11_SMS Spam Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import nltk\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# read the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>v1</th>\n",
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       "      <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",
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      ],
      "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sms=pd.read_csv('spam.csv', encoding='latin-1')\n",
    "sms.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <th>label</th>\n",
       "      <th>text</th>\n",
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       "  <tbody>\n",
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       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
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       "    <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",
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      ],
      "text/plain": [
       "  label                                               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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sms=sms.drop([\"Unnamed: 2\",\"Unnamed: 3\",\"Unnamed: 4\"],axis=1)\n",
    "sms=sms.rename(columns={\"v1\":\"label\",\"v2\":\"text\"})\n",
    "sms.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " no of rows 5572\n"
     ]
    }
   ],
   "source": [
    "print(\" no of rows\", len(sms))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ham     4825\n",
       "spam     747\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sms.label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5572</td>\n",
       "      <td>5572</td>\n",
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       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>5169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>ham</td>\n",
       "      <td>Sorry, I'll call later</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>4825</td>\n",
       "      <td>30</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       label                    text\n",
       "count   5572                    5572\n",
       "unique     2                    5169\n",
       "top      ham  Sorry, I'll call later\n",
       "freq    4825                      30"
      ]
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     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sms.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>text</th>\n",
       "      <th>length</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>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>29</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>155</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>49</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>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                               text  length\n",
       "0   ham  Go until jurong point, crazy.. Available only ...     111\n",
       "1   ham                      Ok lar... Joking wif u oni...      29\n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...     155\n",
       "3   ham  U dun say so early hor... U c already then say...      49\n",
       "4   ham  Nah I don't think he goes to usf, he lives aro...      61"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sms['length']=sms['text'].apply(len)\n",
    "sms.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2507f943a08>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sms['length'].plot(bins=50, kind='hist',figsize=(10,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x000002507FEDFF08>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000002507FEF21C8>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VPVJVH1rSdkZV3VpV90yPz16y7rqqOlxVd1fVZUvav7GqPjit+8WqqrX/dQAAAFhrJzPi+MYklx/Tti/Jbd19YZLbpuVU1UVJ9ia5eNrn+qo6bdrnl5Jck9mNhS9c5jUBAABYQCcMjt39ziQfP6b5yiQ3Ts9vTHLVkvabuvvR7r43yeEkl1TVOUme2d3v6u5O8stL9gEAAGCBrfQcx7O7+6EkmR7PmtrPS/LAku2OTG3nTc+PbV9WVV1TVYeq6tDRo0dXWCIAAABrYa0vjrPceYs9aF9Wd9/Q3Xu6e8/OnTvXrDgAAABO3UqD48PT9NNMj49M7UeSnL9ku11JHpzady3TDgAAwILbscL9Dia5OsmB6fGWJe1vrqqfT3JuZhfBub27H6+qT1fVC5L8UZLvTfJ/rqrydbZ739uOu+6+A1dsYCUAAADzdcLgWFVvSfLCJGdW1ZEkr8osMN5cVa9Icn+SlyZJd99RVTcnuTPJY0mu7e7Hp5f6ocyu0PrUJG+ffgAAAFhwJwyO3f2y46y69Djb70+yf5n2Q0mef0rVAQAAMHdrfXEcAAAAthjBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQAAgCHBEQC2kar6p1V1R1V9qKreUlVfVFVnVNWtVXXP9PjsedcJwGIRHAFgm6iq85L8SJI93f38JKcl2ZtkX5LbuvvCJLdNywDwWYIjAGwvO5I8tap2JHlakgeTXJnkxmn9jUmumlNtACwowREAtonu/miSf5Hk/iQPJflUd78jydnd/dC0zUNJzppflQAsIsERALaJ6dzFK5NckOTcJE+vqpefwv7XVNWhqjp09OjR9SoTgAUkOALA9vHtSe7t7qPd/Zkkv5HkW5I8XFXnJMn0+MhyO3f3Dd29p7v37Ny5c8OKBmD+BEcA2D7uT/KCqnpaVVWSS5PcleRgkqunba5Ocsuc6gNgQe2YdwEAwMbo7j+qqrcmeW+Sx5L8SZIbkjwjyc1V9YrMwuVL51clAItIcASAbaS7X5XkVcc0P5rZ6CMALMtUVQAAAIYERwAAAIZWFRyr6p9W1R1V9aGqektVfVFVnVFVt1bVPdPjs5dsf11VHa6qu6vqstWXDwAAwHpbcXCsqvOS/EiSPd39/CSnJdmbZF+S27r7wiS3Tcupqoum9RcnuTzJ9VV12urKBwAAYL2tdqrqjiRPraodSZ6W5MHMbix847T+xiRXTc+vTHJTdz/a3fcmOZzkklW+PwAAAOtsxcGxuz+a5F9kdtnuh5J8qrvfkeTs7n5o2uahJGdNu5yX5IElL3FkagMAAGCBrWaq6rMzG0W8IMm5SZ5eVS8f7bJMWx/nta+pqkNVdejo0aMrLREAAIA1sJqpqt+e5N7uPtrdn0nyG0m+JcnDVXVOkkyPj0zbH0ly/pL9d2U2tfXzdPcN3b2nu/fs3LlzFSUCAACwWqsJjvcneUFVPa2qKrMbB9+V5GCSq6dtrk5yy/T8YJK9VXV6VV2Q5MIkt6/i/QEAANgAO1a6Y3f/UVW9Ncl7kzyW5E+S3JDkGUlurqpXZBYuXzptf0dV3Zzkzmn7a7v78VXWDwAAwDpbcXBMku5+VZJXHdP8aGajj8ttvz/J/tW8JwAAABtrtbfjAAAAYIsTHAEAABgSHAEAABgSHAEAABgSHAEAABgSHAEAABgSHAEAABgSHAEAABgSHAEAABgSHAEAABgSHAEAABgSHAEAABgSHAEAABgSHAEAABgSHAEAABgSHAEAABgSHAEAABgSHAEAABgSHAEAABjaMe8CAACA1dm9723HXXffgSs2sBK2KiOOAAAADAmOAAAADAmOAAAADAmOAAAADAmOAAAADAmOAAAADAmOAAAADAmOAAAADAmOAAAADAmOAAAADAmOAAAADAmOAAAADAmOAAAADAmOAAAADAmOAAAADAmOAAAADAmOAAAADAmOAAAADAmOAAAADAmOAAAADO2YdwEAwMapqmcleW2S5yfpJD+Q5O4kv5pkd5L7knxXd39iTiUCa2z3vrfNuwS2ACOOALC9vCbJ73T3Vyb52iR3JdmX5LbuvjDJbdMyAHyW4AgA20RVPTPJP0jyuiTp7v/c3Z9McmWSG6fNbkxy1XwqBGBRCY4AsH18WZKjSd5QVX9SVa+tqqcnObu7H0qS6fGs5Xauqmuq6lBVHTp69OjGVQ3A3AmOALB97EjyDUl+qbu/Psl/yilMS+3uG7p7T3fv2blz53rVCMACEhwBYPs4kuRId//RtPzWzILkw1V1TpJMj4/MqT4AFpTgCADbRHd/LMkDVfW8qenSJHcmOZjk6qnt6iS3zKE8ABaY23EAwPbyPyZ5U1V9YZI/T/L9mX2RfHNVvSLJ/UleOsf6AFhAqwqOp3ovqKq6Lskrkjye5Ee6+3dX8/4AwKnp7vcl2bPMqks3uhYANo/VTlU96XtBVdVFSfYmuTjJ5Umur6rTVvn+AAAArLMVB8cV3AvqyiQ3dfej3X1vksNJLlnp+wMAALAxVjPieKr3gjovyQNL9j8ytX0e94kCAABYHKsJjqd6L6hapq2X29B9ogAAABbHai6Os9y9oPZluhdUdz90zL2gjiQ5f8n+u5I8uIr3n5vd+9523HX3HbhiAysBAABYfysecVzBvaAOJtlbVadX1QVJLkxy+0rfHwAAgI2x2vs4nvS9oLr7jqq6ObNw+ViSa7v78VW+PwAAAOtsVcHxVO8F1d37k+xfzXsCAACwsVZ7H0cAAAC2uNVOVQUAALYRF4rcnow4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMLRj3gVsNbv3ve246+47cMUGVgIAy6uq05IcSvLR7n5xVZ2R5FeT7E5yX5Lv6u5PzK9CABaNEUcA2H5+NMldS5b3Jbmtuy9Mctu0DACfJTgCwDZSVbuSXJHktUuar0xy4/T8xiRXbXRdACw2U1UBYHv5V0l+MskXL2k7u7sfSpLufqiqzlpux6q6Jsk1SfLc5z53veuEbclpTywqI44AsE1U1YuTPNLd71nJ/t19Q3fv6e49O3fuXOPqAFhkqw6OVXVaVf1JVf27afmMqrq1qu6ZHp+9ZNvrqupwVd1dVZet9r0BgFPyrUleUlX3JbkpybdV1a8kebiqzkmS6fGR+ZUIwCJai6mqT5xg/8xp+YkT7A9U1b5p+ZVVdVGSvUkuTnJukt+rqr/X3Y+vQQ0AwAl093VJrkuSqnphkp/o7pdX1c8luTrJgenxlrkVCRzXaBorrLdVjTie4gn2Vya5qbsf7e57kxxOcslq3h8AWBMHkryoqu5J8qJpGQA+a7Ujjqdygv15Sd69ZLsjUxsAsMG6+/eT/P70/C+TXDrPegBYbCsOjktPsJ+mu5xwl2Xa+jiv7aptAABsSaacshmtZqrqqZ5gfyTJ+Uv235XkweVe2FXbAAAAFseKg2N3X9fdu7p7d2YXvfn33f3yJAczO7E++dwT7A8m2VtVp1fVBUkuTHL7iisHAABgQ6zFVVWPdSDJzVX1iiT3J3lpknT3HVV1c5I7kzyW5FpXVAUAAFh8axIcT/YE++7en2T/WrwnAAAAG2NVt+MAAABg6xMcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGNox7wJYud373nbcdfcduGIDKwEAALYyI44AAAAMCY4AAAAMmaq6gUwtBQAANiPBcUEIlQAAwKIyVRUAAIAhwREAAIAhwREAAIAhwREAAIAhwREAAIAhwREAAIAhwREAAIAhwREAAIAhwREAtomqOr+q/t+ququq7qiqH53az6iqW6vqnunx2fOuFYDFsmPeBXBiu/e9bd4lALA1PJbkx7v7vVX1xUneU1W3Jvm+JLd194Gq2pdkX5JXzrFOABaMEUcA2Ca6+6Hufu/0/NNJ7kpyXpIrk9w4bXZjkqvmUyEAi2rFwXEl012q6rqqOlxVd1fVZWvxCwAAp66qdif5+iR/lOTs7n4omYXLJGcdZ59rqupQVR06evToRpUKwAJYzYjjE9NdvirJC5JcW1UXZTa95bbuvjDJbdNypnV7k1yc5PIk11fVaaspHgA4dVX1jCS/nuTHuvuvTna/7r6hu/d0956dO3euX4EALJwVB8cVTHe5MslN3f1od9+b5HCSS1b6/gDAqauqL8gsNL6pu39jan64qs6Z1p+T5JF51QfAYlqTcxxPcrrLeUkeWLLbkaltudczFQYA1lhVVZLXJbmru39+yaqDSa6enl+d5JaNrg2Axbbq4HgK011qmbZebkNTYQBgXXxrku9J8m1V9b7p579JciDJi6rqniQvmpYB4LNWdTuO0XSX7n7omOkuR5Kcv2T3XUkeXM37AwAnr7v/IMt/kZskl25kLQBsLisOjicx3eVAPne6y8Ekb66qn09ybpILk9y+0vcHAICNMLqn9n0HrtjASmB+VjPi+MR0lw9W1fumtp/KLDDeXFWvSHJ/kpcmSXffUVU3J7kzsyuyXtvdj6/i/QEAANgAKw6OK5nu0t37k+xf6XsCAACw8VZ1jiMAALC80RRX2GzW5HYcAAAAbF2CIwAAAEOCIwAAAEPb4hxH88sBAABWzogjAAAAQ9tixBEAgO1hpTPN7jtwxRpXAluLEUcAAACGBEcAAACGTFUFAIAVchFGtgsjjgAAAAwJjgAAAAwJjgAAAAwJjgAAAAwJjgAAAAwJjgAAAAwJjgAAAAwJjgAAAAztmHcBAACsj9HN6e87cMUGVrIym71+2EqMOAIAADAkOAIAADAkOAIAADAkOAIAADAkOAIAADDkqqoAAGvM1UDZrvy/v3UZcQQAAGBIcAQAAGBIcAQAAGBIcAQAAGBIcAQAAGDIVVUBADgpi3TFzFEtwNoz4ggAAMCQ4AgAAMCQqaoAANvQRk473QzTSjdDjTBPRhwBAAAYMuK4RS3SyesAAMDmJjgCAADrzsDG5maqKgAAAEOCIwAAAEOmqgIAW95Kp8hthql1i3I10EWpA1gfRhwBAAAYEhwBAAAYMlUVAACYq5VMdV6U6eLbheC4DW2G8zUAAIDFYaoqAAAAQ0YcAQA20PFm/izSrB9XSAWOJTjyOVZ6oFjJwc6UWQAA2Bw2PDhW1eVJXpPktCSv7e4DG10Da28zfHsKwPE5PgMwsqHBsapOS/J/JXlRkiNJ/riqDnb3nRtZB4vPaCTAxpnX8Xk9pkOu9QyY9dhvo14Ptrr1+Ly4GT6DzqvGjR5xvCTJ4e7+8ySpqpuSXJlEcNyiNvoguJI/pI3+49sM/yAB247jMwBDGx0cz0vywJLlI0m+eYNrYJPbyG+GFyn4Hs+ihM1FGTnYDDbDlweboUbWlOMzAEMbHRxrmbb+vI2qrklyzbT411V19yre88wkf7GK/bcTfXXyFgEpX4EAAAerSURBVKav6tXzruCkrKi/Nsnvtqbq1Yvz/9bxrNF/ly9dk1dhrczj+LwuFvzfjYX/+94E9OHqbYs+XI9/C5a85sL24Xoeozc6OB5Jcv6S5V1JHjx2o+6+IckNa/GGVXWou/esxWttdfrq5OmrU6O/Tp6+Yk42/Pi8Hfn7Xj19uHr6cPW2ax8+ZYPf74+TXFhVF1TVFybZm+TgBtcAAHwux2cAhjZ0xLG7H6uqH07yu5ld7vv13X3HRtYAAHwux2cATmTD7+PY3b+d5Lc38C1NqTl5+urk6atTo79Onr5iLuZwfN6O/H2vnj5cPX24etuyD6v78859BwAAgM/a6HMcAQAA2GQERwAAAIYERwAAAIY2/OI4662qvjLJlUnOy+zmxQ8mOdjdd821sAVUVZXkknxuX93eTnz9PPrq5OmrU6O/AGCxOVbPbKmL41TVK5O8LMlNmd3MOJndxHhvkpu6+8C8als0VfUdSa5Pck+Sj07Nu5J8RZL/obvfMa/aFo2+Onn66tToL9i6qupLklyX5KokO6fmR5LckuRAd39yXrVtJj6wr54+XB3H6idtteD4Z0ku7u7PHNP+hUnu6O4L51PZ4qmqu5L819193zHtFyT57e7+qrkUtoD01cnTV6dGf8HWVVW/m+TfJ7mxuz82tf0XSa5O8u3d/aJ51rcZ+MC+evpw9Ryrn7TVpqr+XZJzk3zkmPZzpnU8aUeeHJVd6qNJvmCDa1l0+urk6atTo79g69rd3a9e2jAFyFdX1Q/MqabN5jWZhez7ljY+8YE9ybb5wL4K+nD1HKsnWy04/liS26rqniQPTG3PzexblR+eW1WL6fVJ/riqbsqTfXV+ZtN6Xze3qhaTvjp5+urU6C/Yuj5SVT+Z2Yjjw0lSVWcn+b48+ffOmA/sq6cPV8+xerKlpqomSVU9JU/O467M/lj+uLsfn2thC6iqLkryknxuXx3s7jvnWtgC0lcnT1+dGv0FW1NVPTvJvswu2Hd2ZueWPZzkYJJXd/fH51jeplBV1yX5rsyuXXHsB/abu/t/n1dtm4U+XBuO1TNbLjgCACyaqvr7mX2x/UHnlZ08H9hXr6q+Kk/ecUAfsmKC4zblam8nT1+dPH11avQXbF1VdXt3XzI9/8Ek1yb5t0m+I8lvudI7bA6O1U96yrwLYG5uTvKJJC/s7ud093OS/FdJPpnk1+Za2eLRVydPX50a/QVb19Lzx/77JN/R3T+bWXD87vmUtLlU1ZdU1YGq+tOq+svp566p7Vnzrm8zqKrLlzz/kqp6bVV9oKrePJ1zy4k5Vk+MOG5TVXV3dz/vVNdtR/rq5OmrU6O/YOuqqvcneWFmX9L/bnfvWbLuT7r76+dV22YxuKXJ9yW51C1NTqyq3tvd3zA9f22SjyX5N0m+M8k/7O6r5lnfZuBY/SQjjtvXR6rqJ5d+21RVZ1fVK+Nqb8fSVydPX50a/QVb15ckeU+SQ0nOmAJPquoZmZ1nxont7u5XPxEak9ktTaZpvs+dY12b1Z7u/mfd/ZHu/oUku+dd0CbhWD0RHLevf5zkOUn+Q1V9oqo+nuT3k5yR2dW3eNKxffWJzPrqOdFXx/L/1anRX7BFdffu7v6y7r5genwi/Pxdkn80z9o2ER/YV++sqvqfqurHkzyzqpZ+aSEHnBzH6ompqttYVX1lkl1J3t3df72k/fLu/p35Vbb4qur/6e7vmXcdi6aqvjnJn3b3p6rqaZldiv4bktyR5H/r7k/NtcAFU1VfmORlST7a3b9XVd+d5FuS3Jnkhu7+zFwLBJijY25pctbU/MQtTQ509yfmVdtmUVWvOqbp+u4+Oo2A/x/d/b3zqGuz8Zl5RnDcpqrqRzK7wttdSb4uyY929y3Tus/OhyepqoPLNH9bZuddpLtfsrEVLa6quiPJ13b3Y1V1Q5L/lOTXk1w6tX/nXAtcMFX1psxuzvzUJJ9K8vQkv5lZf1V3Xz3H8gAWVlV9f3e/Yd51bGb68OT4zPykHfMugLn575J8Y3f/dVXtTvLWqtrd3a+Jcy+OtSuzEaDXZnYD50ryTUn+5TyLWlBP6e7Hpud7lvxj+gdV9b55FbXAvrq7v6aqdiT5aJJzu/vxqvqVJO+fc20Ai+xnkwg9q6MPT47PzBPBcfs67Ymh9u6+r6pemNkfwpdmm/0RnIQ9SX40yU8n+Z+7+31V9bfd/R/mXNci+tCSbzDfX1V7uvtQVf29JKZdfr6nTNNVn57kaZldTOPjSU7P517KH2DbqaoPHG9VEreSOAn6cE34zDwRHLevj1XV13X3+5Jk+hblxUlen+Sr51vaYunuv0vyC1X1a9Pjw/G3czw/mOQ1VfXPkvxFkndV1QOZXcTgB+da2WJ6XZI/TXJaZl9M/FpV/XmSFyS5aZ6FASyAs5Ncltk99JaqJP9x48vZlPTh6vnMPHGO4zZVVbuSPLb0EtdL1n1rd//hHMraFKrqiiTf2t0/Ne9aFlVVfXGSL8ssYB/p7ofnXNLCqqpzk6S7H5xuaP3tSe7v7tvnWxnAfFXV65K8obv/YJl1b+7u/3YOZW0q+nD1fGZ+kuAIAADAkPu3AAAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMCQ4AgAAMPT/A+hkknqzyFs/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sms.hist(column='length', by='label', bins=50, figsize=(15,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>text</th>\n",
       "      <th>length</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label                                               text  length\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"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sms.loc[:,'label']=sms.label.map({'ham':0, 'spam':1})\n",
    "sms.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implement Bag of Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<5572x8672 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 73916 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "count=CountVectorizer()\n",
    "input=['REMINDER FROM O2: To get 2.50 pounds free call credit and details of great offers pls reply 2 this text with your valid name, house no and postcode']\n",
    "\n",
    "text=count.fit_transform(sms['text'])\n",
    "\n",
    "x_train, x_test, y_train, y_test= train_test_split(text, sms['label'], test_size=0.20, random_state=1)\n",
    "text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4457, 8672)\n",
      "(1115, 8672)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "\n",
    "input=text[5571]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# implementation of ML Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "              beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "              hidden_layer_sizes=(100,), learning_rate='constant',\n",
       "              learning_rate_init=0.001, max_fun=15000, max_iter=200,\n",
       "              momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n",
       "              power_t=0.5, random_state=None, shuffle=True, solver='adam',\n",
       "              tol=0.0001, validation_fraction=0.1, verbose=False,\n",
       "              warm_start=False)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "model=MLPClassifier()\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 ... 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "prediction=model.predict(x_test)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multinomial NB\n",
      "Accuracy score: 0.97847533632287\n",
      "Precision score: 0.891156462585034\n",
      "Recall score: 0.9424460431654677\n",
      "F1 score: 0.9160839160839161\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "\n",
    "print(\"Multinomial NB\")\n",
    "print(\"Accuracy score: {}\". format(accuracy_score(y_test, prediction)) )\n",
    "print(\"Precision score: {}\". format(precision_score(y_test, prediction)) )\n",
    "print(\"Recall score: {}\". format(recall_score(y_test, prediction)))\n",
    "print(\"F1 score: {}\". format(f1_score(y_test, prediction)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bernoulli NB\n",
      "Accuracy score: 0.9865470852017937\n",
      "Precision score: 0.984375\n",
      "Recall score: 0.9064748201438849\n",
      "F1 score: 0.9438202247191011\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "\n",
    "print(\"Bernoulli NB\")\n",
    "print(\"Accuracy score: {}\". format(accuracy_score(y_test, prediction)) )\n",
    "print(\"Precision score: {}\". format(precision_score(y_test, prediction)) )\n",
    "print(\"Recall score: {}\". format(recall_score(y_test, prediction)))\n",
    "print(\"F1 score: {}\". format(f1_score(y_test, prediction)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP Classifier\n",
      "Accuracy score: 0.9928251121076234\n",
      "Precision score: 1.0\n",
      "Recall score: 0.9424460431654677\n",
      "F1 score: 0.9703703703703704\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "\n",
    "print(\"MLP Classifier\")\n",
    "print(\"Accuracy score: {}\". format(accuracy_score(y_test, prediction)) )\n",
    "print(\"Precision score: {}\". format(precision_score(y_test, prediction)) )\n",
    "print(\"Recall score: {}\". format(recall_score(y_test, prediction)))\n",
    "print(\"F1 score: {}\". format(f1_score(y_test, prediction)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1x8672 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 5 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=sms['text']\n",
    "input=\"To get 2.50 pounds free call credit and details of great offers pls reply 2 this text\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Go until jurong point, crazy.. Available only ...\n",
       "1                           Ok lar... Joking wif u oni...\n",
       "2       Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3       U dun say so early hor... U c already then say...\n",
       "4       Nah I don't think he goes to usf, he lives aro...\n",
       "                              ...                        \n",
       "5567    This is the 2nd time we have tried 2 contact u...\n",
       "5568                Will Ì_ b going to esplanade fr home?\n",
       "5569    Pity, * was in mood for that. So...any other s...\n",
       "5570    The guy did some bitching but I acted like i'd...\n",
       "5571                           Rofl. Its true to its name\n",
       "Name: text, Length: 5572, dtype: object"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[len(data.index)]=\"hfwe ewfjb in jd eif\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Go until jurong point, crazy.. Available only ...\n",
       "1                           Ok lar... Joking wif u oni...\n",
       "2       Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3       U dun say so early hor... U c already then say...\n",
       "4       Nah I don't think he goes to usf, he lives aro...\n",
       "                              ...                        \n",
       "5568                Will Ì_ b going to esplanade fr home?\n",
       "5569    Pity, * was in mood for that. So...any other s...\n",
       "5570    The guy did some bitching but I acted like i'd...\n",
       "5571                           Rofl. Its true to its name\n",
       "5572                                 hfwe ewfjb in jd eif\n",
       "Name: text, Length: 5573, dtype: object"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "text=count.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<5573x8675 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 73921 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "input=text[5572]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "dimension mismatch",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-150-69dca6417756>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    969\u001b[0m         \"\"\"\n\u001b[0;32m    970\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 971\u001b[1;33m         \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    972\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    973\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py\u001b[0m in \u001b[0;36m_predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    683\u001b[0m                                          layer_units[i + 1])))\n\u001b[0;32m    684\u001b[0m         \u001b[1;31m# forward propagate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 685\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_forward_pass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mactivations\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    686\u001b[0m         \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mactivations\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    687\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py\u001b[0m in \u001b[0;36m_forward_pass\u001b[1;34m(self, activations)\u001b[0m\n\u001b[0;32m    102\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_layers_\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    103\u001b[0m             activations[i + 1] = safe_sparse_dot(activations[i],\n\u001b[1;32m--> 104\u001b[1;33m                                                  self.coefs_[i])\n\u001b[0m\u001b[0;32m    105\u001b[0m             \u001b[0mactivations\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mintercepts_\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    106\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\extmath.py\u001b[0m in \u001b[0;36msafe_sparse_dot\u001b[1;34m(a, b, dense_output)\u001b[0m\n\u001b[0;32m    149\u001b[0m             \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    150\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 151\u001b[1;33m         \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0ma\u001b[0m \u001b[1;33m@\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    152\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    153\u001b[0m     if (sparse.issparse(a) and sparse.issparse(b)\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\scipy\\sparse\\base.py\u001b[0m in \u001b[0;36m__matmul__\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m    562\u001b[0m             raise ValueError(\"Scalar operands are not allowed, \"\n\u001b[0;32m    563\u001b[0m                              \"use '*' instead\")\n\u001b[1;32m--> 564\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__mul__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    565\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    566\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__rmatmul__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\scipy\\sparse\\base.py\u001b[0m in \u001b[0;36m__mul__\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m    518\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    519\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 520\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'dimension mismatch'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    521\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    522\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mul_multivector\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: dimension mismatch"
     ]
    }
   ],
   "source": [
    "model.predict(input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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