spam-email-and-sms-classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "24fe3cc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "62d83021",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'encoding': 'Windows-1252', 'confidence': 0.7270322499829184, 'language': ''}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import chardet\n",
    "with open(\"spam.csv\", 'rb') as rawdata:\n",
    "    result = chardet.detect(rawdata.read(100000))\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "518611c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"spam.csv\", encoding=\"Windows-1252\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0d93a09f",
   "metadata": {},
   "outputs": [
    {
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       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     v1                                                 v2 Unnamed: 2  \\\n",
       "0   ham  Go until jurong point, crazy.. Available only ...        NaN   \n",
       "1   ham                      Ok lar... Joking wif u oni...        NaN   \n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...        NaN   \n",
       "3   ham  U dun say so early hor... U c already then say...        NaN   \n",
       "4   ham  Nah I don't think he goes to usf, he lives aro...        NaN   \n",
       "\n",
       "  Unnamed: 3 Unnamed: 4  \n",
       "0        NaN        NaN  \n",
       "1        NaN        NaN  \n",
       "2        NaN        NaN  \n",
       "3        NaN        NaN  \n",
       "4        NaN        NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6b9cea56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d286717",
   "metadata": {},
   "source": [
    "# Steps\n",
    "######  1. Data Cleaning\n",
    "######  2. EDA\n",
    "######  3. Text Preprocessing\n",
    "######  4. Model Building\n",
    "######  5. Evaluation\n",
    "######  6. Improvement"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "581f1d23",
   "metadata": {},
   "source": [
    "# 1. Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "efe77fb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=[\"Unnamed: 2\", \"Unnamed: 3\", \"Unnamed: 4\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "89954f16",
   "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>v1</th>\n",
       "      <th>v2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3481</th>\n",
       "      <td>spam</td>\n",
       "      <td>Do you want a NEW video phone750 anytime any n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3937</th>\n",
       "      <td>ham</td>\n",
       "      <td>WHEN THE FIRST STRIKE IS A RED ONE. THE BIRD +...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>ham</td>\n",
       "      <td>Dear reached railway. What happen to you</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3864</th>\n",
       "      <td>ham</td>\n",
       "      <td>THATåÕS ALRITE GIRL, U KNOW GAIL IS NEVA WRONG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3836</th>\n",
       "      <td>ham</td>\n",
       "      <td>Early bird! Any purchases yet?</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        v1                                                 v2\n",
       "3481  spam  Do you want a NEW video phone750 anytime any n...\n",
       "3937   ham  WHEN THE FIRST STRIKE IS A RED ONE. THE BIRD +...\n",
       "487    ham           Dear reached railway. What happen to you\n",
       "3864   ham  THATåÕS ALRITE GIRL, U KNOW GAIL IS NEVA WRONG...\n",
       "3836   ham                     Early bird! Any purchases yet?"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c1c3920a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={\"v1\": \"target\", \"v2\": \"text\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "611438e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  target                                               text\n",
       "0    ham  Go until jurong point, crazy.. Available only ...\n",
       "1    ham                      Ok lar... Joking wif u oni...\n",
       "2   spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3    ham  U dun say so early hor... U c already then say...\n",
       "4    ham  Nah I don't think he goes to usf, he lives aro..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8d4e46a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()\n",
    "encoder.fit_transform(df[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "557a5430",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"target\"] = encoder.fit_transform(df[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f9f8d0ed",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target                                               text\n",
       "0       0  Go until jurong point, crazy.. Available only ...\n",
       "1       0                      Ok lar... Joking wif u oni...\n",
       "2       1  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3       0  U dun say so early hor... U c already then say...\n",
       "4       0  Nah I don't think he goes to usf, he lives aro..."
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3a155314",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target    0\n",
       "text      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check null values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3d6fbda1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "403"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check duplicated values\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2b96ea04",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop_duplicates(keep=\"first\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7842a669",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4ebfebd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5169, 2)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7ba17c1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target                                               text\n",
       "0       0  Go until jurong point, crazy.. Available only ...\n",
       "1       0                      Ok lar... Joking wif u oni...\n",
       "2       1  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3       0  U dun say so early hor... U c already then say...\n",
       "4       0  Nah I don't think he goes to usf, he lives aro..."
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2af921d",
   "metadata": {},
   "source": [
    "# 2. EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "174bd6e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4516\n",
       "1     653\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"target\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4d3ae1b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "ax.pie(df[\"target\"].value_counts(), labels=[\"Not Spam\", \"Spam\"], autopct=\"%0.2f\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0552bb4c",
   "metadata": {},
   "source": [
    "Data is Imbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f0227d9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "beeec798",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\user\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "32b541e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       111\n",
       "1        29\n",
       "2       155\n",
       "3        49\n",
       "4        61\n",
       "       ... \n",
       "5567    161\n",
       "5568     37\n",
       "5569     57\n",
       "5570    125\n",
       "5571     26\n",
       "Name: text, Length: 5169, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"text\"].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4753e44a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"num_characters\"] = df[\"text\"].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fcdce895",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       [Go, until, jurong, point, ,, crazy, .., Avail...\n",
       "1                [Ok, lar, ..., Joking, wif, u, oni, ...]\n",
       "2       [Free, entry, in, 2, a, wkly, comp, to, win, F...\n",
       "3       [U, dun, say, so, early, hor, ..., U, c, alrea...\n",
       "4       [Nah, I, do, n't, think, he, goes, to, usf, ,,...\n",
       "                              ...                        \n",
       "5567    [This, is, the, 2nd, time, we, have, tried, 2,...\n",
       "5568     [Will, Ì_, b, going, to, esplanade, fr, home, ?]\n",
       "5569    [Pity, ,, *, was, in, mood, for, that, ., So, ...\n",
       "5570    [The, guy, did, some, bitching, but, I, acted,...\n",
       "5571                  [Rofl, ., Its, true, to, its, name]\n",
       "Name: text, Length: 5169, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"text\"].apply(lambda x:nltk.word_tokenize(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "681509fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"num_words\"] = df[\"text\"].apply(lambda x:len(nltk.word_tokenize(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2677749e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       [Go until jurong point, crazy.., Available onl...\n",
       "1                        [Ok lar..., Joking wif u oni...]\n",
       "2       [Free entry in 2 a wkly comp to win FA Cup fin...\n",
       "3       [U dun say so early hor... U c already then sa...\n",
       "4       [Nah I don't think he goes to usf, he lives ar...\n",
       "                              ...                        \n",
       "5567    [This is the 2nd time we have tried 2 contact ...\n",
       "5568              [Will Ì_ b going to esplanade fr home?]\n",
       "5569    [Pity, * was in mood for that., So...any other...\n",
       "5570    [The guy did some bitching but I acted like i'...\n",
       "5571                        [Rofl., Its true to its name]\n",
       "Name: text, Length: 5169, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"text\"].apply(lambda x:nltk.sent_tokenize(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d2877601",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"num_sentences\"] = df[\"text\"].apply(lambda x:len(nltk.sent_tokenize(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b601b8eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>target</th>\n",
       "      <th>text</th>\n",
       "      <th>num_characters</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_sentences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>111</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>155</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>49</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>61</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target                                               text  num_characters  \\\n",
       "0       0  Go until jurong point, crazy.. Available only ...             111   \n",
       "1       0                      Ok lar... Joking wif u oni...              29   \n",
       "2       1  Free entry in 2 a wkly comp to win FA Cup fina...             155   \n",
       "3       0  U dun say so early hor... U c already then say...              49   \n",
       "4       0  Nah I don't think he goes to usf, he lives aro...              61   \n",
       "\n",
       "   num_words  num_sentences  \n",
       "0         24              2  \n",
       "1          8              2  \n",
       "2         37              2  \n",
       "3         13              1  \n",
       "4         15              1  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "782bac20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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>num_characters</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_sentences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5169.000000</td>\n",
       "      <td>5169.000000</td>\n",
       "      <td>5169.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>78.977945</td>\n",
       "      <td>18.453279</td>\n",
       "      <td>1.947185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>58.236293</td>\n",
       "      <td>13.324793</td>\n",
       "      <td>1.362406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>117.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>910.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_characters    num_words  num_sentences\n",
       "count     5169.000000  5169.000000    5169.000000\n",
       "mean        78.977945    18.453279       1.947185\n",
       "std         58.236293    13.324793       1.362406\n",
       "min          2.000000     1.000000       1.000000\n",
       "25%         36.000000     9.000000       1.000000\n",
       "50%         60.000000    15.000000       1.000000\n",
       "75%        117.000000    26.000000       2.000000\n",
       "max        910.000000   220.000000      28.000000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df[[\"num_characters\", \"num_words\", \"num_sentences\"]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e21e9df6",
   "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>num_characters</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_sentences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4516.000000</td>\n",
       "      <td>4516.000000</td>\n",
       "      <td>4516.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>70.459256</td>\n",
       "      <td>17.120903</td>\n",
       "      <td>1.799601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>56.358207</td>\n",
       "      <td>13.493725</td>\n",
       "      <td>1.278465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>52.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>910.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_characters    num_words  num_sentences\n",
       "count     4516.000000  4516.000000    4516.000000\n",
       "mean        70.459256    17.120903       1.799601\n",
       "std         56.358207    13.493725       1.278465\n",
       "min          2.000000     1.000000       1.000000\n",
       "25%         34.000000     8.000000       1.000000\n",
       "50%         52.000000    13.000000       1.000000\n",
       "75%         90.000000    22.000000       2.000000\n",
       "max        910.000000   220.000000      28.000000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ham \n",
    "df[df[\"target\"] == 0][[\"num_characters\", \"num_words\", \"num_sentences\"]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2fc9d2f9",
   "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>num_characters</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_sentences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>653.000000</td>\n",
       "      <td>653.000000</td>\n",
       "      <td>653.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>137.891271</td>\n",
       "      <td>27.667688</td>\n",
       "      <td>2.967841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>30.137753</td>\n",
       "      <td>7.008418</td>\n",
       "      <td>1.483201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>132.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>149.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>157.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>224.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_characters   num_words  num_sentences\n",
       "count      653.000000  653.000000     653.000000\n",
       "mean       137.891271   27.667688       2.967841\n",
       "std         30.137753    7.008418       1.483201\n",
       "min         13.000000    2.000000       1.000000\n",
       "25%        132.000000   25.000000       2.000000\n",
       "50%        149.000000   29.000000       3.000000\n",
       "75%        157.000000   32.000000       4.000000\n",
       "max        224.000000   46.000000       8.000000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# spam \n",
    "df[df[\"target\"] == 1][[\"num_characters\", \"num_words\", \"num_sentences\"]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "36ffb13d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(df[df[\"target\"] == 0][\"num_characters\"])\n",
    "sns.histplot(df[df[\"target\"] == 1][\"num_characters\"], color=\"red\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "418fead4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(df[df[\"target\"] == 0][\"num_words\"])\n",
    "sns.histplot(df[df[\"target\"] == 1][\"num_words\"], color=\"red\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7029c967",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 808.875x750 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df, hue=\"target\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cac5a069",
   "metadata": {},
   "source": [
    "Data has outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "11c9af50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "sns.heatmap(df.corr(), annot=True)\n",
    "plt.yticks(rotation=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20c836bb",
   "metadata": {},
   "source": [
    "# 3. Data/Text Preprocessing\n",
    "* Lower Case\n",
    "* Tokenization (Split into Words)\n",
    "* Removing special characters (#, $, @, %, ^)\n",
    "* Removing stop words and punctuation (., !, is, in , of etc)\n",
    "* Stemming (dancing=dance=dancer set as dance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d85d3fe0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\"]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.corpus import stopwords\n",
    "stopwords.words(\"english\")[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0b6a596b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import string\n",
    "string.punctuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "93620c51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('love', 'danc')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.stem.porter import PorterStemmer\n",
    "ps = PorterStemmer()\n",
    "ps.stem(\"loving\"), ps.stem(\"dancing\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3f40bc03",
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform_text(text):\n",
    "    \n",
    "    # Lower case\n",
    "    text = text.lower()\n",
    "    \n",
    "    # Tokenization\n",
    "    text = nltk.word_tokenize(text) \n",
    "    y = []\n",
    "    for i in text:\n",
    "        if i.isalnum():\n",
    "            y.append(i)\n",
    "            \n",
    "    # Remove Special Characters and stop words\n",
    "    text = y.copy()\n",
    "    y.clear()\n",
    "    \n",
    "    for i in text:\n",
    "        if i not in stopwords.words(\"english\") and i not in string.punctuation:\n",
    "            y.append(i)\n",
    "   \n",
    "    # Stemming\n",
    "    text = y.copy()\n",
    "    y.clear()\n",
    "    for i in text:\n",
    "        y.append(ps.stem(i))\n",
    "    \n",
    "    return \" \".join(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "30476f57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'love yt lectur machin learn'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transform_text(\"I loved the YT Lectures on Machine Learning. How about You\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a3e15372",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"text\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a3cf9a8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'go jurong point crazi avail bugi n great world la e buffet cine got amor wat'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transform_text(df[\"text\"][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b7ce3b79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    go jurong point crazi avail bugi n great world...\n",
       "1                                ok lar joke wif u oni\n",
       "2    free entri 2 wkli comp win fa cup final tkt 21...\n",
       "3                  u dun say earli hor u c alreadi say\n",
       "4                 nah think goe usf live around though\n",
       "5    freemsg hey darl 3 week word back like fun sti...\n",
       "6        even brother like speak treat like aid patent\n",
       "7    per request mell oru minnaminungint nurungu ve...\n",
       "8    winner valu network custom select receivea pri...\n",
       "9    mobil 11 month u r entitl updat latest colour ...\n",
       "Name: text, dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"text\"].apply(transform_text)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d534dcba",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"transformed_text\"] = df[\"text\"].apply(transform_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "66f47db5",
   "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>target</th>\n",
       "      <th>text</th>\n",
       "      <th>num_characters</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_sentences</th>\n",
       "      <th>transformed_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>111</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>go jurong point crazi avail bugi n great world...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>ok lar joke wif u oni</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>155</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>free entri 2 wkli comp win fa cup final tkt 21...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>49</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>u dun say earli hor u c alreadi say</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>61</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>nah think goe usf live around though</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target                                               text  num_characters  \\\n",
       "0       0  Go until jurong point, crazy.. Available only ...             111   \n",
       "1       0                      Ok lar... Joking wif u oni...              29   \n",
       "2       1  Free entry in 2 a wkly comp to win FA Cup fina...             155   \n",
       "3       0  U dun say so early hor... U c already then say...              49   \n",
       "4       0  Nah I don't think he goes to usf, he lives aro...              61   \n",
       "\n",
       "   num_words  num_sentences                                   transformed_text  \n",
       "0         24              2  go jurong point crazi avail bugi n great world...  \n",
       "1          8              2                              ok lar joke wif u oni  \n",
       "2         37              2  free entri 2 wkli comp win fa cup final tkt 21...  \n",
       "3         13              1                u dun say earli hor u c alreadi say  \n",
       "4         15              1               nah think goe usf live around though  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cc43146a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud\n",
    "wc = WordCloud(width=5000, height=5000, min_font_size=10, background_color=\"white\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3a0ed323",
   "metadata": {},
   "outputs": [],
   "source": [
    "spam_wc = wc.generate(df[df[\"target\"] == 1][\"transformed_text\"].str.cat(sep=\" \"))\n",
    "not_spam_wc = wc.generate(df[df[\"target\"] == 0][\"transformed_text\"].str.cat(sep=\" \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f6a55d24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 5))\n",
    "plt.imshow(spam_wc);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b29ea80b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 5))\n",
    "plt.imshow(not_spam_wc);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d1f2aa60",
   "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>target</th>\n",
       "      <th>text</th>\n",
       "      <th>num_characters</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_sentences</th>\n",
       "      <th>transformed_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>111</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>go jurong point crazi avail bugi n great world...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>ok lar joke wif u oni</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>155</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>free entri 2 wkli comp win fa cup final tkt 21...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>49</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>u dun say earli hor u c alreadi say</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>61</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>nah think goe usf live around though</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target                                               text  num_characters  \\\n",
       "0       0  Go until jurong point, crazy.. Available only ...             111   \n",
       "1       0                      Ok lar... Joking wif u oni...              29   \n",
       "2       1  Free entry in 2 a wkly comp to win FA Cup fina...             155   \n",
       "3       0  U dun say so early hor... U c already then say...              49   \n",
       "4       0  Nah I don't think he goes to usf, he lives aro...              61   \n",
       "\n",
       "   num_words  num_sentences                                   transformed_text  \n",
       "0         24              2  go jurong point crazi avail bugi n great world...  \n",
       "1          8              2                              ok lar joke wif u oni  \n",
       "2         37              2  free entri 2 wkli comp win fa cup final tkt 21...  \n",
       "3         13              1                u dun say earli hor u c alreadi say  \n",
       "4         15              1               nah think goe usf live around though  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b403a994",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['free entri 2 wkli comp win fa cup final tkt 21st may text fa 87121 receiv entri question std txt rate c appli 08452810075over18',\n",
       " 'freemsg hey darl 3 week word back like fun still tb ok xxx std chg send rcv',\n",
       " 'winner valu network custom select receivea prize reward claim call claim code kl341 valid 12 hour',\n",
       " 'mobil 11 month u r entitl updat latest colour mobil camera free call mobil updat co free 08002986030',\n",
       " 'six chanc win cash 100 pound txt csh11 send cost 6day tsandc appli repli hl 4 info',\n",
       " 'urgent 1 week free membership prize jackpot txt word claim 81010 c lccltd pobox 4403ldnw1a7rw18',\n",
       " 'xxxmobilemovieclub use credit click wap link next txt messag click http',\n",
       " 'england v macedonia dont miss news txt ur nation team 87077 eg england 87077 tri wale scotland poboxox36504w45wq',\n",
       " 'thank subscript rington uk mobil charg pleas confirm repli ye repli charg',\n",
       " '07732584351 rodger burn msg tri call repli sm free nokia mobil free camcord pleas call 08000930705 deliveri tomorrow',\n",
       " 'sm ac sptv new jersey devil detroit red wing play ice hockey correct incorrect end repli end sptv',\n",
       " 'congrat 1 year special cinema pass 2 call 09061209465 c suprman v matrix3 starwars3 etc 4 free 150pm dont miss',\n",
       " 'valu custom pleas advis follow recent review mob award bonu prize call 09066364589',\n",
       " 'urgent ur award complimentari trip eurodisinc trav aco entry41 claim txt di 87121 morefrmmob shracomorsglsuplt 10 ls1 3aj',\n",
       " 'hear new come ken stuff',\n",
       " 'pleas call custom servic repres 0800 169 6031 guarante cash prize',\n",
       " 'free rington wait collect simpli text password 85069 verifi get usher britney fml',\n",
       " 'gent tri contact last weekend draw show prize guarante call claim code k52 valid 12hr 150ppm',\n",
       " 'winner u special select 2 receiv 4 holiday flight inc speak live oper 2 claim',\n",
       " 'privat 2004 account statement 07742676969 show 786 unredeem bonu point claim call 08719180248 identifi code 45239 expir',\n",
       " 'urgent mobil award bonu caller prize final tri contact u call landlin 09064019788 box42wr29c 150ppm',\n",
       " 'today voda number end 7548 select receiv 350 award match pleas call 08712300220 quot claim code 4041 standard rate app',\n",
       " 'sunshin quiz wkli q win top soni dvd player u know countri algarv txt ansr sp tyron',\n",
       " 'want 2 get laid tonight want real dog locat sent direct 2 ur mob join uk largest dog network bt txting gravel 69888 nt ec2a 150p',\n",
       " 'rcv msg chat svc free hardcor servic text go 69988 u get noth u must age verifi yr network tri',\n",
       " 'freemsg repli text randi sexi femal live local luv hear netcollex ltd 08700621170150p per msg repli stop end',\n",
       " 'custom servic annonc new year deliveri wait pleas call 07046744435 arrang deliveri',\n",
       " 'winner u special select 2 receiv cash 4 holiday flight inc speak live oper 2 claim 0871277810810',\n",
       " 'stop bootydeli invit friend repli see stop send stop frnd 62468',\n",
       " 'bangbab ur order way u receiv servic msg 2 download ur content u goto wap bangb tv ur mobil menu',\n",
       " 'urgent tri contact last weekend draw show prize guarante call claim code s89 valid 12hr',\n",
       " 'pleas call custom servic repres freephon 0808 145 4742 guarante cash prize',\n",
       " 'uniqu enough find 30th august',\n",
       " '500 new mobil 2004 must go txt nokia 89545 collect today 2optout',\n",
       " 'u meet ur dream partner soon ur career 2 flyng start 2 find free txt horo follow ur star sign horo ari',\n",
       " 'text meet someon sexi today u find date even flirt join 4 10p repli name age eg sam 25 18 recd thirtyeight penc',\n",
       " 'u 447801259231 secret admir look 2 make contact r reveal think ur 09058094597',\n",
       " 'congratul ur award 500 cd voucher 125gift guarante free entri 2 100 wkli draw txt music 87066 tnc',\n",
       " 'tri contact repli offer video handset 750 anytim network min unlimit text camcord repli call 08000930705',\n",
       " 'hey realli horni want chat see nake text hot 69698 text charg 150pm unsubscrib text stop 69698',\n",
       " 'ur rington servic chang 25 free credit go choos content stop txt club stop 87070 club4 po box1146 mk45 2wt',\n",
       " 'rington club get uk singl chart mobil week choos top qualiti rington messag free charg',\n",
       " 'hmv bonu special 500 pound genuin hmv voucher answer 4 easi question play send hmv 86688 info',\n",
       " 'custom may claim free camera phone upgrad pay go sim card loyalti call 0845 021 end c appli',\n",
       " 'sm ac blind date 4u rodds1 aberdeen unit kingdom check http sm blind date send hide',\n",
       " 'themob check newest select content game tone gossip babe sport keep mobil fit funki text wap 82468',\n",
       " 'think ur smart win week weekli quiz text play 85222 cs winnersclub po box 84 m26 3uz',\n",
       " 'decemb mobil entitl updat latest colour camera mobil free call mobil updat co free 08002986906',\n",
       " 'call germani 1 penc per minut call fix line via access number 0844 861 85 prepay direct access',\n",
       " 'valentin day special win quiz take partner trip lifetim send go 83600 rcvd',\n",
       " 'fanci shag txt xxuk suzi txt cost per msg tnc websit x',\n",
       " 'ur current 500 pound maxim ur send cash 86688 cc 08708800282',\n",
       " 'xma offer latest motorola sonyericsson nokia free bluetooth doubl min 1000 txt orang call mobileupd8 08000839402',\n",
       " 'discount code rp176781 stop messag repli stop custom servic 08717205546',\n",
       " 'thank rington order refer t91 charg gbp 4 per week unsubscrib anytim call custom servic 09057039994',\n",
       " 'doubl min txt 4 6month free bluetooth orang avail soni nokia motorola phone call mobileupd8 08000839402',\n",
       " '4mth half price orang line rental latest camera phone 4 free phone 11mth call mobilesdirect free 08000938767 updat or2stoptxt',\n",
       " 'free rington text first 87131 poli text get 87131 true tone help 0845 2814032 16 1st free tone txt stop',\n",
       " '100 date servic cal l 09064012103 box334sk38ch',\n",
       " 'free entri weekli competit text word win 80086 18 c',\n",
       " 'send logo 2 ur lover 2 name join heart txt love name1 name2 mobno eg love adam eve 07123456789 87077 yahoo pobox36504w45wq txtno 4 ad 150p',\n",
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      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"target\"] == 1][\"transformed_text\"].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d051e0a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "spam_corpus = []\n",
    "for msg in df[df[\"target\"] == 1][\"transformed_text\"].tolist():\n",
    "    for word in msg.split():\n",
    "        spam_corpus.append(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0c66ec7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9939"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(spam_corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3f511159",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('call', 320),\n",
       " ('free', 191),\n",
       " ('2', 155),\n",
       " ('txt', 141),\n",
       " ('text', 122),\n",
       " ('u', 119),\n",
       " ('ur', 119),\n",
       " ('mobil', 114),\n",
       " ('stop', 104),\n",
       " ('repli', 103),\n",
       " ('claim', 98),\n",
       " ('4', 97),\n",
       " ('prize', 82),\n",
       " ('get', 74),\n",
       " ('new', 64)]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "Counter(spam_corpus).most_common(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5b2360dc",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>call</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>free</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>txt</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>text</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>u</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ur</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mobil</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>stop</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>repli</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>claim</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>prize</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>get</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>new</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0    1\n",
       "0    call  320\n",
       "1    free  191\n",
       "2       2  155\n",
       "3     txt  141\n",
       "4    text  122\n",
       "5       u  119\n",
       "6      ur  119\n",
       "7   mobil  114\n",
       "8    stop  104\n",
       "9   repli  103\n",
       "10  claim   98\n",
       "11      4   97\n",
       "12  prize   82\n",
       "13    get   74\n",
       "14    new   64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(Counter(spam_corpus).most_common(15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f8d695bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1 = pd.DataFrame(Counter(spam_corpus).most_common(10))[0]\n",
    "x2 = pd.DataFrame(Counter(spam_corpus).most_common(10))[1]\n",
    "sns.barplot(x1, x2)\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8417acd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('u', 883),\n",
       " ('go', 404),\n",
       " ('get', 349),\n",
       " ('gt', 288),\n",
       " ('lt', 287),\n",
       " ('2', 284),\n",
       " ('come', 275),\n",
       " ('got', 236),\n",
       " ('know', 236),\n",
       " ('like', 234),\n",
       " ('call', 233),\n",
       " ('time', 219),\n",
       " ('ok', 217),\n",
       " ('love', 216),\n",
       " ('good', 213)]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_spam_corpus = []\n",
    "for msg in df[df[\"target\"] == 0][\"transformed_text\"].tolist():\n",
    "    for word in msg.split():\n",
    "        not_spam_corpus.append(word)\n",
    "        \n",
    "Counter(not_spam_corpus).most_common(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4def03ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(pd.DataFrame(Counter(not_spam_corpus).most_common(10))[0], pd.DataFrame(Counter(not_spam_corpus).most_common(10))[1])\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38b4946e",
   "metadata": {},
   "source": [
    "# 4. Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb8d773c",
   "metadata": {},
   "source": [
    "### To make a text to vector we can use bag of words first. For text data in classification we use Naive Bayes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "3ef64e13",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer()\n",
    "x = cv.fit_transform(df[\"transformed_text\"]).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d88089e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a43ccce4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5169, 6708)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "33f26f2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[\"target\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "125bf8a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "0faf42da",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "0690b9a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB\n",
    "from sklearn.metrics import accuracy_score, precision_score, confusion_matrix\n",
    "\n",
    "gnb = GaussianNB()\n",
    "mnb = MultinomialNB()\n",
    "bnb = BernoulliNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "27ba135a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8800773694390716\n",
      "[[792 104]\n",
      " [ 20 118]]\n",
      "0.5315315315315315\n"
     ]
    }
   ],
   "source": [
    "gnb.fit(x_train, y_train)\n",
    "y_pred1 = gnb.predict(x_test)\n",
    "print(accuracy_score(y_test, y_pred1))\n",
    "print(confusion_matrix(y_test, y_pred1))\n",
    "print(precision_score(y_test, y_pred1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "3aa2910c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9642166344294004\n",
      "[[871  25]\n",
      " [ 12 126]]\n",
      "0.8344370860927153\n"
     ]
    }
   ],
   "source": [
    "mnb.fit(x_train, y_train)\n",
    "y_pred2 = mnb.predict(x_test)\n",
    "print(accuracy_score(y_test, y_pred2))\n",
    "print(confusion_matrix(y_test, y_pred2))\n",
    "print(precision_score(y_test, y_pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "469848d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9700193423597679\n",
      "[[893   3]\n",
      " [ 28 110]]\n",
      "0.9734513274336283\n"
     ]
    }
   ],
   "source": [
    "bnb.fit(x_train, y_train)\n",
    "y_pred3 = bnb.predict(x_test)\n",
    "print(accuracy_score(y_test, y_pred3))\n",
    "print(confusion_matrix(y_test, y_pred3))\n",
    "print(precision_score(y_test, y_pred3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "667c6905",
   "metadata": {},
   "source": [
    "## Using TfidfVectorizer instead of CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "28016e07",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf = TfidfVectorizer()\n",
    "x = tfidf.fit_transform(df[\"transformed_text\"]).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "a4f89e45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "94bdb593",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5169, 6708)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "2d698eef",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[\"target\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "1897c279",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "04d76e30",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "b69cb89f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB\n",
    "from sklearn.metrics import accuracy_score, precision_score, confusion_matrix\n",
    "\n",
    "gnb = GaussianNB()\n",
    "mnb = MultinomialNB()\n",
    "bnb = BernoulliNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "ee34728f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8762088974854932\n",
      "[[793 103]\n",
      " [ 25 113]]\n",
      "0.5231481481481481\n"
     ]
    }
   ],
   "source": [
    "gnb.fit(x_train, y_train)\n",
    "y_pred1 = gnb.predict(x_test)\n",
    "print(accuracy_score(y_test, y_pred1))\n",
    "print(confusion_matrix(y_test, y_pred1))\n",
    "print(precision_score(y_test, y_pred1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "c9dc5535",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9593810444874274\n",
      "[[896   0]\n",
      " [ 42  96]]\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "mnb.fit(x_train, y_train)\n",
    "y_pred2 = mnb.predict(x_test)\n",
    "print(accuracy_score(y_test, y_pred2))\n",
    "print(confusion_matrix(y_test, y_pred2))\n",
    "print(precision_score(y_test, y_pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "04d5bbcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9700193423597679\n",
      "[[893   3]\n",
      " [ 28 110]]\n",
      "0.9734513274336283\n"
     ]
    }
   ],
   "source": [
    "bnb.fit(x_train, y_train)\n",
    "y_pred3 = bnb.predict(x_test)\n",
    "print(accuracy_score(y_test, y_pred3))\n",
    "print(confusion_matrix(y_test, y_pred3))\n",
    "print(precision_score(y_test, y_pred3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c5e7943",
   "metadata": {},
   "source": [
    "mnb performed really well on TfidfVectorizer, since data is imbalanced we give importance to the precision rather than the accuracy.\n",
    "\n",
    "#### TfidfVectorizer and Multinomial Naive Bayes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f00b0e52",
   "metadata": {},
   "source": [
    "# 5. Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "f63c0786",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\anaconda3\\lib\\site-packages\\xgboost\\compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "7c463fa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC(kernel='sigmoid', gamma=1.0)\n",
    "knc = KNeighborsClassifier()\n",
    "mnb = MultinomialNB()\n",
    "dtc = DecisionTreeClassifier(max_depth=5)\n",
    "lrc = LogisticRegression(solver='liblinear', penalty='l1')\n",
    "rfc = RandomForestClassifier(n_estimators=50, random_state=2)\n",
    "abc = AdaBoostClassifier(n_estimators=50, random_state=2)\n",
    "bc = BaggingClassifier(n_estimators=50, random_state=2)\n",
    "etc = ExtraTreesClassifier(n_estimators=50, random_state=2)\n",
    "gbdt = GradientBoostingClassifier(n_estimators=50,random_state=2)\n",
    "xgb = XGBClassifier(n_estimators=50,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "313193d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "clfs = {\n",
    "    'SVC' : svc,\n",
    "    'KN' : knc, \n",
    "    'NB': mnb, \n",
    "    'DT': dtc, \n",
    "    'LR': lrc, \n",
    "    'RF': rfc, \n",
    "    'AdaBoost': abc, \n",
    "    'BgC': bc, \n",
    "    'ETC': etc,\n",
    "    'GBDT':gbdt,\n",
    "    'xgb':xgb\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "dade80d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_classifier(clf,X_train,y_train,X_test,y_test):\n",
    "    clf.fit(X_train,y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test,y_pred)\n",
    "    precision = precision_score(y_test,y_pred)\n",
    "    \n",
    "    return accuracy,precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "947e788d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9729206963249516, 0.9741379310344828)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_classifier(svc,x_train,y_train,x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "f8620100",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For  SVC\n",
      "Accuracy -  0.9729206963249516\n",
      "Precision -  0.9741379310344828\n",
      "For  KN\n",
      "Accuracy -  0.9003868471953579\n",
      "Precision -  1.0\n",
      "For  NB\n",
      "Accuracy -  0.9593810444874274\n",
      "Precision -  1.0\n",
      "For  DT\n",
      "Accuracy -  0.9352030947775629\n",
      "Precision -  0.8380952380952381\n",
      "For  LR\n",
      "Accuracy -  0.9516441005802708\n",
      "Precision -  0.94\n",
      "For  RF\n",
      "Accuracy -  0.9738878143133463\n",
      "Precision -  1.0\n",
      "For  AdaBoost\n",
      "Accuracy -  0.9613152804642167\n",
      "Precision -  0.9454545454545454\n",
      "For  BgC\n",
      "Accuracy -  0.9584139264990329\n",
      "Precision -  0.8625954198473282\n",
      "For  ETC\n",
      "Accuracy -  0.9758220502901354\n",
      "Precision -  0.9829059829059829\n",
      "For  GBDT\n",
      "Accuracy -  0.9526112185686654\n",
      "Precision -  0.9238095238095239\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:35:28] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "For  xgb\n",
      "Accuracy -  0.9690522243713733\n",
      "Precision -  0.9344262295081968\n"
     ]
    }
   ],
   "source": [
    "accuracy_scores = []\n",
    "precision_scores = []\n",
    "\n",
    "for name,clf in clfs.items():\n",
    "    \n",
    "    current_accuracy,current_precision = train_classifier(clf, x_train,y_train,x_test,y_test)\n",
    "    print(\"\")\n",
    "    print(\"For \",name)\n",
    "    print(\"Accuracy - \",current_accuracy)\n",
    "    print(\"Precision - \",current_precision)\n",
    "    \n",
    "    accuracy_scores.append(current_accuracy)\n",
    "    precision_scores.append(current_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "347ca8a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "performance_df = pd.DataFrame({'Algorithm':clfs.keys(),\n",
    "                               'Accuracy':accuracy_scores, \n",
    "                               'Precision':precision_scores}).sort_values('Precision',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "59ba7562",
   "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>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KN</td>\n",
       "      <td>0.900387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NB</td>\n",
       "      <td>0.959381</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.973888</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ETC</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.982906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.972921</td>\n",
       "      <td>0.974138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.961315</td>\n",
       "      <td>0.945455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.951644</td>\n",
       "      <td>0.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>xgb</td>\n",
       "      <td>0.969052</td>\n",
       "      <td>0.934426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>0.952611</td>\n",
       "      <td>0.923810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BgC</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.862595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DT</td>\n",
       "      <td>0.935203</td>\n",
       "      <td>0.838095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Algorithm  Accuracy  Precision\n",
       "1         KN  0.900387   1.000000\n",
       "2         NB  0.959381   1.000000\n",
       "5         RF  0.973888   1.000000\n",
       "8        ETC  0.975822   0.982906\n",
       "0        SVC  0.972921   0.974138\n",
       "6   AdaBoost  0.961315   0.945455\n",
       "4         LR  0.951644   0.940000\n",
       "10       xgb  0.969052   0.934426\n",
       "9       GBDT  0.952611   0.923810\n",
       "7        BgC  0.958414   0.862595\n",
       "3         DT  0.935203   0.838095"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "b935cb11",
   "metadata": {},
   "outputs": [],
   "source": [
    "performance_df1 = pd.melt(performance_df, id_vars = \"Algorithm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "5aff8f04",
   "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>Algorithm</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KN</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.900387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NB</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.959381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RF</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.973888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ETC</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.975822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVC</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.972921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.961315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LR</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.951644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>xgb</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.969052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.952611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BgC</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.958414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DT</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.935203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>KN</td>\n",
       "      <td>Precision</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NB</td>\n",
       "      <td>Precision</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>RF</td>\n",
       "      <td>Precision</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ETC</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.982906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>SVC</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.974138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.945455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LR</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>xgb</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.934426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.923810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>BgC</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.862595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>DT</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.838095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Algorithm   variable     value\n",
       "0         KN   Accuracy  0.900387\n",
       "1         NB   Accuracy  0.959381\n",
       "2         RF   Accuracy  0.973888\n",
       "3        ETC   Accuracy  0.975822\n",
       "4        SVC   Accuracy  0.972921\n",
       "5   AdaBoost   Accuracy  0.961315\n",
       "6         LR   Accuracy  0.951644\n",
       "7        xgb   Accuracy  0.969052\n",
       "8       GBDT   Accuracy  0.952611\n",
       "9        BgC   Accuracy  0.958414\n",
       "10        DT   Accuracy  0.935203\n",
       "11        KN  Precision  1.000000\n",
       "12        NB  Precision  1.000000\n",
       "13        RF  Precision  1.000000\n",
       "14       ETC  Precision  0.982906\n",
       "15       SVC  Precision  0.974138\n",
       "16  AdaBoost  Precision  0.945455\n",
       "17        LR  Precision  0.940000\n",
       "18       xgb  Precision  0.934426\n",
       "19      GBDT  Precision  0.923810\n",
       "20       BgC  Precision  0.862595\n",
       "21        DT  Precision  0.838095"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "dd2ced1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 613.5x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x = 'Algorithm', y='value', \n",
    "               hue = 'variable',data=performance_df1, kind='bar',height=5)\n",
    "plt.ylim(0.5,1.0)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "608ef2b5",
   "metadata": {},
   "source": [
    "# 6. Model Improvement\n",
    "1. Change the max_features parameter of TfIdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "8ece2617",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf = TfidfVectorizer(max_features=3000)\n",
    "x = tfidf.fit_transform(df[\"transformed_text\"]).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "7fa33337",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "716186c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "b4370e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC(kernel='sigmoid', gamma=1.0)\n",
    "knc = KNeighborsClassifier()\n",
    "mnb = MultinomialNB()\n",
    "dtc = DecisionTreeClassifier(max_depth=5)\n",
    "lrc = LogisticRegression(solver='liblinear', penalty='l1')\n",
    "rfc = RandomForestClassifier(n_estimators=50, random_state=2)\n",
    "abc = AdaBoostClassifier(n_estimators=50, random_state=2)\n",
    "bc = BaggingClassifier(n_estimators=50, random_state=2)\n",
    "etc = ExtraTreesClassifier(n_estimators=50, random_state=2)\n",
    "gbdt = GradientBoostingClassifier(n_estimators=50,random_state=2)\n",
    "xgb = XGBClassifier(n_estimators=50,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "16fd7f64",
   "metadata": {},
   "outputs": [],
   "source": [
    "clfs = {\n",
    "    'SVC' : svc,\n",
    "    'KN' : knc, \n",
    "    'NB': mnb, \n",
    "    'DT': dtc, \n",
    "    'LR': lrc, \n",
    "    'RF': rfc, \n",
    "    'AdaBoost': abc, \n",
    "    'BgC': bc, \n",
    "    'ETC': etc,\n",
    "    'GBDT':gbdt,\n",
    "    'xgb':xgb\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "34714857",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_classifier(clf,X_train,y_train,X_test,y_test):\n",
    "    clf.fit(X_train,y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test,y_pred)\n",
    "    precision = precision_score(y_test,y_pred)\n",
    "    \n",
    "    return accuracy,precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "bb1b4934",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "For  SVC\n",
      "Accuracy -  0.9758220502901354\n",
      "Precision -  0.9747899159663865\n",
      "\n",
      "For  KN\n",
      "Accuracy -  0.9052224371373307\n",
      "Precision -  1.0\n",
      "\n",
      "For  NB\n",
      "Accuracy -  0.9709864603481625\n",
      "Precision -  1.0\n",
      "\n",
      "For  DT\n",
      "Accuracy -  0.9323017408123792\n",
      "Precision -  0.8333333333333334\n",
      "\n",
      "For  LR\n",
      "Accuracy -  0.9584139264990329\n",
      "Precision -  0.9702970297029703\n",
      "\n",
      "For  RF\n",
      "Accuracy -  0.9748549323017408\n",
      "Precision -  0.9827586206896551\n",
      "\n",
      "For  AdaBoost\n",
      "Accuracy -  0.960348162475822\n",
      "Precision -  0.9292035398230089\n",
      "\n",
      "For  BgC\n",
      "Accuracy -  0.9574468085106383\n",
      "Precision -  0.8671875\n",
      "\n",
      "For  ETC\n",
      "Accuracy -  0.9748549323017408\n",
      "Precision -  0.9745762711864406\n",
      "\n",
      "For  GBDT\n",
      "Accuracy -  0.9477756286266924\n",
      "Precision -  0.92\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:59:33] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "\n",
      "For  xgb\n",
      "Accuracy -  0.971953578336557\n",
      "Precision -  0.943089430894309\n"
     ]
    }
   ],
   "source": [
    "accuracy_scores = []\n",
    "precision_scores = []\n",
    "\n",
    "for name,clf in clfs.items():\n",
    "    \n",
    "    current_accuracy,current_precision = train_classifier(clf, x_train,y_train,x_test,y_test)\n",
    "    print(\"\")\n",
    "    print(\"For \",name)\n",
    "    print(\"Accuracy - \",current_accuracy)\n",
    "    print(\"Precision - \",current_precision)\n",
    "    \n",
    "    accuracy_scores.append(current_accuracy)\n",
    "    precision_scores.append(current_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "ca15e437",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_df = pd.DataFrame({'Algorithm':clfs.keys(),'Accuracy_max_ft_3000':accuracy_scores,'Precision_max_ft_3000':precision_scores}).sort_values('Precision_max_ft_3000',ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "89f035da",
   "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>Algorithm</th>\n",
       "      <th>Accuracy_max_ft_3000</th>\n",
       "      <th>Precision_max_ft_3000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KN</td>\n",
       "      <td>0.905222</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NB</td>\n",
       "      <td>0.970986</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.982759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.974790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ETC</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.974576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.970297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>xgb</td>\n",
       "      <td>0.971954</td>\n",
       "      <td>0.943089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.960348</td>\n",
       "      <td>0.929204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>0.947776</td>\n",
       "      <td>0.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BgC</td>\n",
       "      <td>0.957447</td>\n",
       "      <td>0.867188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DT</td>\n",
       "      <td>0.932302</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Algorithm  Accuracy_max_ft_3000  Precision_max_ft_3000\n",
       "1         KN              0.905222               1.000000\n",
       "2         NB              0.970986               1.000000\n",
       "5         RF              0.974855               0.982759\n",
       "0        SVC              0.975822               0.974790\n",
       "8        ETC              0.974855               0.974576\n",
       "4         LR              0.958414               0.970297\n",
       "10       xgb              0.971954               0.943089\n",
       "6   AdaBoost              0.960348               0.929204\n",
       "9       GBDT              0.947776               0.920000\n",
       "7        BgC              0.957447               0.867188\n",
       "3         DT              0.932302               0.833333"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "90618db5",
   "metadata": {},
   "outputs": [],
   "source": [
    "performance_df_1 = performance_df.merge(temp_df,on='Algorithm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "c8961df3",
   "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>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Accuracy_max_ft_3000</th>\n",
       "      <th>Precision_max_ft_3000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KN</td>\n",
       "      <td>0.900387</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.905222</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NB</td>\n",
       "      <td>0.959381</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.970986</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.973888</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.982759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ETC</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.982906</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.974576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.972921</td>\n",
       "      <td>0.974138</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.974790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.961315</td>\n",
       "      <td>0.945455</td>\n",
       "      <td>0.960348</td>\n",
       "      <td>0.929204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.951644</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.970297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>xgb</td>\n",
       "      <td>0.969052</td>\n",
       "      <td>0.934426</td>\n",
       "      <td>0.971954</td>\n",
       "      <td>0.943089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>0.952611</td>\n",
       "      <td>0.923810</td>\n",
       "      <td>0.947776</td>\n",
       "      <td>0.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BgC</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.862595</td>\n",
       "      <td>0.957447</td>\n",
       "      <td>0.867188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DT</td>\n",
       "      <td>0.935203</td>\n",
       "      <td>0.838095</td>\n",
       "      <td>0.932302</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Algorithm  Accuracy  Precision  Accuracy_max_ft_3000  Precision_max_ft_3000\n",
       "0         KN  0.900387   1.000000              0.905222               1.000000\n",
       "1         NB  0.959381   1.000000              0.970986               1.000000\n",
       "2         RF  0.973888   1.000000              0.974855               0.982759\n",
       "3        ETC  0.975822   0.982906              0.974855               0.974576\n",
       "4        SVC  0.972921   0.974138              0.975822               0.974790\n",
       "5   AdaBoost  0.961315   0.945455              0.960348               0.929204\n",
       "6         LR  0.951644   0.940000              0.958414               0.970297\n",
       "7        xgb  0.969052   0.934426              0.971954               0.943089\n",
       "8       GBDT  0.952611   0.923810              0.947776               0.920000\n",
       "9        BgC  0.958414   0.862595              0.957447               0.867188\n",
       "10        DT  0.935203   0.838095              0.932302               0.833333"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_df_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "5574030b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf = TfidfVectorizer(max_features=3000)\n",
    "x = tfidf.fit_transform(df[\"transformed_text\"]).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "d3d56da8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "x = scaler.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "79330b3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "43cbe220",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[\"target\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "58e9759a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "3536216e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "c077a763",
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC(kernel='sigmoid', gamma=1.0)\n",
    "knc = KNeighborsClassifier()\n",
    "mnb = MultinomialNB()\n",
    "dtc = DecisionTreeClassifier(max_depth=5)\n",
    "lrc = LogisticRegression(solver='liblinear', penalty='l1')\n",
    "rfc = RandomForestClassifier(n_estimators=50, random_state=2)\n",
    "abc = AdaBoostClassifier(n_estimators=50, random_state=2)\n",
    "bc = BaggingClassifier(n_estimators=50, random_state=2)\n",
    "etc = ExtraTreesClassifier(n_estimators=50, random_state=2)\n",
    "gbdt = GradientBoostingClassifier(n_estimators=50,random_state=2)\n",
    "xgb = XGBClassifier(n_estimators=50,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "143c865c",
   "metadata": {},
   "outputs": [],
   "source": [
    "clfs = {\n",
    "    'SVC' : svc,\n",
    "    'KN' : knc, \n",
    "    'NB': mnb, \n",
    "    'DT': dtc, \n",
    "    'LR': lrc, \n",
    "    'RF': rfc, \n",
    "    'AdaBoost': abc, \n",
    "    'BgC': bc, \n",
    "    'ETC': etc,\n",
    "    'GBDT':gbdt,\n",
    "    'xgb':xgb\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "24d33a6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_classifier(clf,X_train,y_train,X_test,y_test):\n",
    "    clf.fit(X_train,y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test,y_pred)\n",
    "    precision = precision_score(y_test,y_pred)\n",
    "    \n",
    "    return accuracy,precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "c2c807f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "a9063cb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "For  SVC\n",
      "Accuracy -  0.9661508704061895\n",
      "Precision -  0.9256198347107438\n",
      "\n",
      "For  KN\n",
      "Accuracy -  0.9052224371373307\n",
      "Precision -  0.9761904761904762\n",
      "\n",
      "For  NB\n",
      "Accuracy -  0.9787234042553191\n",
      "Precision -  0.9461538461538461\n",
      "\n",
      "For  DT\n",
      "Accuracy -  0.9323017408123792\n",
      "Precision -  0.8333333333333334\n",
      "\n",
      "For  LR\n",
      "Accuracy -  0.9671179883945842\n",
      "Precision -  0.9642857142857143\n",
      "\n",
      "For  RF\n",
      "Accuracy -  0.9748549323017408\n",
      "Precision -  0.9827586206896551\n",
      "\n",
      "For  AdaBoost\n",
      "Accuracy -  0.960348162475822\n",
      "Precision -  0.9292035398230089\n",
      "\n",
      "For  BgC\n",
      "Accuracy -  0.9574468085106383\n",
      "Precision -  0.8671875\n",
      "\n",
      "For  ETC\n",
      "Accuracy -  0.9748549323017408\n",
      "Precision -  0.9745762711864406\n",
      "\n",
      "For  GBDT\n",
      "Accuracy -  0.9477756286266924\n",
      "Precision -  0.92\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19:12:30] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "\n",
      "For  xgb\n",
      "Accuracy -  0.971953578336557\n",
      "Precision -  0.943089430894309\n"
     ]
    }
   ],
   "source": [
    "accuracy_scores = []\n",
    "precision_scores = []\n",
    "\n",
    "for name,clf in clfs.items():\n",
    "    \n",
    "    current_accuracy,current_precision = train_classifier(clf, x_train,y_train,x_test,y_test)\n",
    "    print(\"\")\n",
    "    print(\"For \",name)\n",
    "    print(\"Accuracy - \",current_accuracy)\n",
    "    print(\"Precision - \",current_precision)\n",
    "    \n",
    "    accuracy_scores.append(current_accuracy)\n",
    "    precision_scores.append(current_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "c3cf7384",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_df = pd.DataFrame({'Algorithm':clfs.keys(),'Accuracy_scaling':accuracy_scores,'Precision_scaling':precision_scores}).sort_values('Precision_scaling',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "ea44a62f",
   "metadata": {},
   "outputs": [],
   "source": [
    "performance_df_2 = performance_df_1.merge(temp_df,on='Algorithm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "5a5c0ed7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Accuracy_max_ft_3000</th>\n",
       "      <th>Precision_max_ft_3000</th>\n",
       "      <th>Accuracy_scaling</th>\n",
       "      <th>Precision_scaling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KN</td>\n",
       "      <td>0.900387</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.905222</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.905222</td>\n",
       "      <td>0.976190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NB</td>\n",
       "      <td>0.959381</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.970986</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.978723</td>\n",
       "      <td>0.946154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.973888</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.982759</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.982759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ETC</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.982906</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.974576</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.974576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.972921</td>\n",
       "      <td>0.974138</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.974790</td>\n",
       "      <td>0.966151</td>\n",
       "      <td>0.925620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.961315</td>\n",
       "      <td>0.945455</td>\n",
       "      <td>0.960348</td>\n",
       "      <td>0.929204</td>\n",
       "      <td>0.960348</td>\n",
       "      <td>0.929204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.951644</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.970297</td>\n",
       "      <td>0.967118</td>\n",
       "      <td>0.964286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>xgb</td>\n",
       "      <td>0.969052</td>\n",
       "      <td>0.934426</td>\n",
       "      <td>0.971954</td>\n",
       "      <td>0.943089</td>\n",
       "      <td>0.971954</td>\n",
       "      <td>0.943089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>0.952611</td>\n",
       "      <td>0.923810</td>\n",
       "      <td>0.947776</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.947776</td>\n",
       "      <td>0.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BgC</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.862595</td>\n",
       "      <td>0.957447</td>\n",
       "      <td>0.867188</td>\n",
       "      <td>0.957447</td>\n",
       "      <td>0.867188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DT</td>\n",
       "      <td>0.935203</td>\n",
       "      <td>0.838095</td>\n",
       "      <td>0.932302</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.932302</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Algorithm  Accuracy  Precision  Accuracy_max_ft_3000  \\\n",
       "0         KN  0.900387   1.000000              0.905222   \n",
       "1         NB  0.959381   1.000000              0.970986   \n",
       "2         RF  0.973888   1.000000              0.974855   \n",
       "3        ETC  0.975822   0.982906              0.974855   \n",
       "4        SVC  0.972921   0.974138              0.975822   \n",
       "5   AdaBoost  0.961315   0.945455              0.960348   \n",
       "6         LR  0.951644   0.940000              0.958414   \n",
       "7        xgb  0.969052   0.934426              0.971954   \n",
       "8       GBDT  0.952611   0.923810              0.947776   \n",
       "9        BgC  0.958414   0.862595              0.957447   \n",
       "10        DT  0.935203   0.838095              0.932302   \n",
       "\n",
       "    Precision_max_ft_3000  Accuracy_scaling  Precision_scaling  \n",
       "0                1.000000          0.905222           0.976190  \n",
       "1                1.000000          0.978723           0.946154  \n",
       "2                0.982759          0.974855           0.982759  \n",
       "3                0.974576          0.974855           0.974576  \n",
       "4                0.974790          0.966151           0.925620  \n",
       "5                0.929204          0.960348           0.929204  \n",
       "6                0.970297          0.967118           0.964286  \n",
       "7                0.943089          0.971954           0.943089  \n",
       "8                0.920000          0.947776           0.920000  \n",
       "9                0.867188          0.957447           0.867188  \n",
       "10               0.833333          0.932302           0.833333  "
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "3a7ee93e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf = TfidfVectorizer(max_features=3000)\n",
    "x = tfidf.fit_transform(df[\"transformed_text\"]).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "e781e05c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.hstack((x, df[\"num_characters\"].values.reshape(-1, 1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "d7cc88d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "9808e3a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "2a5ace9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC(kernel='sigmoid', gamma=1.0)\n",
    "knc = KNeighborsClassifier()\n",
    "mnb = MultinomialNB()\n",
    "dtc = DecisionTreeClassifier(max_depth=5)\n",
    "lrc = LogisticRegression(solver='liblinear', penalty='l1')\n",
    "rfc = RandomForestClassifier(n_estimators=50, random_state=2)\n",
    "abc = AdaBoostClassifier(n_estimators=50, random_state=2)\n",
    "bc = BaggingClassifier(n_estimators=50, random_state=2)\n",
    "etc = ExtraTreesClassifier(n_estimators=50, random_state=2)\n",
    "gbdt = GradientBoostingClassifier(n_estimators=50,random_state=2)\n",
    "xgb = XGBClassifier(n_estimators=50,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "e52e0d9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "clfs = {\n",
    "    'SVC' : svc,\n",
    "    'KN' : knc, \n",
    "    'NB': mnb, \n",
    "    'DT': dtc, \n",
    "    'LR': lrc, \n",
    "    'RF': rfc, \n",
    "    'AdaBoost': abc, \n",
    "    'BgC': bc, \n",
    "    'ETC': etc,\n",
    "    'GBDT':gbdt,\n",
    "    'xgb':xgb\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "7b6cfb8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_classifier(clf,X_train,y_train,X_test,y_test):\n",
    "    clf.fit(X_train,y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test,y_pred)\n",
    "    precision = precision_score(y_test,y_pred)\n",
    "    \n",
    "    return accuracy,precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "5406cdb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "For  SVC\n",
      "Accuracy -  0.8665377176015474\n",
      "Precision -  0.0\n",
      "\n",
      "For  KN\n",
      "Accuracy -  0.9352030947775629\n",
      "Precision -  0.8317757009345794\n",
      "\n",
      "For  NB\n",
      "Accuracy -  0.9410058027079303\n",
      "Precision -  1.0\n",
      "\n",
      "For  DT\n",
      "Accuracy -  0.9448742746615088\n",
      "Precision -  0.8785046728971962\n",
      "\n",
      "For  LR\n",
      "Accuracy -  0.9613152804642167\n",
      "Precision -  0.9622641509433962\n",
      "\n",
      "For  RF\n",
      "Accuracy -  0.9680851063829787\n",
      "Precision -  0.981651376146789\n",
      "\n",
      "For  AdaBoost\n",
      "Accuracy -  0.9642166344294004\n",
      "Precision -  0.9316239316239316\n",
      "\n",
      "For  BgC\n",
      "Accuracy -  0.9661508704061895\n",
      "Precision -  0.8992248062015504\n",
      "\n",
      "For  ETC\n",
      "Accuracy -  0.9806576402321083\n",
      "Precision -  0.9758064516129032\n",
      "\n",
      "For  GBDT\n",
      "Accuracy -  0.9516441005802708\n",
      "Precision -  0.9313725490196079\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19:21:31] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "\n",
      "For  xgb\n",
      "Accuracy -  0.9709864603481625\n",
      "Precision -  0.9426229508196722\n"
     ]
    }
   ],
   "source": [
    "accuracy_scores = []\n",
    "precision_scores = []\n",
    "\n",
    "for name,clf in clfs.items():\n",
    "    \n",
    "    current_accuracy,current_precision = train_classifier(clf, x_train,y_train,x_test,y_test)\n",
    "    print(\"\")\n",
    "    print(\"For \",name)\n",
    "    print(\"Accuracy - \",current_accuracy)\n",
    "    print(\"Precision - \",current_precision)\n",
    "    \n",
    "    accuracy_scores.append(current_accuracy)\n",
    "    precision_scores.append(current_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "e32610ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_df = pd.DataFrame({'Algorithm':clfs.keys(),'Accuracy_num_chars':accuracy_scores,'Precision_num_chars':precision_scores}).sort_values('Precision_num_chars',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "b937591b",
   "metadata": {},
   "outputs": [],
   "source": [
    "performance_df_3 = performance_df_2.merge(temp_df,on='Algorithm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "941225b9",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>KN</td>\n",
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       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.973888</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.982759</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.982759</td>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.981651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ETC</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.982906</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.974576</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.974576</td>\n",
       "      <td>0.980658</td>\n",
       "      <td>0.975806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.972921</td>\n",
       "      <td>0.974138</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.974790</td>\n",
       "      <td>0.966151</td>\n",
       "      <td>0.925620</td>\n",
       "      <td>0.866538</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.961315</td>\n",
       "      <td>0.945455</td>\n",
       "      <td>0.960348</td>\n",
       "      <td>0.929204</td>\n",
       "      <td>0.960348</td>\n",
       "      <td>0.929204</td>\n",
       "      <td>0.964217</td>\n",
       "      <td>0.931624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.951644</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.970297</td>\n",
       "      <td>0.967118</td>\n",
       "      <td>0.964286</td>\n",
       "      <td>0.961315</td>\n",
       "      <td>0.962264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>xgb</td>\n",
       "      <td>0.969052</td>\n",
       "      <td>0.934426</td>\n",
       "      <td>0.971954</td>\n",
       "      <td>0.943089</td>\n",
       "      <td>0.971954</td>\n",
       "      <td>0.943089</td>\n",
       "      <td>0.970986</td>\n",
       "      <td>0.942623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>0.952611</td>\n",
       "      <td>0.923810</td>\n",
       "      <td>0.947776</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.947776</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.951644</td>\n",
       "      <td>0.931373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BgC</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.862595</td>\n",
       "      <td>0.957447</td>\n",
       "      <td>0.867188</td>\n",
       "      <td>0.957447</td>\n",
       "      <td>0.867188</td>\n",
       "      <td>0.966151</td>\n",
       "      <td>0.899225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DT</td>\n",
       "      <td>0.935203</td>\n",
       "      <td>0.838095</td>\n",
       "      <td>0.932302</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.932302</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.944874</td>\n",
       "      <td>0.878505</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Algorithm  Accuracy  Precision  Accuracy_max_ft_3000  \\\n",
       "0         KN  0.900387   1.000000              0.905222   \n",
       "1         NB  0.959381   1.000000              0.970986   \n",
       "2         RF  0.973888   1.000000              0.974855   \n",
       "3        ETC  0.975822   0.982906              0.974855   \n",
       "4        SVC  0.972921   0.974138              0.975822   \n",
       "5   AdaBoost  0.961315   0.945455              0.960348   \n",
       "6         LR  0.951644   0.940000              0.958414   \n",
       "7        xgb  0.969052   0.934426              0.971954   \n",
       "8       GBDT  0.952611   0.923810              0.947776   \n",
       "9        BgC  0.958414   0.862595              0.957447   \n",
       "10        DT  0.935203   0.838095              0.932302   \n",
       "\n",
       "    Precision_max_ft_3000  Accuracy_scaling  Precision_scaling  \\\n",
       "0                1.000000          0.905222           0.976190   \n",
       "1                1.000000          0.978723           0.946154   \n",
       "2                0.982759          0.974855           0.982759   \n",
       "3                0.974576          0.974855           0.974576   \n",
       "4                0.974790          0.966151           0.925620   \n",
       "5                0.929204          0.960348           0.929204   \n",
       "6                0.970297          0.967118           0.964286   \n",
       "7                0.943089          0.971954           0.943089   \n",
       "8                0.920000          0.947776           0.920000   \n",
       "9                0.867188          0.957447           0.867188   \n",
       "10               0.833333          0.932302           0.833333   \n",
       "\n",
       "    Accuracy_num_chars  Precision_num_chars  \n",
       "0             0.935203             0.831776  \n",
       "1             0.941006             1.000000  \n",
       "2             0.968085             0.981651  \n",
       "3             0.980658             0.975806  \n",
       "4             0.866538             0.000000  \n",
       "5             0.964217             0.931624  \n",
       "6             0.961315             0.962264  \n",
       "7             0.970986             0.942623  \n",
       "8             0.951644             0.931373  \n",
       "9             0.966151             0.899225  \n",
       "10            0.944874             0.878505  "
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_df_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "e36edb67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultinomialNB TfidfVectorizer with max_features=3000\n",
      "accuracy: 0.9709864603481625\n",
      "precision: 1.0\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf = TfidfVectorizer(max_features=3000)\n",
    "x = tfidf.fit_transform(df[\"transformed_text\"]).toarray()\n",
    "y = df[\"target\"].values\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=2)\n",
    "\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "mnb = MultinomialNB()\n",
    "\n",
    "mnb.fit(x_train,y_train)\n",
    "y_pred = mnb.predict(x_test)\n",
    "print(\"MultinomialNB TfidfVectorizer with max_features=3000\")\n",
    "print(f\"accuracy: {accuracy_score(y_test,y_pred)}\")\n",
    "print(f\"precision: {precision_score(y_test,y_pred)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "8eb94b3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(tfidf,open('vectorizer.pkl','wb'))\n",
    "pickle.dump(mnb,open('model.pkl','wb'))"
   ]
  }
 ],
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