data-lit-logistic-regression - Namratesh.ipynb

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    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['adult-test.csv', 'adult-training.csv']\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
    "\n",
    "import os\n",
    "print(os.listdir(\"../input\"))\n",
    "\n",
    "# Any results you write to the current directory are saved as output\n",
    "\n",
    "\n",
    "columns = ['Age','Workclass','fnlgwt','Education','Education num','Marital Status',\n",
    "           'Occupation','Relationship','Race','Sex','Capital Gain','Capital Loss',\n",
    "           'Hours/Week','Native country','Income']\n",
    "\n",
    "train = pd.read_csv('../input/adult-training.csv', names=columns)\n",
    "test = pd.read_csv('../input/adult-test.csv', names=columns, skiprows=1)\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Workclass</th>\n",
       "      <th>fnlgwt</th>\n",
       "      <th>Education</th>\n",
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       "      <th>Marital Status</th>\n",
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       "      <th>Capital Gain</th>\n",
       "      <th>Capital Loss</th>\n",
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       "      <th>Income</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>39</td>\n",
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       "    <tr>\n",
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       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
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       "      <td>Not-in-family</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
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      "text/plain": [
       "   Age          Workclass  fnlgwt   ...   Hours/Week  Native country  Income\n",
       "0   39          State-gov   77516   ...           40   United-States   <=50K\n",
       "1   50   Self-emp-not-inc   83311   ...           13   United-States   <=50K\n",
       "2   38            Private  215646   ...           40   United-States   <=50K\n",
       "3   53            Private  234721   ...           40   United-States   <=50K\n",
       "4   28            Private  338409   ...           40            Cuba   <=50K\n",
       "\n",
       "[5 rows x 15 columns]"
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     "execution_count": 2,
     "metadata": {},
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   "source": [
    "train.head()"
   ]
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   "execution_count": 3,
   "metadata": {
    "_uuid": "18f62a01d2857ef56a10eac25d6b8eced693ac3f"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32561 entries, 0 to 32560\n",
      "Data columns (total 15 columns):\n",
      "Age               32561 non-null int64\n",
      "Workclass         32561 non-null object\n",
      "fnlgwt            32561 non-null int64\n",
      "Education         32561 non-null object\n",
      "Education num     32561 non-null int64\n",
      "Marital Status    32561 non-null object\n",
      "Occupation        32561 non-null object\n",
      "Relationship      32561 non-null object\n",
      "Race              32561 non-null object\n",
      "Sex               32561 non-null object\n",
      "Capital Gain      32561 non-null int64\n",
      "Capital Loss      32561 non-null int64\n",
      "Hours/Week        32561 non-null int64\n",
      "Native country    32561 non-null object\n",
      "Income            32561 non-null object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_uuid": "bf3581687ea85c6e1be34bebdf9466ed2d2e3b07"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16281 entries, 0 to 16280\n",
      "Data columns (total 15 columns):\n",
      "Age               16281 non-null int64\n",
      "Workclass         16281 non-null object\n",
      "fnlgwt            16281 non-null int64\n",
      "Education         16281 non-null object\n",
      "Education num     16281 non-null int64\n",
      "Marital Status    16281 non-null object\n",
      "Occupation        16281 non-null object\n",
      "Relationship      16281 non-null object\n",
      "Race              16281 non-null object\n",
      "Sex               16281 non-null object\n",
      "Capital Gain      16281 non-null int64\n",
      "Capital Loss      16281 non-null int64\n",
      "Hours/Week        16281 non-null int64\n",
      "Native country    16281 non-null object\n",
      "Income            16281 non-null object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 1.9+ MB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_uuid": "b33a98f1f98640b7c392c66614c3e946834d61ab"
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   "outputs": [
    {
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       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>?</td>\n",
       "      <td>103497</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
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      ],
      "text/plain": [
       "   Age   Workclass  fnlgwt   ...    Hours/Week  Native country   Income\n",
       "0   25     Private  226802   ...            40   United-States   <=50K.\n",
       "1   38     Private   89814   ...            50   United-States   <=50K.\n",
       "2   28   Local-gov  336951   ...            40   United-States    >50K.\n",
       "3   44     Private  160323   ...            40   United-States    >50K.\n",
       "4   18           ?  103497   ...            30   United-States   <=50K.\n",
       "\n",
       "[5 rows x 15 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_uuid": "af4fde972b61b64bd64f716364a37a29bac4e0cf"
   },
   "outputs": [],
   "source": [
    "train.replace(' ?', np.nan, inplace=True)\n",
    "test.replace(' ?', np.nan, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_uuid": "625a9cd5824ce2fe4b1981678b3ad503091da0b3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                  0\n",
       "Workclass         1836\n",
       "fnlgwt               0\n",
       "Education            0\n",
       "Education num        0\n",
       "Marital Status       0\n",
       "Occupation        1843\n",
       "Relationship         0\n",
       "Race                 0\n",
       "Sex                  0\n",
       "Capital Gain         0\n",
       "Capital Loss         0\n",
       "Hours/Week           0\n",
       "Native country     583\n",
       "Income               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_uuid": "a71e37f60e39c4155b323572fda7077da2c74db3",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                 0\n",
       "Workclass         963\n",
       "fnlgwt              0\n",
       "Education           0\n",
       "Education num       0\n",
       "Marital Status      0\n",
       "Occupation        966\n",
       "Relationship        0\n",
       "Race                0\n",
       "Sex                 0\n",
       "Capital Gain        0\n",
       "Capital Loss        0\n",
       "Hours/Week          0\n",
       "Native country    274\n",
       "Income              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_uuid": "e331b8f3c20976689b901853429df2f08757b09b"
   },
   "outputs": [],
   "source": [
    "train['Income'] = train['Income'].apply(lambda x: 1 if x==' >50K' else 0)\n",
    "test['Income'] = test['Income'].apply(lambda x: 1 if x==' >50K.' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_uuid": "5a6f14aa9ee2fefc75569eb57c533f33711d45ba"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAEvdJREFUeJzt3X+s3fV93/Hnqzg/GtrEJlyQZzszUa00tBo/ZoEzpiiFzhiIYjQFyVG1eJE1/+NtZKrUOps2lKSRiDSVNNKKhII7J8pCGE2GBSjUckDtJgUw4UcAB9klHr4zxW4NZC1qOtL3/jifGw7Ote+55vqeYz7Ph3R0vt/393PO9/0991y/7vfHOU5VIUnqzy+MuwFJ0ngYAJLUKQNAkjplAEhSpwwASeqUASBJnTIAJKlTBoAkdcoAkKROLRl3Aydz7rnn1urVq8fdhiSdUR599NG/rKqpucZNdACsXr2avXv3jrsNSTqjJPnfo4zzEJAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnqlAEgSZ0yACSpUwaAJHVqoj8JrDPH6u33jmW9B2++bizrld4K3AOQpE6NFABJlia5K8kPk+xL8qEk5yTZnWR/u1/WxibJl5McSPJkkkuHnmdzG78/yebTtVGSpLmNugfwB8B3qupXgYuAfcB2YE9VrQH2tHmAa4A17bYVuBUgyTnATcDlwGXATTOhIUlafHOeA0jybuDDwL8EqKq/A/4uyUbgI23YTuBB4HeBjcBXq6qA77W9h+Vt7O6qOtaedzewAfjGwm1O38Z1HF7SmWmUPYD3A0eBP0ryWJKvJDkbOL+qXgBo9+e18SuAQ0OPn261E9XfIMnWJHuT7D169Oi8N0iSNJpRAmAJcClwa1VdAvwNrx/umU1mqdVJ6m8sVN1WVWurau3U1Jz/n4Ek6RSNEgDTwHRVPdTm72IQCC+2Qzu0+yND41cNPX4lcPgkdUnSGMwZAFX1F8ChJB9opauAZ4BdwMyVPJuBu9v0LuCT7WqgdcAr7RDR/cD6JMvayd/1rSZJGoNRPwj2b4CvJ3k78BzwKQbhcWeSLcDzwA1t7H3AtcAB4NU2lqo6luTzwCNt3OdmTgi/1XgyVtKZYKQAqKrHgbWzLLpqlrEFbDvB8+wAdsynQUnS6eEngSWpUwaAJHXKAJCkThkAktQpA0CSOmUASFKnDABJ6pQBIEmdMgAkqVNv6f8T2K9kkKQTcw9AkjplAEhSpwwASeqUASBJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnqlAEgSZ0yACSpUwaAJHVqpABIcjDJD5I8nmRvq52TZHeS/e1+WasnyZeTHEjyZJJLh55ncxu/P8nm07NJkqRRzGcP4Deq6uKqWtvmtwN7qmoNsKfNA1wDrGm3rcCtMAgM4CbgcuAy4KaZ0JAkLb43cwhoI7CzTe8Erh+qf7UGvgcsTbIcuBrYXVXHquolYDew4U2sX5L0JowaAAX8SZJHk2xttfOr6gWAdn9eq68ADg09drrVTlSXJI3BqP8l5BVVdTjJecDuJD88ydjMUquT1N/44EHAbAV43/veN2J7kqT5GmkPoKoOt/sjwLcZHMN/sR3aod0facOngVVDD18JHD5J/fh13VZVa6tq7dTU1Py2RpI0sjkDIMnZSX55ZhpYDzwF7AJmruTZDNzdpncBn2xXA60DXmmHiO4H1idZ1k7+rm81SdIYjHII6Hzg20lmxv+3qvpOkkeAO5NsAZ4Hbmjj7wOuBQ4ArwKfAqiqY0k+DzzSxn2uqo4t2JZIkuZlzgCoqueAi2ap/xVw1Sz1Arad4Ll2ADvm36YkaaH5SWBJ6pQBIEmdMgAkqVMGgCR1ygCQpE4ZAJLUKQNAkjplAEhSpwwASeqUASBJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnqlAEgSZ0yACSpUwaAJHXKAJCkThkAktQpA0CSOmUASFKnDABJ6tTIAZDkrCSPJbmnzV+Q5KEk+5N8M8nbW/0dbf5AW7566Dk+0+rPJrl6oTdGkjS6+ewB3AjsG5r/InBLVa0BXgK2tPoW4KWq+hXgljaOJBcCm4BfAzYAf5jkrDfXviTpVI0UAElWAtcBX2nzAa4E7mpDdgLXt+mNbZ62/Ko2fiNwR1X9pKp+BBwALluIjZAkzd+oewBfAn4H+Ps2/17g5ap6rc1PAyva9ArgEEBb/kob/7P6LI/5mSRbk+xNsvfo0aPz2BRJ0nzMGQBJPgocqapHh8uzDK05lp3sMa8Xqm6rqrVVtXZqamqu9iRJp2jJCGOuAD6W5FrgncC7GewRLE2ypP2VvxI43MZPA6uA6SRLgPcAx4bqM4YfI52S1dvvHdu6D9583djWLS2EOfcAquozVbWyqlYzOIn73ar6LeAB4ONt2Gbg7ja9q83Tln+3qqrVN7WrhC4A1gAPL9iWSJLmZZQ9gBP5XeCOJL8HPAbc3uq3A19LcoDBX/6bAKrq6SR3As8ArwHbquqnb2L9kqQ3YV4BUFUPAg+26eeY5Sqeqvpb4IYTPP4LwBfm26QkaeH5SWBJ6pQBIEmdMgAkqVMGgCR1ygCQpE4ZAJLUKQNAkjplAEhSpwwASeqUASBJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnqlAEgSZ0yACSpUwaAJHXKAJCkThkAktQpA0CSOmUASFKn5gyAJO9M8nCSJ5I8neSzrX5BkoeS7E/yzSRvb/V3tPkDbfnqoef6TKs/m+Tq07VRkqS5jbIH8BPgyqq6CLgY2JBkHfBF4JaqWgO8BGxp47cAL1XVrwC3tHEkuRDYBPwasAH4wyRnLeTGSJJGN2cA1MBft9m3tVsBVwJ3tfpO4Po2vbHN05ZflSStfkdV/aSqfgQcAC5bkK2QJM3bSOcAkpyV5HHgCLAb+HPg5ap6rQ2ZBla06RXAIYC2/BXgvcP1WR4jSVpkIwVAVf20qi4GVjL4q/2Dsw1r9znBshPV3yDJ1iR7k+w9evToKO1Jkk7BvK4CqqqXgQeBdcDSJEvaopXA4TY9DawCaMvfAxwbrs/ymOF13FZVa6tq7dTU1HzakyTNwyhXAU0lWdqmfxH4TWAf8ADw8TZsM3B3m97V5mnLv1tV1eqb2lVCFwBrgIcXakMkSfOzZO4hLAd2tit2fgG4s6ruSfIMcEeS3wMeA25v428HvpbkAIO//DcBVNXTSe4EngFeA7ZV1U8XdnMkSaOaMwCq6kngklnqzzHLVTxV9bfADSd4ri8AX5h/m5KkheYngSWpUwaAJHXKAJCkThkAktQpA0CSOmUASFKnDABJ6pQBIEmdMgAkqVOjfBWEpFms3n7vWNZ78ObrxrJevfW4ByBJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnqlAEgSZ0yACSpUwaAJHXKAJCkThkAktQpA0CSOmUASFKn5gyAJKuSPJBkX5Knk9zY6uck2Z1kf7tf1upJ8uUkB5I8meTSoefa3MbvT7L59G2WJGkuo+wBvAb8dlV9EFgHbEtyIbAd2FNVa4A9bR7gGmBNu20FboVBYAA3AZcDlwE3zYSGJGnxzRkAVfVCVX2/Tf9fYB+wAtgI7GzDdgLXt+mNwFdr4HvA0iTLgauB3VV1rKpeAnYDGxZ0ayRJI5vXOYAkq4FLgIeA86vqBRiEBHBeG7YCODT0sOlWO1FdkjQGIwdAkl8C/hj4dFX9+GRDZ6nVSerHr2drkr1J9h49enTU9iRJ8zRSACR5G4N//L9eVd9q5RfboR3a/ZFWnwZWDT18JXD4JPU3qKrbqmptVa2dmpqaz7ZIkuZhlKuAAtwO7Kuq3x9atAuYuZJnM3D3UP2T7WqgdcAr7RDR/cD6JMvayd/1rSZJGoMlI4y5AvgXwA+SPN5q/x64GbgzyRbgeeCGtuw+4FrgAPAq8CmAqjqW5PPAI23c56rq2IJshSRp3uYMgKr6n8x+/B7gqlnGF7DtBM+1A9gxnwYlSaeHnwSWpE4ZAJLUKQNAkjplAEhSpwwASeqUASBJnTIAJKlTBoAkdWqUTwJLmiCrt987tnUfvPm6sa1bC889AEnqlAEgSZ0yACSpUwaAJHXKAJCkThkAktQpA0CSOmUASFKnDABJ6pQBIEmdMgAkqVMGgCR1ygCQpE4ZAJLUKQNAkjplAEhSp+YMgCQ7khxJ8tRQ7Zwku5Psb/fLWj1JvpzkQJInk1w69JjNbfz+JJtPz+ZIkkY1yh7AfwU2HFfbDuypqjXAnjYPcA2wpt22ArfCIDCAm4DLgcuAm2ZCQ5I0HnMGQFX9KXDsuPJGYGeb3glcP1T/ag18D1iaZDlwNbC7qo5V1UvAbn4+VCRJi+hUzwGcX1UvALT781p9BXBoaNx0q52o/nOSbE2yN8neo0ePnmJ7kqS5LPRJ4MxSq5PUf75YdVtVra2qtVNTUwvanCTpdacaAC+2Qzu0+yOtPg2sGhq3Ejh8krokaUxONQB2ATNX8mwG7h6qf7JdDbQOeKUdIrofWJ9kWTv5u77VJEljsmSuAUm+AXwEODfJNIOreW4G7kyyBXgeuKENvw+4FjgAvAp8CqCqjiX5PPBIG/e5qjr+xLIkaRHNGQBV9YkTLLpqlrEFbDvB8+wAdsyrO0nSaeMngSWpUwaAJHXKAJCkThkAktQpA0CSOmUASFKnDABJ6pQBIEmdMgAkqVMGgCR1ygCQpE7N+V1AkjRj9fZ7x7LegzdfN5b1vtW5ByBJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnqlAEgSZ0yACSpU34VhKSJ51dQnB7uAUhSpxY9AJJsSPJskgNJti/2+iVJA4saAEnOAv4LcA1wIfCJJBcuZg+SpIHFPgdwGXCgqp4DSHIHsBF4ZpH7kKQ5jevcAyzO+YfFPgS0Ajg0ND/dapKkRbbYewCZpVZvGJBsBba22b9O8uxp7+p15wJ/uYjrO1VnQp9nQo9gnwvtTOjzTOiRfPFN9fkPRxm02AEwDawaml8JHB4eUFW3AbctZlMzkuytqrXjWPd8nAl9ngk9gn0utDOhzzOhR1icPhf7ENAjwJokFyR5O7AJ2LXIPUiSWOQ9gKp6Lcm/Bu4HzgJ2VNXTi9mDJGlg0T8JXFX3Afct9npHNJZDT6fgTOjzTOgR7HOhnQl9ngk9wiL0maqae5Qk6S3Hr4KQpE51GQBJViV5IMm+JE8nubHVz0myO8n+dr9szH2+M8nDSZ5ofX621S9I8lDr85vthPrYJTkryWNJ7mnzE9dnkoNJfpDk8SR7W23Sfu5Lk9yV5IftPfqhCezxA+01nLn9OMmnJ63P1uu/a78/TyX5Rvu9mqj3ZpIbW39PJ/l0q53217LLAABeA367qj4IrAO2ta+k2A7sqao1wJ42P04/Aa6sqouAi4ENSdYBXwRuaX2+BGwZY4/DbgT2Dc1Pap+/UVUXD11iN2k/9z8AvlNVvwpcxOA1nageq+rZ9hpeDPxj4FXg20xYn0lWAP8WWFtVv87g4pNNTNB7M8mvA/+KwTclXAR8NMkaFuO1rKrub8DdwD8DngWWt9py4Nlx9zbU47uA7wOXM/hwyJJW/xBw/wT0t7K9Sa8E7mHwob9J7PMgcO5xtYn5uQPvBn5EOz83iT3O0vN64H9NYp+8/u0D5zC46OUe4OpJem8CNwBfGZr/j8DvLMZr2esewM8kWQ1cAjwEnF9VLwC0+/PG19lAO6zyOHAE2A38OfByVb3WhkzK12l8icGb9u/b/HuZzD4L+JMkj7ZPncNk/dzfDxwF/qgdTvtKkrMnrMfjbQK+0aYnqs+q+j/AfwaeB14AXgEeZbLem08BH07y3iTvAq5l8IHZ0/5adh0ASX4J+GPg01X143H3M5uq+mkNdrNXMthF/OBswxa3qzdK8lHgSFU9OlyeZegkXHJ2RVVdyuAbabcl+fC4GzrOEuBS4NaqugT4G8Z/SOqE2rHzjwH/fdy9zKYdN98IXAD8A+BsBj/7443tvVlV+xgcktoNfAd4gsFh6tOu2wBI8jYG//h/vaq+1covJlneli9n8Ff3RKiql4EHGZyzWJpk5jMcP/d1GmNwBfCxJAeBOxgcBvoSk9cnVXW43R9hcMz6Mibr5z4NTFfVQ23+LgaBMEk9DrsG+H5VvdjmJ63P3wR+VFVHq+r/Ad8C/gkT9t6sqtur6tKq+jBwDNjPIryWXQZAkgC3A/uq6veHFu0CNrfpzQzODYxNkqkkS9v0LzJ4M+8DHgA+3oaNvc+q+kxVrayq1QwOB3y3qn6LCeszydlJfnlmmsGx66eYoJ97Vf0FcCjJB1rpKgZflz4xPR7nE7x++Acmr8/ngXVJ3tV+72dez0l7b57X7t8H/HMGr+npfy3HeYJmjCdd/imDXb4ngcfb7VoGx633MEjfPcA5Y+7zHwGPtT6fAv5Tq78feBg4wGDX+x3jfk2Hev4IcM8k9tn6eaLdngb+Q6tP2s/9YmBv+7n/D2DZpPXY+nwX8FfAe4Zqk9jnZ4Eftt+hrwHvmMD35p8xCKYngKsW67X0k8CS1KkuDwFJkgwASeqWASBJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJI69f8BGGj3opPUeLgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(train['Age']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_uuid": "5ae13491a2213f5dd35c35255dd303db9e8f841c"
   },
   "outputs": [],
   "source": [
    "train['Workclass'].fillna(' 0', inplace=True)\n",
    "test['Workclass'].fillna(' 0', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_uuid": "c654da8e500a732c81ed644f6b044cebd20e0486"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n",
      "/opt/conda/lib/python3.6/site-packages/seaborn/categorical.py:3672: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x=\"Workclass\", y=\"Income\", data=train, kind=\"bar\", size = 6, \n",
    "palette = \"muted\")\n",
    "plt.xticks(rotation=45);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_uuid": "8c83b05c1a5daebf6bedc225b853a5141c528ea5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " Private             22696\n",
       " Self-emp-not-inc     2541\n",
       " Local-gov            2093\n",
       " 0                    1836\n",
       " State-gov            1298\n",
       " Self-emp-inc         1116\n",
       " Federal-gov           960\n",
       " Without-pay            14\n",
       " Never-worked            7\n",
       "Name: Workclass, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Workclass'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_uuid": "76ff0681d05d32e274921827acb048c0a8012fae"
   },
   "outputs": [],
   "source": [
    "train['Workclass'].replace(' Without-pay', ' Never-worked', inplace=True)\n",
    "test['Workclass'].replace(' Without-pay', ' Never-worked', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_uuid": "ae32b53d8a51c494299d3697c0ce6277f73decc5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.256100e+04\n",
       "mean     1.897784e+05\n",
       "std      1.055500e+05\n",
       "min      1.228500e+04\n",
       "25%      1.178270e+05\n",
       "50%      1.783560e+05\n",
       "75%      2.370510e+05\n",
       "max      1.484705e+06\n",
       "Name: fnlgwt, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['fnlgwt'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_uuid": "83308fc94ba3eb92c588079d9f1c6f304bcd7da8"
   },
   "outputs": [],
   "source": [
    "train['fnlgwt'] = train['fnlgwt'].apply(lambda x: np.log1p(x))\n",
    "test['fnlgwt'] = test['fnlgwt'].apply(lambda x: np.log1p(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_uuid": "83b3f732072a8d0ba8e124fbcb1d4c3354609318"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    32561.000000\n",
       "mean        11.983778\n",
       "std          0.630738\n",
       "min          9.416216\n",
       "25%         11.676981\n",
       "50%         12.091542\n",
       "75%         12.376035\n",
       "max         14.210727\n",
       "Name: fnlgwt, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['fnlgwt'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_uuid": "b044822d5908bf4f303bf405d9b1bdae5719061f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n",
      "/opt/conda/lib/python3.6/site-packages/seaborn/categorical.py:3672: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x=\"Education\",y=\"Income\",data=train,kind=\"bar\", size = 7, \n",
    "palette = \"muted\")\n",
    "plt.xticks(rotation=60);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_uuid": "9309fedec959ade8fb9278f3379ec06c8b7a830b"
   },
   "outputs": [],
   "source": [
    "def primary(x):\n",
    "    if x in [' 1st-4th', ' 5th-6th', ' 7th-8th', ' 9th', ' 10th', ' 11th', ' 12th']:\n",
    "        return ' Primary'\n",
    "    else:\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_uuid": "5f8120987c917cd82a28686087f77c58a1b90530"
   },
   "outputs": [],
   "source": [
    "train['Education'] = train['Education'].apply(primary)\n",
    "test['Education'] = test['Education'].apply(primary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_uuid": "e23e3fffb5b450e3d05f7933a4753dbfc6e1eba3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n",
      "/opt/conda/lib/python3.6/site-packages/seaborn/categorical.py:3672: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x=\"Education\",y=\"Income\",data=train,kind=\"bar\", size = 6, \n",
    "palette = \"muted\")\n",
    "plt.xticks(rotation=60);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_uuid": "709d5aa9b0352e9ecc647781b6529cadc612cd47"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n",
      "/opt/conda/lib/python3.6/site-packages/seaborn/categorical.py:3672: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x=\"Education num\",y=\"Income\",data=train,kind=\"bar\", size = 6, \n",
    "palette = \"muted\")\n",
    "plt.xticks(rotation=60);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_uuid": "73f1d2bc3378d48a04786298ad3d3f9c6ccc74ec"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n",
      "/opt/conda/lib/python3.6/site-packages/seaborn/categorical.py:3672: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x=\"Marital Status\",y=\"Income\",data=train,kind=\"bar\", size = 5, \n",
    "palette = \"muted\")\n",
    "plt.xticks(rotation=60);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_uuid": "e17130fbaa89b67dabe1ff92d8547e6ea302549b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " Married-civ-spouse       14976\n",
       " Never-married            10683\n",
       " Divorced                  4443\n",
       " Separated                 1025\n",
       " Widowed                    993\n",
       " Married-spouse-absent      418\n",
       " Married-AF-spouse           23\n",
       "Name: Marital Status, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Marital Status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_uuid": "2510fdf63f99bd85b8e2e0c10b40bc7d94587a29"
   },
   "outputs": [],
   "source": [
    "train['Marital Status'].replace(' Married-AF-spouse', ' Married-civ-spouse', inplace=True)\n",
    "test['Marital Status'].replace(' Married-AF-spouse', ' Married-civ-spouse', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_uuid": "91e1728496cf2b36d61f3d47e9dc23920d731342"
   },
   "outputs": [],
   "source": [
    "train['Occupation'].fillna(' 0', inplace=True)\n",
    "test['Occupation'].fillna(' 0', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_uuid": "1333a381aa3916a3019a1c40036df033d7bb620e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n",
      "/opt/conda/lib/python3.6/site-packages/seaborn/categorical.py:3672: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x=\"Occupation\",y=\"Income\",data=train,kind=\"bar\", size = 8, \n",
    "palette = \"muted\")\n",
    "plt.xticks(rotation=60);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_uuid": "234293966300324d9b2b9c345028346f3cef0a85"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " Prof-specialty       4140\n",
       " Craft-repair         4099\n",
       " Exec-managerial      4066\n",
       " Adm-clerical         3770\n",
       " Sales                3650\n",
       " Other-service        3295\n",
       " Machine-op-inspct    2002\n",
       " 0                    1843\n",
       " Transport-moving     1597\n",
       " Handlers-cleaners    1370\n",
       " Farming-fishing       994\n",
       " Tech-support          928\n",
       " Protective-serv       649\n",
       " Priv-house-serv       149\n",
       " Armed-Forces            9\n",
       "Name: Occupation, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Occupation'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_uuid": "fbaa8b2d458f7d0d8c1b7f7df42929a31245c621"
   },
   "outputs": [],
   "source": [
    "train['Occupation'].replace(' Armed-Forces', ' 0', inplace=True)\n",
    "test['Occupation'].replace(' Armed-Forces', ' 0', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_uuid": "e36dc7d758729648ad69e458d79561e501ee81be"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " Husband           13193\n",
       " Not-in-family      8305\n",
       " Own-child          5068\n",
       " Unmarried          3446\n",
       " Wife               1568\n",
       " Other-relative      981\n",
       "Name: Relationship, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Relationship'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_uuid": "fda357269a58d74262d9073235d8eb187b07e6f6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " White                 27816\n",
       " Black                  3124\n",
       " Asian-Pac-Islander     1039\n",
       " Amer-Indian-Eskimo      311\n",
       " Other                   271\n",
       "Name: Race, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Race'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_uuid": "ab78b7f40a850248de64a235792caba7d372eb95"
   },
   "outputs": [],
   "source": [
    "train['Native country'].fillna(' 0', inplace=True)\n",
    "test['Native country'].fillna(' 0', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_uuid": "fdccd1d58c88a73d9851787d64f48ecc58c07251"
   },
   "outputs": [],
   "source": [
    "def native(country):\n",
    "    if country in [' United-States', ' Cuba', ' 0']:\n",
    "        return 'US'\n",
    "    elif country in [' England', ' Germany', ' Canada', ' Italy', ' France', ' Greece', ' Philippines']:\n",
    "        return 'Western'\n",
    "    elif country in [' Mexico', ' Puerto-Rico', ' Honduras', ' Jamaica', ' Columbia', ' Laos', ' Portugal', ' Haiti',\n",
    "                     ' Dominican-Republic', ' El-Salvador', ' Guatemala', ' Peru', \n",
    "                     ' Trinadad&Tobago', ' Outlying-US(Guam-USVI-etc)', ' Nicaragua', ' Vietnam', ' Holand-Netherlands' ]:\n",
    "        return 'Poor' # no offence\n",
    "    elif country in [' India', ' Iran', ' Cambodia', ' Taiwan', ' Japan', ' Yugoslavia', ' China', ' Hong']:\n",
    "        return 'Eastern'\n",
    "    elif country in [' South', ' Poland', ' Ireland', ' Hungary', ' Scotland', ' Thailand', ' Ecuador']:\n",
    "        return 'Poland team'\n",
    "    \n",
    "    else: \n",
    "        return country    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_uuid": "59fa16b60bf5f7d785062b60e94601d01ed9890b"
   },
   "outputs": [],
   "source": [
    "train['Native country'] = train['Native country'].apply(native)\n",
    "test['Native country'] = test['Native country'].apply(native)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_uuid": "a511926d8a26285fac8c37d45901b10cb1781b33",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "US             29848\n",
       "Poor            1415\n",
       "Western          677\n",
       "Eastern          386\n",
       "Poland team      235\n",
       "Name: Native country, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Native country'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_uuid": "3a7864de5c5b3f65b378de352867d7051f75d6ba"
   },
   "outputs": [],
   "source": [
    "joint = pd.concat([train, test], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_uuid": "bdcd3b6c219fb85df99d157f7b08801cbe412e0b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                 int64\n",
       "Workclass          object\n",
       "fnlgwt            float64\n",
       "Education          object\n",
       "Education num       int64\n",
       "Marital Status     object\n",
       "Occupation         object\n",
       "Relationship       object\n",
       "Race               object\n",
       "Sex                object\n",
       "Capital Gain        int64\n",
       "Capital Loss        int64\n",
       "Hours/Week          int64\n",
       "Native country     object\n",
       "Income              int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joint.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_uuid": "1713e4da4b373f5a11d58166113ab808038cf3d7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Workclass 8\n",
      "Education 10\n",
      "Marital Status 6\n",
      "Occupation 14\n",
      "Relationship 6\n",
      "Race 5\n",
      "Sex 2\n",
      "Native country 5\n"
     ]
    }
   ],
   "source": [
    "categorical_features = joint.select_dtypes(include=['object']).axes[1]\n",
    "\n",
    "for col in categorical_features:\n",
    "    print (col, joint[col].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "_uuid": "8cadb24b9f8f415c57826f18faf66b2968964e78"
   },
   "outputs": [],
   "source": [
    "#one-hot encode\n",
    "for col in categorical_features:\n",
    "    joint = pd.concat([joint, pd.get_dummies(joint[col], prefix=col, prefix_sep=':')], axis=1)\n",
    "    joint.drop(col, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "_uuid": "22c678c3275a72f7214e9f2a8eaf67f1b9172a21"
   },
   "outputs": [],
   "source": [
    "train = joint.head(train.shape[0])\n",
    "test = joint.tail(test.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "_uuid": "9aa4a61ea1093c29ebf96635927dd89973029783"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:9: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  if __name__ == '__main__':\n",
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:10: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    }
   ],
   "source": [
    "Xtrain = train.drop('Income', axis=1)\n",
    "ytrain = train['Income']\n",
    "\n",
    "Xtest = test.drop('Income', axis=1)\n",
    "ytest = test['Income']\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(Xtrain)\n",
    "Xtrain = scaler.transform(Xtrain)\n",
    "Xtest = scaler.transform(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "_uuid": "fcd295f3674589ae5f128a4dcb654d79281157c2"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "_uuid": "b0a0007f1cb6028cbb19fe8b1547e00386587e4a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(Xtrain, ytrain)\n",
    "\n",
    "Ytrain = model.predict(Xtrain)\n",
    "Ytest = model.predict(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "_uuid": "fdcbf8b16fbee67eea337b1a55ba9e7da4cc694b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.93      0.91     24720\n",
      "           1       0.74      0.60      0.66      7841\n",
      "\n",
      "   micro avg       0.85      0.85      0.85     32561\n",
      "   macro avg       0.81      0.77      0.78     32561\n",
      "weighted avg       0.85      0.85      0.85     32561\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(ytrain, Ytrain))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "_uuid": "2b00993c24b1277523b689f920a4dcb73dc2b476"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.93      0.91     12435\n",
      "           1       0.73      0.60      0.66      3846\n",
      "\n",
      "   micro avg       0.85      0.85      0.85     16281\n",
      "   macro avg       0.81      0.77      0.78     16281\n",
      "weighted avg       0.85      0.85      0.85     16281\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(ytest, Ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "_uuid": "9c924f4af5bf4633a09f68b6f5645c80b1060b76"
   },
   "outputs": [],
   "source": [
    "# now by  by scracth\n",
    "\n",
    "def sigmoid(z):\n",
    "  \n",
    "  return 1 / (1 + np.exp(-z))\n",
    "\n",
    "\n",
    "def predict(features, weights):\n",
    " \n",
    "  return sigmoid( np.dot(features, weights) )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "_uuid": "0b561da42b8a433be3111d8786e5750850529b88"
   },
   "outputs": [],
   "source": [
    "def cost_function_for_all_training_samples(features, labels, weights):\n",
    "  m = features.shape[0]  # m = number of samples\n",
    "  predictions = predict(features, weights)\n",
    "  return -(1/m) * np.sum( labels*np.log(predictions) + (1-labels)*np.log(1-predictions) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "_uuid": "20b188df40150e5f51f17edc75529bd9deb73c61"
   },
   "outputs": [],
   "source": [
    "def decision_boundary(probability, threshold=0.5):\n",
    "  return 1 if probability >= threshold else 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "_uuid": "1651d6ef67e9b64d1c815e18d2ea740191ee34e5"
   },
   "outputs": [],
   "source": [
    "def calculate_gradient(features, labels, weights):\n",
    "    predictions = predict(features, weights)\n",
    "    matrixOfAggregateSlopeOfCostFunction = np.dot(features.T, predictions - labels)\n",
    "    return matrixOfAggregateSlopeOfCostFunction\n",
    "  \n",
    "\n",
    "def update_weights(features, labels, weights, lr):\n",
    "\n",
    "    matrixOfAggregateSlopeOfCostFunction = calculate_gradient(features, labels, weights)\n",
    "\n",
    "    m = len(features)\n",
    "    averageCostDerivativeForEachFeature = matrixOfAggregateSlopeOfCostFunction / m\n",
    "\n",
    "    gradient = averageCostDerivativeForEachFeature * lr\n",
    "\n",
    "    return weights - gradient\n",
    "\n",
    "\n",
    "def fit(features, labels, weights, lr, iterations):\n",
    "\n",
    "    for i in range(iterations):\n",
    "        weights = update_weights(features, labels, weights, lr)\n",
    "        if i % 100 == 0:\n",
    "          cost = cost_function_for_all_training_samples(features, labels, weights)\n",
    "          print(\"iteration:\", str(i), \"cost:\", str(cost))\n",
    "\n",
    "    return weights\n",
    "  \n",
    "  \n",
    "def classify(predictions):\n",
    "  '''\n",
    "  input  - N element array of predictions between 0 and 1\n",
    "  output - N element array of 0s (False) and 1s (True)\n",
    "  '''\n",
    "  decide = np.vectorize(decision_boundary)\n",
    "  return decide(predictions).flatten()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "_uuid": "7cfeefbb816c520f406c0eca15c7bb01f58d3bae"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration: 0 cost: 0.6484338908190735\n",
      "iteration: 100 cost: 0.5130357510040386\n",
      "iteration: 200 cost: 0.4950341658704987\n",
      "iteration: 300 cost: 0.4821666991003447\n",
      "iteration: 400 cost: 0.4721929775652031\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:4: RuntimeWarning: divide by zero encountered in log\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration: 500 cost: 0.4641760769597606\n",
      "iteration: 600 cost: 0.45759853847782495\n",
      "iteration: 700 cost: 0.4521280119700136\n",
      "iteration: 800 cost: 0.44753255237845674\n",
      "iteration: 900 cost: 0.44364210297706125\n",
      "iteration: 1000 cost: 0.44032772845912715\n",
      "iteration: 1100 cost: 0.4374892462866999\n",
      "iteration: 1200 cost: 0.4350473585381868\n",
      "iteration: 1300 cost: 0.43293838146986824\n",
      "iteration: 1400 cost: 0.431110562954192\n",
      "iteration: 1500 cost: 0.4295214852557169\n",
      "iteration: 1600 cost: 0.42813609403288266\n",
      "iteration: 1700 cost: 0.4269249442556957\n",
      "iteration: 1800 cost: 0.4258639251907581\n",
      "iteration: 1900 cost: 0.42493347147535115\n",
      "iteration: 2000 cost: 0.42411118395704933\n",
      "iteration: 2100 cost: 0.4233899891693866\n",
      "iteration: 2200 cost: 0.4227456107688152\n",
      "iteration: 2300 cost: 0.4221854771657948\n",
      "iteration: 2400 cost: 0.4216694752360071\n",
      "iteration: 2500 cost: inf\n",
      "iteration: 2600 cost: inf\n",
      "iteration: 2700 cost: inf\n",
      "iteration: 2800 cost: inf\n",
      "iteration: 2900 cost: inf\n",
      "iteration: 3000 cost: inf\n"
     ]
    }
   ],
   "source": [
    "initial_weights = [0] * Xtrain.shape[1]\n",
    "lr = 0.2\n",
    "iterations = 3001\n",
    "\n",
    "weights = fit(Xtrain, ytrain, initial_weights, lr, iterations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "_uuid": "f363c7c1d05491488e4877dccf1077c52fdd24cd"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "59e5f0b41d5d73ec7ad011e4cd361ba3c661dbce"
   },
   "source": []
  }
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