raceClassifier-ngrams1-2-no(punc-stop)-submit.ipynb

{
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    "<a href=\"https://colab.research.google.com/github/em-chiu/intersection_project/blob/main/EC%20Classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DSgFvHzhSPNK",
    "outputId": "fe6a4603-759f-483e-b192-e891f58f83e0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /Users/emilychiu/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to /Users/emilychiu/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     /Users/emilychiu/nltk_data...\n",
      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
      "[nltk_data]       date!\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('stopwords')\n",
    "nltk.download('punkt')\n",
    "import urllib.request\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import TweetTokenizer\n",
    "import re\n",
    "import numpy as np\n",
    "from collections import Counter\n",
    "from sklearn import preprocessing\n",
    "import string\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "import pandas as pd # data processing\n",
    "import csv\n",
    "import sys\n",
    "import unidecode\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the data to df, try1 works\n",
    "df = pd.read_csv('FTR_labels_copy.csv', header=None, encoding = \"ISO-8859-1\")\\\n",
    "        .drop(0, axis=1)\\\n",
    "        .rename(columns={1: 'tweet'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tweet</td>\n",
       "      <td>label</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Elle ne devait pas dÃ©gager la nazi ??? https:...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>je dois faire un oral en anglais sur Hitler et...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>@ambrassmoi_ A Ã§a ð¤ de ressembler Ã  la cr...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>\"Par curiositÃ©, jâai voulu Ã©couter les dis...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                               tweet      2\n",
       "0                                              tweet  label\n",
       "1  Elle ne devait pas dÃ©gager la nazi ??? https:...      1\n",
       "2  je dois faire un oral en anglais sur Hitler et...      0\n",
       "3  @ambrassmoi_ A Ã§a ð¤ de ressembler Ã  la cr...      0\n",
       "4  \"Par curiositÃ©, jâai voulu Ã©couter les dis...      0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop first row \n",
    "# by selecting all rows from first row onwards\n",
    "df = df.iloc[1: , :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2856, 2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view dimensions of dataset\n",
    "df.shape\n",
    "# 2857 instances and 2 attributes in dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet', 'label'], dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename column names\n",
    "col_names = ['tweet', 'label']\n",
    "df.columns = col_names\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Elle ne devait pas dÃ©gager la nazi ??? https:...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>je dois faire un oral en anglais sur Hitler et...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>@ambrassmoi_ A Ã§a ð¤ de ressembler Ã  la cr...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>\"Par curiositÃ©, jâai voulu Ã©couter les dis...</td>\n",
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       "    </tr>\n",
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       "      <th>5</th>\n",
       "      <td>@tical10 @ExBarcaFan74523 @ConflitsFrance Mdr ...</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "                                               tweet label\n",
       "1  Elle ne devait pas dÃ©gager la nazi ??? https:...     1\n",
       "2  je dois faire un oral en anglais sur Hitler et...     0\n",
       "3  @ambrassmoi_ A Ã§a ð¤ de ressembler Ã  la cr...     0\n",
       "4  \"Par curiositÃ©, jâai voulu Ã©couter les dis...     0\n",
       "5  @tical10 @ExBarcaFan74523 @ConflitsFrance Mdr ...     1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview after renaming\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2856 entries, 1 to 2856\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   tweet   2856 non-null   object\n",
      " 1   label   2856 non-null   object\n",
      "dtypes: object(2)\n",
      "memory usage: 44.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# summary of data\n",
    "df.info()\n",
    "# no missing data\n",
    "# Categorical variables have data type object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2 categorical variables\n",
      "\n",
      "The categorical variables are :\n",
      "\n",
      " ['tweet', 'label']\n"
     ]
    }
   ],
   "source": [
    "# find categorical variables\n",
    "categorical = [var for var in df.columns if df[var].dtype=='O']\n",
    "\n",
    "print('There are {} categorical variables\\n'.format(len(categorical)))\n",
    "\n",
    "print('The categorical variables are :\\n\\n', categorical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Elle ne devait pas dÃ©gager la nazi ??? https:...</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>je dois faire un oral en anglais sur Hitler et...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>@ambrassmoi_ A Ã§a ð¤ de ressembler Ã  la cr...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>\"Par curiositÃ©, jâai voulu Ã©couter les dis...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>@tical10 @ExBarcaFan74523 @ConflitsFrance Mdr ...</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "                                               tweet label\n",
       "1  Elle ne devait pas dÃ©gager la nazi ??? https:...     1\n",
       "2  je dois faire un oral en anglais sur Hitler et...     0\n",
       "3  @ambrassmoi_ A Ã§a ð¤ de ressembler Ã  la cr...     0\n",
       "4  \"Par curiositÃ©, jâai voulu Ã©couter les dis...     0\n",
       "5  @tical10 @ExBarcaFan74523 @ConflitsFrance Mdr ...     1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view the categorical variables\n",
    "df[categorical].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet    0\n",
       "label    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check missing values in categorical variables\n",
    "df[categorical].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"0. Moi\\n                                                                                                                                                    4\n",
      "@flo__lfc @leo2saucisson @ActuFoot_ CrÃ¨ve                                                                                                                   2\n",
      "\"ððððð\\n                                                                                                                                      2\n",
      "\"\"\"A Bright Room Called Day\"\", la cinglante leÃ§on d'histoire de Tony Kushner. Ma critique dans Les SoirÃ©es de Paris â¶ï¸Â https://t.co/Ydl1PqJv7AÂ \\n    2\n",
      "\"En ce moment sur LFM #CrÃ¨ve-cÅur#Mauvais Åil et toujours Le Morning sur LFM de 07h Ã  10h du Lundi au Vendredi.\\n                                        2\n",
      "                                                                                                                                                            ..\n",
      "de toutes faÃ§ons, c'est une mascarade. personne n'est mort et le masque sert Ã  rien #nazi #GiletsJaunes                                                    1\n",
      "Ah je crÃ¨ve de froid                                                                                                                                        1\n",
      "@Weatherboy_fr @ButchCoolidge11 @DamienRieu Bah oui câest les franÃ§ais qui ont Ã©lus hitler câest connuâ¦ gÃ©nie !                                     1\n",
      "@mormach Pitoyable cette information dirigÃ©e !!! On en crÃ¨ve !!!                                                                                           1\n",
      "Sal Noir https://t.co/9Sr7f4Mzr5 https://t.co/53F9rBpJsf                                                                                                     1\n",
      "Name: tweet, Length: 2847, dtype: int64\n",
      "0    1929\n",
      "1     927\n",
      "Name: label, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# view frequency counts of values in categorical variables\n",
    "for var in categorical: \n",
    "    print(df[var].value_counts())\n",
    "\n",
    "# 0        1929\n",
    "# 1         927"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"0. Moi\\n                                                                                                                                                    0.001401\n",
      "@flo__lfc @leo2saucisson @ActuFoot_ CrÃ¨ve                                                                                                                   0.000700\n",
      "\"ððððð\\n                                                                                                                                      0.000700\n",
      "\"\"\"A Bright Room Called Day\"\", la cinglante leÃ§on d'histoire de Tony Kushner. Ma critique dans Les SoirÃ©es de Paris â¶ï¸Â https://t.co/Ydl1PqJv7AÂ \\n    0.000700\n",
      "\"En ce moment sur LFM #CrÃ¨ve-cÅur#Mauvais Åil et toujours Le Morning sur LFM de 07h Ã  10h du Lundi au Vendredi.\\n                                        0.000700\n",
      "                                                                                                                                                               ...   \n",
      "de toutes faÃ§ons, c'est une mascarade. personne n'est mort et le masque sert Ã  rien #nazi #GiletsJaunes                                                    0.000350\n",
      "Ah je crÃ¨ve de froid                                                                                                                                        0.000350\n",
      "@Weatherboy_fr @ButchCoolidge11 @DamienRieu Bah oui câest les franÃ§ais qui ont Ã©lus hitler câest connuâ¦ gÃ©nie !                                     0.000350\n",
      "@mormach Pitoyable cette information dirigÃ©e !!! On en crÃ¨ve !!!                                                                                           0.000350\n",
      "Sal Noir https://t.co/9Sr7f4Mzr5 https://t.co/53F9rBpJsf                                                                                                     0.000350\n",
      "Name: tweet, Length: 2847, dtype: float64\n",
      "0    0.67542\n",
      "1    0.32458\n",
      "Name: label, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# view frequency distribution of categorical variables\n",
    "for var in categorical:   \n",
    "    print(df[var].value_counts()/float(len(df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1929\n",
       "1     927\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # check frequency distribution of values in label variable\n",
    "df.label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1       elle ne devait pas dã©gager la nazi ??? https:...\n",
       "2       je dois faire un oral en anglais sur hitler et...\n",
       "3       @ambrassmoi_ a ã§a ð¤ de ressembler ã  la cr...\n",
       "4       \"par curiositã©, jâai voulu ã©couter les dis...\n",
       "5       @tical10 @exbarcafan74523 @conflitsfrance mdr ...\n",
       "                              ...                        \n",
       "2852                           kevin câest un sal noir.\n",
       "2853    bougnoule bamboula sale arabe , elle est belle...\n",
       "2854      @b_mahrezz tu viens de le traiter de sal noir ?\n",
       "2855                     sal noir https://t.co/jraabftel8\n",
       "2856    sal noir https://t.co/9sr7f4mzr5 https://t.co/...\n",
       "Name: lowered_tweet, Length: 2856, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lowercase all words\n",
    "def make_lower(a_string):\n",
    "    return a_string.lower()\n",
    "\n",
    "df['lowered_tweet'] = df['tweet'].apply(make_lower)\n",
    "df['lowered_tweet']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet</th>\n",
       "      <th>label</th>\n",
       "      <th>lowered_tweet</th>\n",
       "      <th>tokenized_tweet</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0</td>\n",
       "      <td>je dois faire un oral en anglais sur hitler et...</td>\n",
       "      <td>[je, dois, faire, un, oral, en, anglais, sur, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>@ambrassmoi_ A Ã§a ð¤ de ressembler Ã  la cr...</td>\n",
       "      <td>0</td>\n",
       "      <td>@ambrassmoi_ a ã§a ð¤ de ressembler ã  la cr...</td>\n",
       "      <td>[@ambrassmoi_, a, ã, §, a, ð, , ¤, , de, res...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>\"Par curiositÃ©, jâai voulu Ã©couter les dis...</td>\n",
       "      <td>0</td>\n",
       "      <td>\"par curiositã©, jâai voulu ã©couter les dis...</td>\n",
       "      <td>[\", par, curiositã, ©, ,, jâ, , , ai, voulu,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>@tical10 @ExBarcaFan74523 @ConflitsFrance Mdr ...</td>\n",
       "      <td>1</td>\n",
       "      <td>@tical10 @exbarcafan74523 @conflitsfrance mdr ...</td>\n",
       "      <td>[@tical10, @exbarcafan74523, @conflitsfrance, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               tweet label  \\\n",
       "1  Elle ne devait pas dÃ©gager la nazi ??? https:...     1   \n",
       "2  je dois faire un oral en anglais sur Hitler et...     0   \n",
       "3  @ambrassmoi_ A Ã§a ð¤ de ressembler Ã  la cr...     0   \n",
       "4  \"Par curiositÃ©, jâai voulu Ã©couter les dis...     0   \n",
       "5  @tical10 @ExBarcaFan74523 @ConflitsFrance Mdr ...     1   \n",
       "\n",
       "                                       lowered_tweet  \\\n",
       "1  elle ne devait pas dã©gager la nazi ??? https:...   \n",
       "2  je dois faire un oral en anglais sur hitler et...   \n",
       "3  @ambrassmoi_ a ã§a ð¤ de ressembler ã  la cr...   \n",
       "4  \"par curiositã©, jâai voulu ã©couter les dis...   \n",
       "5  @tical10 @exbarcafan74523 @conflitsfrance mdr ...   \n",
       "\n",
       "                                     tokenized_tweet  \n",
       "1  [elle, ne, devait, pas, dã, ©, gager, la, nazi...  \n",
       "2  [je, dois, faire, un, oral, en, anglais, sur, ...  \n",
       "3  [@ambrassmoi_, a, ã, §, a, ð, , ¤, , de, res...  \n",
       "4  [\", par, curiositã, ©, ,, jâ, , , ai, voulu,...  \n",
       "5  [@tical10, @exbarcafan74523, @conflitsfrance, ...  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer = nltk.TweetTokenizer()\n",
    "# keeps hashtags together, subset of word_tokenize\n",
    "\n",
    "df['tokenized_tweet'] = df['lowered_tweet'].apply(tokenizer.tokenize)\n",
    "df.head()\n",
    "\n",
    "# https://stackoverflow.com/questions/48363461/passing-a-pandas-dataframe-column-to-an-nltk-tokenizer\n",
    "#df['tweet'] = df['tweet'].apply(nltk.TweetTokenizer)\n",
    "# this overwrote what i did"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rejoin_string(a_list):\n",
    "    return ' '.join(a_list)\n",
    "\n",
    "\n",
    "df['tokenized_tweet_joined'] = df['tokenized_tweet'].apply(rejoin_string)\n",
    "# rejoins into string, that's been tokenized\n",
    "# holds spaces between tokens but now altogether"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>tweet</th>\n",
       "      <th>label</th>\n",
       "      <th>lowered_tweet</th>\n",
       "      <th>tokenized_tweet</th>\n",
       "      <th>tokenized_tweet_joined</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Elle ne devait pas dÃ©gager la nazi ??? https:...</td>\n",
       "      <td>1</td>\n",
       "      <td>elle ne devait pas dã©gager la nazi ??? https:...</td>\n",
       "      <td>[elle, ne, devait, pas, dã, ©, gager, la, nazi...</td>\n",
       "      <td>elle ne devait pas dã © gager la nazi ? ? ? ht...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>je dois faire un oral en anglais sur Hitler et...</td>\n",
       "      <td>0</td>\n",
       "      <td>je dois faire un oral en anglais sur hitler et...</td>\n",
       "      <td>[je, dois, faire, un, oral, en, anglais, sur, ...</td>\n",
       "      <td>je dois faire un oral en anglais sur hitler et...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>@ambrassmoi_ A Ã§a ð¤ de ressembler Ã  la cr...</td>\n",
       "      <td>0</td>\n",
       "      <td>@ambrassmoi_ a ã§a ð¤ de ressembler ã  la cr...</td>\n",
       "      <td>[@ambrassmoi_, a, ã, §, a, ð, , ¤, , de, res...</td>\n",
       "      <td>@ambrassmoi_ a ã § a ð  ¤  de ressembler ã l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>\"Par curiositÃ©, jâai voulu Ã©couter les dis...</td>\n",
       "      <td>0</td>\n",
       "      <td>\"par curiositã©, jâai voulu ã©couter les dis...</td>\n",
       "      <td>[\", par, curiositã, ©, ,, jâ, , , ai, voulu,...</td>\n",
       "      <td>\" par curiositã © , jâ   ai voulu ã © couter...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>@tical10 @ExBarcaFan74523 @ConflitsFrance Mdr ...</td>\n",
       "      <td>1</td>\n",
       "      <td>@tical10 @exbarcafan74523 @conflitsfrance mdr ...</td>\n",
       "      <td>[@tical10, @exbarcafan74523, @conflitsfrance, ...</td>\n",
       "      <td>@tical10 @exbarcafan74523 @conflitsfrance mdr ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               tweet label  \\\n",
       "1  Elle ne devait pas dÃ©gager la nazi ??? https:...     1   \n",
       "2  je dois faire un oral en anglais sur Hitler et...     0   \n",
       "3  @ambrassmoi_ A Ã§a ð¤ de ressembler Ã  la cr...     0   \n",
       "4  \"Par curiositÃ©, jâai voulu Ã©couter les dis...     0   \n",
       "5  @tical10 @ExBarcaFan74523 @ConflitsFrance Mdr ...     1   \n",
       "\n",
       "                                       lowered_tweet  \\\n",
       "1  elle ne devait pas dã©gager la nazi ??? https:...   \n",
       "2  je dois faire un oral en anglais sur hitler et...   \n",
       "3  @ambrassmoi_ a ã§a ð¤ de ressembler ã  la cr...   \n",
       "4  \"par curiositã©, jâai voulu ã©couter les dis...   \n",
       "5  @tical10 @exbarcafan74523 @conflitsfrance mdr ...   \n",
       "\n",
       "                                     tokenized_tweet  \\\n",
       "1  [elle, ne, devait, pas, dã, ©, gager, la, nazi...   \n",
       "2  [je, dois, faire, un, oral, en, anglais, sur, ...   \n",
       "3  [@ambrassmoi_, a, ã, §, a, ð, , ¤, , de, res...   \n",
       "4  [\", par, curiositã, ©, ,, jâ, , , ai, voulu,...   \n",
       "5  [@tical10, @exbarcafan74523, @conflitsfrance, ...   \n",
       "\n",
       "                              tokenized_tweet_joined  \n",
       "1  elle ne devait pas dã © gager la nazi ? ? ? ht...  \n",
       "2  je dois faire un oral en anglais sur hitler et...  \n",
       "3  @ambrassmoi_ a ã § a ð  ¤  de ressembler ã l...  \n",
       "4  \" par curiositã © , jâ   ai voulu ã © couter...  \n",
       "5  @tical10 @exbarcafan74523 @conflitsfrance mdr ...  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['tweet'] = df['tweet'].astype(pd.StringDtype())\n",
    "\n",
    "# df['tokenized_tweet'] = df['tokenized_tweet'].astype(str)\n",
    "# must feed str into vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df['tokenized_tweet_joined'] # list of tokenized tweets\n",
    "y = df['label'] # list of labels of tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split X and y into training and testing sets\n",
    "# randomizes data in splitting\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
    "# indicating random state ensures data split will be same\n",
    "#https://stackoverflow.com/questions/28064634/random-state-pseudo-random-number-in-scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2284,), (572,))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the shape of X_train and X_test\n",
    "\n",
    "X_train.shape, X_test.shape\n",
    "# ((2284, 10), (572, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2284,), (572,))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape, y_test.shape\n",
    "# ((2284,), (572,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check data types in X_train\n",
    "\n",
    "X_train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "# Initialize our vectorizer (featurizer, turning text to numbers)\n",
    "#only looking at numbers produced, creating features\n",
    "# takes each tweet turning into feature vector, each column corresponds to word\n",
    "vectorizer = TfidfVectorizer(ngram_range=(1,2)) \n",
    "# range of n-grams in the final matrix as new features\n",
    "\n",
    "# fit vectorizer w/ train data (training data)\n",
    "# This makes your vocab matrix, learns words in vocab\n",
    "vectorizer.fit(X_train)\n",
    "\n",
    "# This transforms your documents into vectors (numbers).\n",
    "X_train = vectorizer.transform(X_train)\n",
    "X_test = vectorizer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to test and evaluate models. \n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score, f1_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# For plotting\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def build_and_eval_model(model, X, y):\n",
    "    print(\"Running report for model:\", model) \n",
    "    \n",
    "    # Fit our model.\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    \n",
    "    # predicting results based on testing data\n",
    "    y_pred = model.predict(X_test)\n",
    "    # Make predicted probabilites of our testing data\n",
    "#     y_pred_proba = model.predict_proba(X_test)\n",
    "# https://stackoverflow.com/questions/15111408/how-does-sklearn-svm-svcs-function-predict-proba-work-internally\n",
    "\n",
    "    # Get the evaluation metrics \n",
    "    print('Model accuracy score: {0:0.4f}'. format(accuracy_score(y_test, y_pred)))\n",
    "    # y_test are true class labels and y_pred are predicted class labels in test-set, don't use train data\n",
    "    \n",
    "    # f1-score is always lower than accuracy measures, they embed precision and recall into their computation\n",
    "#     print('F1 Score: %.4f' % f1_score(y_test, y_pred, pos_label='0'))\n",
    "    # https://datascience.stackexchange.com/questions/54436/valueerror-pos-label-1-is-not-a-valid-label-arrayn-y-dtype-u1\n",
    "    # data not much, low f1score; cross fold validation to generate data articificially\n",
    "    f1score = f1_score(y_test, y_pred, average='macro')\n",
    "    print('macro F-1 score : {}'.format(np.round(f1score,5)))\n",
    "    # macro avg f1score = unweighted mean of all the per-class F1 scores, treats all classes equally important\n",
    "    # weighted-averaged F1 score is calculated by taking the mean of all per-class F1 scores while considering each class’s support\n",
    "    ## ‘weight’ refers to proportion of each class’s support relative to the sum of all support values\n",
    "    # https://towardsdatascience.com/micro-macro-weighted-averages-of-f1-score-clearly-explained-b603420b292f\n",
    "    \n",
    "    # compare the train-set and test-set accuracy to check for overfitting \n",
    "    y_pred_train = model.predict(X_train) \n",
    "    # overlearned training data = training accuracy higher (but not over so b/c not 99)\n",
    "    ## close enough to \"possibly\" overfit but not dramatic difference --> trust model accuracy score (minor point)\n",
    "    print('Training set accuracy score: {0:0.4f}'. format(accuracy_score(y_train, y_pred_train)))\n",
    "    print('Test set accuracy score: {:.4f}'.format(model.score(X_test, y_test)))\n",
    "\n",
    "    # Print the classification report. \n",
    "    print(metrics.classification_report(y_test, y_pred))\n",
    "    \n",
    "        \n",
    "    # Plot the confusion matrix\n",
    "    fig, ax = plt.subplots(figsize=(13,13))\n",
    "    \n",
    "    metrics.plot_confusion_matrix(estimator=model, \n",
    "                                  X=X_test, \n",
    "                                  y_true=y_test, \n",
    "                                  ax=ax)\n",
    "    \n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    print('Confusion matrix\\n\\n', cm)\n",
    "    print('\\nTrue Positives(TP) = ', cm[0,0])\n",
    "    print('\\nTrue Negatives(TN) = ', cm[1,1])\n",
    "    print('\\nFalse Positives(FP) = ', cm[0,1])\n",
    "    print('\\nFalse Negatives(FN) = ', cm[1,0])\n",
    "\n",
    "#     # visualize confusion matrix with seaborn heatmap\n",
    "#     cm_matrix = pd.DataFrame(data=cm, columns=['Actual Positive:1', 'Actual Negative:0'], \n",
    "#                                  index=['Predict Positive:1', 'Predict Negative:0'])\n",
    "#     sns.heatmap(cm_matrix, annot=True, fmt='d', cmap='YlGnBu')\n",
    "    \n",
    "    \n",
    "    # print classification accuracy, same as test set score & model accuracy score\n",
    "    classification_accuracy = (cm[0,0] + cm[1,1]) / float(cm[0,0] + cm[1,1] + cm[0,1] + cm[1,0])\n",
    "    print('Classification accuracy : {0:0.4f}'.format(classification_accuracy))\n",
    "    \n",
    "    # print classification error\n",
    "    classification_error = (cm[0,1] + cm[1,0]) / float(cm[0,0] + cm[1,1] + cm[0,1] + cm[1,0])\n",
    "    print('Classification error : {0:0.4f}'.format(classification_error))\n",
    "\n",
    "    # Return the fitted model. \n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running report for model: LogisticRegression()\n",
      "Model accuracy score: 0.7762\n",
      "macro F-1 score : 0.69286\n",
      "Training set accuracy score: 0.8546\n",
      "Test set accuracy score: 0.7762\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.95      0.85       390\n",
      "           1       0.79      0.40      0.53       182\n",
      "\n",
      "    accuracy                           0.78       572\n",
      "   macro avg       0.78      0.68      0.69       572\n",
      "weighted avg       0.78      0.78      0.75       572\n",
      "\n",
      "Confusion matrix\n",
      "\n",
      " [[371  19]\n",
      " [109  73]]\n",
      "\n",
      "True Positives(TP) =  371\n",
      "\n",
      "True Negatives(TN) =  73\n",
      "\n",
      "False Positives(FP) =  19\n",
      "\n",
      "False Negatives(FN) =  109\n",
      "Classification accuracy : 0.7762\n",
      "Classification error : 0.2238\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr = LogisticRegression()\n",
    "build_and_eval_model(lr, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running report for model: LinearSVC()\n",
      "Model accuracy score: 0.7902\n",
      "macro F-1 score : 0.74629\n",
      "Training set accuracy score: 0.9996\n",
      "Test set accuracy score: 0.7902\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.88      0.85       390\n",
      "           1       0.70      0.59      0.64       182\n",
      "\n",
      "    accuracy                           0.79       572\n",
      "   macro avg       0.76      0.74      0.75       572\n",
      "weighted avg       0.78      0.79      0.78       572\n",
      "\n",
      "Confusion matrix\n",
      "\n",
      " [[345  45]\n",
      " [ 75 107]]\n",
      "\n",
      "True Positives(TP) =  345\n",
      "\n",
      "True Negatives(TN) =  107\n",
      "\n",
      "False Positives(FP) =  45\n",
      "\n",
      "False Negatives(FN) =  75\n",
      "Classification accuracy : 0.7902\n",
      "Classification error : 0.2098\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearSVC()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "lsvc = svm.LinearSVC()\n",
    "build_and_eval_model(lsvc, X, y)\n",
    "# class is imbalanced but not a lot of data is impacting 0 detection better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running report for model: MultinomialNB(alpha=0.05)\n",
      "Model accuracy score: 0.7605\n",
      "macro F-1 score : 0.68572\n",
      "Training set accuracy score: 0.9987\n",
      "Test set accuracy score: 0.7605\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.92      0.84       390\n",
      "           1       0.70      0.43      0.53       182\n",
      "\n",
      "    accuracy                           0.76       572\n",
      "   macro avg       0.74      0.67      0.69       572\n",
      "weighted avg       0.75      0.76      0.74       572\n",
      "\n",
      "Confusion matrix\n",
      "\n",
      " [[357  33]\n",
      " [104  78]]\n",
      "\n",
      "True Positives(TP) =  357\n",
      "\n",
      "True Negatives(TN) =  78\n",
      "\n",
      "False Positives(FP) =  33\n",
      "\n",
      "False Negatives(FN) =  104\n",
      "Classification accuracy : 0.7605\n",
      "Classification error : 0.2395\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MultinomialNB(alpha=0.05)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mnb = MultinomialNB(alpha=.05)\n",
    "build_and_eval_model(mnb, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running report for model: SVC()\n",
      "Model accuracy score: 0.7605\n",
      "macro F-1 score : 0.66534\n",
      "Training set accuracy score: 0.9978\n",
      "Test set accuracy score: 0.7605\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.95      0.84       390\n",
      "           1       0.76      0.36      0.49       182\n",
      "\n",
      "    accuracy                           0.76       572\n",
      "   macro avg       0.76      0.65      0.67       572\n",
      "weighted avg       0.76      0.76      0.73       572\n",
      "\n",
      "Confusion matrix\n",
      "\n",
      " [[370  20]\n",
      " [117  65]]\n",
      "\n",
      "True Positives(TP) =  370\n",
      "\n",
      "True Negatives(TN) =  65\n",
      "\n",
      "False Positives(FP) =  20\n",
      "\n",
      "False Negatives(FN) =  117\n",
      "Classification accuracy : 0.7605\n",
      "Classification error : 0.2395\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "svc = svm.SVC()\n",
    "build_and_eval_model(svc, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running report for model: RandomForestClassifier()\n",
      "Model accuracy score: 0.7710\n",
      "macro F-1 score : 0.69675\n",
      "Training set accuracy score: 0.9996\n",
      "Test set accuracy score: 0.7710\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.93      0.85       390\n",
      "           1       0.74      0.43      0.55       182\n",
      "\n",
      "    accuracy                           0.77       572\n",
      "   macro avg       0.76      0.68      0.70       572\n",
      "weighted avg       0.77      0.77      0.75       572\n",
      "\n",
      "Confusion matrix\n",
      "\n",
      " [[362  28]\n",
      " [103  79]]\n",
      "\n",
      "True Positives(TP) =  362\n",
      "\n",
      "True Negatives(TN) =  79\n",
      "\n",
      "False Positives(FP) =  28\n",
      "\n",
      "False Negatives(FN) =  103\n",
      "Classification accuracy : 0.7710\n",
      "Classification error : 0.2290\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier()\n",
    "\n",
    "build_and_eval_model(rf, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running report for model: DecisionTreeClassifier(random_state=0)\n",
      "Model accuracy score: 0.6976\n",
      "macro F-1 score : 0.65778\n",
      "Training set accuracy score: 0.9996\n",
      "Test set accuracy score: 0.6976\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.76      0.77       390\n",
      "           1       0.52      0.56      0.54       182\n",
      "\n",
      "    accuracy                           0.70       572\n",
      "   macro avg       0.66      0.66      0.66       572\n",
      "weighted avg       0.70      0.70      0.70       572\n",
      "\n",
      "Confusion matrix\n",
      "\n",
      " [[297  93]\n",
      " [ 80 102]]\n",
      "\n",
      "True Positives(TP) =  297\n",
      "\n",
      "True Negatives(TN) =  102\n",
      "\n",
      "False Positives(FP) =  93\n",
      "\n",
      "False Negatives(FN) =  80\n",
      "Classification accuracy : 0.6976\n",
      "Classification error : 0.3024\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(random_state=0)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt = DecisionTreeClassifier(random_state=0)\n",
    "build_and_eval_model(dt, X, y)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "include_colab_link": true,
   "provenance": []
  },
  "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}