AI PROBLEM MODEL.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f4298ff2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a65c1590",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('labeled_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4054aaf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First</th>\n",
       "      <th>count</th>\n",
       "      <th>hate_speech</th>\n",
       "      <th>offensive_language</th>\n",
       "      <th>neither</th>\n",
       "      <th>class</th>\n",
       "      <th>tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>@mayasolovely: As a woman you shouldn't compl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>@mleew17: boy dats cold...tyga dwn bad for cu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>@UrKindOfBrand Dawg!!!! RT @80sbaby4life: You...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>!!!!!!!!! RT @C_G_Anderson: @viva_based she lo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>@ShenikaRoberts: The shit you hear about me m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24778</th>\n",
       "      <td>25291</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>you's a muthaf***in lie &amp;#8220;@LifeAsKing: @2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24779</th>\n",
       "      <td>25292</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>you've gone and broke the wrong heart baby, an...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24780</th>\n",
       "      <td>25294</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>young buck wanna eat!!.. dat nigguh like I ain...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24781</th>\n",
       "      <td>25295</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>youu got wild bitches tellin you lies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24782</th>\n",
       "      <td>25296</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>~~Ruffled | Ntac Eileen Dahlia - Beautiful col...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24783 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       First  count  hate_speech  offensive_language  neither  class  \\\n",
       "0          0      3            0                   0        3      2   \n",
       "1          1      3            0                   3        0      1   \n",
       "2          2      3            0                   3        0      1   \n",
       "3          3      3            0                   2        1      1   \n",
       "4          4      6            0                   6        0      1   \n",
       "...      ...    ...          ...                 ...      ...    ...   \n",
       "24778  25291      3            0                   2        1      1   \n",
       "24779  25292      3            0                   1        2      2   \n",
       "24780  25294      3            0                   3        0      1   \n",
       "24781  25295      6            0                   6        0      1   \n",
       "24782  25296      3            0                   0        3      2   \n",
       "\n",
       "                                                   tweet  \n",
       "0       @mayasolovely: As a woman you shouldn't compl...  \n",
       "1       @mleew17: boy dats cold...tyga dwn bad for cu...  \n",
       "2       @UrKindOfBrand Dawg!!!! RT @80sbaby4life: You...  \n",
       "3      !!!!!!!!! RT @C_G_Anderson: @viva_based she lo...  \n",
       "4       @ShenikaRoberts: The shit you hear about me m...  \n",
       "...                                                  ...  \n",
       "24778  you's a muthaf***in lie &#8220;@LifeAsKing: @2...  \n",
       "24779  you've gone and broke the wrong heart baby, an...  \n",
       "24780  young buck wanna eat!!.. dat nigguh like I ain...  \n",
       "24781              youu got wild bitches tellin you lies  \n",
       "24782  ~~Ruffled | Ntac Eileen Dahlia - Beautiful col...  \n",
       "\n",
       "[24783 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "87c9699d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns = ['First','count'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "86f25628",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hate_speech</th>\n",
       "      <th>offensive_language</th>\n",
       "      <th>neither</th>\n",
       "      <th>class</th>\n",
       "      <th>tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>@mayasolovely: As a woman you shouldn't compl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>@mleew17: boy dats cold...tyga dwn bad for cu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>@UrKindOfBrand Dawg!!!! RT @80sbaby4life: You...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>!!!!!!!!! RT @C_G_Anderson: @viva_based she lo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>@ShenikaRoberts: The shit you hear about me m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24778</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>you's a muthaf***in lie &amp;#8220;@LifeAsKing: @2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24779</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>you've gone and broke the wrong heart baby, an...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24780</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>young buck wanna eat!!.. dat nigguh like I ain...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24781</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>youu got wild bitches tellin you lies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24782</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>~~Ruffled | Ntac Eileen Dahlia - Beautiful col...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24783 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       hate_speech  offensive_language  neither  class  \\\n",
       "0                0                   0        3      2   \n",
       "1                0                   3        0      1   \n",
       "2                0                   3        0      1   \n",
       "3                0                   2        1      1   \n",
       "4                0                   6        0      1   \n",
       "...            ...                 ...      ...    ...   \n",
       "24778            0                   2        1      1   \n",
       "24779            0                   1        2      2   \n",
       "24780            0                   3        0      1   \n",
       "24781            0                   6        0      1   \n",
       "24782            0                   0        3      2   \n",
       "\n",
       "                                                   tweet  \n",
       "0       @mayasolovely: As a woman you shouldn't compl...  \n",
       "1       @mleew17: boy dats cold...tyga dwn bad for cu...  \n",
       "2       @UrKindOfBrand Dawg!!!! RT @80sbaby4life: You...  \n",
       "3      !!!!!!!!! RT @C_G_Anderson: @viva_based she lo...  \n",
       "4       @ShenikaRoberts: The shit you hear about me m...  \n",
       "...                                                  ...  \n",
       "24778  you's a muthaf***in lie &#8220;@LifeAsKing: @2...  \n",
       "24779  you've gone and broke the wrong heart baby, an...  \n",
       "24780  young buck wanna eat!!.. dat nigguh like I ain...  \n",
       "24781              youu got wild bitches tellin you lies  \n",
       "24782  ~~Ruffled | Ntac Eileen Dahlia - Beautiful col...  \n",
       "\n",
       "[24783 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ed7b7575",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hate_speech</th>\n",
       "      <th>offensive_language</th>\n",
       "      <th>neither</th>\n",
       "      <th>class</th>\n",
       "      <th>tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Good</td>\n",
       "      <td>@mayasolovely: As a woman you shouldn't compl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Bad</td>\n",
       "      <td>@mleew17: boy dats cold...tyga dwn bad for cu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Bad</td>\n",
       "      <td>@UrKindOfBrand Dawg!!!! RT @80sbaby4life: You...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Bad</td>\n",
       "      <td>!!!!!!!!! RT @C_G_Anderson: @viva_based she lo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Bad</td>\n",
       "      <td>@ShenikaRoberts: The shit you hear about me m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24778</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Bad</td>\n",
       "      <td>you's a muthaf***in lie &amp;#8220;@LifeAsKing: @2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24779</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Good</td>\n",
       "      <td>you've gone and broke the wrong heart baby, an...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24780</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Bad</td>\n",
       "      <td>young buck wanna eat!!.. dat nigguh like I ain...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24781</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Bad</td>\n",
       "      <td>youu got wild bitches tellin you lies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24782</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Good</td>\n",
       "      <td>~~Ruffled | Ntac Eileen Dahlia - Beautiful col...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24783 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       hate_speech  offensive_language  neither class  \\\n",
       "0                0                   0        3  Good   \n",
       "1                0                   3        0   Bad   \n",
       "2                0                   3        0   Bad   \n",
       "3                0                   2        1   Bad   \n",
       "4                0                   6        0   Bad   \n",
       "...            ...                 ...      ...   ...   \n",
       "24778            0                   2        1   Bad   \n",
       "24779            0                   1        2  Good   \n",
       "24780            0                   3        0   Bad   \n",
       "24781            0                   6        0   Bad   \n",
       "24782            0                   0        3  Good   \n",
       "\n",
       "                                                   tweet  \n",
       "0       @mayasolovely: As a woman you shouldn't compl...  \n",
       "1       @mleew17: boy dats cold...tyga dwn bad for cu...  \n",
       "2       @UrKindOfBrand Dawg!!!! RT @80sbaby4life: You...  \n",
       "3      !!!!!!!!! RT @C_G_Anderson: @viva_based she lo...  \n",
       "4       @ShenikaRoberts: The shit you hear about me m...  \n",
       "...                                                  ...  \n",
       "24778  you's a muthaf***in lie &#8220;@LifeAsKing: @2...  \n",
       "24779  you've gone and broke the wrong heart baby, an...  \n",
       "24780  young buck wanna eat!!.. dat nigguh like I ain...  \n",
       "24781              youu got wild bitches tellin you lies  \n",
       "24782  ~~Ruffled | Ntac Eileen Dahlia - Beautiful col...  \n",
       "\n",
       "[24783 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class'].replace({0:'Bad',1:'Bad',2:'Good'},inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "49a73594",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">hate_speech</th>\n",
       "      <th colspan=\"5\" halign=\"left\">offensive_language</th>\n",
       "      <th colspan=\"8\" halign=\"left\">neither</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>...</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bad</th>\n",
       "      <td>20620.0</td>\n",
       "      <td>0.324442</td>\n",
       "      <td>0.674819</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20620.0</td>\n",
       "      <td>2.847672</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20620.0</td>\n",
       "      <td>0.085645</td>\n",
       "      <td>0.287201</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Good</th>\n",
       "      <td>4163.0</td>\n",
       "      <td>0.062935</td>\n",
       "      <td>0.253524</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4163.0</td>\n",
       "      <td>0.264233</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4163.0</td>\n",
       "      <td>2.845544</td>\n",
       "      <td>0.795181</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      hate_speech                                               \\\n",
       "            count      mean       std  min  25%  50%  75%  max   \n",
       "class                                                            \n",
       "Bad       20620.0  0.324442  0.674819  0.0  0.0  0.0  0.0  7.0   \n",
       "Good       4163.0  0.062935  0.253524  0.0  0.0  0.0  0.0  3.0   \n",
       "\n",
       "      offensive_language            ...            neither            \\\n",
       "                   count      mean  ...  75%  max    count      mean   \n",
       "class                               ...                                \n",
       "Bad              20620.0  2.847672  ...  3.0  9.0  20620.0  0.085645   \n",
       "Good              4163.0  0.264233  ...  1.0  4.0   4163.0  2.845544   \n",
       "\n",
       "                                          \n",
       "            std  min  25%  50%  75%  max  \n",
       "class                                     \n",
       "Bad    0.287201  0.0  0.0  0.0  0.0  4.0  \n",
       "Good   0.795181  2.0  2.0  3.0  3.0  9.0  \n",
       "\n",
       "[2 rows x 24 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('class').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a7446b6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Bad'] = df['class'].apply(lambda x: 1 if x == 'Bad' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8be0ba4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hate_speech</th>\n",
       "      <th>offensive_language</th>\n",
       "      <th>neither</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24778</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24779</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24780</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24781</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24782</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24783 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       hate_speech  offensive_language  neither\n",
       "0                0                   0        3\n",
       "1                0                   3        0\n",
       "2                0                   3        0\n",
       "3                0                   2        1\n",
       "4                0                   6        0\n",
       "...            ...                 ...      ...\n",
       "24778            0                   2        1\n",
       "24779            0                   1        2\n",
       "24780            0                   3        0\n",
       "24781            0                   6        0\n",
       "24782            0                   0        3\n",
       "\n",
       "[24783 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df[['hate_speech','offensive_language','neither']]\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4c5687eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hate_speech</th>\n",
       "      <th>offensive_language</th>\n",
       "      <th>neither</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>hate_speech</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.261808</td>\n",
       "      <td>-0.157599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>offensive_language</th>\n",
       "      <td>-0.261808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.684406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neither</th>\n",
       "      <td>-0.157599</td>\n",
       "      <td>-0.684406</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    hate_speech  offensive_language   neither\n",
       "hate_speech            1.000000           -0.261808 -0.157599\n",
       "offensive_language    -0.261808            1.000000 -0.684406\n",
       "neither               -0.157599           -0.684406  1.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cor = new_df.corr()\n",
    "cor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a4da594c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data=cor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c8e59668",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data=cor, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "421f8ba3",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(df.tweet, df.Bad, test_size = 0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5dd4ee3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22786                             Where's my money, bitch?\n",
       "14756    RT @CommonBlackGirI: When a hoe says hi to bae...\n",
       "10336    I got 99 problems and a bitch ain't one...\\nSh...\n",
       "15483    RT @HighSchoolSuckz: i am that horrible friend...\n",
       "22565    Well isnt that a bitch 2 wildcards are going t...\n",
       "                               ...                        \n",
       "556      \"Stop being a pussy, son, and shove that needl...\n",
       "4189                         @MrWoofless Don't be a bitch.\n",
       "22701    When her pussy stank RT @BasedGodTobi: When yo...\n",
       "10922    I swear girls with good pussy think that's the...\n",
       "14093    RT @ATypicalFemaIe: when a bitch gets smart an...\n",
       "Name: tweet, Length: 19826, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b7ccb7eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = CountVectorizer()\n",
    "x_train_count = cv.fit_transform(x_train.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "93d5313f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<19826x30989 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 259429 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "10ed99a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = MultinomialNB()\n",
    "model.fit(x_train_count, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "972d5920",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "badMessage = [\"Fuck you\"]\n",
    "badMessage_count = cv.transform(badMessage)\n",
    "model.predict(badMessage_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "38bf6c9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9074036715755497"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test_count = cv.transform(x_test)\n",
    "model.score(x_test_count, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7edc772d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(x_test_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4614a4ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 435  406]\n",
      " [  53 4063]]\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test,y_pred)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d21d4062",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.52      0.65       841\n",
      "           1       0.91      0.99      0.95      4116\n",
      "\n",
      "    accuracy                           0.91      4957\n",
      "   macro avg       0.90      0.75      0.80      4957\n",
      "weighted avg       0.91      0.91      0.90      4957\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cca0cc7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hate_speech</th>\n",
       "      <th>offensive_language</th>\n",
       "      <th>neither</th>\n",
       "      <th>class</th>\n",
       "      <th>tweet</th>\n",
       "      <th>Bad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Good</td>\n",
       "      <td>@mayasolovely: As a woman you shouldn't compl...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Bad</td>\n",
       "      <td>@mleew17: boy dats cold...tyga dwn bad for cu...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Bad</td>\n",
       "      <td>@UrKindOfBrand Dawg!!!! RT @80sbaby4life: You...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Bad</td>\n",
       "      <td>!!!!!!!!! RT @C_G_Anderson: @viva_based she lo...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Bad</td>\n",
       "      <td>@ShenikaRoberts: The shit you hear about me m...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24778</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Bad</td>\n",
       "      <td>you's a muthaf***in lie &amp;#8220;@LifeAsKing: @2...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24779</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Good</td>\n",
       "      <td>you've gone and broke the wrong heart baby, an...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24780</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Bad</td>\n",
       "      <td>young buck wanna eat!!.. dat nigguh like I ain...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24781</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Bad</td>\n",
       "      <td>youu got wild bitches tellin you lies</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24782</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Good</td>\n",
       "      <td>~~Ruffled | Ntac Eileen Dahlia - Beautiful col...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24783 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       hate_speech  offensive_language  neither class  \\\n",
       "0                0                   0        3  Good   \n",
       "1                0                   3        0   Bad   \n",
       "2                0                   3        0   Bad   \n",
       "3                0                   2        1   Bad   \n",
       "4                0                   6        0   Bad   \n",
       "...            ...                 ...      ...   ...   \n",
       "24778            0                   2        1   Bad   \n",
       "24779            0                   1        2  Good   \n",
       "24780            0                   3        0   Bad   \n",
       "24781            0                   6        0   Bad   \n",
       "24782            0                   0        3  Good   \n",
       "\n",
       "                                                   tweet  Bad  \n",
       "0       @mayasolovely: As a woman you shouldn't compl...    0  \n",
       "1       @mleew17: boy dats cold...tyga dwn bad for cu...    1  \n",
       "2       @UrKindOfBrand Dawg!!!! RT @80sbaby4life: You...    1  \n",
       "3      !!!!!!!!! RT @C_G_Anderson: @viva_based she lo...    1  \n",
       "4       @ShenikaRoberts: The shit you hear about me m...    1  \n",
       "...                                                  ...  ...  \n",
       "24778  you's a muthaf***in lie &#8220;@LifeAsKing: @2...    1  \n",
       "24779  you've gone and broke the wrong heart baby, an...    0  \n",
       "24780  young buck wanna eat!!.. dat nigguh like I ain...    1  \n",
       "24781              youu got wild bitches tellin you lies    1  \n",
       "24782  ~~Ruffled | Ntac Eileen Dahlia - Beautiful col...    0  \n",
       "\n",
       "[24783 rows x 6 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ee9df867",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['alpha', 'class_prior', 'fit_prior'])\n"
     ]
    }
   ],
   "source": [
    "print(model.get_params().keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ff166dc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=MultinomialNB(), n_jobs=-1,\n",
       "             param_grid={'alpha': [0.6], 'fit_prior': [False]},\n",
       "             scoring='roc_auc')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import PowerTransformer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import numpy as np\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "param_grid = {\n",
    "    \"alpha\" : [0.6] ,\n",
    "    \"fit_prior\" : [False]\n",
    "}\n",
    "grid_search = GridSearchCV(model,param_grid,n_jobs=-1,cv=10,scoring='roc_auc')\n",
    "grid_search.fit(x_train_count, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "53823b7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_scores = grid_search.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "86724da4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'mean_fit_time': array([0.02396731]), 'std_fit_time': array([0.00956069]), 'mean_score_time': array([0.00575616]), 'std_score_time': array([0.0017996]), 'param_alpha': masked_array(data=[0.6],\n",
      "             mask=[False],\n",
      "       fill_value='?',\n",
      "            dtype=object), 'param_fit_prior': masked_array(data=[False],\n",
      "             mask=[False],\n",
      "       fill_value='?',\n",
      "            dtype=object), 'params': [{'alpha': 0.6, 'fit_prior': False}], 'split0_test_score': array([0.96055968]), 'split1_test_score': array([0.960172]), 'split2_test_score': array([0.96670875]), 'split3_test_score': array([0.95440332]), 'split4_test_score': array([0.95891892]), 'split5_test_score': array([0.95328055]), 'split6_test_score': array([0.95259584]), 'split7_test_score': array([0.96668492]), 'split8_test_score': array([0.96554399]), 'split9_test_score': array([0.96090361]), 'mean_test_score': array([0.95997716]), 'std_test_score': array([0.00504033]), 'rank_test_score': array([1])}\n"
     ]
    }
   ],
   "source": [
    "print(grid_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "99bf833a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_alpha</th>\n",
       "      <th>param_fit_prior</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>split5_test_score</th>\n",
       "      <th>split6_test_score</th>\n",
       "      <th>split7_test_score</th>\n",
       "      <th>split8_test_score</th>\n",
       "      <th>split9_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.023967</td>\n",
       "      <td>0.009561</td>\n",
       "      <td>0.005756</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>0.6</td>\n",
       "      <td>False</td>\n",
       "      <td>{'alpha': 0.6, 'fit_prior': False}</td>\n",
       "      <td>0.96056</td>\n",
       "      <td>0.960172</td>\n",
       "      <td>0.966709</td>\n",
       "      <td>0.954403</td>\n",
       "      <td>0.958919</td>\n",
       "      <td>0.953281</td>\n",
       "      <td>0.952596</td>\n",
       "      <td>0.966685</td>\n",
       "      <td>0.965544</td>\n",
       "      <td>0.960904</td>\n",
       "      <td>0.959977</td>\n",
       "      <td>0.00504</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time param_alpha  \\\n",
       "0       0.023967      0.009561         0.005756          0.0018         0.6   \n",
       "\n",
       "  param_fit_prior                              params  split0_test_score  \\\n",
       "0           False  {'alpha': 0.6, 'fit_prior': False}            0.96056   \n",
       "\n",
       "   split1_test_score  split2_test_score  split3_test_score  split4_test_score  \\\n",
       "0           0.960172           0.966709           0.954403           0.958919   \n",
       "\n",
       "   split5_test_score  split6_test_score  split7_test_score  split8_test_score  \\\n",
       "0           0.953281           0.952596           0.966685           0.965544   \n",
       "\n",
       "   split9_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "0           0.960904         0.959977         0.00504                1  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(grid_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "618e9cb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.02396731]),\n",
       " 'std_fit_time': array([0.00956069]),\n",
       " 'mean_score_time': array([0.00575616]),\n",
       " 'std_score_time': array([0.0017996]),\n",
       " 'param_alpha': masked_array(data=[0.6],\n",
       "              mask=[False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_fit_prior': masked_array(data=[False],\n",
       "              mask=[False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'alpha': 0.6, 'fit_prior': False}],\n",
       " 'split0_test_score': array([0.96055968]),\n",
       " 'split1_test_score': array([0.960172]),\n",
       " 'split2_test_score': array([0.96670875]),\n",
       " 'split3_test_score': array([0.95440332]),\n",
       " 'split4_test_score': array([0.95891892]),\n",
       " 'split5_test_score': array([0.95328055]),\n",
       " 'split6_test_score': array([0.95259584]),\n",
       " 'split7_test_score': array([0.96668492]),\n",
       " 'split8_test_score': array([0.96554399]),\n",
       " 'split9_test_score': array([0.96090361]),\n",
       " 'mean_test_score': array([0.95997716]),\n",
       " 'std_test_score': array([0.00504033]),\n",
       " 'rank_test_score': array([1])}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "499b2845",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}