income-prediction-on-uci-adult-dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "dc20f0a99fd04fa1c6a078e748116df1da646c71"
   },
   "source": [
    "# Consensus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "a171a930e23459ecbdb4d50dbffd2c18ca8e8ced"
   },
   "source": [
    "### Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "_uuid": "fbbeca12041ad15f3af35a5554028ee2dfa4c105"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.plotting import scatter_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "4e491dd82265049fba9ef6332e82597f38a56c6a"
   },
   "source": [
    "### Loading *csv* file in *dataframe*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "_uuid": "07fc092a747b81e8111f30d1b195528742022a73"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   age          workclass  fnlwgt   education  education-num  \\\n",
      "0   39          State-gov   77516   Bachelors             13   \n",
      "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
      "2   38            Private  215646     HS-grad              9   \n",
      "3   53            Private  234721        11th              7   \n",
      "4   28            Private  338409   Bachelors             13   \n",
      "\n",
      "        marital-status          occupation    relationship    race      sex  \\\n",
      "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
      "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
      "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
      "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
      "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
      "\n",
      "   capital-gain  capital-loss  hours-per-week         country  salary  \n",
      "0          2174             0              40   United-States   <=50K  \n",
      "1             0             0              13   United-States   <=50K  \n",
      "2             0             0              40   United-States   <=50K  \n",
      "3             0             0              40   United-States   <=50K  \n",
      "4             0             0              40            Cuba   <=50K  \n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"./adult2.csv\",1,\",\")\n",
    "data = [df]\n",
    "print (df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "b3de43268bbfe322d8ca104891c2a8e1523afdfd"
   },
   "source": [
    "### Convert *salary* to integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "_uuid": "6ff675da6eeb9c18988b051b8b71e2fe6e2db3e2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   age          workclass  fnlwgt   education  education-num  \\\n",
      "0   39          State-gov   77516   Bachelors             13   \n",
      "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
      "2   38            Private  215646     HS-grad              9   \n",
      "3   53            Private  234721        11th              7   \n",
      "4   28            Private  338409   Bachelors             13   \n",
      "5   37            Private  284582     Masters             14   \n",
      "6   49            Private  160187         9th              5   \n",
      "7   52   Self-emp-not-inc  209642     HS-grad              9   \n",
      "8   31            Private   45781     Masters             14   \n",
      "9   42            Private  159449   Bachelors             13   \n",
      "\n",
      "           marital-status          occupation    relationship    race  \\\n",
      "0           Never-married        Adm-clerical   Not-in-family   White   \n",
      "1      Married-civ-spouse     Exec-managerial         Husband   White   \n",
      "2                Divorced   Handlers-cleaners   Not-in-family   White   \n",
      "3      Married-civ-spouse   Handlers-cleaners         Husband   Black   \n",
      "4      Married-civ-spouse      Prof-specialty            Wife   Black   \n",
      "5      Married-civ-spouse     Exec-managerial            Wife   White   \n",
      "6   Married-spouse-absent       Other-service   Not-in-family   Black   \n",
      "7      Married-civ-spouse     Exec-managerial         Husband   White   \n",
      "8           Never-married      Prof-specialty   Not-in-family   White   \n",
      "9      Married-civ-spouse     Exec-managerial         Husband   White   \n",
      "\n",
      "       sex  capital-gain  capital-loss  hours-per-week         country  salary  \n",
      "0     Male          2174             0              40   United-States       1  \n",
      "1     Male             0             0              13   United-States       1  \n",
      "2     Male             0             0              40   United-States       1  \n",
      "3     Male             0             0              40   United-States       1  \n",
      "4   Female             0             0              40            Cuba       1  \n",
      "5   Female             0             0              40   United-States       1  \n",
      "6   Female             0             0              16         Jamaica       1  \n",
      "7     Male             0             0              45   United-States       0  \n",
      "8   Female         14084             0              50   United-States       0  \n",
      "9     Male          5178             0              40   United-States       0  \n"
     ]
    }
   ],
   "source": [
    "salary_map={' <=50K':1,' >50K':0}\n",
    "df['salary']=df['salary'].map(salary_map).astype(int)\n",
    "    \n",
    "print (df.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "25725d2c6ec72126121896d5da97c5f5740d26c2"
   },
   "source": [
    "### convert *sex* into *integer*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "_uuid": "1a4309556f21a2bd25b28b00b05c5b6f4cdabdf6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   age          workclass  fnlwgt   education  education-num  \\\n",
      "0   39          State-gov   77516   Bachelors             13   \n",
      "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
      "2   38            Private  215646     HS-grad              9   \n",
      "3   53            Private  234721        11th              7   \n",
      "4   28            Private  338409   Bachelors             13   \n",
      "5   37            Private  284582     Masters             14   \n",
      "6   49            Private  160187         9th              5   \n",
      "7   52   Self-emp-not-inc  209642     HS-grad              9   \n",
      "8   31            Private   45781     Masters             14   \n",
      "9   42            Private  159449   Bachelors             13   \n",
      "\n",
      "           marital-status          occupation    relationship    race  sex  \\\n",
      "0           Never-married        Adm-clerical   Not-in-family   White    1   \n",
      "1      Married-civ-spouse     Exec-managerial         Husband   White    1   \n",
      "2                Divorced   Handlers-cleaners   Not-in-family   White    1   \n",
      "3      Married-civ-spouse   Handlers-cleaners         Husband   Black    1   \n",
      "4      Married-civ-spouse      Prof-specialty            Wife   Black    0   \n",
      "5      Married-civ-spouse     Exec-managerial            Wife   White    0   \n",
      "6   Married-spouse-absent       Other-service   Not-in-family   Black    0   \n",
      "7      Married-civ-spouse     Exec-managerial         Husband   White    1   \n",
      "8           Never-married      Prof-specialty   Not-in-family   White    0   \n",
      "9      Married-civ-spouse     Exec-managerial         Husband   White    1   \n",
      "\n",
      "   capital-gain  capital-loss  hours-per-week         country  salary  \n",
      "0          2174             0              40   United-States       1  \n",
      "1             0             0              13   United-States       1  \n",
      "2             0             0              40   United-States       1  \n",
      "3             0             0              40   United-States       1  \n",
      "4             0             0              40            Cuba       1  \n",
      "5             0             0              40   United-States       1  \n",
      "6             0             0              16         Jamaica       1  \n",
      "7             0             0              45   United-States       0  \n",
      "8         14084             0              50   United-States       0  \n",
      "9          5178             0              40   United-States       0  \n",
      "----------------------------------------\n",
      "<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",
      "fnlwgt            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 int64\n",
      "capital-gain      32561 non-null int64\n",
      "capital-loss      32561 non-null int64\n",
      "hours-per-week    32561 non-null int64\n",
      "country           32561 non-null object\n",
      "salary            32561 non-null int64\n",
      "dtypes: int64(8), object(7)\n",
      "memory usage: 3.7+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "df['sex'] = df['sex'].map({' Male':1,' Female':0}).astype(int)\n",
    "\n",
    "print (df.head(10))\n",
    "print ((\"-\"*40))\n",
    "print (df.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "ca53e75c89aacaf4b6edeb8b821c0d4843753040"
   },
   "source": [
    "### Find correlation between columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "_uuid": "f46804e6f1a567a1e1d21ade76f2895b8b469eff"
   },
   "outputs": [],
   "source": [
    "def plot_correlation(df, size=15):\n",
    "    corr= df.corr()\n",
    "    fig, ax =plt.subplots(figsize=(size,size))\n",
    "    ax.matshow(corr)\n",
    "    plt.xticks(range(len(corr.columns)),corr.columns)\n",
    "    plt.yticks(range(len(corr.columns)),corr.columns)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "_uuid": "1e691165e894259a589d1272e130856c19e2b94d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_correlation(df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "b2a1ad4fe0670e111b351dd11dc9604993d240ac"
   },
   "source": [
    "### Categorise in US and Non-US candidates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "_uuid": "2d73662014c2f6a6b078d23d308f2382b67ab558"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               salary\n",
      "country                              \n",
      " ?                           0.749571\n",
      " Cambodia                    0.631579\n",
      " Canada                      0.677686\n",
      " China                       0.733333\n",
      " Columbia                    0.966102\n",
      " Cuba                        0.736842\n",
      " Dominican-Republic          0.971429\n",
      " Ecuador                     0.857143\n",
      " El-Salvador                 0.915094\n",
      " England                     0.666667\n",
      " France                      0.586207\n",
      " Germany                     0.678832\n",
      " Greece                      0.724138\n",
      " Guatemala                   0.953125\n",
      " Haiti                       0.909091\n",
      " Holand-Netherlands          1.000000\n",
      " Honduras                    0.923077\n",
      " Hong                        0.700000\n",
      " Hungary                     0.769231\n",
      " India                       0.600000\n",
      " Iran                        0.581395\n",
      " Ireland                     0.791667\n",
      " Italy                       0.657534\n",
      " Jamaica                     0.876543\n",
      " Japan                       0.612903\n",
      " Laos                        0.888889\n",
      " Mexico                      0.948678\n",
      " Nicaragua                   0.941176\n",
      " Outlying-US(Guam-USVI-etc)  1.000000\n",
      " Peru                        0.935484\n",
      " Philippines                 0.691919\n",
      " Poland                      0.800000\n",
      " Portugal                    0.891892\n",
      " Puerto-Rico                 0.894737\n",
      " Scotland                    0.750000\n",
      " South                       0.800000\n",
      " Taiwan                      0.607843\n",
      " Thailand                    0.833333\n",
      " Trinadad&Tobago             0.894737\n",
      " United-States               0.754165\n",
      " Vietnam                     0.925373\n",
      " Yugoslavia                  0.625000\n"
     ]
    }
   ],
   "source": [
    "print (df[['country','salary']].groupby(['country']).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "66d96cac567e056776406027ed95e9dec70dc0b4"
   },
   "source": [
    "### Drop empty value marked as '?'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "_uuid": "93e342e709ec51180b9d31d43387acd21c38b99e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32561, 15)\n",
      "(30162, 15)\n",
      "   age          workclass  fnlwgt   education  education-num  \\\n",
      "0   39          State-gov   77516   Bachelors             13   \n",
      "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
      "2   38            Private  215646     HS-grad              9   \n",
      "3   53            Private  234721        11th              7   \n",
      "4   28            Private  338409   Bachelors             13   \n",
      "5   37            Private  284582     Masters             14   \n",
      "6   49            Private  160187         9th              5   \n",
      "7   52   Self-emp-not-inc  209642     HS-grad              9   \n",
      "8   31            Private   45781     Masters             14   \n",
      "9   42            Private  159449   Bachelors             13   \n",
      "\n",
      "           marital-status          occupation    relationship    race  sex  \\\n",
      "0           Never-married        Adm-clerical   Not-in-family   White    1   \n",
      "1      Married-civ-spouse     Exec-managerial         Husband   White    1   \n",
      "2                Divorced   Handlers-cleaners   Not-in-family   White    1   \n",
      "3      Married-civ-spouse   Handlers-cleaners         Husband   Black    1   \n",
      "4      Married-civ-spouse      Prof-specialty            Wife   Black    0   \n",
      "5      Married-civ-spouse     Exec-managerial            Wife   White    0   \n",
      "6   Married-spouse-absent       Other-service   Not-in-family   Black    0   \n",
      "7      Married-civ-spouse     Exec-managerial         Husband   White    1   \n",
      "8           Never-married      Prof-specialty   Not-in-family   White    0   \n",
      "9      Married-civ-spouse     Exec-managerial         Husband   White    1   \n",
      "\n",
      "   capital-gain  capital-loss  hours-per-week         country  salary  \n",
      "0          2174             0              40   United-States       1  \n",
      "1             0             0              13   United-States       1  \n",
      "2             0             0              40   United-States       1  \n",
      "3             0             0              40   United-States       1  \n",
      "4             0             0              40            Cuba       1  \n",
      "5             0             0              40   United-States       1  \n",
      "6             0             0              16         Jamaica       1  \n",
      "7             0             0              45   United-States       0  \n",
      "8         14084             0              50   United-States       0  \n",
      "9          5178             0              40   United-States       0  \n"
     ]
    }
   ],
   "source": [
    "print (df.shape)\n",
    "df['country'] = df['country'].replace(' ?',np.nan)\n",
    "df['workclass'] = df['workclass'].replace(' ?',np.nan)\n",
    "df['occupation'] = df['occupation'].replace(' ?',np.nan)\n",
    "\n",
    "df.dropna(how='any',inplace=True)\n",
    "\n",
    "print (df.shape)\n",
    "print (df.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "_uuid": "9b5560ad29d5b630524a52423283e430df93bca7"
   },
   "outputs": [],
   "source": [
    "\n",
    "for dataset in data:\n",
    "    dataset.loc[dataset['country'] != ' United-States', 'country'] = 'Non-US'\n",
    "    dataset.loc[dataset['country'] == ' United-States', 'country'] = 'US'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "_uuid": "57b341aa545cdf313db569f68ee316b62910ba09"
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
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       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
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       "      <th>sex</th>\n",
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       "      <th>hours-per-week</th>\n",
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       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
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       "      <td>13</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>US</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Non-US</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>284582</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>160187</td>\n",
       "      <td>9th</td>\n",
       "      <td>5</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>Non-US</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>209642</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>45781</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>14084</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42</td>\n",
       "      <td>Private</td>\n",
       "      <td>159449</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>5178</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education-num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "5   37            Private  284582     Masters             14   \n",
       "6   49            Private  160187         9th              5   \n",
       "7   52   Self-emp-not-inc  209642     HS-grad              9   \n",
       "8   31            Private   45781     Masters             14   \n",
       "9   42            Private  159449   Bachelors             13   \n",
       "\n",
       "           marital-status          occupation    relationship    race  sex  \\\n",
       "0           Never-married        Adm-clerical   Not-in-family   White    1   \n",
       "1      Married-civ-spouse     Exec-managerial         Husband   White    1   \n",
       "2                Divorced   Handlers-cleaners   Not-in-family   White    1   \n",
       "3      Married-civ-spouse   Handlers-cleaners         Husband   Black    1   \n",
       "4      Married-civ-spouse      Prof-specialty            Wife   Black    0   \n",
       "5      Married-civ-spouse     Exec-managerial            Wife   White    0   \n",
       "6   Married-spouse-absent       Other-service   Not-in-family   Black    0   \n",
       "7      Married-civ-spouse     Exec-managerial         Husband   White    1   \n",
       "8           Never-married      Prof-specialty   Not-in-family   White    0   \n",
       "9      Married-civ-spouse     Exec-managerial         Husband   White    1   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week country  salary  \n",
       "0          2174             0              40      US       1  \n",
       "1             0             0              13      US       1  \n",
       "2             0             0              40      US       1  \n",
       "3             0             0              40      US       1  \n",
       "4             0             0              40  Non-US       1  \n",
       "5             0             0              40      US       1  \n",
       "6             0             0              16  Non-US       1  \n",
       "7             0             0              45      US       0  \n",
       "8         14084             0              50      US       0  \n",
       "9          5178             0              40      US       0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "304708e76f7ccc0f6fc6f5e4d6b50d9cd132f041"
   },
   "source": [
    "### Convert *country* in *integer*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "_uuid": "d5fac4b0c4f179150d2c1815c599a84e38d17eae"
   },
   "outputs": [],
   "source": [
    "df['country'] = df['country'].map({'US':1,'Non-US':0}).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "_uuid": "68e5145678ed67e65998e7a2977ffacd4ff72c43"
   },
   "outputs": [
    {
     "data": {
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       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>284582</td>\n",
       "      <td>Masters</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>Private</td>\n",
       "      <td>160187</td>\n",
       "      <td>9th</td>\n",
       "      <td>5</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>209642</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>Private</td>\n",
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       "      <td>14</td>\n",
       "      <td>Never-married</td>\n",
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       "      <td>0</td>\n",
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       "      <td>159449</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>5178</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education-num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "5   37            Private  284582     Masters             14   \n",
       "6   49            Private  160187         9th              5   \n",
       "7   52   Self-emp-not-inc  209642     HS-grad              9   \n",
       "8   31            Private   45781     Masters             14   \n",
       "9   42            Private  159449   Bachelors             13   \n",
       "\n",
       "           marital-status          occupation    relationship    race  sex  \\\n",
       "0           Never-married        Adm-clerical   Not-in-family   White    1   \n",
       "1      Married-civ-spouse     Exec-managerial         Husband   White    1   \n",
       "2                Divorced   Handlers-cleaners   Not-in-family   White    1   \n",
       "3      Married-civ-spouse   Handlers-cleaners         Husband   Black    1   \n",
       "4      Married-civ-spouse      Prof-specialty            Wife   Black    0   \n",
       "5      Married-civ-spouse     Exec-managerial            Wife   White    0   \n",
       "6   Married-spouse-absent       Other-service   Not-in-family   Black    0   \n",
       "7      Married-civ-spouse     Exec-managerial         Husband   White    1   \n",
       "8           Never-married      Prof-specialty   Not-in-family   White    0   \n",
       "9      Married-civ-spouse     Exec-managerial         Husband   White    1   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  country  salary  \n",
       "0          2174             0              40        1       1  \n",
       "1             0             0              13        1       1  \n",
       "2             0             0              40        1       1  \n",
       "3             0             0              40        1       1  \n",
       "4             0             0              40        0       1  \n",
       "5             0             0              40        1       1  \n",
       "6             0             0              16        0       1  \n",
       "7             0             0              45        1       0  \n",
       "8         14084             0              50        1       0  \n",
       "9          5178             0              40        1       0  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "fd4be34334fc1e59cd8a9233214b7bf40840ac46"
   },
   "source": [
    "### Data visualisation using histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "_uuid": "d357e8cb3896c18a67942d572bd0e3926e0450b7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x= df['hours-per-week']\n",
    "plt.hist(x,bins=None,density=True,normed=None,histtype='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "_uuid": "5eb3b5acefbd709ca1ab6a3189324a69dc3664d5"
   },
   "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>salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relationship</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Husband</th>\n",
       "      <td>0.544331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Not-in-family</th>\n",
       "      <td>0.893477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other-relative</th>\n",
       "      <td>0.960630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Own-child</th>\n",
       "      <td>0.985670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unmarried</th>\n",
       "      <td>0.933686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wife</th>\n",
       "      <td>0.506401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   salary\n",
       "relationship             \n",
       " Husband         0.544331\n",
       " Not-in-family   0.893477\n",
       " Other-relative  0.960630\n",
       " Own-child       0.985670\n",
       " Unmarried       0.933686\n",
       " Wife            0.506401"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['relationship','salary']].groupby(['relationship']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "_uuid": "c5fb5a744f9d8d9ab093b6b2fccdd5576ed152fa"
   },
   "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>salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital-status</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Divorced</th>\n",
       "      <td>0.892738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Married-AF-spouse</th>\n",
       "      <td>0.523810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Married-civ-spouse</th>\n",
       "      <td>0.545041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Married-spouse-absent</th>\n",
       "      <td>0.916216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Never-married</th>\n",
       "      <td>0.951676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Separated</th>\n",
       "      <td>0.929712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Widowed</th>\n",
       "      <td>0.903265</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          salary\n",
       "marital-status                  \n",
       " Divorced               0.892738\n",
       " Married-AF-spouse      0.523810\n",
       " Married-civ-spouse     0.545041\n",
       " Married-spouse-absent  0.916216\n",
       " Never-married          0.951676\n",
       " Separated              0.929712\n",
       " Widowed                0.903265"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['marital-status','salary']].groupby(['marital-status']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "8c6a21be751694230d3f7a69f9fe5d15ce39d010"
   },
   "source": [
    "### Categorise marital-status into single and couple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "_uuid": "ce155bd8a7f5bea428ac5e6ab0fbaf8676bdc692"
   },
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>country</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Single</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Couple</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Single</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Couple</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>Couple</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>284582</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Couple</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>160187</td>\n",
       "      <td>9th</td>\n",
       "      <td>5</td>\n",
       "      <td>Single</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>209642</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Couple</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>45781</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Single</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>14084</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42</td>\n",
       "      <td>Private</td>\n",
       "      <td>159449</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Couple</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>5178</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education-num marital-status  \\\n",
       "0   39          State-gov   77516   Bachelors             13         Single   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13         Couple   \n",
       "2   38            Private  215646     HS-grad              9         Single   \n",
       "3   53            Private  234721        11th              7         Couple   \n",
       "4   28            Private  338409   Bachelors             13         Couple   \n",
       "5   37            Private  284582     Masters             14         Couple   \n",
       "6   49            Private  160187         9th              5         Single   \n",
       "7   52   Self-emp-not-inc  209642     HS-grad              9         Couple   \n",
       "8   31            Private   45781     Masters             14         Single   \n",
       "9   42            Private  159449   Bachelors             13         Couple   \n",
       "\n",
       "           occupation    relationship    race  sex  capital-gain  \\\n",
       "0        Adm-clerical   Not-in-family   White    1          2174   \n",
       "1     Exec-managerial         Husband   White    1             0   \n",
       "2   Handlers-cleaners   Not-in-family   White    1             0   \n",
       "3   Handlers-cleaners         Husband   Black    1             0   \n",
       "4      Prof-specialty            Wife   Black    0             0   \n",
       "5     Exec-managerial            Wife   White    0             0   \n",
       "6       Other-service   Not-in-family   Black    0             0   \n",
       "7     Exec-managerial         Husband   White    1             0   \n",
       "8      Prof-specialty   Not-in-family   White    0         14084   \n",
       "9     Exec-managerial         Husband   White    1          5178   \n",
       "\n",
       "   capital-loss  hours-per-week  country  salary  \n",
       "0             0              40        1       1  \n",
       "1             0              13        1       1  \n",
       "2             0              40        1       1  \n",
       "3             0              40        1       1  \n",
       "4             0              40        0       1  \n",
       "5             0              40        1       1  \n",
       "6             0              16        0       1  \n",
       "7             0              45        1       0  \n",
       "8             0              50        1       0  \n",
       "9             0              40        1       0  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df['marital-status'] = df['marital-status'].replace([' Divorced',' Married-spouse-absent',' Never-married',' Separated',' Widowed'],'Single')\n",
    "df['marital-status'] = df['marital-status'].replace([' Married-AF-spouse',' Married-civ-spouse'],'Couple')\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "_uuid": "772a8021aa908fb6ca9b4334b1a9af8d4c3514b5"
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital-status</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>Couple</th>\n",
       "      <td>0.545009</td>\n",
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       "      <td>0.931637</td>\n",
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      "text/plain": [
       "                  salary\n",
       "marital-status          \n",
       "Couple          0.545009\n",
       "Single          0.931637"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['marital-status','salary']].groupby(['marital-status']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "_uuid": "3664ae4247c0603f4e819d8329e2f62aa4e884f9"
   },
   "outputs": [
    {
     "data": {
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       "      <th rowspan=\"5\" valign=\"top\">Couple</th>\n",
       "      <th>Husband</th>\n",
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       "      <th>Not-in-family</th>\n",
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       "      <th rowspan=\"4\" valign=\"top\">Single</th>\n",
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       "      <th>Own-child</th>\n",
       "      <td>0.988818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unmarried</th>\n",
       "      <td>0.933686</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  salary\n",
       "marital-status relationship             \n",
       "Couple          Husband         0.544331\n",
       "                Not-in-family   0.714286\n",
       "                Other-relative  0.857143\n",
       "                Own-child       0.821429\n",
       "                Wife            0.506401\n",
       "Single          Not-in-family   0.893802\n",
       "                Other-relative  0.976623\n",
       "                Own-child       0.988818\n",
       "                Unmarried       0.933686"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['marital-status','relationship','salary']].groupby(['marital-status','relationship']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "_uuid": "72843c3ce65658daafd1098bf804944a7855ec67"
   },
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Not-in-family</th>\n",
       "      <th>Couple</th>\n",
       "      <td>0.714286</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">Other-relative</th>\n",
       "      <th>Couple</th>\n",
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       "      <th>Unmarried</th>\n",
       "      <th>Single</th>\n",
       "      <td>0.933686</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                                  salary\n",
       "relationship    marital-status          \n",
       " Husband        Couple          0.544331\n",
       " Not-in-family  Couple          0.714286\n",
       "                Single          0.893802\n",
       " Other-relative Couple          0.857143\n",
       "                Single          0.976623\n",
       " Own-child      Couple          0.821429\n",
       "                Single          0.988818\n",
       " Unmarried      Single          0.933686\n",
       " Wife           Couple          0.506401"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['marital-status','relationship','salary']].groupby(['relationship','marital-status']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "_uuid": "2bfe8c8f6746d11dcd65e73df11e9e91605e121b"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>age</th>\n",
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       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>284582</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>160187</td>\n",
       "      <td>9th</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>209642</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>45781</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>14084</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42</td>\n",
       "      <td>Private</td>\n",
       "      <td>159449</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>5178</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education-num  marital-status  \\\n",
       "0   39          State-gov   77516   Bachelors             13               1   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13               0   \n",
       "2   38            Private  215646     HS-grad              9               1   \n",
       "3   53            Private  234721        11th              7               0   \n",
       "4   28            Private  338409   Bachelors             13               0   \n",
       "5   37            Private  284582     Masters             14               0   \n",
       "6   49            Private  160187         9th              5               1   \n",
       "7   52   Self-emp-not-inc  209642     HS-grad              9               0   \n",
       "8   31            Private   45781     Masters             14               1   \n",
       "9   42            Private  159449   Bachelors             13               0   \n",
       "\n",
       "           occupation    relationship    race  sex  capital-gain  \\\n",
       "0        Adm-clerical   Not-in-family   White    1          2174   \n",
       "1     Exec-managerial         Husband   White    1             0   \n",
       "2   Handlers-cleaners   Not-in-family   White    1             0   \n",
       "3   Handlers-cleaners         Husband   Black    1             0   \n",
       "4      Prof-specialty            Wife   Black    0             0   \n",
       "5     Exec-managerial            Wife   White    0             0   \n",
       "6       Other-service   Not-in-family   Black    0             0   \n",
       "7     Exec-managerial         Husband   White    1             0   \n",
       "8      Prof-specialty   Not-in-family   White    0         14084   \n",
       "9     Exec-managerial         Husband   White    1          5178   \n",
       "\n",
       "   capital-loss  hours-per-week  country  salary  \n",
       "0             0              40        1       1  \n",
       "1             0              13        1       1  \n",
       "2             0              40        1       1  \n",
       "3             0              40        1       1  \n",
       "4             0              40        0       1  \n",
       "5             0              40        1       1  \n",
       "6             0              16        0       1  \n",
       "7             0              45        1       0  \n",
       "8             0              50        1       0  \n",
       "9             0              40        1       0  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df['marital-status'] = df['marital-status'].map({'Couple':0,'Single':1})\n",
    "   \n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "_uuid": "8b60dcf5dd1eb62de72b419efd644d503773d278"
   },
   "outputs": [
    {
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       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>Prof-specialty</td>\n",
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       "      <td>Black</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>284582</td>\n",
       "      <td>Masters</td>\n",
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       "      <td>9th</td>\n",
       "      <td>5</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>7</th>\n",
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       "      <td>Self-emp-not-inc</td>\n",
       "      <td>209642</td>\n",
       "      <td>HS-grad</td>\n",
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       "      <td>Exec-managerial</td>\n",
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       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education-num  marital-status  \\\n",
       "0   39          State-gov   77516   Bachelors             13               1   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13               0   \n",
       "2   38            Private  215646     HS-grad              9               1   \n",
       "3   53            Private  234721        11th              7               0   \n",
       "4   28            Private  338409   Bachelors             13               0   \n",
       "5   37            Private  284582     Masters             14               0   \n",
       "6   49            Private  160187         9th              5               1   \n",
       "7   52   Self-emp-not-inc  209642     HS-grad              9               0   \n",
       "8   31            Private   45781     Masters             14               1   \n",
       "9   42            Private  159449   Bachelors             13               0   \n",
       "\n",
       "           occupation  relationship    race  sex  capital-gain  capital-loss  \\\n",
       "0        Adm-clerical             3   White    1          2174             0   \n",
       "1     Exec-managerial             2   White    1             0             0   \n",
       "2   Handlers-cleaners             3   White    1             0             0   \n",
       "3   Handlers-cleaners             2   Black    1             0             0   \n",
       "4      Prof-specialty             1   Black    0             0             0   \n",
       "5     Exec-managerial             1   White    0             0             0   \n",
       "6       Other-service             3   Black    0             0             0   \n",
       "7     Exec-managerial             2   White    1             0             0   \n",
       "8      Prof-specialty             3   White    0         14084             0   \n",
       "9     Exec-managerial             2   White    1          5178             0   \n",
       "\n",
       "   hours-per-week  country  salary  \n",
       "0              40        1       1  \n",
       "1              13        1       1  \n",
       "2              40        1       1  \n",
       "3              40        1       1  \n",
       "4              40        0       1  \n",
       "5              40        1       1  \n",
       "6              16        0       1  \n",
       "7              45        1       0  \n",
       "8              50        1       0  \n",
       "9              40        1       0  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rel_map = {' Unmarried':0,' Wife':1,' Husband':2,' Not-in-family':3,' Own-child':4,' Other-relative':5}\n",
    "\n",
    "df['relationship'] = df['relationship'].map(rel_map)\n",
    "    \n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "c4dca9d9b71971d049755a27a40cd2d4e23f4c94"
   },
   "source": [
    "### Analyse *race*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "_uuid": "a6e24d514ebae6ddfd5ea1016391ca4e77502126"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>salary</th>\n",
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       "      <th>race</th>\n",
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       "      <th>Amer-Indian-Eskimo</th>\n",
       "      <td>0.881119</td>\n",
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       "      <td>0.722905</td>\n",
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       "      <td>0.870075</td>\n",
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       "      <th>Other</th>\n",
       "      <td>0.909091</td>\n",
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      ],
      "text/plain": [
       "                       salary\n",
       "race                         \n",
       " Amer-Indian-Eskimo  0.881119\n",
       " Asian-Pac-Islander  0.722905\n",
       " Black               0.870075\n",
       " Other               0.909091\n",
       " White               0.736282"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['race','salary']].groupby('race').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "_uuid": "f71f4fc02ac6c21d7822faa9311565284cd87111"
   },
   "outputs": [
    {
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>284582</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>160187</td>\n",
       "      <td>9th</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>209642</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>45781</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14084</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42</td>\n",
       "      <td>Private</td>\n",
       "      <td>159449</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5178</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education-num  marital-status  \\\n",
       "0   39          State-gov   77516   Bachelors             13               1   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13               0   \n",
       "2   38            Private  215646     HS-grad              9               1   \n",
       "3   53            Private  234721        11th              7               0   \n",
       "4   28            Private  338409   Bachelors             13               0   \n",
       "5   37            Private  284582     Masters             14               0   \n",
       "6   49            Private  160187         9th              5               1   \n",
       "7   52   Self-emp-not-inc  209642     HS-grad              9               0   \n",
       "8   31            Private   45781     Masters             14               1   \n",
       "9   42            Private  159449   Bachelors             13               0   \n",
       "\n",
       "           occupation  relationship  race  sex  capital-gain  capital-loss  \\\n",
       "0        Adm-clerical             3     0    1          2174             0   \n",
       "1     Exec-managerial             2     0    1             0             0   \n",
       "2   Handlers-cleaners             3     0    1             0             0   \n",
       "3   Handlers-cleaners             2     3    1             0             0   \n",
       "4      Prof-specialty             1     3    0             0             0   \n",
       "5     Exec-managerial             1     0    0             0             0   \n",
       "6       Other-service             3     3    0             0             0   \n",
       "7     Exec-managerial             2     0    1             0             0   \n",
       "8      Prof-specialty             3     0    0         14084             0   \n",
       "9     Exec-managerial             2     0    1          5178             0   \n",
       "\n",
       "   hours-per-week  country  salary  \n",
       "0              40        1       1  \n",
       "1              13        1       1  \n",
       "2              40        1       1  \n",
       "3              40        1       1  \n",
       "4              40        0       1  \n",
       "5              40        1       1  \n",
       "6              16        0       1  \n",
       "7              45        1       0  \n",
       "8              50        1       0  \n",
       "9              40        1       0  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "race_map={' White':0,' Amer-Indian-Eskimo':1,' Asian-Pac-Islander':2,' Black':3,' Other':4}\n",
    "\n",
    "\n",
    "df['race']= df['race'].map(race_map)\n",
    "    \n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "_uuid": "c9cc890c516847bebab8720f84c23fa4ffbcbb49"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adm-clerical</th>\n",
       "      <td>0.866165</td>\n",
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       "    <tr>\n",
       "      <th>Armed-Forces</th>\n",
       "      <td>0.888889</td>\n",
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       "    <tr>\n",
       "      <th>Craft-repair</th>\n",
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       "      <th>Exec-managerial</th>\n",
       "      <td>0.514780</td>\n",
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       "    <tr>\n",
       "      <th>Farming-fishing</th>\n",
       "      <td>0.883721</td>\n",
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       "      <th>Handlers-cleaners</th>\n",
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       "      <th>Other-service</th>\n",
       "      <td>0.958904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Priv-house-serv</th>\n",
       "      <td>0.993007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prof-specialty</th>\n",
       "      <td>0.551511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Protective-serv</th>\n",
       "      <td>0.673913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>0.729353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tech-support</th>\n",
       "      <td>0.695175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transport-moving</th>\n",
       "      <td>0.797074</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      salary\n",
       "occupation                  \n",
       " Adm-clerical       0.866165\n",
       " Armed-Forces       0.888889\n",
       " Craft-repair       0.774690\n",
       " Exec-managerial    0.514780\n",
       " Farming-fishing    0.883721\n",
       " Handlers-cleaners  0.938519\n",
       " Machine-op-inspct  0.875381\n",
       " Other-service      0.958904\n",
       " Priv-house-serv    0.993007\n",
       " Prof-specialty     0.551511\n",
       " Protective-serv    0.673913\n",
       " Sales              0.729353\n",
       " Tech-support       0.695175\n",
       " Transport-moving   0.797074"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['occupation','salary']].groupby(['occupation']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "_uuid": "c190048f25c39a60c4a1386ddd5d00306df8095e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workclass</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Federal-gov</th>\n",
       "      <td>0.612937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Local-gov</th>\n",
       "      <td>0.705370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Private</th>\n",
       "      <td>0.781208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Self-emp-inc</th>\n",
       "      <td>0.441341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Self-emp-not-inc</th>\n",
       "      <td>0.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State-gov</th>\n",
       "      <td>0.731040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Without-pay</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     salary\n",
       "workclass                  \n",
       " Federal-gov       0.612937\n",
       " Local-gov         0.705370\n",
       " Private           0.781208\n",
       " Self-emp-inc      0.441341\n",
       " Self-emp-not-inc  0.714286\n",
       " State-gov         0.731040\n",
       " Without-pay       1.000000"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['workclass','salary']].groupby(['workclass']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "_uuid": "3f13b700584617231384a855001a331b78911f8b"
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
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       "      <td>Adm-clerical</td>\n",
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       "      <td>13</td>\n",
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       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
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       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>private</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>209642</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>self_employed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>45781</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14084</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>private</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42</td>\n",
       "      <td>Private</td>\n",
       "      <td>159449</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5178</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>private</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education-num  marital-status  \\\n",
       "0   39          State-gov   77516   Bachelors             13               1   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13               0   \n",
       "2   38            Private  215646     HS-grad              9               1   \n",
       "3   53            Private  234721        11th              7               0   \n",
       "4   28            Private  338409   Bachelors             13               0   \n",
       "5   37            Private  284582     Masters             14               0   \n",
       "6   49            Private  160187         9th              5               1   \n",
       "7   52   Self-emp-not-inc  209642     HS-grad              9               0   \n",
       "8   31            Private   45781     Masters             14               1   \n",
       "9   42            Private  159449   Bachelors             13               0   \n",
       "\n",
       "           occupation  relationship  race  sex  capital-gain  capital-loss  \\\n",
       "0        Adm-clerical             3     0    1          2174             0   \n",
       "1     Exec-managerial             2     0    1             0             0   \n",
       "2   Handlers-cleaners             3     0    1             0             0   \n",
       "3   Handlers-cleaners             2     3    1             0             0   \n",
       "4      Prof-specialty             1     3    0             0             0   \n",
       "5     Exec-managerial             1     0    0             0             0   \n",
       "6       Other-service             3     3    0             0             0   \n",
       "7     Exec-managerial             2     0    1             0             0   \n",
       "8      Prof-specialty             3     0    0         14084             0   \n",
       "9     Exec-managerial             2     0    1          5178             0   \n",
       "\n",
       "   hours-per-week  country  salary employment_type  \n",
       "0              40        1       1            govt  \n",
       "1              13        1       1   self_employed  \n",
       "2              40        1       1         private  \n",
       "3              40        1       1         private  \n",
       "4              40        0       1         private  \n",
       "5              40        1       1         private  \n",
       "6              16        0       1         private  \n",
       "7              45        1       0   self_employed  \n",
       "8              50        1       0         private  \n",
       "9              40        1       0         private  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def f(x):\n",
    "    if x['workclass'] == ' Federal-gov' or x['workclass']== ' Local-gov' or x['workclass']==' State-gov': return 'govt'\n",
    "    elif x['workclass'] == ' Private':return 'private'\n",
    "    elif x['workclass'] == ' Self-emp-inc' or x['workclass'] == ' Self-emp-not-inc': return 'self_employed'\n",
    "    else: return 'without_pay'\n",
    "    \n",
    "    \n",
    "df['employment_type']=df.apply(f, axis=1)\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "_uuid": "7343baa3f06541ddb48712f41754ac0545e7cce9"
   },
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employment_type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>govt</th>\n",
       "      <td>0.692702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>private</th>\n",
       "      <td>0.781208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>self_employed</th>\n",
       "      <td>0.632242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>without_pay</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   salary\n",
       "employment_type          \n",
       "govt             0.692702\n",
       "private          0.781208\n",
       "self_employed    0.632242\n",
       "without_pay      1.000000"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['employment_type','salary']].groupby(['employment_type']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "_uuid": "1c283c2ee271637e9e860dee3e5dbd01cc8b57d8"
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
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       "      <th>hours-per-week</th>\n",
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       "      <th>employment_type</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>284582</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>160187</td>\n",
       "      <td>9th</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>209642</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>45781</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42</td>\n",
       "      <td>Private</td>\n",
       "      <td>159449</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5178</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education-num  marital-status  \\\n",
       "0   39          State-gov   77516   Bachelors             13               1   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13               0   \n",
       "2   38            Private  215646     HS-grad              9               1   \n",
       "3   53            Private  234721        11th              7               0   \n",
       "4   28            Private  338409   Bachelors             13               0   \n",
       "5   37            Private  284582     Masters             14               0   \n",
       "6   49            Private  160187         9th              5               1   \n",
       "7   52   Self-emp-not-inc  209642     HS-grad              9               0   \n",
       "8   31            Private   45781     Masters             14               1   \n",
       "9   42            Private  159449   Bachelors             13               0   \n",
       "\n",
       "           occupation  relationship  race  sex  capital-gain  capital-loss  \\\n",
       "0        Adm-clerical             3     0    1          2174             0   \n",
       "1     Exec-managerial             2     0    1             0             0   \n",
       "2   Handlers-cleaners             3     0    1             0             0   \n",
       "3   Handlers-cleaners             2     3    1             0             0   \n",
       "4      Prof-specialty             1     3    0             0             0   \n",
       "5     Exec-managerial             1     0    0             0             0   \n",
       "6       Other-service             3     3    0             0             0   \n",
       "7     Exec-managerial             2     0    1             0             0   \n",
       "8      Prof-specialty             3     0    0         14084             0   \n",
       "9     Exec-managerial             2     0    1          5178             0   \n",
       "\n",
       "   hours-per-week  country  salary  employment_type  \n",
       "0              40        1       1                0  \n",
       "1              13        1       1                2  \n",
       "2              40        1       1                1  \n",
       "3              40        1       1                1  \n",
       "4              40        0       1                1  \n",
       "5              40        1       1                1  \n",
       "6              16        0       1                1  \n",
       "7              45        1       0                2  \n",
       "8              50        1       0                1  \n",
       "9              40        1       0                1  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employment_map = {'govt':0,'private':1,'self_employed':2,'without_pay':3}\n",
    "\n",
    "df['employment_type'] = df['employment_type'].map(employment_map)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "_uuid": "c94ffd747ba64db3eb47adccdcc512314b99f14a"
   },
   "outputs": [
    {
     "data": {
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       "      <th>7th-8th</th>\n",
       "      <td>0.937163</td>\n",
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       "      <th>9th</th>\n",
       "      <td>0.945055</td>\n",
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       "      <th>Assoc-acdm</th>\n",
       "      <td>0.746032</td>\n",
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       "      <th>Assoc-voc</th>\n",
       "      <td>0.736802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bachelors</th>\n",
       "      <td>0.578509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Doctorate</th>\n",
       "      <td>0.253333</td>\n",
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       "      <th>HS-grad</th>\n",
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       "      <td>0.435771</td>\n",
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       "    <tr>\n",
       "      <th>Preschool</th>\n",
       "      <td>1.000000</td>\n",
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       "      <th>Prof-school</th>\n",
       "      <td>0.250923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Some-college</th>\n",
       "      <td>0.799940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 salary\n",
       "education              \n",
       " 10th          0.928049\n",
       " 11th          0.943702\n",
       " 12th          0.923077\n",
       " 1st-4th       0.960265\n",
       " 5th-6th       0.958333\n",
       " 7th-8th       0.937163\n",
       " 9th           0.945055\n",
       " Assoc-acdm    0.746032\n",
       " Assoc-voc     0.736802\n",
       " Bachelors     0.578509\n",
       " Doctorate     0.253333\n",
       " HS-grad       0.835671\n",
       " Masters       0.435771\n",
       " Preschool     1.000000\n",
       " Prof-school   0.250923\n",
       " Some-college  0.799940"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['education','salary']].groupby(['education']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "_uuid": "2461de98eb79e7d6b2fac571c1d44223e5ea949f"
   },
   "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>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>country</th>\n",
       "      <th>salary</th>\n",
       "      <th>employment_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>77516</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>83311</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>215646</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>234721</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>338409</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37</td>\n",
       "      <td>284582</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49</td>\n",
       "      <td>160187</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52</td>\n",
       "      <td>209642</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31</td>\n",
       "      <td>45781</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14084</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42</td>\n",
       "      <td>159449</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5178</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  fnlwgt  education-num  marital-status  relationship  race  sex  \\\n",
       "0   39   77516             13               1             3     0    1   \n",
       "1   50   83311             13               0             2     0    1   \n",
       "2   38  215646              9               1             3     0    1   \n",
       "3   53  234721              7               0             2     3    1   \n",
       "4   28  338409             13               0             1     3    0   \n",
       "5   37  284582             14               0             1     0    0   \n",
       "6   49  160187              5               1             3     3    0   \n",
       "7   52  209642              9               0             2     0    1   \n",
       "8   31   45781             14               1             3     0    0   \n",
       "9   42  159449             13               0             2     0    1   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  country  salary  \\\n",
       "0          2174             0              40        1       1   \n",
       "1             0             0              13        1       1   \n",
       "2             0             0              40        1       1   \n",
       "3             0             0              40        1       1   \n",
       "4             0             0              40        0       1   \n",
       "5             0             0              40        1       1   \n",
       "6             0             0              16        0       1   \n",
       "7             0             0              45        1       0   \n",
       "8         14084             0              50        1       0   \n",
       "9          5178             0              40        1       0   \n",
       "\n",
       "   employment_type  \n",
       "0                0  \n",
       "1                2  \n",
       "2                1  \n",
       "3                1  \n",
       "4                1  \n",
       "5                1  \n",
       "6                1  \n",
       "7                2  \n",
       "8                1  \n",
       "9                1  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(labels=['workclass','education','occupation'],axis=1,inplace=True)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "_uuid": "b53a66635f486a2feb7c367a88ea027bb056aab7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Bv1ST9X7wn2Dwzf1L3dfMSuDWJD861lTHmwU+XgOfZ/BT/EhfcLb0F9B9d/0AcEdV/f6488xVVW+rqpVVNc3gxcebq2qiZqlV9W/APUme1a26kMHV2ZPibuDFSR7X/XtfyAS90DzH8K1OtgB/M8Ys8+p+2dLvABur6tvjzjOsqr5cVU+rqunua2YWeEH3f3SS/DXwMoAkzwTOYsQ3ibP0F/YS4JcZzKC/2H38wrhDnYHeCHw4yW3A84HfHXOe7+t+ArkBuBX4MoOvh7FftZnko8A/Ac9KMpvkcmAHcFGSuxi8+2THiZ5jTBn/CPhhYG/39fK+Ccs3URbIuBM4r3sb5y5gy6h/YvKKXElqiDN9SWqIpS9JDbH0Jakhlr4kNcTSl6SGWPqS1BBLX5IaYulLUkP+D6YQCHL/4SprAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x= df['education-num']\n",
    "plt.hist(x,bins=None,density=True,normed=None,histtype='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "_uuid": "c4c08faf1bd3cb64ec33f68095516f5edcf23af2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=df['capital-gain']\n",
    "plt.hist(x,bins=None,normed=None)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "_uuid": "c8222c4c470815ad7d50237c4fd0e35166aea299"
   },
   "outputs": [],
   "source": [
    "df.loc[(df['capital-gain'] > 0),'capital-gain'] = 1\n",
    "df.loc[(df['capital-gain'] == 0 ,'capital-gain')]= 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "_uuid": "f5b842506edaa875730e2a0a3b988c5abc9fabf4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>country</th>\n",
       "      <th>salary</th>\n",
       "      <th>employment_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>77516</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>83311</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>215646</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>234721</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>338409</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37</td>\n",
       "      <td>284582</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49</td>\n",
       "      <td>160187</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52</td>\n",
       "      <td>209642</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31</td>\n",
       "      <td>45781</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42</td>\n",
       "      <td>159449</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>37</td>\n",
       "      <td>280464</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30</td>\n",
       "      <td>141297</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>23</td>\n",
       "      <td>122272</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>32</td>\n",
       "      <td>205019</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>34</td>\n",
       "      <td>245487</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>25</td>\n",
       "      <td>176756</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>32</td>\n",
       "      <td>186824</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>38</td>\n",
       "      <td>28887</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>43</td>\n",
       "      <td>292175</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>40</td>\n",
       "      <td>193524</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>54</td>\n",
       "      <td>302146</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>35</td>\n",
       "      <td>76845</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>43</td>\n",
       "      <td>117037</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2042</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>59</td>\n",
       "      <td>109015</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>56</td>\n",
       "      <td>216851</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  fnlwgt  education-num  marital-status  relationship  race  sex  \\\n",
       "0    39   77516             13               1             3     0    1   \n",
       "1    50   83311             13               0             2     0    1   \n",
       "2    38  215646              9               1             3     0    1   \n",
       "3    53  234721              7               0             2     3    1   \n",
       "4    28  338409             13               0             1     3    0   \n",
       "5    37  284582             14               0             1     0    0   \n",
       "6    49  160187              5               1             3     3    0   \n",
       "7    52  209642              9               0             2     0    1   \n",
       "8    31   45781             14               1             3     0    0   \n",
       "9    42  159449             13               0             2     0    1   \n",
       "10   37  280464             10               0             2     3    1   \n",
       "11   30  141297             13               0             2     2    1   \n",
       "12   23  122272             13               1             4     0    0   \n",
       "13   32  205019             12               1             3     3    1   \n",
       "15   34  245487              4               0             2     1    1   \n",
       "16   25  176756              9               1             4     0    1   \n",
       "17   32  186824              9               1             0     0    1   \n",
       "18   38   28887              7               0             2     0    1   \n",
       "19   43  292175             14               1             0     0    0   \n",
       "20   40  193524             16               0             2     0    1   \n",
       "21   54  302146              9               1             0     3    0   \n",
       "22   35   76845              5               0             2     3    1   \n",
       "23   43  117037              7               0             2     0    1   \n",
       "24   59  109015              9               1             0     0    0   \n",
       "25   56  216851             13               0             2     0    1   \n",
       "\n",
       "    capital-gain  capital-loss  hours-per-week  country  salary  \\\n",
       "0              1             0              40        1       1   \n",
       "1              0             0              13        1       1   \n",
       "2              0             0              40        1       1   \n",
       "3              0             0              40        1       1   \n",
       "4              0             0              40        0       1   \n",
       "5              0             0              40        1       1   \n",
       "6              0             0              16        0       1   \n",
       "7              0             0              45        1       0   \n",
       "8              1             0              50        1       0   \n",
       "9              1             0              40        1       0   \n",
       "10             0             0              80        1       0   \n",
       "11             0             0              40        0       0   \n",
       "12             0             0              30        1       1   \n",
       "13             0             0              50        1       1   \n",
       "15             0             0              45        0       1   \n",
       "16             0             0              35        1       1   \n",
       "17             0             0              40        1       1   \n",
       "18             0             0              50        1       1   \n",
       "19             0             0              45        1       0   \n",
       "20             0             0              60        1       0   \n",
       "21             0             0              20        1       1   \n",
       "22             0             0              40        1       1   \n",
       "23             0          2042              40        1       1   \n",
       "24             0             0              40        1       1   \n",
       "25             0             0              40        1       0   \n",
       "\n",
       "    employment_type  \n",
       "0                 0  \n",
       "1                 2  \n",
       "2                 1  \n",
       "3                 1  \n",
       "4                 1  \n",
       "5                 1  \n",
       "6                 1  \n",
       "7                 2  \n",
       "8                 1  \n",
       "9                 1  \n",
       "10                1  \n",
       "11                0  \n",
       "12                1  \n",
       "13                1  \n",
       "15                1  \n",
       "16                2  \n",
       "17                1  \n",
       "18                1  \n",
       "19                2  \n",
       "20                1  \n",
       "21                1  \n",
       "22                0  \n",
       "23                1  \n",
       "24                1  \n",
       "25                0  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "_uuid": "b577703c4cbd367b0af2b5741befd412f41a4360"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=df['capital-loss']\n",
    "plt.hist(x,bins=None)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "_uuid": "d36e6768fdf84aab93dff249fd8f33992d2f92ac"
   },
   "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>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>country</th>\n",
       "      <th>salary</th>\n",
       "      <th>employment_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>77516</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>83311</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>215646</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>234721</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>338409</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37</td>\n",
       "      <td>284582</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49</td>\n",
       "      <td>160187</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52</td>\n",
       "      <td>209642</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31</td>\n",
       "      <td>45781</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42</td>\n",
       "      <td>159449</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  fnlwgt  education-num  marital-status  relationship  race  sex  \\\n",
       "0   39   77516             13               1             3     0    1   \n",
       "1   50   83311             13               0             2     0    1   \n",
       "2   38  215646              9               1             3     0    1   \n",
       "3   53  234721              7               0             2     3    1   \n",
       "4   28  338409             13               0             1     3    0   \n",
       "5   37  284582             14               0             1     0    0   \n",
       "6   49  160187              5               1             3     3    0   \n",
       "7   52  209642              9               0             2     0    1   \n",
       "8   31   45781             14               1             3     0    0   \n",
       "9   42  159449             13               0             2     0    1   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  country  salary  \\\n",
       "0             1             0              40        1       1   \n",
       "1             0             0              13        1       1   \n",
       "2             0             0              40        1       1   \n",
       "3             0             0              40        1       1   \n",
       "4             0             0              40        0       1   \n",
       "5             0             0              40        1       1   \n",
       "6             0             0              16        0       1   \n",
       "7             0             0              45        1       0   \n",
       "8             1             0              50        1       0   \n",
       "9             1             0              40        1       0   \n",
       "\n",
       "   employment_type  \n",
       "0                0  \n",
       "1                2  \n",
       "2                1  \n",
       "3                1  \n",
       "4                1  \n",
       "5                1  \n",
       "6                1  \n",
       "7                2  \n",
       "8                1  \n",
       "9                1  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df['capital-loss'] > 0),'capital-loss'] = 1\n",
    "df.loc[(df['capital-loss'] == 0 ,'capital-loss')]= 0\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "_uuid": "0d87318b52a9a07514443ae84ee11539ab707f66"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30162"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "8524c97d937f6cfd57b1eee4288bfbe6b4c405d0"
   },
   "source": [
    "## Applying model for learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "8509e784320903a6df764c7ee952cce02dd94d4f",
    "collapsed": true
   },
   "source": [
    "### Divide data in training, validation and test dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "ab003b4f1d279420c5ab1473b48313fca3a67ece"
   },
   "source": [
    "#### 50% training data, 20% validation data, 30% test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "_uuid": "724e7bed803be921b289d636ca8bb190825ab63e"
   },
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X= df.drop(['salary'],axis=1)\n",
    "y=df['salary']\n",
    "\n",
    "split_size=0.3\n",
    "\n",
    "#Creation of Train and Test dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=split_size,random_state=22)\n",
    "\n",
    "#Creation of Train and validation dataset\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train,y_train,test_size=0.2,random_state=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "_uuid": "3a5928ad3c2c409f092ab45b78064474822a31df"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dataset: (16890, 12)(16890,)\n",
      "Validation dataset: (4223, 12)(4223,)\n",
      "Test dataset: (9049, 12)(9049,)\n"
     ]
    }
   ],
   "source": [
    "print (\"Train dataset: {0}{1}\".format(X_train.shape, y_train.shape))\n",
    "print (\"Validation dataset: {0}{1}\".format(X_val.shape, y_val.shape))\n",
    "print (\"Test dataset: {0}{1}\".format(X_test.shape, y_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "cb273c746b97171b9ed8977bde9d0fd1d1f66e1c"
   },
   "source": [
    "### Let's select few algorithm used for classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "_uuid": "e698b53644b10a7c02ce3fc6fd79118e66c5ee20"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "_uuid": "70adf937d53f29dd2e91832a22cc4a7f9c030c28"
   },
   "outputs": [],
   "source": [
    "models = []\n",
    "names = ['3NN','5NN','7NN','9NN']\n",
    "\n",
    "# models.append((LogisticRegression()))\n",
    "# models.append((RandomForestClassifier(n_estimators=100)))\n",
    "# models.append((MLPClassifier()))\n",
    "# models.append((GaussianNB()))\n",
    "# models.append((DecisionTreeClassifier()))\n",
    "# models.append((SVC()))\n",
    "models.append((KNeighborsClassifier(n_neighbors=3)))\n",
    "models.append((KNeighborsClassifier(n_neighbors=5)))\n",
    "models.append((KNeighborsClassifier(n_neighbors=7)))\n",
    "models.append((KNeighborsClassifier(n_neighbors=9)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "_uuid": "e1863b73952bf8b95bb63518008e146bff2e98c9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "           metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n",
      "           weights='uniform'), KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
      "           weights='uniform'), KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "           metric_params=None, n_jobs=None, n_neighbors=7, p=2,\n",
      "           weights='uniform'), KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "           metric_params=None, n_jobs=None, n_neighbors=9, p=2,\n",
      "           weights='uniform')]\n"
     ]
    }
   ],
   "source": [
    "print (models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "_uuid": "a4bc46f4505a5b0a930ad8169e8598dfbb92e00f"
   },
   "outputs": [],
   "source": [
    "from sklearn import model_selection\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "_uuid": "a15da3ec8514a73903d59c8f5c0e6a6b6f20622b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empiric Error rates for each KNN classifier\n",
      "----------------------------------------\n",
      "3NN: 0.30831162680558843\n",
      "----------------------------------------\n",
      "5NN: 0.290551740468861\n",
      "----------------------------------------\n",
      "7NN: 0.27563343594601\n",
      "----------------------------------------\n",
      "9NN: 0.2742126450390717\n"
     ]
    }
   ],
   "source": [
    "\n",
    "kfold = model_selection.KFold(n_splits=5,random_state=7)\n",
    "\n",
    "print ('Empiric Error rates for each KNN classifier')\n",
    "for i in range(0,len(models)):    \n",
    "    cv_result = model_selection.cross_val_score(models[i],X_train,y_train,cv=kfold,scoring='accuracy')\n",
    "    score=models[i].fit(X_train,y_train)\n",
    "    prediction = models[i].predict(X_val)\n",
    "    acc_score = accuracy_score(y_val,prediction)     \n",
    "    print ('-'*40)\n",
    "    print ('{0}: {1}'.format(names[i],1-acc_score))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "46467ef4f67479d0aa35d13c242484d6d83fa6f5"
   },
   "source": [
    "##### Let's proceed further with 9NN algorithm as it showed good accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "697a04c3d4866639f742fd772145fafe66ea67f0"
   },
   "source": [
    "#### Let's predict our test data and see prediction results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "_uuid": "b629ec76287d2e82feea1022b487021729f18fec"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "_uuid": "07e94d5eb08c2b90785f19433df37b83b2c5b121"
   },
   "outputs": [],
   "source": [
    "bestknn = KNeighborsClassifier(n_neighbors=9)\n",
    "bestknn.fit(X_train,y_train)\n",
    "prediction = bestknn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "_uuid": "e06e4946abfc87ac0019f00684ee72c6efdab418"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\n",
      "Accuracy score:\n",
      "0.7300254171731683\n",
      "----------------------------------------\n",
      "Empiric Error rate:\n",
      "0.26997458282683173\n",
      "----------------------------------------\n",
      "Confusion Matrix:\n",
      "[[ 223 2020]\n",
      " [ 423 6383]]\n",
      "----------------------------------------\n",
      "Classification Matrix:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.35      0.10      0.15      2243\n",
      "           1       0.76      0.94      0.84      6806\n",
      "\n",
      "   micro avg       0.73      0.73      0.73      9049\n",
      "   macro avg       0.55      0.52      0.50      9049\n",
      "weighted avg       0.66      0.73      0.67      9049\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print ('-'*40)\n",
    "print ('Accuracy score:')\n",
    "print (accuracy_score(y_test,prediction))\n",
    "print ('-'*40)\n",
    "print ('Empiric Error rate:')\n",
    "print (1-accuracy_score(y_test,prediction))\n",
    "print ('-'*40)\n",
    "print ('Confusion Matrix:')\n",
    "print (confusion_matrix(y_test,prediction))\n",
    "print ('-'*40)\n",
    "print ('Classification Matrix:')\n",
    "print (classification_report(y_test,prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "bc6eca70161d45ca1e1325ef5b1734bbc396073b"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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