let's look at kNN and see how it performs. With kNN the data will need to be scaled as well, so I'll configure a pipeline."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b203cb1a-b7a0-4b58-87a9-ab8f743a1967",
   "metadata": {},
   "source": [
    "#### Configure Random k Nearest Neighors Pipelines"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44a01688-56c2-431c-9027-f07b8e40d845",
   "metadata": {},
   "source": [
    "kNN with CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "2c9cf668-3abb-4932-9bda-a69ad8573649",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2022-12-23T01:04:54.366262Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "knn_pipe = Pipeline([\n",
    "    ('cv', CountVectorizer()),\n",
    "    ('ss', StandardScaler(with_mean=False)),\n",
    "    ('knn', KNeighborsClassifier())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "f98dee5a-1148-4dc4-9a86-85aa30e286ec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:04:54.369730Z",
     "iopub.status.busy": "2022-12-23T01:04:54.369579Z",
     "iopub.status.idle": "2022-12-23T01:04:54.892390Z",
     "shell.execute_reply": "2022-12-23T01:04:54.892041Z",
     "shell.execute_reply.started": "2022-12-23T01:04:54.369711Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;cv&#x27;, CountVectorizer()),\n",
       "                (&#x27;ss&#x27;, StandardScaler(with_mean=False)),\n",
       "                (&#x27;knn&#x27;, KNeighborsClassifier())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;cv&#x27;, CountVectorizer()),\n",
       "                (&#x27;ss&#x27;, StandardScaler(with_mean=False)),\n",
       "                (&#x27;knn&#x27;, KNeighborsClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CountVectorizer</label><div class=\"sk-toggleable__content\"><pre>CountVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler(with_mean=False)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-42\" type=\"checkbox\" ><label for=\"sk-estimator-id-42\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('cv', CountVectorizer()),\n",
       "                ('ss', StandardScaler(with_mean=False)),\n",
       "                ('knn', KNeighborsClassifier())])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit with default parameters\n",
    "knn_pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "3e9e3359-cbe7-40a9-90c9-ac9b25bdac7d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:04:54.893297Z",
     "iopub.status.busy": "2022-12-23T01:04:54.893202Z",
     "iopub.status.idle": "2022-12-23T01:05:05.335663Z",
     "shell.execute_reply": "2022-12-23T01:05:05.335335Z",
     "shell.execute_reply.started": "2022-12-23T01:04:54.893286Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.735533647663952, 0.6050150021431633)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# score train and test with default parameters\n",
    "knn_pipe.score(X_train, y_train), knn_pipe.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e460762-157d-4b19-a599-8d65f06bef3b",
   "metadata": {},
   "source": [
    "kNN with TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "03df8e3a-1bf7-4453-8eab-29a3da6232a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:05:05.336324Z",
     "iopub.status.busy": "2022-12-23T01:05:05.336239Z",
     "iopub.status.idle": "2022-12-23T01:05:05.338911Z",
     "shell.execute_reply": "2022-12-23T01:05:05.338599Z",
     "shell.execute_reply.started": "2022-12-23T01:05:05.336313Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "knn_pipe_tf = Pipeline([\n",
    "    ('tf', TfidfVectorizer()),\n",
    "    ('ss', StandardScaler(with_mean=False)),\n",
    "    ('knn', KNeighborsClassifier())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "15716e9d-6fe1-4774-b7ea-eb6621151af9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:05:05.339551Z",
     "iopub.status.busy": "2022-12-23T01:05:05.339472Z",
     "iopub.status.idle": "2022-12-23T01:05:05.814523Z",
     "shell.execute_reply": "2022-12-23T01:05:05.814198Z",
     "shell.execute_reply.started": "2022-12-23T01:05:05.339542Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-14 {color: black;background-color: white;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-14 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-14 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;tf&#x27;, TfidfVectorizer()),\n",
       "                (&#x27;ss&#x27;, StandardScaler(with_mean=False)),\n",
       "                (&#x27;knn&#x27;, KNeighborsClassifier())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-43\" type=\"checkbox\" ><label for=\"sk-estimator-id-43\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;tf&#x27;, TfidfVectorizer()),\n",
       "                (&#x27;ss&#x27;, StandardScaler(with_mean=False)),\n",
       "                (&#x27;knn&#x27;, KNeighborsClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-44\" type=\"checkbox\" ><label for=\"sk-estimator-id-44\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-45\" type=\"checkbox\" ><label for=\"sk-estimator-id-45\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler(with_mean=False)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-46\" type=\"checkbox\" ><label for=\"sk-estimator-id-46\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('tf', TfidfVectorizer()),\n",
       "                ('ss', StandardScaler(with_mean=False)),\n",
       "                ('knn', KNeighborsClassifier())])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit with default parameters\n",
    "knn_pipe_tf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "e73750ba-c073-45c2-bc10-e66b0afaeea8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:05:05.820585Z",
     "iopub.status.busy": "2022-12-23T01:05:05.820447Z",
     "iopub.status.idle": "2022-12-23T01:05:17.155922Z",
     "shell.execute_reply": "2022-12-23T01:05:17.155522Z",
     "shell.execute_reply.started": "2022-12-23T01:05:05.820572Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8129732818974139, 0.6140162880411487)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# score train and test with default parameters\n",
    "knn_pipe_tf.score(X_train, y_train), knn_pipe_tf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0261581-7d96-428c-aedd-d614b1b97021",
   "metadata": {},
   "source": [
    "#### GridSearch\n",
    "\n",
    "GridSearch to use coarse hyperparameter searching, instead of default parameters, in order to find and select the best model for tuning."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a38c6be3-95a4-435f-8c72-03a5ce17b535",
   "metadata": {},
   "source": [
    "GridSearching both CountVectorizer and TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "edadd78f-d16e-4cdf-af55-c100b02e915b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:05:17.157019Z",
     "iopub.status.busy": "2022-12-23T01:05:17.156694Z",
     "iopub.status.idle": "2022-12-23T01:05:17.160474Z",
     "shell.execute_reply": "2022-12-23T01:05:17.160009Z",
     "shell.execute_reply.started": "2022-12-23T01:05:17.157004Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# set two\n",
    "cv_knn_params = {\n",
    "    'cv__max_df': [0.85, 0.95],\n",
    "    'cv__max_features':[5000, 10_000],\n",
    "    'cv__min_df': [6],\n",
    "    'cv__ngram_range': [(1, 2), (1,3)],\n",
    "    'cv__stop_words': ['english'],\n",
    "    'knn__n_neighbors': [5, 7],\n",
    "    'knn__weights': ['uniform', 'distance'],\n",
    "    'knn__p': [1, 2]\n",
    "}\n",
    "\n",
    "# set one\n",
    "# cv_knn_params = {\n",
    "#     'cv__max_df': [0.85],\n",
    "#     'cv__max_features':[ 5000],\n",
    "#     'cv__min_df': [6],\n",
    "#     'cv__ngram_range': [(1, 2)],\n",
    "#     'cv__stop_words': ['english'],\n",
    "#     'knn__n_neighbors': [5, 7, 9],\n",
    "#     'knn__weights': ['uniform', 'distance'],\n",
    "#     'knn__p': [1, 2]\n",
    "# }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "24ff88cc-5caf-4fc0-ae94-eed083991b19",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:05:17.161266Z",
     "iopub.status.busy": "2022-12-23T01:05:17.161170Z",
     "iopub.status.idle": "2022-12-23T01:05:17.163452Z",
     "shell.execute_reply": "2022-12-23T01:05:17.163045Z",
     "shell.execute_reply.started": "2022-12-23T01:05:17.161255Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # configure GridSearch\n",
    "# gs_cv_knn = GridSearchCV(knn_pipe, cv_knn_params, n_jobs=-1, verbose=1)\n",
    "\n",
    "# # fit with training data\n",
    "# gs_cv_knn.fit(X_train, y_train)\n",
    "\n",
    "# # output the best params after GridSearch is complete\n",
    "# gs_cv_knn.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "0f217ed6-9040-4559-8874-c616e94970b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:05:17.164283Z",
     "iopub.status.busy": "2022-12-23T01:05:17.164189Z",
     "iopub.status.idle": "2022-12-23T01:05:17.166545Z",
     "shell.execute_reply": "2022-12-23T01:05:17.166138Z",
     "shell.execute_reply.started": "2022-12-23T01:05:17.164271Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # get train and test scores from the model fit with the parameters from \n",
    "# # GridSearch\n",
    "# gs_cv_knn.score(X_train, y_train), gs_cv_knn.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08fa5717-33ee-4d72-856b-a05791fb0823",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-20T17:22:59.589777Z",
     "iopub.status.busy": "2022-12-20T17:22:59.589348Z"
    },
    "tags": []
   },
   "source": [
    "kNN w/CountVectorizer Results\n",
    "- set two (0.9992141734533505, 0.6217316759537077)\n",
    "    - 'cv__max_df': 0.85,\n",
    "    - 'cv__max_features': 5000,\n",
    "    - 'cv__min_df': 6,\n",
    "    - 'cv__ngram_range': (1, 3),\n",
    "    - 'cv__stop_words': 'english',\n",
    "    - 'knn__n_neighbors': 5,\n",
    "    - 'knn__p': 2,\n",
    "    - 'knn__weights': 'distance'\n",
    "\n",
    "- set one (0.9992141734533505, 0.6243034719245607)\n",
    "    - 'cv__max_df': 0.85,\n",
    "    - 'cv__max_features': 5000,\n",
    "    - 'cv__min_df': 6,\n",
    "    - 'cv__ngram_range': (1, 2),\n",
    "    - 'cv__stop_words': 'english',\n",
    "    - 'knn__n_neighbors': 5,\n",
    "    - 'knn__weights': 'distance'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "4701106b-53a2-403e-af24-bd2e5b534f7c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:05:17.167485Z",
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     "shell.execute_reply": "2022-12-23T01:05:17.170546Z",
     "shell.execute_reply.started": "2022-12-23T01:05:17.167472Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# set two\n",
    "tdf_knn_params = {\n",
    "    'tf__stop_words': ['english'],\n",
    "    'tf__max_features': [7_500, 10_000],\n",
    "    'tf__min_df': [6],\n",
    "    'tf__ngram_range': [(1,2), (1,3)],\n",
    "    'knn__n_neighbors': [5, 7],\n",
    "    'knn__weights': ['uniform', 'distance'],\n",
    "    'knn__p': [1, 2]\n",
    "}\n",
    "\n",
    "# set one\n",
    "# tdf_knn_params = {\n",
    "#     'tf__stop_words': ['english'],\n",
    "#     'tf__max_features': [10_000],\n",
    "#     'tf__min_df': [6],\n",
    "#     'tf__ngram_range': [(1,2)],\n",
    "#     'knn__n_neighbors': [5, 7, 9],\n",
    "#     'knn__weights': ['uniform', 'distance']\n",
    "# }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "c64a203c-a686-4b60-aeb1-858bb344a11a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:05:17.171878Z",
     "iopub.status.busy": "2022-12-23T01:05:17.171777Z",
     "iopub.status.idle": "2022-12-23T01:05:17.174509Z",
     "shell.execute_reply": "2022-12-23T01:05:17.174065Z",
     "shell.execute_reply.started": "2022-12-23T01:05:17.171866Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # configure GridSearch\n",
    "# gs_tdf_knn = GridSearchCV(knn_pipe_tf, tdf_knn_params, n_jobs=-1, verbose=1)\n",
    "\n",
    "# # fit with training data\n",
    "# gs_tdf_knn.fit(X_train, y_train)\n",
    "\n",
    "# # output the best params after GridSearch is complete\n",
    "# gs_tdf_knn.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "0d0867c6-0045-4daf-98a2-a79161017e44",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:05:17.175181Z",
     "iopub.status.busy": "2022-12-23T01:05:17.175089Z",
     "iopub.status.idle": "2022-12-23T01:05:17.177454Z",
     "shell.execute_reply": "2022-12-23T01:05:17.177078Z",
     "shell.execute_reply.started": "2022-12-23T01:05:17.175169Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # get train and test scores from the model fit with the parameters from \n",
    "# # GridSearch\n",
    "# gs_tdf_knn.score(X_train, y_train), gs_tdf_knn.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8537b4d1-27d8-42da-8bd5-26650a3da977",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-20T17:32:24.252548Z",
     "iopub.status.busy": "2022-12-20T17:32:24.252075Z",
     "iopub.status.idle": "2022-12-20T17:34:26.737075Z",
     "shell.execute_reply": "2022-12-20T17:34:26.736434Z",
     "shell.execute_reply.started": "2022-12-20T17:32:24.252513Z"
    },
    "tags": []
   },
   "source": [
    "kNN w/TfidfVectorizer Results:\n",
    "- set two (0.9993570510072868, 0.5225032147449635)\n",
    "    - 'knn__n_neighbors': 5,\n",
    "    - 'knn__p': 2,\n",
    "    - 'knn__weights': 'distance',\n",
    "    - 'tf__max_features': 7500,\n",
    "    - 'tf__min_df': 6,\n",
    "    - 'tf__ngram_range': (1, 3),\n",
    "    - 'tf__stop_words': 'english'\n",
    "\n",
    "- set one (0.9992141734533505, 0.6243034719245607)\n",
    "    - 'knn__n_neighbors': 5,\n",
    "    - 'knn__weights': 'distance',\n",
    "    - 'tf__max_features': 10000,\n",
    "    - 'tf__min_df': 6,\n",
    "    - 'tf__ngram_range': (1, 2),\n",
    "    - 'tf__stop_words': 'english'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c3a114d-ae42-4d05-b28d-8c6a4ce67fde",
   "metadata": {},
   "source": [
    "This model is not generalizing to the test data very well.  With two grid searches, I was only able to raise the accuracy score about 2 percentage points."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c77b7c0-3ab7-4ed5-814f-a307b350033f",
   "metadata": {},
   "source": [
    "#### Best KNN Model\n",
    "\n",
    "The best performing KNN:\n",
    "- Train: 0.999\n",
    "- Test:  0.624\n",
    "\n",
    "Params\n",
    "- 'cv__max_df': 0.85,\n",
    "- 'cv__max_features': 5000,\n",
    "- 'cv__min_df': 6,\n",
    "- 'cv__ngram_range': (1, 2),\n",
    "- 'cv__stop_words': 'english',\n",
    "- 'knn__n_neighbors': 5,\n",
    "- 'knn__weights': 'distance'\n",
    "\n",
    "When considering this kNN model in the context of NLP, it does occur to me that it probably isn't the best choice as kNN suffers with high dimensionality.  I was also considering if the scaling/standardization was needed or not (it was included in the pipeline), due to the features being on the same scale, especially with a TfidfVectorizer.  Since I've decided not to choose this model, I did not spend additional time investigating this."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a9ebf73-b325-4c0e-922c-360fef0e89d0",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "740408e2-8300-40a0-9858-4e423140b10b",
   "metadata": {},
   "source": [
    "#### Build Logistic Regression Pipelines\n",
    "\n",
    "To continue exploring various models, I'll configure pipelines for both CountVectorizer and TfidfVectorizer with logistic regression to see the different performance and to be able to GridSearch for optimal parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e2c1a3b-049f-4f14-9427-629c0d57cf76",
   "metadata": {},
   "source": [
    "Logistic Regression CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "48cbe854-4ab2-4521-a086-75c07ece24c7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:05:17.178318Z",
     "iopub.status.busy": "2022-12-23T01:05:17.178170Z",
     "iopub.status.idle": "2022-12-23T01:05:17.181090Z",
     "shell.execute_reply": "2022-12-23T01:05:17.180625Z",
     "shell.execute_reply.started": "2022-12-23T01:05:17.178305Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "lr_cv_pipe = Pipeline([\n",
    "    ('cv', CountVectorizer()),\n",
    "    ('lr', LogisticRegression(solver='liblinear', max_iter=10_000))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "e46baa12-3c61-4514-8195-e93c44b18208",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:05:17.181988Z",
     "iopub.status.busy": "2022-12-23T01:05:17.181849Z",
     "iopub.status.idle": "2022-12-23T01:05:18.511316Z",
     "shell.execute_reply": "2022-12-23T01:05:18.510926Z",
     "shell.execute_reply.started": "2022-12-23T01:05:17.181974Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "                (&#x27;lr&#x27;, LogisticRegression(max_iter=10000, solver=&#x27;liblinear&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-47\" type=\"checkbox\" ><label for=\"sk-estimator-id-47\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;cv&#x27;, CountVectorizer()),\n",
       "                (&#x27;lr&#x27;, LogisticRegression(max_iter=10000, solver=&#x27;liblinear&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-48\" type=\"checkbox\" ><label for=\"sk-estimator-id-48\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CountVectorizer</label><div class=\"sk-toggleable__content\"><pre>CountVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-49\" type=\"checkbox\" ><label for=\"sk-estimator-id-49\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=10000, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div></div></div>"
      ],
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       "Pipeline(steps=[('cv', CountVectorizer()),\n",
       "                ('lr', LogisticRegression(max_iter=10000, solver='liblinear'))])"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit with mostly defaults, choosing liblinear to be able to use l1 and l2\n",
    "# also increased max_iter for convergence\n",
    "lr_cv_pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "d08be2f6-114f-4a00-9f0c-fb9bfb550ce5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:05:18.512021Z",
     "iopub.status.busy": "2022-12-23T01:05:18.511935Z",
     "iopub.status.idle": "2022-12-23T01:05:19.116335Z",
     "shell.execute_reply": "2022-12-23T01:05:19.116044Z",
     "shell.execute_reply.started": "2022-12-23T01:05:18.512010Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9360622946135162, 0.8062580368624089)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# score train and test with mostly default parameters\n",
    "lr_cv_pipe.score(X_train, y_train), lr_cv_pipe.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ebe3859-e9a8-4fa3-9246-d07b610eca2a",
   "metadata": {},
   "source": [
    "Logistic Regression TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "7a7ebd63-60be-4994-85c1-87f9efe0f88b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:05:19.117073Z",
     "iopub.status.busy": "2022-12-23T01:05:19.116984Z",
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     "shell.execute_reply": "2022-12-23T01:05:19.119306Z",
     "shell.execute_reply.started": "2022-12-23T01:05:19.117062Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "lr_tdf_pipe = Pipeline([\n",
    "    ('tf', TfidfVectorizer()),\n",
    "    ('lr', LogisticRegression(solver='liblinear', max_iter=10_000))\n",
    "])"
   ]
  },
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     "shell.execute_reply.started": "2022-12-23T01:05:19.120321Z"
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    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-16 {color: black;background-color: white;}#sk-container-id-16 pre{padding: 0;}#sk-container-id-16 div.sk-toggleable {background-color: white;}#sk-container-id-16 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-16 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-16 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-16 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-16 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-16 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-16 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-16 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-16 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-16 div.sk-item {position: relative;z-index: 1;}#sk-container-id-16 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-16 div.sk-item::before, #sk-container-id-16 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-16 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-16 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-16 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-16 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-16 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-16 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-16 div.sk-label-container {text-align: center;}#sk-container-id-16 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-16 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;tf&#x27;, TfidfVectorizer()),\n",
       "                (&#x27;lr&#x27;, LogisticRegression(max_iter=10000, solver=&#x27;liblinear&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-50\" type=\"checkbox\" ><label for=\"sk-estimator-id-50\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;tf&#x27;, TfidfVectorizer()),\n",
       "                (&#x27;lr&#x27;, LogisticRegression(max_iter=10000, solver=&#x27;liblinear&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-51\" type=\"checkbox\" ><label for=\"sk-estimator-id-51\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-52\" type=\"checkbox\" ><label for=\"sk-estimator-id-52\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=10000, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('tf', TfidfVectorizer()),\n",
       "                ('lr', LogisticRegression(max_iter=10000, solver='liblinear'))])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit with mostly defaults, choosing liblinear to be able to use l1 and l2\n",
    "# also increased max_iter for convergence\n",
    "lr_tdf_pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "505a96d4-882d-48be-9bee-01be56055924",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:05:19.666893Z",
     "iopub.status.busy": "2022-12-23T01:05:19.666805Z",
     "iopub.status.idle": "2022-12-23T01:05:20.270753Z",
     "shell.execute_reply": "2022-12-23T01:05:20.270460Z",
     "shell.execute_reply.started": "2022-12-23T01:05:19.666882Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8624089155593656, 0.8088298328332619)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# score train and test with mostly default parameters\n",
    "lr_tdf_pipe.score(X_train, y_train), lr_tdf_pipe.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9fa69de-a71b-4920-83d0-865da29bff80",
   "metadata": {},
   "source": [
    "#### GridSearch\n",
    "\n",
    "GridSearch to try and tune the hyperparameters for the various pipelines."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39b276fb-24f4-49e8-b4f5-226516645655",
   "metadata": {},
   "source": [
    "GridSearching both CountVectorizer and TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "359887c6-3aef-4ee4-90c1-3125b729c587",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:05:20.271481Z",
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     "shell.execute_reply": "2022-12-23T01:05:20.274727Z",
     "shell.execute_reply.started": "2022-12-23T01:05:20.271470Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# set three\n",
    "cv_params = {\n",
    "    'cv__stop_words': [None, 'english'],\n",
    "    'cv__max_df': [0.85, 0.9, .95],\n",
    "    'cv__min_df': [4, 5, 6],\n",
    "    'cv__ngram_range': [(1,2), (1,3), (2,3)],\n",
    "    'cv__max_features': [13_000, 14_000, 15_000],\n",
    "    'lr__C': [0.08, 0.1, 0.2],\n",
    "    'lr__penalty': ['l1', 'l2']\n",
    "}\n",
    "\n",
    "# set two\n",
    "# cv_params = {\n",
    "#     'cv__stop_words': [None, 'english'],\n",
    "#     'cv__max_df': [0.8, 0.9, .95, 1.0],\n",
    "#     'cv__min_df': [2, 5, 6, 7],\n",
    "#     'cv__ngram_range': [(1,2), (1,3), (2,3)],\n",
    "#     'cv__max_features': [12_500, 15_000, 17_500],\n",
    "#     'lr__C': [0.01, 0.1, 0.5, 1],\n",
    "#     'lr__penalty': ['l1', 'l2']\n",
    "# }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "257544cc-8f5d-4e9d-b2e9-c8c45917d726",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:05:20.276233Z",
     "iopub.status.busy": "2022-12-23T01:05:20.276026Z",
     "iopub.status.idle": "2022-12-23T01:05:20.278305Z",
     "shell.execute_reply": "2022-12-23T01:05:20.277931Z",
     "shell.execute_reply.started": "2022-12-23T01:05:20.276218Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # configure GridSearch\n",
    "# gs_cv = GridSearchCV(lr_cv_pipe, cv_params, n_jobs=-1, verbose=1)\n",
    "\n",
    "# # fit with training data\n",
    "# gs_cv.fit(X_train, y_train)\n",
    "\n",
    "# # output the best params after GridSearch is complete\n",
    "# gs_cv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "a1f67728-dc76-4fe6-9043-06d6e93c802a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:05:20.278856Z",
     "iopub.status.busy": "2022-12-23T01:05:20.278773Z",
     "iopub.status.idle": "2022-12-23T01:05:20.280898Z",
     "shell.execute_reply": "2022-12-23T01:05:20.280586Z",
     "shell.execute_reply.started": "2022-12-23T01:05:20.278845Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # get train and test scores from the model fit with the parameters from \n",
    "# # GridSearch\n",
    "# gs_cv.score(X_train, y_train), gs_cv.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fd9c6c0-ff17-4146-9a85-2813bad66342",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-18T09:14:55.185321Z",
     "iopub.status.busy": "2022-12-18T09:14:55.184636Z",
     "iopub.status.idle": "2022-12-18T10:14:37.422604Z",
     "shell.execute_reply": "2022-12-18T10:14:37.421970Z",
     "shell.execute_reply.started": "2022-12-18T09:14:55.185255Z"
    },
    "tags": []
   },
   "source": [
    "Logistic Regression w/CountVectorizer Results\n",
    "\n",
    "- set two same scores as one since same hps found\n",
    "- (0.9422060294327761, 0.8148306900985856)\n",
    "    - 'cv__max_df': 0.9,\n",
    "    - 'cv__max_features': 15000,\n",
    "    - 'cv__min_df': 5,\n",
    "    - 'cv__ngram_range': (1, 2),\n",
    "    - 'cv__stop_words': None,\n",
    "    - 'lr__C': 0.1,\n",
    "    - 'lr__penalty': 'l2'\n",
    "\n",
    "- set one (0.9422060294327761, 0.8148306900985856)\n",
    "    - 'cv__max_df': 0.9,\n",
    "    - 'cv__max_features': 15000,\n",
    "    - 'cv__min_df': 5,\n",
    "    - 'cv__ngram_range': (1, 2),\n",
    "    - 'cv__stop_words': None,\n",
    "    - 'lr__C': 0.1,\n",
    "    - 'lr__penalty': 'l2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "bd245f1a-e12b-4636-8e54-d321ce49c304",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:05:20.281787Z",
     "iopub.status.busy": "2022-12-23T01:05:20.281606Z",
     "iopub.status.idle": "2022-12-23T01:05:20.284644Z",
     "shell.execute_reply": "2022-12-23T01:05:20.284280Z",
     "shell.execute_reply.started": "2022-12-23T01:05:20.281767Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# set two\n",
    "tdf_params = {\n",
    "    'tf__stop_words': [None, 'english'],\n",
    "    'tf__max_features': [12_500, 15_000, 17_500],\n",
    "    'tf__ngram_range': [(1,2), (1,3),(2,3)],\n",
    "    'lr__C': [5, 10, 15],\n",
    "    'lr__penalty': ['l1', 'l2']\n",
    "}\n",
    "\n",
    "# set one\n",
    "# tdf_params = {\n",
    "#     'tf__stop_words': [None, 'english'],\n",
    "#     'tf__max_features': [1000,5000, 10_000, 15_000],\n",
    "#     'tf__ngram_range': [(1,1), (1,2), (2,2)],\n",
    "#     'lr__C': [0.1, 0.5, 1, 10],\n",
    "#     'lr__penalty': ['l1', 'l2']\n",
    "# }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "a734aa55-eb0b-40bc-81ec-87c7ed9207ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:05:20.285376Z",
     "iopub.status.busy": "2022-12-23T01:05:20.285285Z",
     "iopub.status.idle": "2022-12-23T01:05:20.287893Z",
     "shell.execute_reply": "2022-12-23T01:05:20.287504Z",
     "shell.execute_reply.started": "2022-12-23T01:05:20.285365Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # configure GridSearch\n",
    "# gs_tdf = GridSearchCV(lr_tdf_pipe, tdf_params, n_jobs=-1, verbose=1)\n",
    "\n",
    "# # fit with training data\n",
    "# gs_tdf.fit(X_train, y_train)\n",
    "\n",
    "# # output the best params after GridSearch is complete\n",
    "# gs_tdf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "efe6f99e-203b-4835-a031-86f95d6fe50a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:05:20.288643Z",
     "iopub.status.busy": "2022-12-23T01:05:20.288553Z",
     "iopub.status.idle": "2022-12-23T01:05:20.290894Z",
     "shell.execute_reply": "2022-12-23T01:05:20.290520Z",
     "shell.execute_reply.started": "2022-12-23T01:05:20.288631Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # get train and test scores from the model fit with the parameters from \n",
    "# # GridSearch\n",
    "\n",
    "# gs_tdf.score(X_train, y_train), gs_tdf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd07d0b9-fdd0-4e20-bfb9-241a2941ee7b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-19T05:27:58.068838Z",
     "iopub.status.busy": "2022-12-19T05:27:58.067705Z",
     "iopub.status.idle": "2022-12-19T05:52:56.664413Z",
     "shell.execute_reply": "2022-12-19T05:52:56.662811Z",
     "shell.execute_reply.started": "2022-12-19T05:27:58.068775Z"
    },
    "tags": []
   },
   "source": [
    "Logistic Regression w/TfidfVectorizer\n",
    "- set two (0.9614230604372053, 0.8180454350621518)\n",
    "    - 'lr__C': 5,\n",
    "    - 'lr__penalty': 'l2',\n",
    "    - 'tf__max_features': 17500,\n",
    "    - 'tf__ngram_range': (1, 2),\n",
    "    - 'tf__stop_words': None\n",
    "\n",
    "- set one (0.9794256322331761, 0.8174024860694384)\n",
    "    - 'lr__C': 10,\n",
    "    - 'lr__penalty': 'l2',\n",
    "    - 'tf__max_features': 15000,\n",
    "    - 'tf__ngram_range': (1, 2),\n",
    "    - 'tf__stop_words': None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d323fc94-5406-4e91-9001-2760ee7975a5",
   "metadata": {},
   "source": [
    "#### Best Logistic Regression Model\n",
    "\n",
    "For the purposes of this project, even though the logistic regression model is performing better than some of the other models, knowing that this data doesn't meet assumptions required for logistic regression, such as normality, I will not choose logistic regression as a production model.\n",
    "\n",
    "The best performing Logistic Regression:\n",
    "- Train: 0.961\n",
    "- Test:  0.818\n",
    "\n",
    "Params\n",
    "- 'lr__C': 5,\n",
    "- 'lr__penalty': 'l2',\n",
    "- 'tf__max_features': 17500,\n",
    "- 'tf__ngram_range': (1, 2),\n",
    "- 'tf__stop_words': None\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1216180-c678-489a-b93e-50be27311d24",
   "metadata": {},
   "source": [
    "## Model Selection and Tuning\n",
    "\n",
    "Now that we have tried a number of different models, we'll select the best one, given the information we have collected, and then attempt to further refine/tune that model as the final model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "970c05cd-489a-4f66-9d9c-843c8e823d31",
   "metadata": {},
   "source": [
    "### Selection: Random Forest\n",
    "\n",
    "After going through a couple of Grid Search attempts through each of the models, above, the leading model was a Random Forest model that had the highest accuracy and was appropriate for NLP data.  As mentioned, logistic regression is excluded from this given the assumptions that model has about the underlying data, and kNN due to the high dimensionality of the data.\n",
    "\n",
    "In this section, we'll continue to Grid Search to tune the Random Forest model, starting from the best parameter set so far:\n",
    "\n",
    "The best performing Random Forest:\n",
    "- Train: .986\n",
    "- Test:  .802\n",
    "\n",
    "Params\n",
    "- 'cv__max_df': 0.85,\n",
    "- 'cv__max_features': 5000,\n",
    "- 'cv__min_df': 6,\n",
    "- 'cv__ngram_range': (1, 2),\n",
    "- 'cv__stop_words': 'english',\n",
    "- 'rf__max_depth': 200,\n",
    "- 'rf__max_features': 'log2',\n",
    "- 'rf__max_samples': 0.9,\n",
    "- 'rf__n_estimators': 750\n",
    "\n",
    "Using this information, we'll further refine the Grid Search to try and achieve a better score on the test data.  I will bring the pipeline and parameters down from the Random Forest section and start to search for parameters to improve performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "56d26a62-670c-4ce8-b3f3-89d490dc4005",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:05:20.291587Z",
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     "shell.execute_reply.started": "2022-12-23T01:05:20.291570Z"
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   "source": [
    "# this looks like a leading model, so trying to tune it a bit further\n",
    "# reducing the sets so I can iterate through some grid searches more quickly\n",
    "\n",
    "# set six \n",
    "# try adding more max samples feature to try and control subsample size\n",
    "# and address some of the overfitting\n",
    "cv_rf_params = {\n",
    "    'cv__stop_words': [None],\n",
    "    'cv__max_df': [0.85],\n",
    "    'cv__min_df': [6],\n",
    "    'cv__ngram_range': [(1,2)],\n",
    "    'cv__max_features': [12_000, 15_000],\n",
    "    'rf__n_estimators': [2_000, 3_000],\n",
    "    'rf__max_depth': [750],\n",
    "    'rf__max_samples': [0.5, 0.6, 0.7, 0.85],\n",
    "    'rf__max_features': ['log2']\n",
    "}\n",
    "\n",
    "# set five\n",
    "# cv_rf_params = {\n",
    "#     'cv__stop_words': [None],\n",
    "#     'cv__max_df': [0.85],\n",
    "#     'cv__min_df': [6],\n",
    "#     'cv__ngram_range': [(1,2)],\n",
    "#     'cv__max_features': [12_000, 13_500, 15_000],\n",
    "#     'rf__n_estimators': [2_000, 3_000, 4_000],\n",
    "#     'rf__max_depth': [750],\n",
    "#     'rf__max_samples': [0.85],\n",
    "#     'rf__max_features': ['log2']\n",
    "# }\n",
    "\n",
    "# set four\n",
    "# cv_rf_params = {\n",
    "#     'cv__stop_words': [None],\n",
    "#     'cv__max_df': [0.85, 0.90],\n",
    "#     'cv__min_df': [6],\n",
    "#     'cv__ngram_range': [(1,2), (1,3)],\n",
    "#     'cv__max_features': [15_000, 20_000],\n",
    "#     'rf__n_estimators': [1000, 2500],\n",
    "#     'rf__max_depth': [750, 1000],\n",
    "#     'rf__max_samples': [0.85],\n",
    "#     'rf__max_features': ['log2']\n",
    "# }\n",
    "\n",
    "# set three\n",
    "# cv_rf_params = {\n",
    "#     'cv__stop_words': ['english', None],\n",
    "#     'cv__max_df': [0.85, 0.90],\n",
    "#     'cv__min_df': [6],\n",
    "#     'cv__ngram_range': [(1,2)],\n",
    "#     'cv__max_features': [10_000, 15_000],\n",
    "#     'rf__n_estimators': [1000],\n",
    "#     'rf__max_depth': [500, 750],\n",
    "#     'rf__max_samples': [0.85],\n",
    "#     'rf__max_features': ['log2']\n",
    "# }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "183594ec-a905-45d0-93dd-930becd70101",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:05:20.296212Z",
     "iopub.status.busy": "2022-12-23T01:05:20.295818Z",
     "iopub.status.idle": "2022-12-23T01:43:58.098506Z",
     "shell.execute_reply": "2022-12-23T01:43:58.097539Z",
     "shell.execute_reply.started": "2022-12-23T01:05:20.296197Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matt/opt/anaconda3/lib/python3.9/site-packages/joblib/externals/loky/process_executor.py:702: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'cv__max_df': 0.85,\n",
       " 'cv__max_features': 12000,\n",
       " 'cv__min_df': 6,\n",
       " 'cv__ngram_range': (1, 2),\n",
       " 'cv__stop_words': None,\n",
       " 'rf__max_depth': 750,\n",
       " 'rf__max_features': 'log2',\n",
       " 'rf__max_samples': 0.85,\n",
       " 'rf__n_estimators': 2000}"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# configure GridSearch\n",
    "gs_rf_cv = GridSearchCV(rf_cv, cv_rf_params, n_jobs=-1, verbose=1)\n",
    "\n",
    "# fit with training data\n",
    "gs_rf_cv.fit(X_train, y_train)\n",
    "\n",
    "# output the best params after GridSearch is complete\n",
    "gs_rf_cv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "c36f9cf2-43b7-4646-a000-afaf932ae416",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:43:58.101087Z",
     "iopub.status.busy": "2022-12-23T01:43:58.100944Z",
     "iopub.status.idle": "2022-12-23T01:44:21.864275Z",
     "shell.execute_reply": "2022-12-23T01:44:21.863945Z",
     "shell.execute_reply.started": "2022-12-23T01:43:58.101068Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9994284897842549, 0.8122588941277326)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get train and test scores from the model fit with the parameters from \n",
    "# GridSearch\n",
    "gs_rf_cv.score(X_train, y_train), gs_rf_cv.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7211d2f1-a417-4f29-99b6-1d8358d10941",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-20T23:04:01.541725Z",
     "iopub.status.busy": "2022-12-20T23:04:01.541305Z",
     "iopub.status.idle": "2022-12-20T23:47:49.726965Z",
     "shell.execute_reply": "2022-12-20T23:47:49.725953Z",
     "shell.execute_reply.started": "2022-12-20T23:04:01.541664Z"
    },
    "tags": []
   },
   "source": [
    "Random Forest Tuning Results<br>\n",
    "Best Result is set six:\n",
    "- set six (0.9994284897842549, 0.8156879554222032)\n",
    "    - 'cv__max_df': 0.85,\n",
    "    - 'cv__max_features': 12000,\n",
    "    - 'cv__min_df': 6,\n",
    "    - 'cv__ngram_range': (1, 2),\n",
    "    - 'cv__stop_words': None,\n",
    "    - 'rf__max_depth': 750,\n",
    "    - 'rf__max_features': 'log2',\n",
    "    - 'rf__max_samples': 0.85,\n",
    "    - 'rf__n_estimators': 2000\n",
    " \n",
    "- set five (0.9994284897842549, 0.812473210458637)\n",
    "    - 'cv__max_df': 0.85,\n",
    "    - 'cv__max_features': 12000,\n",
    "    - 'cv__min_df': 6,\n",
    "    - 'cv__ngram_range': (1, 2),\n",
    "    - 'cv__stop_words': None,\n",
    "    - 'rf__max_depth': 750,\n",
    "    - 'rf__max_features': 'log2',\n",
    "    - 'rf__max_samples': 0.85,\n",
    "    - 'rf__n_estimators': 3000\n",
    "\n",
    "\n",
    "- set four (0.9996428061151593, 0.8101157308186884)\n",
    "    - 'cv__max_df': 0.85,\n",
    "    - 'cv__max_features': 15000,\n",
    "    - 'cv__min_df': 6,\n",
    "    - 'cv__ngram_range': (1, 2),\n",
    "    - 'cv__stop_words': None,\n",
    "    - 'rf__max_depth': 750,\n",
    "    - 'rf__max_features': 'log2',\n",
    "    - 'rf__max_samples': 0.85,\n",
    "    - 'rf__n_estimators': 2500\n",
    "\n",
    "- set three (0.9996428061151593, 0.8152593227603944)\n",
    "    - 'cv__max_df': 0.9,\n",
    "    - 'cv__max_features': 15000,\n",
    "    - 'cv__min_df': 6,\n",
    "    - 'cv__ngram_range': (1, 2),\n",
    "    - 'cv__stop_words': None,\n",
    "    - 'rf__max_depth': 750,\n",
    "    - 'rf__max_features': 'log2',\n",
    "    - 'rf__max_samples': 0.85,\n",
    "    - 'rf__n_estimators': 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9517e905-79fb-44de-b3b9-b7bf9cdd6358",
   "metadata": {},
   "source": [
    "Even though all the models are still showing signs of overfitting on the training data, they are performing well on the test data.  I will move forward with the selected model, using the parameters from 'set six'.  This shows a 1.3 percentage point increase over the model that was found during the initial Grid Search, and is significantly better than the baseline of 52.4%.  Using this model, we'll do some further evaluation below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e16c28d-3e46-42f9-9369-752f6bf60f12",
   "metadata": {},
   "source": [
    "## Model Evalution\n",
    "\n",
    "From the modeling steps above, I've selected the Random Forest classifier for the production model.  Here I'll evaluate the model, focusing on accuracy, but also investigate what the model may not be classifying well."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c85df47d-52fe-4640-888d-4598c7d2d4fd",
   "metadata": {},
   "source": [
    "#### Classification Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "e44ab09b-f055-4b04-9b7c-79b49744a3f5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:44:21.865317Z",
     "iopub.status.busy": "2022-12-23T01:44:21.865234Z",
     "iopub.status.idle": "2022-12-23T01:44:28.022914Z",
     "shell.execute_reply": "2022-12-23T01:44:28.022383Z",
     "shell.execute_reply.started": "2022-12-23T01:44:21.865306Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Xbox       0.85      0.73      0.79      2222\n",
      " Playstation       0.78      0.89      0.83      2444\n",
      "\n",
      "    accuracy                           0.81      4666\n",
      "   macro avg       0.82      0.81      0.81      4666\n",
      "weighted avg       0.82      0.81      0.81      4666\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, \n",
    "                            gs_rf_cv.predict(X_test), \n",
    "                            target_names=['Xbox', 'Playstation']\n",
    "                           ))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "226dd7b2-d952-4602-ad80-612381e2db00",
   "metadata": {},
   "source": [
    "For Playstation, the majority class, this report is showing that the model was good at capturing many of the positive classifications with the recall score of 0.89. Precision is a bit lower than recall, but refers to when the model predicted postive, 78% of those positive predications were correctly identified as positive.  A higher precision gives us more confidence that if the model predicts positive, the record will actually be positive.  \n",
    "\n",
    "In the classification report, we see the opposite is true for the minority class (Xbox). This could be due to some of the words used--when looking through the project, like the feature importances section below, there were words that showed high importance that appeared to be Xbox related.  Perhaps this explains some of the increased precision for the Xbox class.\n",
    "\n",
    "Overall, there are generally tradeoffs between precision and recall, so looking at the f1 score gives us a look at the balance between these tradeoffs.  The scores in this case are right around the overall accuracy and are good in the context of trying to exceed the 75% accuracy target for this POC."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f15f95f6-bde9-415b-b919-6baa1e9cadf9",
   "metadata": {},
   "source": [
    "#### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "92b213f3-4826-492e-b842-3c4821194c2a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:44:28.024045Z",
     "iopub.status.busy": "2022-12-23T01:44:28.023927Z",
     "iopub.status.idle": "2022-12-23T01:44:34.365750Z",
     "shell.execute_reply": "2022-12-23T01:44:34.365395Z",
     "shell.execute_reply.started": "2022-12-23T01:44:28.024034Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_estimator(gs_rf_cv, X_test, y_test, \n",
    "                                      cmap='Blues')\n",
    "\n",
    "plt.title('Random Forest Confusion Matrix\\n(1 = Playstation, 0 = Xbox)');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8882d9af-e4bc-4719-9ca9-a8be54dcff6f",
   "metadata": {},
   "source": [
    "When viewing the confusion matrix, it looks like the most problematic area for prediction are the false positives, observations that the model thought would be 1/Playstation, but were actually 0/Xbox.  This makes sense given what we saw with the precision and recall scores from the classification report.  I'm most interested in the false positives so I will try and look at some of the text from those observations below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e475c494-19b8-44d0-831b-f650798e600e",
   "metadata": {},
   "source": [
    "#### ROC "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1f48a59-33fe-4d60-a965-905fe528b7a0",
   "metadata": {},
   "source": [
    "We'll plot the ROC AUC for the selected Random Forest model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "e149af2d-782f-467f-9c5f-17cf3238ae58",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:44:34.366905Z",
     "iopub.status.busy": "2022-12-23T01:44:34.366628Z",
     "iopub.status.idle": "2022-12-23T01:44:40.902449Z",
     "shell.execute_reply": "2022-12-23T01:44:40.901883Z",
     "shell.execute_reply.started": "2022-12-23T01:44:34.366891Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# from a classification lesson\n",
    "# Create an ax object\n",
    "ax = plt.gca()\n",
    "\n",
    "# Use RocCurveDisplay for both estimators\n",
    "RocCurveDisplay.from_estimator(gs_rf_cv, X_test, y_test, \n",
    "                               ax=ax, name='Random Forest')\n",
    "\n",
    "# Add 'worst case scenario' line\n",
    "plt.plot([0,1], [0,1], label='no prediction value', \n",
    "         linestyle='--', color='gray')\n",
    "\n",
    "# Necessary to label the baseline\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a5633bd-30c1-44d9-969d-e044873aa29f",
   "metadata": {},
   "source": [
    "This ROC AUC is fairly high (0.89).  In this chart, you are looking at the area above the dotted line, which represents if the model had no prediction value, and under the curved blue line.  The more area under the curve (AUC) the better the model is at discriminating between classes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ace6d77-0f6e-442c-aaa1-278b58b844cf",
   "metadata": {},
   "source": [
    "#### Specific Classification Examples\n",
    "\n",
    "I will attempt to look at some of the specific examples, both positive and negative in order to understand any elements that might be contributing to the classification errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "9359f6b9-c4c0-460f-8eae-cbf9a831548c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:44:40.903289Z",
     "iopub.status.busy": "2022-12-23T01:44:40.903161Z",
     "iopub.status.idle": "2022-12-23T01:44:47.005406Z",
     "shell.execute_reply": "2022-12-23T01:44:47.005035Z",
     "shell.execute_reply.started": "2022-12-23T01:44:40.903273Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# make predictions using model\n",
    "y_preds = gs_rf_cv.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "5fc42a1c-2fc0-4072-b03d-0fb5d2b9885e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:44:47.006079Z",
     "iopub.status.busy": "2022-12-23T01:44:47.005987Z",
     "iopub.status.idle": "2022-12-23T01:44:53.114157Z",
     "shell.execute_reply": "2022-12-23T01:44:53.113786Z",
     "shell.execute_reply.started": "2022-12-23T01:44:47.006068Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# get predicted probabilities using model\n",
    "y_probs = gs_rf_cv.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "0a935287-f391-4ea6-8719-f7a10644be60",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:44:53.114948Z",
     "iopub.status.busy": "2022-12-23T01:44:53.114863Z",
     "iopub.status.idle": "2022-12-23T01:44:53.122220Z",
     "shell.execute_reply": "2022-12-23T01:44:53.121928Z",
     "shell.execute_reply.started": "2022-12-23T01:44:53.114937Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# build a dataframe using the information from X_test, y_test, preds, and probs\n",
    "# https://stackoverflow.com/questions/48947194/add-randomforestclassifier-predict-proba-results-to-original-dataframe\n",
    "\n",
    "eval_df = pd.DataFrame(X_test)\n",
    "eval_df['target'] = y_test\n",
    "eval_df['pred'] = y_preds\n",
    "eval_df['prob_0'] = y_probs[:,0] \n",
    "eval_df['prob_1'] = y_probs[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "f4fce717-9ae6-407e-836f-eb86cbcb7c90",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:44:53.123017Z",
     "iopub.status.busy": "2022-12-23T01:44:53.122868Z",
     "iopub.status.idle": "2022-12-23T01:44:53.128523Z",
     "shell.execute_reply": "2022-12-23T01:44:53.128196Z",
     "shell.execute_reply.started": "2022-12-23T01:44:53.123006Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>target</th>\n",
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       "      <th>prob_1</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16782</th>\n",
       "      <td>will not let I buy   game normally I would think this be too personal but I remember everyone have issue download last month s  game with gold and be wonder if it be maybe a large problem</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7212</th>\n",
       "      <td>free trial go recently get back into gaming and notice the free trial be not there anyone else</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.441944</td>\n",
       "      <td>0.558056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1139</th>\n",
       "      <td>which game be you skip on the 4 to play on the 5 for the first time watch dog legion bring this to my attention out in  week on 4 or   week 5 seem silly to play it on 4 when you could test out your 5 with it</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.348500</td>\n",
       "      <td>0.651500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16635</th>\n",
       "      <td>be it possible to do the  year of  gamepass conversion from gold if you ve already do it I buy the  gamepass conversion a year ago it expire this week I be wonder if its possible to get a year of gold then buy the gamepass ultimate conversion again to get another year of ultimate I more or less live check to check and can not guarantee I will have that money every month to do it so it be more ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.774500</td>\n",
       "      <td>0.225500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4806</th>\n",
       "      <td>question for 5 backwards compatibility early today ign just release a video talk about how vague the 5 s backward compatibility will be able to work which accord to a statement from ubisoft mean that mainly 4 title will be playable on the   while 3 and 2 game will not but now that statement be be redact and it seem like we may only get to play 4 game on the   but nothing be be really make offi...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.406500</td>\n",
       "      <td>0.593500</td>\n",
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      "text/plain": [
       "                                                                                                                                                                                                                                                                                                                                                                                                                  text  \\\n",
       "16782                                                                                                                                                                                                                      will not let I buy   game normally I would think this be too personal but I remember everyone have issue download last month s  game with gold and be wonder if it be maybe a large problem   \n",
       "7212                                                                                                                                                                                                                                                                                                                    free trial go recently get back into gaming and notice the free trial be not there anyone else   \n",
       "1139                                                                                                                                                                                                   which game be you skip on the 4 to play on the 5 for the first time watch dog legion bring this to my attention out in  week on 4 or   week 5 seem silly to play it on 4 when you could test out your 5 with it   \n",
       "16635  be it possible to do the  year of  gamepass conversion from gold if you ve already do it I buy the  gamepass conversion a year ago it expire this week I be wonder if its possible to get a year of gold then buy the gamepass ultimate conversion again to get another year of ultimate I more or less live check to check and can not guarantee I will have that money every month to do it so it be more ...   \n",
       "4806   question for 5 backwards compatibility early today ign just release a video talk about how vague the 5 s backward compatibility will be able to work which accord to a statement from ubisoft mean that mainly 4 title will be playable on the   while 3 and 2 game will not but now that statement be be redact and it seem like we may only get to play 4 game on the   but nothing be be really make offi...   \n",
       "\n",
       "       target  pred    prob_0    prob_1  \n",
       "16782       0     0  0.660000  0.340000  \n",
       "7212        1     1  0.441944  0.558056  \n",
       "1139        1     1  0.348500  0.651500  \n",
       "16635       0     0  0.774500  0.225500  \n",
       "4806        1     1  0.406500  0.593500  "
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41596096-f136-4c09-a00b-36716554efe0",
   "metadata": {},
   "source": [
    "Trying to focus in on the false positive records that were prevalent in the confusion matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "b1291c51-9642-4cef-872d-98b4a1f3383b",
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       "      <th>11042</th>\n",
       "      <td>another pre order sale do you think that  be go to do another pre order sale I hope so so more people can try and get it</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.237000</td>\n",
       "      <td>0.763000</td>\n",
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       "    <tr>\n",
       "      <th>12224</th>\n",
       "      <td>tell I why any opinion anybody have experience with this game be it worth download I tend to be a fan of story drive game be it similar to the last of we type of game it kind of give I alan wake vibe from look at the trailer</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.243500</td>\n",
       "      <td>0.756500</td>\n",
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       "    <tr>\n",
       "      <th>18505</th>\n",
       "      <td>I get my  account scamme the person delete all of my email and then add there how can I get it back the also change the password</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.244667</td>\n",
       "      <td>0.755333</td>\n",
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       "    <tr>\n",
       "      <th>11885</th>\n",
       "      <td>any 4k ole 120hz tv recommendation just got pre order time to complete the experience</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.259834</td>\n",
       "      <td>0.740166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16042</th>\n",
       "      <td>can I get my money back for a pre order that I do not cancel I pre order tannenberg a couple of week ago for £   and I get an email from  yesterday say that my pre order have be cancel can I still get my money back</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.260833</td>\n",
       "      <td>0.739167</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16921</th>\n",
       "      <td>how to connect original  to computer monitor dvi or vga do some research on my side it seem there be few or no option have anyone have experience do this I would need a converter or some sort to go from a v to the monitor and sound</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.498500</td>\n",
       "      <td>0.501500</td>\n",
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       "      <th>9875</th>\n",
       "      <td>boot help stop ok so basically there ’ this one man in  that boot people I know that the new update come out but supposedly there be still a way to boot I be wonder what can be do to stop this and if not I would enjoy to boot he myself with my phone but I know that most be fake any help</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.498500</td>\n",
       "      <td>0.501500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13265</th>\n",
       "      <td>why do ppl like achievement I be curious what be it about achievement that get people hype</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.498962</td>\n",
       "      <td>0.501038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>why be the cable for the original  so unconvential this be a question which have be in the back of my mind for a while but I ve never think to try and research it until now but I can not seem to find any article or answer on google for some reason do anyone know why  decide to go with this design</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.499500</td>\n",
       "      <td>0.500500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17439</th>\n",
       "      <td>controller not work at certain angle hey everyone so I recently buy the cyberpunk  controller and I notice when I press the a button at certain angle mainly if it ’ the rim of the button that it ’s not register the button as be press I test this on my old controller and it be not happen at all no matter how hard or where I press it the button activate so I send back the controller and recently...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.499500</td>\n",
       "      <td>0.500500</td>\n",
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       "<p>601 rows × 5 columns</p>\n",
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      "text/plain": [
       "                                                                                                                                                                                                                                                                                                                                                                                                                  text  \\\n",
       "11042                                                                                                                                                                                                                                                                                         another pre order sale do you think that  be go to do another pre order sale I hope so so more people can try and get it   \n",
       "12224                                                                                                                                                                                 tell I why any opinion anybody have experience with this game be it worth download I tend to be a fan of story drive game be it similar to the last of we type of game it kind of give I alan wake vibe from look at the trailer   \n",
       "18505                                                                                                                                                                                                                                                                                 I get my  account scamme the person delete all of my email and then add there how can I get it back the also change the password   \n",
       "11885                                                                                                                                                                                                                                                                                                                            any 4k ole 120hz tv recommendation just got pre order time to complete the experience   \n",
       "16042                                                                                                                                                                                           can I get my money back for a pre order that I do not cancel I pre order tannenberg a couple of week ago for £   and I get an email from  yesterday say that my pre order have be cancel can I still get my money back   \n",
       "...                                                                                                                                                                                                                                                                                                                                                                                                                ...   \n",
       "16921                                                                                                                                                                          how to connect original  to computer monitor dvi or vga do some research on my side it seem there be few or no option have anyone have experience do this I would need a converter or some sort to go from a v to the monitor and sound   \n",
       "9875                                                                                                                   boot help stop ok so basically there ’ this one man in  that boot people I know that the new update come out but supposedly there be still a way to boot I be wonder what can be do to stop this and if not I would enjoy to boot he myself with my phone but I know that most be fake any help   \n",
       "13265                                                                                                                                                                                                                                                                                                                       why do ppl like achievement I be curious what be it about achievement that get people hype   \n",
       "9995                                                                                                         why be the cable for the original  so unconvential this be a question which have be in the back of my mind for a while but I ve never think to try and research it until now but I can not seem to find any article or answer on google for some reason do anyone know why  decide to go with this design   \n",
       "17439  controller not work at certain angle hey everyone so I recently buy the cyberpunk  controller and I notice when I press the a button at certain angle mainly if it ’ the rim of the button that it ’s not register the button as be press I test this on my old controller and it be not happen at all no matter how hard or where I press it the button activate so I send back the controller and recently...   \n",
       "\n",
       "       target  pred    prob_0    prob_1  \n",
       "11042       0     1  0.237000  0.763000  \n",
       "12224       0     1  0.243500  0.756500  \n",
       "18505       0     1  0.244667  0.755333  \n",
       "11885       0     1  0.259834  0.740166  \n",
       "16042       0     1  0.260833  0.739167  \n",
       "...       ...   ...       ...       ...  \n",
       "16921       0     1  0.498500  0.501500  \n",
       "9875        0     1  0.498500  0.501500  \n",
       "13265       0     1  0.498962  0.501038  \n",
       "9995        0     1  0.499500  0.500500  \n",
       "17439       0     1  0.499500  0.500500  \n",
       "\n",
       "[601 rows x 5 columns]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_df[\n",
    "    (eval_df['target'] == 0) &\n",
    "    (eval_df['pred'] == 1)\n",
    "].sort_values(by='prob_1', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dde55a2-9024-409d-a7af-58f3d7208265",
   "metadata": {},
   "source": [
    "It looks like there are multiple observations that are above 70% probability for 1/Playstation, but were actually from the Xbox subreddit.  Let's investigate those a bit more closely."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "a8958a6e-e830-4d26-9dd2-951aa8ea6220",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2022-12-23T01:44:53.139131Z"
    },
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   },
   "outputs": [],
   "source": [
    "# show the text of posts that were from the Xbox subreddit, but predicted\n",
    "# to be from the playstation subreddit with 70% probability or higher\n",
    "# save in a variable to re-use for the wordcloud\n",
    "\n",
    "fps = eval_df.loc[eval_df[\n",
    "        (eval_df['target'] == 0) &\n",
    "        (eval_df['pred'] == 1) &\n",
    "        (eval_df['prob_1'] > 0.7)\n",
    "    ].index].sort_values(by='prob_1', ascending=False)[['text']]"
   ]
  },
  {
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   "id": "dc59acc1-4bcb-42b9-9a71-6adc091deb90",
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       "13"
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   "source": [
    "len(fps)"
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       "      <th>11042</th>\n",
       "      <td>another pre order sale do you think that  be go to do another pre order sale I hope so so more people can try and get it</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12224</th>\n",
       "      <td>tell I why any opinion anybody have experience with this game be it worth download I tend to be a fan of story drive game be it similar to the last of we type of game it kind of give I alan wake vibe from look at the trailer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18505</th>\n",
       "      <td>I get my  account scamme the person delete all of my email and then add there how can I get it back the also change the password</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11885</th>\n",
       "      <td>any 4k ole 120hz tv recommendation just got pre order time to complete the experience</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16042</th>\n",
       "      <td>can I get my money back for a pre order that I do not cancel I pre order tannenberg a couple of week ago for £   and I get an email from  yesterday say that my pre order have be cancel can I still get my money back</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11817</th>\n",
       "      <td>anyone know why cyberpunk be only  usd seem cheap think something be wrong when pre order it</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18560</th>\n",
       "      <td>need help contact  anyway I can get a call with  I ’m have complication at the moment my account got hack and they change everything in a matter of a couple minute even got pass my 2fa but I ’m not worried since I have very recent billing info to prove it be mine but I ’m have more trouble actually get in contact with they</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11822</th>\n",
       "      <td>so will store get more stock like tomorrow will I have another chance at preordere again or do I got to wait for release</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16127</th>\n",
       "      <td>rock of age  unavailable hey guy I notice roa  be come out yesterday today and got really hype on the come soon section but now it say unavailable and have be remove from come soon despite   release date in the story anybody else have success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10689</th>\n",
       "      <td>really annoying account recovery I have and  account that I stop use in  it be delete from my  and I do not know the email password or phone number for the account all I know be the username I know it seem like it be a stretch but be there anyway to get my account back any info be helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17628</th>\n",
       "      <td>my  account be steal my  account be steal I ve lose so many game and countless hour of work on they be there anyway I can get my account back all the detail be change</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9787</th>\n",
       "      <td>I preorder cyberpunk what be the  store preorder bonus and I preordere on  because I feel like there will be exclusive content on  becuaas they be kinda partner up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11895</th>\n",
       "      <td>what ’s  shipping like  seem to be the safe bet when it come to preordering what ’ their shipping process like be they reputable for ship arrive on time safe packaging obviously mail slow atm but hopefully  can get the console out on time actually there ’ a good chance of they shipping console a bit early to combat slow shipping time so I would not be surprised if some of we get our system a d...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                                                                                                                                                                                                                                                                  text\n",
       "11042                                                                                                                                                                                                                                                                                         another pre order sale do you think that  be go to do another pre order sale I hope so so more people can try and get it\n",
       "12224                                                                                                                                                                                 tell I why any opinion anybody have experience with this game be it worth download I tend to be a fan of story drive game be it similar to the last of we type of game it kind of give I alan wake vibe from look at the trailer\n",
       "18505                                                                                                                                                                                                                                                                                 I get my  account scamme the person delete all of my email and then add there how can I get it back the also change the password\n",
       "11885                                                                                                                                                                                                                                                                                                                            any 4k ole 120hz tv recommendation just got pre order time to complete the experience\n",
       "16042                                                                                                                                                                                           can I get my money back for a pre order that I do not cancel I pre order tannenberg a couple of week ago for £   and I get an email from  yesterday say that my pre order have be cancel can I still get my money back\n",
       "11817                                                                                                                                                                                                                                                                                                                     anyone know why cyberpunk be only  usd seem cheap think something be wrong when pre order it\n",
       "18560                                                                             need help contact  anyway I can get a call with  I ’m have complication at the moment my account got hack and they change everything in a matter of a couple minute even got pass my 2fa but I ’m not worried since I have very recent billing info to prove it be mine but I ’m have more trouble actually get in contact with they\n",
       "11822                                                                                                                                                                                                                                                                                         so will store get more stock like tomorrow will I have another chance at preordere again or do I got to wait for release\n",
       "16127                                                                                                                                                               rock of age  unavailable hey guy I notice roa  be come out yesterday today and got really hype on the come soon section but now it say unavailable and have be remove from come soon despite   release date in the story anybody else have success\n",
       "10689                                                                                                                really annoying account recovery I have and  account that I stop use in  it be delete from my  and I do not know the email password or phone number for the account all I know be the username I know it seem like it be a stretch but be there anyway to get my account back any info be helpful\n",
       "17628                                                                                                                                                                                                                                           my  account be steal my  account be steal I ve lose so many game and countless hour of work on they be there anyway I can get my account back all the detail be change\n",
       "9787                                                                                                                                                                                                                                               I preorder cyberpunk what be the  store preorder bonus and I preordere on  because I feel like there will be exclusive content on  becuaas they be kinda partner up\n",
       "11895  what ’s  shipping like  seem to be the safe bet when it come to preordering what ’ their shipping process like be they reputable for ship arrive on time safe packaging obviously mail slow atm but hopefully  can get the console out on time actually there ’ a good chance of they shipping console a bit early to combat slow shipping time so I would not be surprised if some of we get our system a d..."
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5677c91b-a087-4a39-969f-097753b172da",
   "metadata": {},
   "source": [
    "There doesn't seem to be any obvious pattern in these posts, although I do see variations of \"pre order\" and \"account\" show up fairly often.  But largely, these are posts that I could see being classified as either since they are most related to general order/pre order and account issues.  Both platforms would have accounts and pre-orders, so I can see how the model may have a difficult time classifying these posts."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e813166-6ac5-4b17-931a-ec72b6292e35",
   "metadata": {},
   "source": [
    "##### Word Cloud\n",
    "\n",
    "Let's create word cloud with the 25 most frequent words from the high-percent-probability posts that we were looking at above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "43069f5c-fce9-4f49-8937-f93cefef1e13",
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    },
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   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# https://www.python-graph-gallery.com/wordcloud/\n",
    "# https://stackoverflow.com/questions/43606339/generate-word-cloud-from-single-column-pandas-dataframe\n",
    "\n",
    "plt.figure(figsize=(12,8))#\n",
    "\n",
    "# Create the wordcloud object\n",
    "wordcloud = WordCloud(width=1920,\n",
    "                      height=1080,\n",
    "                      max_words=25,\n",
    "                      background_color='beige'\n",
    "                     ).generate(' '.join(fps['text']))\n",
    "\n",
    "# Display the generated image:\n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis(\"off\")\n",
    "plt.margins(x=0, y=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d57673a4-dde1-4b79-86c1-39cdc110523b",
   "metadata": {},
   "source": [
    "Looking through this word cloud, there aren't a lot of words that would be specific to Playstation or Xbox, so I can understand that it may have been difficult for the model to distinguish between the classes for these posts."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5136342d-577d-4dec-a91a-de6d4ed311ad",
   "metadata": {},
   "source": [
    "#### Check Feature Importances\n",
    "\n",
    "Check the feature importance values to see if there is anything interesting here.  Given the large amount of features that is feeding the model, I'm not expecting to find anything particularly meaningful, but thought it would be worthwhile to see."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "96302c37-ba05-4e38-bfb5-56f7c2d32b4f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:44:54.050414Z",
     "iopub.status.busy": "2022-12-23T01:44:54.050324Z",
     "iopub.status.idle": "2022-12-23T01:44:54.355638Z",
     "shell.execute_reply": "2022-12-23T01:44:54.355290Z",
     "shell.execute_reply.started": "2022-12-23T01:44:54.050402Z"
    }
   },
   "outputs": [],
   "source": [
    "# create a dataframe by extracting feature importances and set the index\n",
    "# to the feature names\n",
    "\n",
    "feats = pd.DataFrame(\n",
    "    gs_rf_cv.best_estimator_.named_steps.rf.feature_importances_,\n",
    "    index=gs_rf_cv.best_estimator_.named_steps.cv.get_feature_names_out())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "f13e2474-8c18-45e5-8b8e-e2ad66c5df2b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:44:54.356332Z",
     "iopub.status.busy": "2022-12-23T01:44:54.356236Z",
     "iopub.status.idle": "2022-12-23T01:44:54.358985Z",
     "shell.execute_reply": "2022-12-23T01:44:54.358644Z",
     "shell.execute_reply.started": "2022-12-23T01:44:54.356321Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# rename the column\n",
    "feats.rename({0: 'importance'}, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "135c1d4c-4c00-41ed-8946-f139a6bb5be2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-23T01:44:54.359614Z",
     "iopub.status.busy": "2022-12-23T01:44:54.359534Z",
     "iopub.status.idle": "2022-12-23T01:44:54.365845Z",
     "shell.execute_reply": "2022-12-23T01:44:54.365423Z",
     "shell.execute_reply.started": "2022-12-23T01:44:54.359603Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<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>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>series</th>\n",
       "      <td>0.011594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>the series</th>\n",
       "      <td>0.005991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>0.005646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>game pass</th>\n",
       "      <td>0.005412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pass</th>\n",
       "      <td>0.004166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>be</th>\n",
       "      <td>0.003469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>my one</th>\n",
       "      <td>0.003228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>the</th>\n",
       "      <td>0.003165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>to</th>\n",
       "      <td>0.002927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gold</th>\n",
       "      <td>0.002824</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            importance\n",
       "series        0.011594\n",
       "the series    0.005991\n",
       "one           0.005646\n",
       "game pass     0.005412\n",
       "pass          0.004166\n",
       "be            0.003469\n",
       "my one        0.003228\n",
       "the           0.003165\n",
       "to            0.002927\n",
       "gold          0.002824"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the top 10 features by importance\n",
    "feats.sort_values(by='importance', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "553054f9-f799-4296-b7cf-1a524550c6f8",
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   "source": [
    "This is where it would be helpful to have domain expertise to see if any of these terms are truly meaningful.  I just did a google search for game pass, and that actually does return what looks to be some subscription service for Xbox, so that is pretty neat that although it wasn't obvious to me that this was related to Microsoft or Xbox that the model did find this and it's an important piece to how the posts are classified.  \n",
    "\n",
    "It also looks like the current generation of Xbox consoles have \"series\" in the name, so I can see how that would be important here as well.  Perhaps this accounts for the slighly higher precision score in the classification report for Xbox over Playstation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83a5c287-753e-4844-b9f0-df526e955bc6",
   "metadata": {},
   "source": [
    "## Conclusions and Next Steps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "284eff50-02d3-4947-8735-f2e183902e44",
   "metadata": {},
   "source": [
    "The modeling that was performed improved the accuracy from a 52% baseline accuracy, given the majority class, to 82% accuracy with the chosen Random Forest model after tuning.  This exceeds the 75% target for Kontaku, so they should proceed with the remainder of their analysis.  This proof-of-concept model should give them a good data point to use when performing some additional internal analysis about the cost-benefit of moving towards a more automated and scalable classification and moderation approach.\n",
    "\n",
    "### Next Steps\n",
    "For potential next steps relating to this effort, I would put forward:\n",
    "1. Proceed with cost-benefit analysis\n",
    "2. Model improvement\n",
    "    - Look at additional techniques, such as boosting, stacking, and sentiment analysis to see if the model performance can be improved\n",
    "3. Evaluate the cost of model deployment\n",
    "    - Having the model is great, but there will be other costs relating to putting this model into production, which should be understood by Kotaku in order to make the best business decision they can## Conclusions and Next Steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5048d61a-a45b-47ad-a4a3-cfc49548bf23",
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   "outputs": [],
   "source": []
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