Predict.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> Gender Classification\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data pre-processing\n",
    "### Using Machine Learning to predict gender from an indivdual's name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We load our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/names_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>index</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index       name sex\n",
       "0      0       Mary   F\n",
       "1      1       Anna   F\n",
       "2      2       Emma   F\n",
       "3      3  Elizabeth   F\n",
       "4      4     Minnie   F"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preview first few rows\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 95025 entries, 0 to 95024\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   index   95025 non-null  int64 \n",
      " 1   name    95025 non-null  object\n",
      " 2   sex     95025 non-null  object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 2.2+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "285075"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning - Check columns for name consistency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'name', 'sex'], dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index     int64\n",
       "name     object\n",
       "sex      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for data types\n",
    "# We'll probably have to convert the name and sex columns to value types\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index    0\n",
       "name     0\n",
       "sex      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for duplicate values\n",
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "181800"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check to see if our dataset is balanced\n",
    "# Number of Female names\n",
    "data[data.sex == 'F'].size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "103275"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of Male names\n",
    "data[data.sex == 'M'].size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copyt the data frame for the future we want modify\n",
    "data_names = data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_names.sex.replace({'F': 0, 'M': 1}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_names.sex.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index     int64\n",
       "name     object\n",
       "sex       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_names.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xfeatures = data_names['name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Extraction\n",
    "cv = CountVectorizer()\n",
    "X = cv.fit_transform(Xfeatures)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# Save Vectorizer\n",
    "from sklearn.externals import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "gender_vectorizer = open('gender_vectorier.pkl', 'wb')\n",
    "joblib.dump(cv, gender_vectorizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "gender_vectorizer.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
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       " 'abayomi',\n",
       " 'abb',\n",
       " 'abba',\n",
       " 'abbaas',\n",
       " 'abbagail',\n",
       " 'abbagale',\n",
       " 'abbagayle',\n",
       " 'abbas',\n",
       " 'abbe',\n",
       " 'abbee',\n",
       " 'abbegail',\n",
       " 'abbegale',\n",
       " 'abbegayle',\n",
       " 'abbey',\n",
       " 'abbeygail',\n",
       " 'abbeygale',\n",
       " 'abbi',\n",
       " 'abbie',\n",
       " 'abbiegail',\n",
       " 'abbiegale',\n",
       " 'abbiegayle',\n",
       " 'abbigael',\n",
       " 'abbigail',\n",
       " 'abbigaile',\n",
       " 'abbigal',\n",
       " 'abbigale',\n",
       " 'abbigayl',\n",
       " 'abbigayle',\n",
       " 'abbilyn',\n",
       " 'abbot',\n",
       " 'abbott',\n",
       " 'abbra',\n",
       " 'abbriella',\n",
       " 'abbrielle',\n",
       " 'abby',\n",
       " 'abbye',\n",
       " 'abbygael',\n",
       " 'abbygail',\n",
       " 'abbygaile',\n",
       " 'abbygale',\n",
       " 'abbygayl',\n",
       " 'abbygayle',\n",
       " 'abbylynn',\n",
       " 'abcde',\n",
       " 'abd',\n",
       " 'abdala',\n",
       " 'abdalah',\n",
       " 'abdalla',\n",
       " 'abdallah',\n",
       " 'abdalrahman',\n",
       " 'abdalrhman',\n",
       " 'abdel',\n",
       " 'abdelaziz',\n",
       " 'abdelhadi',\n",
       " 'abdelkareem',\n",
       " 'abdelkarim',\n",
       " 'abdellah',\n",
       " 'abdelrahman',\n",
       " 'abdelrhman',\n",
       " 'abderrahman',\n",
       " 'abdi',\n",
       " 'abdias',\n",
       " 'abdiasis',\n",
       " 'abdiaziz',\n",
       " 'abdiel',\n",
       " 'abdifatah',\n",
       " 'abdihafid',\n",
       " 'abdihakim',\n",
       " 'abdihamid',\n",
       " 'abdikadir',\n",
       " 'abdikarim',\n",
       " 'abdikhaliq',\n",
       " 'abdilahi',\n",
       " 'abdimalik',\n",
       " 'abdinajib',\n",
       " 'abdinasir',\n",
       " 'abdiqani',\n",
       " 'abdirahim',\n",
       " 'abdirahin',\n",
       " 'abdirahman',\n",
       " 'abdirashid',\n",
       " 'abdirisaq',\n",
       " 'abdirizak',\n",
       " 'abdisalam',\n",
       " 'abdisalan',\n",
       " 'abdisamad',\n",
       " 'abdishakur',\n",
       " 'abdiwahab',\n",
       " 'abdiwali',\n",
       " 'abdo',\n",
       " 'abdon',\n",
       " 'abdou',\n",
       " 'abdoul',\n",
       " 'abdoulaye',\n",
       " 'abdoulaziz',\n",
       " 'abdoulie',\n",
       " 'abdourahman',\n",
       " 'abdourahmane',\n",
       " 'abdrahman',\n",
       " 'abdrew',\n",
       " 'abdu',\n",
       " 'abdual',\n",
       " 'abduallah',\n",
       " 'abduel',\n",
       " 'abdul',\n",
       " 'abdula',\n",
       " 'abdulah',\n",
       " 'abdulahad',\n",
       " 'abdulahi',\n",
       " 'abdulai',\n",
       " 'abdulaye',\n",
       " 'abdulazeem',\n",
       " 'abdulazeez',\n",
       " 'abdulaziz',\n",
       " 'abdulbari',\n",
       " 'abdulbasit',\n",
       " 'abdule',\n",
       " 'abdulelah',\n",
       " 'abdulhadi',\n",
       " 'abdulhakeem',\n",
       " 'abdulhakim',\n",
       " 'abdulhalim',\n",
       " 'abdulhameed',\n",
       " 'abdulhamid',\n",
       " 'abduljabbar',\n",
       " 'abduljaleel',\n",
       " 'abduljalil',\n",
       " 'abdulkadir',\n",
       " 'abdulkareem',\n",
       " 'abdulkarim',\n",
       " 'abdulkhaliq',\n",
       " 'abdull',\n",
       " 'abdulla',\n",
       " 'abdullah',\n",
       " 'abdullahi',\n",
       " 'abdullatif',\n",
       " 'abdulloh',\n",
       " 'abdulmajeed',\n",
       " 'abdulmajid',\n",
       " 'abdulmalek',\n",
       " 'abdulmalik',\n",
       " 'abdulmohsen',\n",
       " 'abdulnasir',\n",
       " 'abdulqadir',\n",
       " 'abdulraheem',\n",
       " 'abdulrahim',\n",
       " 'abdulrahman',\n",
       " 'abdulrazaq',\n",
       " 'abdulrehman',\n",
       " 'abdulrhman',\n",
       " 'abdulsalam',\n",
       " 'abdulsamad',\n",
       " 'abdulwadud',\n",
       " 'abdulwahab',\n",
       " 'abdulwahid',\n",
       " 'abdur',\n",
       " 'abdurahman',\n",
       " 'abdurahmon',\n",
       " 'abdurraheem',\n",
       " 'abdurrahim',\n",
       " 'abdurrahmaan',\n",
       " 'abdurrahman',\n",
       " 'abdurrehman',\n",
       " 'abdussamad',\n",
       " 'abe',\n",
       " 'abeal',\n",
       " 'abed',\n",
       " 'abedallah',\n",
       " 'abedalrahman',\n",
       " 'abednego',\n",
       " 'abeeha',\n",
       " 'abeer',\n",
       " 'abeera',\n",
       " 'abegail',\n",
       " 'abegale',\n",
       " 'abegayle',\n",
       " 'abel',\n",
       " 'abela',\n",
       " 'abelardo',\n",
       " 'abelina',\n",
       " 'abelino',\n",
       " 'abell',\n",
       " 'abella',\n",
       " 'abem',\n",
       " 'aben',\n",
       " 'abena',\n",
       " 'abenezer',\n",
       " 'abeni',\n",
       " 'aber',\n",
       " 'aberdeen',\n",
       " 'aberham',\n",
       " 'abernathy',\n",
       " 'abert',\n",
       " 'abery',\n",
       " 'abey',\n",
       " 'abgail',\n",
       " 'abha',\n",
       " 'abhay',\n",
       " 'abheek',\n",
       " 'abhi',\n",
       " 'abhigna',\n",
       " 'abhijay',\n",
       " 'abhijeet',\n",
       " 'abhijit',\n",
       " 'abhijot',\n",
       " 'abhik',\n",
       " 'abhilash',\n",
       " 'abhimanyu',\n",
       " 'abhinav',\n",
       " 'abhinay',\n",
       " 'abhinaya',\n",
       " 'abhiraam',\n",
       " 'abhiraj',\n",
       " 'abhiram',\n",
       " 'abhirup',\n",
       " 'abhishek',\n",
       " 'abhyuday',\n",
       " 'abi',\n",
       " 'abia',\n",
       " 'abiageal',\n",
       " 'abiah',\n",
       " 'abian',\n",
       " 'abianna',\n",
       " 'abibail',\n",
       " 'abid',\n",
       " 'abida',\n",
       " 'abidah',\n",
       " 'abidan',\n",
       " 'abie',\n",
       " 'abiegail',\n",
       " 'abiel',\n",
       " 'abiela',\n",
       " 'abiella',\n",
       " 'abiezer',\n",
       " 'abigael',\n",
       " 'abigaelle',\n",
       " 'abigahil',\n",
       " 'abigai',\n",
       " 'abigail',\n",
       " 'abigaile',\n",
       " 'abigailgrace',\n",
       " 'abigaille',\n",
       " 'abigailmarie',\n",
       " 'abigailrose',\n",
       " 'abigal',\n",
       " 'abigale',\n",
       " 'abigayil',\n",
       " 'abigayl',\n",
       " 'abigayle',\n",
       " 'abigeal',\n",
       " 'abigel',\n",
       " 'abigial',\n",
       " 'abiha',\n",
       " 'abihail',\n",
       " 'abijah',\n",
       " 'abilene',\n",
       " 'abilgail',\n",
       " 'abilio',\n",
       " 'abilyn',\n",
       " 'abilynn',\n",
       " 'abimael',\n",
       " 'abimbola',\n",
       " 'abimelec',\n",
       " 'abin',\n",
       " 'abinadab',\n",
       " 'abinadi',\n",
       " 'abinav',\n",
       " 'abinaya',\n",
       " 'abiodun',\n",
       " 'abiola',\n",
       " 'abiona',\n",
       " 'abir',\n",
       " 'abira',\n",
       " 'abiram',\n",
       " 'abirami',\n",
       " 'abisag',\n",
       " 'abisai',\n",
       " 'abish',\n",
       " 'abisha',\n",
       " 'abishai',\n",
       " 'abishek',\n",
       " 'abisola',\n",
       " 'abiud',\n",
       " 'abiyah',\n",
       " 'abla',\n",
       " 'able',\n",
       " 'abnel',\n",
       " 'abner',\n",
       " 'abney',\n",
       " 'abony',\n",
       " 'abou',\n",
       " 'aboubacar',\n",
       " 'aboubakar',\n",
       " 'abra',\n",
       " 'abraam',\n",
       " 'abraar',\n",
       " 'abrah',\n",
       " 'abraham',\n",
       " 'abrahan',\n",
       " 'abraheem',\n",
       " 'abrahem',\n",
       " 'abrahim',\n",
       " 'abrahm',\n",
       " 'abram',\n",
       " 'abran',\n",
       " 'abranda',\n",
       " 'abrar',\n",
       " 'abraxas',\n",
       " 'abrea',\n",
       " 'abreana',\n",
       " 'abreanna',\n",
       " 'abree',\n",
       " 'abreia',\n",
       " 'abren',\n",
       " 'abreona',\n",
       " 'abreonna',\n",
       " 'abrey',\n",
       " 'abreya',\n",
       " 'abrham',\n",
       " 'abri',\n",
       " 'abria',\n",
       " 'abriah',\n",
       " 'abrial',\n",
       " 'abriam',\n",
       " 'abrian',\n",
       " 'abriana',\n",
       " 'abrianna',\n",
       " 'abriannah',\n",
       " 'abrianne',\n",
       " 'abrie',\n",
       " 'abriel',\n",
       " 'abriela',\n",
       " 'abriele',\n",
       " 'abriella',\n",
       " 'abrielle',\n",
       " 'abrien',\n",
       " 'abrienne',\n",
       " 'abrigail',\n",
       " 'abrihet',\n",
       " 'abril',\n",
       " 'abrille',\n",
       " 'abrina',\n",
       " 'abrion',\n",
       " 'abriona',\n",
       " 'abrionna',\n",
       " 'abrish',\n",
       " 'abriya',\n",
       " 'abriyah',\n",
       " 'abriyana',\n",
       " 'abrom',\n",
       " 'abron',\n",
       " 'abrum',\n",
       " 'abry',\n",
       " 'abryana',\n",
       " 'abryanna',\n",
       " 'abryella',\n",
       " 'abryelle',\n",
       " 'abryl',\n",
       " 'absalat',\n",
       " 'absalom',\n",
       " 'absalon',\n",
       " 'abshir',\n",
       " 'absidy',\n",
       " 'abtin',\n",
       " 'abu',\n",
       " 'abubacar',\n",
       " 'abubacarr',\n",
       " 'abubakar',\n",
       " 'abubakarr',\n",
       " 'abubakary',\n",
       " 'abubaker',\n",
       " 'abubakr',\n",
       " 'abuk',\n",
       " 'abukar',\n",
       " 'abundio',\n",
       " 'aby',\n",
       " 'abyade',\n",
       " 'abyan',\n",
       " 'abygael',\n",
       " 'abygail',\n",
       " 'abygaile',\n",
       " 'abygale',\n",
       " 'abygayle',\n",
       " 'abyssinia',\n",
       " 'ac',\n",
       " 'acacia',\n",
       " 'acacius',\n",
       " 'acadia',\n",
       " 'acamas',\n",
       " 'acari',\n",
       " 'acasia',\n",
       " 'accacia',\n",
       " 'accalia',\n",
       " 'access',\n",
       " 'accie',\n",
       " 'accursio',\n",
       " 'ace',\n",
       " 'acea',\n",
       " 'acein',\n",
       " 'acel',\n",
       " 'acelin',\n",
       " 'acelino',\n",
       " 'acelyn',\n",
       " 'acelynn',\n",
       " 'acen',\n",
       " 'acencion',\n",
       " 'aceon',\n",
       " 'acer',\n",
       " 'aceson',\n",
       " 'acesyn',\n",
       " 'aceton',\n",
       " 'acey',\n",
       " 'aceyn',\n",
       " 'achai',\n",
       " 'achaia',\n",
       " 'achan',\n",
       " 'achante',\n",
       " 'achanti',\n",
       " 'achary',\n",
       " 'achazia',\n",
       " 'achel',\n",
       " 'acheron',\n",
       " 'achille',\n",
       " 'achilles',\n",
       " 'achilleus',\n",
       " 'achillies',\n",
       " 'achintya',\n",
       " 'achol',\n",
       " 'achraf',\n",
       " 'achsa',\n",
       " 'achsah',\n",
       " 'achyut',\n",
       " 'achyuth',\n",
       " 'acia',\n",
       " 'aciano',\n",
       " 'acie',\n",
       " 'aciel',\n",
       " 'acil',\n",
       " 'acire',\n",
       " 'ackeem',\n",
       " 'ackley',\n",
       " 'acob',\n",
       " 'acquanetta',\n",
       " 'acquanette',\n",
       " 'acsa',\n",
       " 'acura',\n",
       " 'acxel',\n",
       " 'acy',\n",
       " 'ad',\n",
       " 'ada',\n",
       " 'adabel',\n",
       " 'adabella',\n",
       " 'adabelle',\n",
       " 'adacia',\n",
       " 'adae',\n",
       " 'adael',\n",
       " 'adaelyn',\n",
       " 'adaeze',\n",
       " 'adagio',\n",
       " 'adah',\n",
       " 'adahir',\n",
       " 'adahli',\n",
       " 'adahlia',\n",
       " 'adahy',\n",
       " 'adai',\n",
       " 'adaia',\n",
       " 'adaiah',\n",
       " 'adaija',\n",
       " 'adaijah',\n",
       " 'adailyn',\n",
       " 'adain',\n",
       " 'adair',\n",
       " 'adaira',\n",
       " 'adaire',\n",
       " 'adairis',\n",
       " 'adaisha',\n",
       " 'adaisia',\n",
       " 'adaja',\n",
       " 'adajah',\n",
       " 'adaku',\n",
       " 'adal',\n",
       " 'adala',\n",
       " 'adalade',\n",
       " 'adalae',\n",
       " 'adalai',\n",
       " 'adalaide',\n",
       " 'adalay',\n",
       " 'adalaya',\n",
       " 'adalayde',\n",
       " 'adalbert',\n",
       " 'adalberto',\n",
       " 'adale',\n",
       " 'adalea',\n",
       " 'adaleah',\n",
       " 'adalee',\n",
       " 'adaleen',\n",
       " 'adaleena',\n",
       " 'adalei',\n",
       " 'adaleia',\n",
       " 'adaleigh',\n",
       " 'adaleine',\n",
       " 'adalen',\n",
       " 'adalena',\n",
       " 'adalene',\n",
       " 'adaley',\n",
       " 'adaleya',\n",
       " 'adaleyza',\n",
       " 'adalhi',\n",
       " 'adali',\n",
       " 'adalia',\n",
       " 'adaliah',\n",
       " 'adalicia',\n",
       " 'adalid',\n",
       " 'adalida',\n",
       " 'adalie',\n",
       " 'adaliene',\n",
       " 'adalin',\n",
       " 'adalina',\n",
       " 'adalind',\n",
       " 'adalinda',\n",
       " 'adaline',\n",
       " 'adalinn',\n",
       " 'adalinne',\n",
       " 'adalis',\n",
       " 'adalisa',\n",
       " 'adalise',\n",
       " 'adalisse',\n",
       " 'adalius',\n",
       " 'adaliyah',\n",
       " 'adaliz',\n",
       " 'adalize',\n",
       " 'adallyn',\n",
       " 'adaly',\n",
       " 'adalya',\n",
       " 'adalye',\n",
       " 'adalyn',\n",
       " 'adalyna',\n",
       " 'adalynd',\n",
       " 'adalyne',\n",
       " 'adalynn',\n",
       " 'adalynne',\n",
       " 'adalys',\n",
       " 'adalyse',\n",
       " 'adam',\n",
       " 'adama',\n",
       " 'adamae',\n",
       " 'adamari',\n",
       " 'adamarie',\n",
       " 'adamaris',\n",
       " 'adamariz',\n",
       " 'adamary',\n",
       " 'adamarys',\n",
       " 'adamina',\n",
       " 'adamm',\n",
       " 'adamma',\n",
       " 'adammichael',\n",
       " 'adamo',\n",
       " 'adams',\n",
       " 'adan',\n",
       " 'adana',\n",
       " 'adaneli',\n",
       " 'adanelly',\n",
       " 'adanely',\n",
       " 'adanna',\n",
       " 'adannaya',\n",
       " 'adante',\n",
       " 'adanya',\n",
       " 'adaobi',\n",
       " 'adaora',\n",
       " 'adar',\n",
       " 'adara',\n",
       " 'adarah',\n",
       " 'adari',\n",
       " 'adaria',\n",
       " 'adarian',\n",
       " 'adarien',\n",
       " 'adarion',\n",
       " 'adarious',\n",
       " 'adarius',\n",
       " 'adarrius',\n",
       " 'adarryl',\n",
       " 'adarryll',\n",
       " 'adarsh',\n",
       " 'adaryl',\n",
       " 'adaryll',\n",
       " 'adasha',\n",
       " 'adashia',\n",
       " 'adasia',\n",
       " 'adason',\n",
       " 'adassa',\n",
       " 'adasyn',\n",
       " 'adaugo',\n",
       " 'adaure',\n",
       " 'adavia',\n",
       " 'adavion',\n",
       " 'adaya',\n",
       " 'adayah',\n",
       " 'adayla',\n",
       " 'adaysha',\n",
       " 'adayshia',\n",
       " 'adaysia',\n",
       " 'adbeel',\n",
       " 'adbiel',\n",
       " 'add',\n",
       " 'adda',\n",
       " 'addah',\n",
       " 'addai',\n",
       " 'addalee',\n",
       " 'addaleigh',\n",
       " 'addaley',\n",
       " 'addalie',\n",
       " 'addalin',\n",
       " 'addalina',\n",
       " 'addaline',\n",
       " 'addalyn',\n",
       " 'addalyne',\n",
       " 'addalynn',\n",
       " 'addalynne',\n",
       " 'addam',\n",
       " 'addan',\n",
       " ...]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Features \n",
    "X\n",
    "# Labels\n",
    "y = data_names.sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6398163206734908"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use Naive Bayes Classifier\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "clf = MultinomialNB()\n",
    "clf.fit(X_train, y_train)\n",
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy is 63.98163206734908 %\n"
     ]
    }
   ],
   "source": [
    "# Model Accuracy\n",
    "print(f'Model accuracy is {clf.score(X_test, y_test)*100} %')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample Prediction\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_name = ['Mike']\n",
    "vect = cv.transform(sample_name).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Female is 0, Male is 1\n",
    "clf.predict(vect)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sample prediction of random names\n",
    "sample_names = ['Mark', 'Larry', 'Samantha', 'Olivia', 'Kia', 'Moussa']\n",
    "vect_random = cv.transform(sample_names).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 0, 0, 1])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(vect_random)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put it all in a function\n",
    "def gender_predictor(name):\n",
    "    test_name = [name]\n",
    "    vector = cv.transform(test_name).toarray()\n",
    "    if clf.predict(vector) == 0:\n",
    "        print('Female')\n",
    "    else:\n",
    "        print('Male')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male\n"
     ]
    }
   ],
   "source": [
    "gender_predictor('Obama')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving our model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "gender_model = open('gender_model.pkl', 'wb')\n",
    "joblib.dump(clf, gender_model)\n",
    "gender_model.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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