A5-Kaggle.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\hwunrow\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\__init__.py:886: MatplotlibDeprecationWarning: \n",
      "examples.directory is deprecated; in the future, examples will be found relative to the 'datapath' directory.\n",
      "  \"found relative to the 'datapath' directory.\".format(key))\n"
     ]
    }
   ],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split, KFold, cross_val_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vxNUYrAtsdOv"
   },
   "source": [
    "# Investigate Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 433
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1054,
     "status": "error",
     "timestamp": 1564252172689,
     "user": {
      "displayName": "Han Yong Wunrow",
      "photoUrl": "https://lh5.googleusercontent.com/-ywThDaCRYPQ/AAAAAAAAAAI/AAAAAAAAAAc/03SlI3Hqgno/s64/photo.jpg",
      "userId": "14597859047699288310"
     },
     "user_tz": 420
    },
    "id": "hcfqKg6AsdOw",
    "outputId": "80f4999e-6bfd-4a84-ca1d-db6bd740d6a8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'age', 'workclass', 'fnlwgt', 'education', 'education-num',\n",
       "       'marital-status', 'occupation', 'relationship', 'race', 'sex',\n",
       "       'capital-gain', 'capital-loss', 'hours-per-week', 'native-country',\n",
       "       'Category'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = pd.read_csv(\"income_train.csv\")\n",
    "train_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_YdcheeFsdO0"
   },
   "outputs": [],
   "source": [
    "# group numeric and categorical features\n",
    "numeric_features = ['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week', 'Category']\n",
    "categorical_features = ['workclass', 'education', 'martial-status', 'occupation', 'relationship', 'race', \n",
    "                        'sex', 'native-country']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reformat target to 0 and 1\n",
    "target = 'Category'\n",
    "train_data.loc[train_data[target] == \"<=50K\", target] = 0\n",
    "train_data.loc[train_data[target] == \">50K\", target] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number low income individuals: 17304 (75.92%)\n",
      "Number high income individuals: 5488 (24.08%)\n"
     ]
    },
    {
     "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": [
    "# Histogram of >50K and <=50K\n",
    "sns.countplot(x=target, data=train_data, order=sorted(train_data[target].unique()))\n",
    "low_income = train_data[train_data[target] == 0]\n",
    "high_income = train_data[train_data[target] == 1]\n",
    "\n",
    "print(f'Number low income individuals: {len(low_income)} ({len(low_income) * 100.0 / len(train_data):.2f}%)')\n",
    "print(f'Number high income individuals: {len(high_income)} ({len(high_income) * 100.0 / len(train_data):.2f}%)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28a9c92d630>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation matric with numeric features\n",
    "sns.heatmap(train_data[numeric_features].corr(),annot=True, fmt = \".2f\", cmap = \"PiYG\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\hwunrow\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\seaborn\\categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n",
      "c:\\users\\hwunrow\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\seaborn\\categorical.py:3672: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\Users\\hwunrow\\AppData\\Roaming\\Python\\Python37\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look at education num\n",
    "g = sns.factorplot(x=\"education-num\",y=\"Category\",data=train_data,kind=\"bar\",size = 6,palette = \"muted\")\n",
    "g = g.set_ylabels(\">50K probability\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look at age\n",
    "g = sns.FacetGrid(train_data, col='Category')\n",
    "g = g.map(sns.distplot, \"age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look at hours-per week\n",
    "g  = sns.factorplot(x=\"hours-per-week\",y=\"Category\",data=train_data,kind=\"bar\",size = 6,palette = \"muted\")\n",
    "g = g.set_ylabels(\">50K probability\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "categorical_features = ['workclass', 'education', 'martial-status', 'occupation', 'relationship', 'race', \n",
    "                        'sex', 'native-country']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look at workclass\n",
    "g = sns.factorplot(x=\"workclass\",y=\"Category\",data=train_data,kind=\"bar\", size = 6 ,\n",
    "palette = \"muted\")\n",
    "g = g.set_ylabels(\"Income >50K Probability\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look at education\n",
    "g = sns.factorplot(x=\"education\",y=\"Category\",data=train_data,kind=\"bar\", size = 6 ,\n",
    "palette = \"muted\")\n",
    "g.despine(left=True)\n",
    "g = g.set_ylabels(\"Income >50K Probability\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Never-married' 'Divorced' 'Married-civ-spouse' 'Separated'\n",
      " 'Married-AF-spouse' 'Widowed' 'Married-spouse-absent']\n"
     ]
    },
    {
     "data": {
      "image/png": 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TD0zy1/2HJklSM0nL6t3AXwE3AVTVOcAhfQYlSdKoSZLV9lV11rR5N/cRjCRJM5kkWV2R5F50dw5OchDw816jkiRpxCT3s3o5sBi4b5IVwM9oo1pIkrRRrDNZVdXFwH5Jbg9sVVXX9B+WJEmrrTNZJdkZeB6wJ7CwDRcIVXVkr5FJktSZ5DurU2mJ6lzguyN/65Rk/yQXJrkoyTEzPP/SJOd2dyH+RpL7zSF2SdIWYpLvrLatqqPmWnGSBcCJwBOA5cDSJEuq6oKRYh+tqnd25Q8A3gzsP9f3kiRt3iZpWX0oyUuS3C3Jnab+JnjdPsBFVXVxVd0InAwcOFqgGyh3yu3prjiUJGnUJMnqRuAE4Fus7gJcNsHrdgMuHZle3s1bQ5KXJ/kpcDww4/dgSQ5PsizJssWLF0/w1pKkzckk3YBHAfeuqivmWHdmmLdWy6mqTgROTPJs4K+B589QZjHt8vkZ65Akbd4maVmdD/zuNtS9HNhjZHp34LIx5U8Gnnob3keStJmbpGV1C3B2kq8CN0zNnODS9aXAXkkWASto4wk+e7RAkr2q6ifd5P8CfoIkSdNMkqw+0/3NSVXdnOQI4DRgAfC+qjo/yXHAsqpaAhyRZD/aILm/ZoYuQEmSJhnB4oNJtgH27mZdWFU3TVJ5VZ1K+53W6LxjRx6/Yg6xSpK2UJOMYLEv8EHgEtpFE3skeX5VndFvaJIkNZN0A74JeGJVXQjtZozAx4CH9hmYJElTJrkacOupRAVQVT8Gtu4vJEmS1jRJy2pZkvcCH+qmn8OEYwNKkrQhTJKsXka7p9WRtO+szgD+uc+gJEkaNUmyWgi8rareDKsGqL1dr1FJkjRiku+svgxsNzK9HfClfsKRJGltkySrbavq2qmJ7vH2/YUkSdKaJklWv03yh1MTSR4KXNdfSJIkrWmS76z+HPhEkqlBaO8GHNxfSJIkrWmS4ZaWJrkvcB/a1YA/mnS4JUmSNoRJWlYADwf27Mo/JAlVdVJvUUmSNGKSsQE/BNwLOJt2uxBoN0A0WUmSNopJWlYPA+5XVd6hV5K0SUxyNeB5wK59ByJJ0mwmaVntAlyQ5CzWvFPwAb1FJUnSiEmS1ev6DkKSpHEmuXT9axsjEEmSZjNrskpyDe2qv7WeAqqqduwtKkmSRsyarKpqh40ZiCRJs5nkakBJkjYpk5UkafBMVpKkwTNZSZIGz2QlSRo8k5UkafBMVpKkwTNZSZIGz2QlSRo8k5UkafBMVpKkwTNZSZIGz2QlSRo8k5UkafBMVpKkwTNZSZIGz2QlSRo8k5UkafBMVpKkwTNZSZIGz2QlSRq8XpNVkv2TXJjkoiTHzPD8UUkuSHJOki8nuUef8UiS5qfeklWSBcCJwP8A7gccmuR+04p9H3hYVT0Q+CRwfF/xSJLmrz5bVvsAF1XVxVV1I3AycOBogar6alX9rpv8NrB7j/FIkuapPpPVbsClI9PLu3mzeRHwbz3GI0map/pMVplhXs1YMHku8DDghFmePzzJsiTLFi9evAFDlCTNBwt7rHs5sMfI9O7AZdMLJdkPeA3wJ1V1w0wVVdViYCpLzZjwJEmbrz5bVkuBvZIsSrINcAiwZLRAkocA7wIOqKrLe4xFkjSP9Zasqupm4AjgNOCHwClVdX6S45Ic0BU7AbgD8IkkZydZMkt1kqQtWJ/dgFTVqcCp0+YdO/J4vz7fX5K0eXAEC0nS4JmsJEmDZ7KSJA2eyUqSNHgmK0nS4JmsJEmDZ7KSJA2eyUqSNHgmK0nS4JmsJEmDZ7KSJA2eyUqSNHgmK0nS4JmsJEmDZ7KSJA2eyUqSNHgmK0nS4JmsJEmDZ7KSJA2eyUqSNHgmK0nS4JmsJEmDZ7KSJA2eyUqSNHgLN3UAkoZr+212WOO/tKmYrCTN6jF7P2VThyABdgNKkuYBk5UkafBMVpKkwTNZSZIGz2QlSRo8k5UkafBMVpKkwTNZSZIGz2QlSRo8k5UkafAcbmkzdPTRR7Ny5Up23XVXjj/++E0djiStN5PVZmjlypWsWLFiU4chDYIf3jYPJitJm7XN4cObCddkJUmDtzkk3PXlBRaSpMHrNVkl2T/JhUkuSnLMDM//cZLvJbk5yUF9xiJJmr966wZMsgA4EXgCsBxYmmRJVV0wUuy/gMOAV/YVx3zx4zcetsHquunXv1j1f0PUu/crP7DedUjS+ujzO6t9gIuq6mKAJCcDBwKrklVVXdI9d2uPcUiS5rk+uwF3Ay4dmV7ezZMkaU76TFaZYV7dpoqSw5MsS7Js8eLF6xmWJGm+6bMbcDmwx8j07sBlt6WiqloMTGWp25TwJEnzV58tq6XAXkkWJdkGOARY0uP7SZI2U70lq6q6GTgCOA34IXBKVZ2f5LgkBwAkeXiS5cAzgXclOb+veLYkd95uAXfdfiF33m7Bpg5FkjaIXkewqKpTgVOnzTt25PFSWvegNqAj97nrpg5BWi9veNGTNlhdv/rFzd3/FRuk3te+97T1rkNz5wgWkqTBM1lJkgbPgWylnjhStrThmKyknjhStrTh2A0oSRo8W1aS1IMfnPClDVbXjb/+3ar/G6LeB71qv/WuY2OzZSVJGjyTlSRp8ExWkqTBM1lJkgbPCywkbda2XRiguv+ar0xWkjZrD9rVAZ03B3YDSpIGz5aVNOLbr3jFBqvr+l/+ctX/DVHvH73tbetdhzRf2bKSJA2eyUqSNHgmK0nS4JmsJEmD5wUW03gPIkkaHpPVNN6DSJKGx25ASdLgbRYtq2cfe/oGq+uKK68DYOWV122Qej963L7rXYckbek2i2QlDdEdt956jf/SbXXH7XZa4/+WyGQl9eQFixZt6hC0mXjpww/d1CFscn5nJUkaPFtW0yzYduc1/kuSNj2T1TR3vP9zNnUIkqRp7AaUJA2eyUqSNHgmK0nS4JmsJEmDZ7KSJA2eyUqSNHgmK0nS4JmsJEmDZ7KSJA2eyUqSNHgmK0nS4JmsJEmDZ7KSJA2eyUqSNHgmK0nS4PWarJLsn+TCJBclOWaG52+X5OPd899Jsmef8UiS5qfeklWSBcCJwP8A7gccmuR+04q9CPh1Vd0beAvwj33FI0mav/psWe0DXFRVF1fVjcDJwIHTyhwIfLB7/EngvydJjzFJkuahVFU/FScHAftX1Yu76T8FHlFVR4yUOa8rs7yb/mlX5oppdR0OHN5Nbgtc30vQq+0CXLHOUsPmMgyDyzAMLsO6XVFV+/dY/3pZ2GPdM7WQpmfGScpQVYuBxRsiqEkkWVZVD9tY79cHl2EYXIZhcBnmvz67AZcDe4xM7w5cNluZJAuBnYBf9RiTJGke6jNZLQX2SrIoyTbAIcCSaWWWAM/vHh8EfKX66peUJM1bvXUDVtXNSY4ATgMWAO+rqvOTHAcsq6olwHuBDyW5iNaiOqSveOZoo3U59shlGAaXYRhchnmutwssJEnaUBzBQpI0eCYrSdLgDSJZJakkbxqZfmWS123CkDa4JMcl2W+G+bckOTvJ+Ul+kOSoJFsl2bMbhurt6/m+leRDI9MLk/wyyefnUMe+M5VPcuYcY3ldklfOofxLkzxvLu8xV0le0637c7rt8Ige32u2bfHvSf73bahv1frc0NsiydO6eO87Mm/PJNd162nqb5u5xj3mPd+S5M9Hpk9L8p6R6Tcl+b9JPjnDayvJyiQP66bnvJ+PievMadN7dr8Rna38nPbzIVvXsm6A+needN8fRLICbgCenmSXjfFm3WXyfda/YPp0VR1bVV+aofh1VfXgqro/8ATgfwL/r3vu+qo6cj3D+S3wgCTbddNPAFbMsY61lgegqh61nrGNVVXvrKqT+qo/ySOBJwN/WFUPBPYDLu3pvRYy+7bYGpjogJ1h3w30si0OBb7B2hc9/bTbX6f+btyA73km8CiAJFvRfgR7/5HnHwV8uaoOmuG1vwVuD9yum57zfj593W6s/XwLtzMT7vtU1Sb/A64F/gr42276lcDrusd3AT5FuxR+KfBoWpK9BNh5pI6LgP82U/nu+dfRrqb5IvDRGWI4nTY+4RnAD4GHA/8K/AT4m5FynwG+C5wPHD5tGY4DvgM8povvWFYf8B8ADurKPhT4WlfPzcDdRub/ELgJOAH4GfD5dSzvPYAvA+d0/3+ve/4DwJu7+r8DPBd4P/Br4Oe0KzIB/g9wDfA72q/jH9LNfxNwNXAl7QT+LeCrwEeBC6aWeSSeV3Xr+xzg9SPzXwNcCHwJ+Bjwyln2ged1r/0B8KGRbfZK4PeBs0bK7gmcM0Mdd+u239nAecBjR7bNm4DvdevoLt38o7r1cQ7waeCO3fxl3bb5LvAV4NJu/lnAStqJ8XrgGd38Y0fW4Urg97v5hwGfAD7X1XNttx0vBs7t1ueraSfV64Afd4+/35U7t4vtMyP1/BT4L9rVs5cAv+hhW9yhi2Nv4EfT1vt5ExzP/wBc0L3/G0f2x3cCX++W88nd/G1p++W53d8vu/l/TTsWvgjcETi1W3/37bbZed3f97r3ubnbJsu791hG28+vpo05uk+37JfTkuLfdtvhsK7uK7ttdDhwVbd+r6XtU9fSjs0fAP/Z1Xv9XNctLZl+oavnPODgbv4lXYxndX/37uaPO7YPGj33rGP/fyLt+P0ebT+6wyzb/MtdmXOBA0e2+Y9ow+KdQxsWb/sx23nc+fd9tPPsxcCR3fyTafv+2cAJY/erTZ2oRk4mO3YbbSfWTFYfBR7TPf494Ifd47cBL+gePwL40jrKv4528tlulhhOB/6xe/wK2g+Y70b7pLYcuHP33J26/9t1O8TU/AKeNVLfJcDRI9MfoP2WbGvawTJ1wryedlk/3Ub/E9rBeCJdslrH8n4OeH73+IXAZ0be7/Pdun1gt8O9o9sp9qX9pGAX4JvAjt1r3k1LwtvSDt6VwJ2AU2g7+2+BRTMcJE+kfRAILbF+Hvhj2gF+LrB9t30vYuaD+P60A32Xaev4dVPlu7jv2T1+NfDXM9Tzl8BruscLgB1Gts1zusfHAv/UPT6P9mHkx7QD6xPd9vkNsF9X5sXANd3j5bTfAgI8vltXvw/8G7BtN38JsLR7fFj3mqnluRZ4JO2A343Wo7Av7SRxXreOFnbr8wu0g36rbtkvBx7Xrc8nddvikpH1s0G2RVfHc4H3do/PpLU8oZ24pk4sZwMnzvDaO3XbcupK451H9sd/72Laq1sv23bb7P1dmfvSks5eXfmvAm+g9TZ8k3aSf/LIsh4FfLh7fEv32i8DTwVupY09+gVaEjq0W+dLaS3o5d3jP6clprd02/68bt0uAg6mnWCvpR2bf9mt2xO6cnPdz58BvHtkeqeRc8XUfvs8Vh/z447tmZLVWvs/7Rg/A7j9yLFz7AyxLWT1eWCXbhnSbfNiddJ5H+38PNt2Hnf+PZN2Pt2Fdn7Zmgk/AFVVr8MtzUlVXZ3kJOBI2gExZT/gfiPj2+6YZAfg47QTz/tpLZePr6M8wJKqGq17uqkfLZ8LnF9VPwdIcjFtpI0rgSOTPK0rtwftwLqSdrB8alp9H2dt9wEeAPxHF+PWwO5JdqJt8K91g/l+gnaQjtY10/I+Enh69/hDwPEjr/kEsG9VnZNkD9oBemr33E3AH9FOtv+V5Ha0nfN62knjCuDMqvpVkg/TWr5nVdXPZlimJ3Z/3++m79Ctlx2AT1fV7wCSTP9R+JTHA5+sbkzIqpppFJNTgGfRPs0d3P1NtxR4X5KtaQf22d38W1m9vj4M/Gu3vneknZQeS1uHLwP+jPYJ+MQk1wHbsPr3iFcD90zyalqrewfayfMhwMpuHW5Fa2VN+Y9py/Mc2nbdpyt7p5HndqJ9gn1sF9uttE+6d6Ml1AfRWoA30D6Bf5+1re+2gHZif2v3+ORu+nvd9E+r6sFjXns1bR96T5Iv0E7oU06pqluBn3TH1H1pvRDvAKiqHyX5FfA04N6sbtE/ita6OpvWqtw6yTtoSeK1Se5AW5fX0Xpe/gC4kfaho4CP0BL9s7rn3gbcmdbavg9tX/+P7vEi2nr/NO2E//Mu9p1p2+GJ3fLdg5Zs57JuzwXemOQfaQnp6yPPfWzk/1u6x+OO7Zmstf8n+RPaXS++2Z1vtqGt0+kC/F2SP+4ydUIbAAAI6UlEQVSWfzdazw20noVvdo8/TDtHv5WZt/O48+8XquoG4IYkl4/UP5GhfGc15a2024bcfmTeVsAja3Uf+W5VdQ1thd87yV1on6T+dR3loX1iAiDJ+7sviE8dea8buv+3jjyeml6YZF/axnhkVT2IdkLYtitzfVXdMm15fsvaQkuED+4O+uuq6ond/EpyT1rim37Cnm15p6tZ3v83wDGsPiimYrmK9klrO9rJ46qReqbHP9PyTNXz9yPr/N5V9d4Z4mmFkz1GvqB/aff6tcpN83HgWUn2BqqqfpLkESP1HFBVZ9A+6a6g/dh8toszVr1XVd1SVafTTmCX0j79Xgv8abd9ngj8oit+Oa275zpay/R2I/On1uF9utdPGV1nC2ndJMfT9vHLaSePKW+gtSY+SPskurKL4dW0E/Vo7L1siyR3pn14eE+SS2hdigcns98NobsQ4uwk76mqm2mJ+FO0/fTfR4pOf/9i7fFBr6Yl5d1o++K3aSftXWgn+6tpXaGn0xLLX9KO+aL1HnyAdkx+cVq9j2Z1N/YnaMfY12ldnXemtdxCa+V8rVt3f9Adm6Ox/j0t6f10ruu2qn7M6lbY3yc5dpZ1M9uxMDX/5m6Z6bbLNgCz7P+hfWCa2h/uV1Uvmn7s0D5E3QV4aLfP/YLV57a1ttuY7Tzu/Dt6Tr2FOQ5KMahk1X0CPYWWsKZ8ERgdqf3BXdmiffp5M62peeW48jO81wu6lfk/Z3p+FjvR7r/1u+4qqT+aw2unXAjcpftyfyrG+1fVVbST3MnAP9F2ntF4Z1veM1n9JfhzaN+RzeTTwA+q6txuemvaiWBX2o4D8BLaDvQj2o67Yzf/0HUs02nAC7tPuCTZLcldad0PT0uyXffp6indslw6sjO/k9Z186zuREmSO01/g6r6aRfna+laSVX1nZF6liS5B3B5Vb2bNjrKH3Yv34rWBQvwbOAbVfUb4HdJppbtT4Ff0r4nyUj5Z3XrCtrB+9iqejvtO5FbaN1M92R1a+plTLsgZURoCeo9tJPm3bv5N9M+ne9EO9GcRvvSeepEvjOt2/kMWqtjm+49njLDe6zvtjgIOKmq7lFVe1bVHrTu6MfMskxU1ZO617+4e9+dqupUWhfb6PH3zLQrXe/VrbMLu7ie08W6N60r7ZG01s6DaAnrLrRkdQGthUVVfYrWYnpEVV1NO6E+iNY7cgLwsG59Qtt/r+7W7RnAX9BaBd+n9Szc0u0PF3breucunq2TTF3g8Rtaz8QLad27c163Se4O/K6qPgy8kdX7J6zuKTiY1S2f2Y7tS2hJD1pX59ZdPDPt/98GHp3k3l2Z7ZPsPf3Y6dbV5VV1U5LH0VqOU35v5Hx1KPCNMdt5ovPviGto+/46DaYbcMSbGFlYWpPzxCTn0OI9A3hp99zHaU3fwyYsv77+HXhpV/eFtB1hTqrqxrTbp7y964q6PfBvSa6h6xKkfZo/bYaXz7a870vyKtrJ9gWzvPWraevlPNpJ96qq+mWSo4E3JTmerkVQVdcn+QDtpPcN2kGy55hl+mKS3we+1X0AvxZ4blV9L8nHaa2C/6R9kp3p9ecn+Vvga0luoZ1EDpuh6MdpJ6JFs4SyL/CqJDd1MUy1rH4L3D/Jd2knnakTw+uBxUneT/vUdwatq/WLwIeTvJx20cRU0rgr8Iy0293cCjytqi5I8g/AvyT5J9pJcTY30U6in+nWyY9G5n+T9l3ku2kJ83zgcUnOpSWqM0fW53u6eZ+b/gbruy1oJ6N/mDbvU7QkP8nNUXcAPptkW9p6+4uR5y6kXVj034CXdvvZPwPv7JbzZtqHhk/TkvkiVn8XdD1t2+0KLEpydlf/ed3xWLQT31u7er5J607bhXZhx6dpLdZraB/CVlTVLWndjrd06+7GJP8P+MckP6CdP6a6Q19A+75mR1ri2In23eNc1u0fACckuZW2zV828tztknyH9sFq6gPUbMf2u7t1fBbtg95UK3tfpu3/3TF+GPCxtG5qaBev/HhabB8BPpdkGWvum9D2x+cneRftO95/6ZZ/pu08p/NvVV2Z5JvdeenfqupVs5V1uCVt9pJcW1V32AD1nE774nzZ+ke1Zek+/Hy+qtb6jdSWrutufVhNu4+f1jSobkBJkmZiy0qSNHi2rCRJg2eykiQNnslKkjR4JitpHZLcPd1I30kenGSdv83LLCPVjyn/1CT321DlpM2NyUoaI8nCqrqsVo/0/WDWHAZrQ3kqbVicDVVO2qyYrLRZSrsPz4+SvCfJeUk+kmS/7geIP0myT/d3ZpLvd//v0732sCSfSPI54ItdXeel3bvpONrwQ2cnOXi2OtYR2z8kuSDtHlpvTPIo4ADaD0bPTnKvJC9JsjTtHmef6kYemKnc6Vl9D6ddut/skOT+Sc7qyp2TZK9+1rS0cQxxBAtpQ7k38EzabR+W0kZheAzthP9/aSNc/HFV3Zx2Y8y/o40NCG3InwdWG8h3T1g1wsGxtB9wHgGQZMcxdawlbSippwH3rapKsnNVXZU2+OmqH80muaobNockfwO8qKreMUO52d7qpcDbquojXZKdbQgoaV4wWWlz9rOpsRCTnE+7cV91Q/vsSTfKedfqKFaPAQhrj5Y+m3F1zGTcqOSjHtAlqZ1pI6fPNPzWON8CXpNkd+Bfq+onc3y9NCh2A2pzNn3k/NFR9RfSjXJeVQ+gDT667Uj52UY1n25cHcCcRiUf9QHgiKr6A9oYhmvV21k1Avdomar6KK0FeR1wWpLHT7g80iDZstKWbGqUc5h54NyZTB8lep11VNWTph6njVa9fVWdmuTbtJvczVTvDsDP0+5N9JyR95he7hLaCNxnsXqkeNJuNXNxVb29e/xA2k0IpXnJlpW2ZMfT7iv0TSb/TuertJvLnZ3k4NtQxw7A57tRqb/G6tGqT6aNmP39tFtovBb4Du2mgKMjYE8v90bgZUnOpI0wPuVg2ojkZ9PuU3bShMsnDZJjA0qSBs+WlSRp8ExWkqTBM1lJkgbPZCVJGjyTlSRp8ExWkqTBM1lJkgbv/wNb9xXsH1BQkgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look at martial-status\n",
    "print(train_data['marital-status'].unique())\n",
    "g = sns.factorplot(x=\"marital-status\",y=\"Category\",data=train_data,kind=\"bar\", size = 6 ,\n",
    "palette = \"muted\")\n",
    "g.despine(left=True)\n",
    "g = g.set_ylabels(\"Income >50K Probability\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look at occupation\n",
    "g = sns.factorplot(x=\"occupation\",y=\"Category\",data=train_data,kind=\"bar\", size = 6 ,\n",
    "palette = \"muted\")\n",
    "g.despine(left=True)\n",
    "g = g.set_ylabels(\"Income >50K Probability\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Not-in-family', 'Husband', 'Own-child', 'Unmarried', 'Wife',\n",
       "       'Other-relative'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look at relationship\n",
    "g = sns.factorplot(x=\"relationship\",y=\"Category\",data=train_data,kind=\"bar\", size = 6 ,\n",
    "palette = \"muted\")\n",
    "g.despine(left=True)\n",
    "g = g.set_ylabels(\"Income >50K Probability\")\n",
    "train_data['relationship'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o07L4MfBlYCVARNxJ9iAjMzPbRJRJFltFxG110zqrCMbMzFpTmWTxmKTdSE/Ok3Q08I9KozIzs5ZSptfZzwBTgT0kLQLuJ7ur26xLg7ZYDXSmdzPb0BUmi4hYCBwqaWugX0Q8U31YtqE7ec8n+zoEM+tBhclC0g7AB4ERwICsuyiIiBMrjczMzFpGmcNQM4FZwF2AjymYmW2CyiSLLSLipMojMbM+c9NBB/fKepYN6A8Syzo6emWdB998U+Xr2FSUuRrqUkkfl/RySTvVXpVHZmZmLaNMy2IFcBZwKuny2fT+yqqCMjOz1lKmZXES8KqIGBERI9OrVKKQNFbSfEkLJJ3SYP5Bkm6X1Jnu38jPO0HSfel1QrmPY2ZmVSiTLOYBz3e3Ykn9gfOAI4DRwHGSRtct9hDwIeDndWV3Ar4KvAEYA3xV0o7djcHMzHpGmcNQq4C5km4Altcmlrh0dgywIN2ngaTpwDjgnlwdD6R59VdZHQ78rvaEPkm/A8YCvygRr5mZ9bAyyeKq9OquXYCHc+MdZC2FdS27yzrEYGZmPaDMHdwXSxoI7J4mzY+IlSXqVqPqSsZVqqykCcAEgOHDh5es2szMuqvMHdyHABcDD5DtxIdJOiEibi4o2gEMy40PBRaXjKsDOKSu7I31C0XEVLJ+q2hrayubiMzMuvSDL17TK+t58rHn1rz3xjonnv3u9Spf5gT32cBhEXFwRBxEdj7hOyXKzQZGSRqZWibjgRkl47oWOEzSjunE9mFpmpmZ9YEyyWKziJhfG4mIvwGbFRWKiE5gItlO/q/AZRExT9JkSUcCSNpfUgdwDHCBpHmp7FLg62QJZzYwuXay28zMel+ZE9ztki4CLk3jHwDmlKk8ImaS9S2Vn3Zabng22SGmRmWnAdPKrMfMzKpVJll8iuyZFieSnbO4GTi/yqDMzKy1lEkWA4BzI+IcWHOz3eaVRmVmZi2lzDmL64Etc+NbAr+vJhwzM2tFZZLFFhHxbG0kDW9VXUhmZtZqyiSL5yTtWxuRtB+wrLqQzMys1ZQ5Z/F54HJJtRvqXg4cW11IZmbWasp09zFb0h7Aq8muhrq3ZHcfZma2kSjTsgDYHxiRlt9HEhFxSWVRmZlZSynTN9SlwG7AXLLuyiHr1M/JwsxsE1GmZdEGjI4Id9RnZraJKnM11N3AkKoDMTOz1lWmZTEIuEfSbbz4SXlHVhaVmZm1lDLJ4mtVB2FmZq2tzKWzN/VGIGa28dshnfrcwadANzhdJgtJz9D4MagCIiK2qywqM9soHb9qdV+HYOuoy2QREdv2ZiBmZta6ylwNZWZmmzgnCzMzK+RkYWZmhZwszMyskJOFmZkVcrIwM7NCThZmZlao7PMszMysB209cLsXvbc6Jwszsz5w4G5H9XUI3VLpYShJYyXNl7RA0ikN5m8u6Zdp/p8ljUjTR0haJmluev2oyjjNzKy5yloWkvoD5wFvBzqA2ZJmRMQ9ucU+CjwREa+SNB74FnBsmvf3iNi7qvjMzKy8KlsWY4AFEbEwIlYA04FxdcuMAy5Ow1cAb5OkCmMyM7N1UGWy2AV4ODfekaY1XCYiOoGngJ3TvJGS7pB0k6Q3N1qBpAmS2iW1P/rooz0bvZmZrVFlsmjUQqjv8ryrZf4BDI+IfYCTgJ9LesklAxExNSLaIqJt8ODB6x2wmZk1VmWy6ACG5caHAou7WkbSAGB7YGlELI+IxwEiYg7wd2D3CmM1M7MmqkwWs4FRkkZKGgiMB2bULTMDOCENHw38ISJC0uB0ghxJrwRGAQsrjNXMzJqo7GqoiOiUNBG4FugPTIuIeZImA+0RMQO4CLhU0gJgKVlCATgImCypE1gFfDIillYVq5mZNVfpTXkRMROYWTfttNzwC8AxDcpdCVxZZWxmZlae+4YyM7NCThZmZlbIycLMzAo5WZiZWSEnCzMzK+RkYWZmhZwszMyskJOFmZkVcrIwM7NCThZmZlbIycLMzAo5WZiZWSEnCzMzK+RkYWZmhZwszMyskJOFmZkVcrIwM7NCThZmZlbIycLMzAo5WZiZWSEnCzMzK+RkYWZmhZwszMyskJOFmZkVqjRZSBorab6kBZJOaTB/c0m/TPP/LGlEbt6X0/T5kg6vMk4zM2uusmQhqT9wHnAEMBo4TtLousU+CjwREa8CvgN8K5UdDYwHXguMBc5P9ZmZWR+osmUxBlgQEQsjYgUwHRhXt8w44OI0fAXwNklK06dHxPKIuB9YkOozM7M+UGWy2AV4ODfekaY1XCYiOoGngJ1LljUzs14yoMK61WBalFymTFkkTQAmpNFnJc3vVoTVGAQ81hsr0rdP6I3VrI9e2xZ8tdFXpqX03vfiRG+LNeRtUfPZc7qctWuZ8lUmiw5gWG58KLC4i2U6JA0AtgeWlixLREwFpvZgzOtNUntEtPV1HK3A22Itb4u1vC3W2pC2RZWHoWYDoySNlDSQ7IT1jLplZgC1n8dHA3+IiEjTx6erpUYCo4DbKozVzMyaqKxlERGdkiYC1wL9gWkRMU/SZKA9ImYAFwGXSlpA1qIYn8rOk3QZcA/QCXwmIlZVFauZmTWn7Ie89RRJE9LhsU2et8Va3hZreVustSFtCycLMzMr5O4+zMyskJNFFyR9R9Lnc+PXSrowN362pJMk/bqL8hfW7liX9J/VR1wtSaskzZX0F0m3S3pTmj5C0t3rWOeNktbpShBJ75UUkvYoWG6mpB3WZR0N6npA0l1pG1wnaUgP1PlTSUc3mf81SSfXTSv12bu7rp4sl7bVoDR8a3fX2aTe2vew9npJN0K5ZRvGnf/f7EmShkq6WtJ9kv4u6VxJAyXtLekdueVe8jfdEDhZdO1WoLZD7Ed2PfRrc/PfBGzWVeGI+FhE3JNGN/hkASyLiL0jYi/gy8D/6+N4jgP+SLoooisR8Y6IeLIH1/uWtA3a6bu/a6nPvi7SJewvGV4fEfGmnqgnqX0Pa68z1yGe/P9mj0g9T/wPcFVEjAJ2B7YBzgD2Bt7RpHh319UnXR85WXTtFlKyIEsSdwPPSNpR0ubAa4A7gG0kXSHpXkk/S1+aNb+aJZ0JbJl+Bf0szTte0m1p2gUbYL9X2wFP1E9MrYz/Sy2PNa2PNG9S7lf5mXXl+km6WNI3yqxc0jbAgWR9i41P014u6ea0Te+W9OY0Pf8L9ypJcyTNU3ZDZ62+ZyWdkWKbJelfSoRxM/CqVP6HktpTvafn6t1f0q2p3tskbVvwuc6UdI+kOyV9u8H8j0uaA7wXeJ4saSDpt5I6JD0maYWkX0n6QFrn4+mX7m/I7lc6WdLsFOsdaXsskHSppOuARZK+Kekm4HMNYnhA0unp73tXrXUjaWdlra07JF1A7sZaSc+m920kXZ8rOy5NHyHpr5J+nOK6TtKWJf4G3dl2X08tjX7KtWjT3/5baTv8XtKYNH+hpCPTMltI+kmK+Q5Jb2kQwluBFyLiJwDp6s0vAB8DpgDHpu/msWn50bn1nJiLs+G+IcU5WdKfgQO6s216TET41cULeAAYDnwC+CTwdbJfCAeS7SwOIeuiZChZ4v0T8K+p7I1AWxp+Nlfna4BrgM3S+PnAB/v6s5bYFquAucC96TPvl6aPAO5Ow1sBW6ThUWSXSEPWmeStwFZpfKfcNnoj8Avg1G7EcjxwURq+FdgX+GKtDrJLtbfN/Q0H1a13S7Lkv3MaD+DdaXgK8JUm34daXT8AvlVXb//0mfYEBgILgf3TvO2AAQ3q/CnZPUY7AfNZe9HJDun9a8DJaXjn2mcHvpHq3xf4LbACeAWwF9nl5qcDR6W/17lp3grgK2Qt4nbgb6ney8nuIt4yxX9+oxhz2+CzafjTwIVp+HvAaWn4nWmb1rbVs+l9ALBdGh5E1uebyL5DncDead5lwPEF38Pa69gm2662bacAF+Tm38ja/80AjkjDvwKuS9tnL2Bumv5F4CdpeA/gIdL3PBfXicB3GsR7R5r3g9y0r5F9bzdP2+HxtM4u9w0pzvf15T6gyju4Nwa11sWbgHPI+qd6E9nOsnYc9raI6ACQNJfsi//HJnW+DdgPmK2sEbIl8EgFsfe0ZRGxN4CkA4BLJL2ubpnNgB9I2pvsn3r3NP1Qsn+25wEiYmmuzAXAZRFxRjdiOQ74bhqensavAaZJ2ozsUMDcBuVOlPTeNDyMLKE9TrYTrZ17mgO8vcm6b5C0CriTbMcL8L7UUhkAvJysl+UA/hERswEi4umCz/Q08AJwYWoFNDoX9jqynfJTaXwRqXUBzI+IxcDi9L26DjiG7AbX3SNisaTVZK2x49NnXyXpTuBfgCcjYlkq+8uCWP8nvc8hS0gAB9WGI+I3kl7S8iRLDN+UdBCwmuz/qdaKuz/3N5tD9n/UyJrv4ZpKs8NlXW27/wL+HBETaGwFWbIFuAtYHhErJd2Vi+Ffge+nz3avpAfJvtt31n22RpeWdjX9NxGxHFgu6RGy7dBs37AKuLKLz9ArnCyaq523eD3ZL9GHyX5lPA1MS8sszy2/iuJtKuDiiPhyz4baeyLiT+nQzuC6WV8A/kn2q6wf2T8wdP0PA9k2fouksyPihS6WWUPSzmRN/tdJCrJf8wFMItthvZPsRs+zIuKSXLlDyJLWARHxvKQbgS3S7JWRfr6R/oap+T8nTZsREael4bdExJq+fJT1MHAyWQviCUk/TfU2/MySfgLsAyyOiDXHsSO7iXUM2Q5jPDAxfc68S8hab88AW6d6hpPdvJr/Hio3vpq130kBp5LtFKdGxAEppq8Bz+bKf1HSufUx5tTqrv++F12H/wGy78x+aYf8AGv/BvX/R1tKGkb2IwDgRxHxo0aVFmy72cB+knaq+5FSk//br67FERGrtfacTZkOpuYB/5afIGk7sh8ljW4obrTfaLZveCH6+MZkn7No7hbgXcDSiFiVvmw7kB0z/FM36lmZfvECXA8cLellAJJ2klSqI69WkY5T9yf7VZ63Pdmv6dXAv6dlIPuV+xFJW6XyO+XKXATMBC5XuROqRwOXRMSuETEiIoYB95Mlikci4sepzn0bxPZEShR7kB3+6lL6e9dOop7WZNHtgOeAp5Sd6zgiTb8XeIWk/dNn3lbSgIj4cKrzRTthZedhto+ImcDnyU6K1tuZ7Ff/q8gOb1yePvvLuojtZtJOU9LLyXZGY8kO2QyW9MG0XD/W/sIHmNwoxgI3kyUDJB0B7Nhgme3J/kYr03H/pt/7iHg49zdomCjS+pptu98CZwK/UcE5oybyn213sgRd32np9cBWtW2afmycTXYo7J9AmXW39L7ByaK5u8iOKc6qm/ZU/tdlCVOBOyX9LLKrML4CXJcOAfyO7NBFq6udpJ9LtsM6ocEvnfOBEyTNImumPwcQEb8lOxzSnsq/6LLBiDgHuJ2sRVD0nTyO7Nhy3pVk/5RzJd1B9gvv3LplfkvWYriT7NzTLHpARPyFbMc9j6y1eUuavoLsePr3Jf2F7O+8RVf1kO1Mfp3iu4mslVZvMXB4quveNO1K4JVd1PkrskNVBwM/BH4P7EZ2vkLAmSm2T9L1YZ+yTgcOknQ7cBjZcf16PwPaJLWT7XzvbbBMkTXfw/Q6k4JtFxGXAz8GZnT3xHlyPtA/HZr6JfChdAgpv44gu/DgGEn3AX8ja1n/J3AD2Qnt/Anul2j1fYPv4DYzs0JuWZiZWSEnCzMzK+RkYWZmhZwszMyskJOFmZkVcrIwM7NCThZmPUQZ/0/ZRslfbLP1oLU9pp5PdmPhRSrZA62k/pLOUtYL7J2SPtF3n8SsOfcNZbb+Xg18OCI+XeuDKHX3cL2kPcnuVP4lcGxEzE59Bi0j69TvqYjYX1m397dIui4i7u+zT2LWBScLs/X3YETUug8p3QOtpMOAPbX2aW7bk/UG62RhLcfJwmz9PQfd74E2Tf9sRFzbW4GarSufszDrOd3qgRa4FvhUrUdiSbtL2roP4jYr5JaFWQ+JiL+kXm/nkT3Fbk0PtKm30e+nXk+XkT1b40Ky3l5vV/a0m0eB9/RF7GZF3OusmZkV8mEoMzMr5GRhZmaFnCzMzKyQk4WZmRVysjAzs0JOFmZmVsjJwszMCjlZmJlZof8PWbllY6ikNSEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look at race\n",
    "g = sns.barplot(x=\"race\",y=\"Category\",data=train_data)\n",
    "g = g.set_ylabel(\"Income >50K Probability\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "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": [
    "# look at sex\n",
    "g = sns.barplot(x=\"sex\",y=\"Category\",data=train_data)\n",
    "g = g.set_ylabel(\"Income >50K Probability\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "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": [
    "# look at native-country\n",
    "g = sns.barplot(x=\"native-country\",y=\"Category\",data=train_data)\n",
    "g = g.set_ylabel(\"Income >50K Probability\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iA4ZHKNmsdO2"
   },
   "source": [
    "## Encode Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert Sex value to 0 and 1\n",
    "train_data[\"sex\"] = train_data[\"sex\"].map({\"Male\": 0, \"Female\":1})\n",
    "\n",
    "# Create Married Column - Binary Yes(1) or No(0)\n",
    "train_data[\"marital-status\"] = train_data[\"marital-status\"].replace(['Never-married','Divorced','Separated','Widowed'], 'Single')\n",
    "train_data[\"marital-status\"] = train_data[\"marital-status\"].replace(['Married-civ-spouse','Married-spouse-absent','Married-AF-spouse'], 'Married')\n",
    "train_data[\"marital-status\"] = train_data[\"marital-status\"].map({\"Married\":1, \"Single\":0})\n",
    "train_data[\"marital-status\"] = train_data[\"marital-status\"].astype(int)\n",
    "\n",
    "# group together White and Asians\n",
    "train_data['race'] = train_data['race'].map({'White': 1, 'Asian-Pac-Islander': 1, 'Black':0, 'Amer-Indian-Eskimo':0, 'Other':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mw4mooJqsdO3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset with Dropped Labels\n"
     ]
    },
    {
     "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>Id</th>\n",
       "      <th>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24859</td>\n",
       "      <td>39</td>\n",
       "      <td>77516</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27412</td>\n",
       "      <td>38</td>\n",
       "      <td>215646</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3074</td>\n",
       "      <td>52</td>\n",
       "      <td>209642</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24024</td>\n",
       "      <td>31</td>\n",
       "      <td>45781</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14084</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9623</td>\n",
       "      <td>42</td>\n",
       "      <td>159449</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5178</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  age  fnlwgt  education-num  marital-status  sex  capital-gain  \\\n",
       "0  24859   39   77516             13               0    0          2174   \n",
       "1  27412   38  215646              9               0    0             0   \n",
       "2   3074   52  209642              9               1    0             0   \n",
       "3  24024   31   45781             14               0    1         14084   \n",
       "4   9623   42  159449             13               1    0          5178   \n",
       "\n",
       "   capital-loss  hours-per-week  Category  \n",
       "0             0              40         0  \n",
       "1             0              40         0  \n",
       "2             0              45         1  \n",
       "3             0              50         1  \n",
       "4             0              40         1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the data you don't want to use\n",
    "train_data.drop(labels=[\"education\",\"relationship\"], axis = 1, inplace = True)\n",
    "print('Dataset with Dropped Labels')\n",
    "train_data.head()\n",
    "features = ['age', 'fnlwgt', 'education-num', 'martial-status', 'sex', 'capital-gain', 'capital-loss', 'hours-per-week']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HIUZxtW7sdPF"
   },
   "source": [
    "# Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Split Data: X\n",
      "[[    39  77516     13 ...   2174      0     40]\n",
      " [    38 215646      9 ...      0      0     40]\n",
      " [    52 209642      9 ...      0      0     45]\n",
      " ...\n",
      " [    40 154374      9 ...      0      0     40]\n",
      " [    58 151910      9 ...      0      0     40]\n",
      " [    52 287927      9 ...  15024      0     40]]\n",
      "Split Data: Y\n",
      "[0 0 1 ... 1 0 1]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hwunrow\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hwunrow\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hwunrow\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hwunrow\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hwunrow\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hwunrow\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hwunrow\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hwunrow\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hwunrow\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hwunrow\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: 0.798991 (0.007524)\n",
      "KNN: 0.772062 (0.006738)\n",
      "CART: 0.801569 (0.010278)\n",
      "RF: 0.842867 (0.008760)\n",
      "GB: 0.859211 (0.010565)\n"
     ]
    }
   ],
   "source": [
    "# Split-out Validation Dataset and Create Test Variables\n",
    "array = train_data.values\n",
    "X = array[:,1:9]\n",
    "Y = array[:,9]\n",
    "print('Split Data: X')\n",
    "print(X)\n",
    "print('Split Data: Y')\n",
    "print(Y)\n",
    "validation_size = 0.20\n",
    "seed = 7\n",
    "num_folds = 10\n",
    "scoring = 'accuracy'\n",
    "X_train, X_validation, Y_train, Y_validation = train_test_split(X,Y,\n",
    "    test_size=validation_size,random_state=seed)\n",
    "\n",
    "# Params for Random Forest\n",
    "num_trees = 100\n",
    "max_features = 3\n",
    "\n",
    "#Spot Check 5 Algorithms (LR, KNN, CART, RF)\n",
    "models = []\n",
    "models.append(('LR', LogisticRegression()))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('CART', DecisionTreeClassifier()))\n",
    "models.append(('RF', RandomForestClassifier(n_estimators=num_trees, max_features=max_features)))\n",
    "models.append(('GB', GradientBoostingClassifier()))\n",
    "\n",
    "# evalutate each model in turn\n",
    "results = []\n",
    "names = []\n",
    "for name, model in models:\n",
    "    kfold = KFold(n_splits=10, random_state=seed)\n",
    "    cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.862667 using {'learning_rate': 0.075, 'max_depth': 5, 'max_features': 5, 'n_estimators': 100}\n",
      "0.860528 (0.011048) with: {'learning_rate': 0.1, 'max_depth': 4, 'max_features': 3, 'n_estimators': 100}\n",
      "0.861350 (0.010412) with: {'learning_rate': 0.1, 'max_depth': 4, 'max_features': 3, 'n_estimators': 150}\n",
      "0.861405 (0.010703) with: {'learning_rate': 0.1, 'max_depth': 4, 'max_features': 3, 'n_estimators': 200}\n",
      "0.861899 (0.010980) with: {'learning_rate': 0.1, 'max_depth': 4, 'max_features': 4, 'n_estimators': 100}\n",
      "0.861679 (0.011162) with: {'learning_rate': 0.1, 'max_depth': 4, 'max_features': 4, 'n_estimators': 150}\n",
      "0.862392 (0.009950) with: {'learning_rate': 0.1, 'max_depth': 4, 'max_features': 4, 'n_estimators': 200}\n",
      "0.861241 (0.010952) with: {'learning_rate': 0.1, 'max_depth': 4, 'max_features': 5, 'n_estimators': 100}\n",
      "0.862228 (0.011046) with: {'learning_rate': 0.1, 'max_depth': 4, 'max_features': 5, 'n_estimators': 150}\n",
      "0.861405 (0.009613) with: {'learning_rate': 0.1, 'max_depth': 4, 'max_features': 5, 'n_estimators': 200}\n",
      "0.861789 (0.011817) with: {'learning_rate': 0.1, 'max_depth': 5, 'max_features': 3, 'n_estimators': 100}\n",
      "0.860966 (0.009389) with: {'learning_rate': 0.1, 'max_depth': 5, 'max_features': 3, 'n_estimators': 150}\n",
      "0.860912 (0.009200) with: {'learning_rate': 0.1, 'max_depth': 5, 'max_features': 3, 'n_estimators': 200}\n",
      "0.861679 (0.010537) with: {'learning_rate': 0.1, 'max_depth': 5, 'max_features': 4, 'n_estimators': 100}\n",
      "0.861405 (0.009088) with: {'learning_rate': 0.1, 'max_depth': 5, 'max_features': 4, 'n_estimators': 150}\n",
      "0.860199 (0.008768) with: {'learning_rate': 0.1, 'max_depth': 5, 'max_features': 4, 'n_estimators': 200}\n",
      "0.861460 (0.011225) with: {'learning_rate': 0.1, 'max_depth': 5, 'max_features': 5, 'n_estimators': 100}\n",
      "0.860473 (0.009103) with: {'learning_rate': 0.1, 'max_depth': 5, 'max_features': 5, 'n_estimators': 150}\n",
      "0.861186 (0.008626) with: {'learning_rate': 0.1, 'max_depth': 5, 'max_features': 5, 'n_estimators': 200}\n",
      "0.862173 (0.009665) with: {'learning_rate': 0.1, 'max_depth': 6, 'max_features': 3, 'n_estimators': 100}\n",
      "0.861954 (0.008857) with: {'learning_rate': 0.1, 'max_depth': 6, 'max_features': 3, 'n_estimators': 150}\n",
      "0.859540 (0.008327) with: {'learning_rate': 0.1, 'max_depth': 6, 'max_features': 3, 'n_estimators': 200}\n",
      "0.861570 (0.008846) with: {'learning_rate': 0.1, 'max_depth': 6, 'max_features': 4, 'n_estimators': 100}\n",
      "0.860857 (0.009471) with: {'learning_rate': 0.1, 'max_depth': 6, 'max_features': 4, 'n_estimators': 150}\n",
      "0.859321 (0.008403) with: {'learning_rate': 0.1, 'max_depth': 6, 'max_features': 4, 'n_estimators': 200}\n",
      "0.862228 (0.009778) with: {'learning_rate': 0.1, 'max_depth': 6, 'max_features': 5, 'n_estimators': 100}\n",
      "0.860034 (0.009424) with: {'learning_rate': 0.1, 'max_depth': 6, 'max_features': 5, 'n_estimators': 150}\n",
      "0.859047 (0.009069) with: {'learning_rate': 0.1, 'max_depth': 6, 'max_features': 5, 'n_estimators': 200}\n",
      "0.859760 (0.011811) with: {'learning_rate': 0.075, 'max_depth': 4, 'max_features': 3, 'n_estimators': 100}\n",
      "0.860966 (0.011609) with: {'learning_rate': 0.075, 'max_depth': 4, 'max_features': 3, 'n_estimators': 150}\n",
      "0.861734 (0.011377) with: {'learning_rate': 0.075, 'max_depth': 4, 'max_features': 3, 'n_estimators': 200}\n",
      "0.860582 (0.011523) with: {'learning_rate': 0.075, 'max_depth': 4, 'max_features': 4, 'n_estimators': 100}\n",
      "0.861460 (0.011118) with: {'learning_rate': 0.075, 'max_depth': 4, 'max_features': 4, 'n_estimators': 150}\n",
      "0.861570 (0.011907) with: {'learning_rate': 0.075, 'max_depth': 4, 'max_features': 4, 'n_estimators': 200}\n",
      "0.861844 (0.010999) with: {'learning_rate': 0.075, 'max_depth': 4, 'max_features': 5, 'n_estimators': 100}\n",
      "0.861954 (0.011413) with: {'learning_rate': 0.075, 'max_depth': 4, 'max_features': 5, 'n_estimators': 150}\n",
      "0.862392 (0.010197) with: {'learning_rate': 0.075, 'max_depth': 4, 'max_features': 5, 'n_estimators': 200}\n",
      "0.861405 (0.010695) with: {'learning_rate': 0.075, 'max_depth': 5, 'max_features': 3, 'n_estimators': 100}\n",
      "0.861625 (0.009611) with: {'learning_rate': 0.075, 'max_depth': 5, 'max_features': 3, 'n_estimators': 150}\n",
      "0.862502 (0.009506) with: {'learning_rate': 0.075, 'max_depth': 5, 'max_features': 3, 'n_estimators': 200}\n",
      "0.861515 (0.010727) with: {'learning_rate': 0.075, 'max_depth': 5, 'max_features': 4, 'n_estimators': 100}\n",
      "0.861625 (0.010210) with: {'learning_rate': 0.075, 'max_depth': 5, 'max_features': 4, 'n_estimators': 150}\n",
      "0.861405 (0.010967) with: {'learning_rate': 0.075, 'max_depth': 5, 'max_features': 4, 'n_estimators': 200}\n",
      "0.862667 (0.010302) with: {'learning_rate': 0.075, 'max_depth': 5, 'max_features': 5, 'n_estimators': 100}\n",
      "0.862173 (0.010992) with: {'learning_rate': 0.075, 'max_depth': 5, 'max_features': 5, 'n_estimators': 150}\n",
      "0.861295 (0.009792) with: {'learning_rate': 0.075, 'max_depth': 5, 'max_features': 5, 'n_estimators': 200}\n",
      "0.861241 (0.009637) with: {'learning_rate': 0.075, 'max_depth': 6, 'max_features': 3, 'n_estimators': 100}\n",
      "0.861899 (0.010338) with: {'learning_rate': 0.075, 'max_depth': 6, 'max_features': 3, 'n_estimators': 150}\n",
      "0.859760 (0.009316) with: {'learning_rate': 0.075, 'max_depth': 6, 'max_features': 3, 'n_estimators': 200}\n",
      "0.862008 (0.010053) with: {'learning_rate': 0.075, 'max_depth': 6, 'max_features': 4, 'n_estimators': 100}\n",
      "0.861570 (0.010747) with: {'learning_rate': 0.075, 'max_depth': 6, 'max_features': 4, 'n_estimators': 150}\n",
      "0.859979 (0.008439) with: {'learning_rate': 0.075, 'max_depth': 6, 'max_features': 4, 'n_estimators': 200}\n",
      "0.862392 (0.009385) with: {'learning_rate': 0.075, 'max_depth': 6, 'max_features': 5, 'n_estimators': 100}\n",
      "0.861405 (0.009100) with: {'learning_rate': 0.075, 'max_depth': 6, 'max_features': 5, 'n_estimators': 150}\n",
      "0.859869 (0.008851) with: {'learning_rate': 0.075, 'max_depth': 6, 'max_features': 5, 'n_estimators': 200}\n",
      "0.857072 (0.011546) with: {'learning_rate': 0.05, 'max_depth': 4, 'max_features': 3, 'n_estimators': 100}\n",
      "0.860034 (0.011504) with: {'learning_rate': 0.05, 'max_depth': 4, 'max_features': 3, 'n_estimators': 150}\n",
      "0.860802 (0.011504) with: {'learning_rate': 0.05, 'max_depth': 4, 'max_features': 3, 'n_estimators': 200}\n",
      "0.856908 (0.012311) with: {'learning_rate': 0.05, 'max_depth': 4, 'max_features': 4, 'n_estimators': 100}\n",
      "0.861131 (0.010507) with: {'learning_rate': 0.05, 'max_depth': 4, 'max_features': 4, 'n_estimators': 150}\n",
      "0.861899 (0.010822) with: {'learning_rate': 0.05, 'max_depth': 4, 'max_features': 4, 'n_estimators': 200}\n",
      "0.858937 (0.011683) with: {'learning_rate': 0.05, 'max_depth': 4, 'max_features': 5, 'n_estimators': 100}\n",
      "0.861131 (0.010691) with: {'learning_rate': 0.05, 'max_depth': 4, 'max_features': 5, 'n_estimators': 150}\n",
      "0.862118 (0.011261) with: {'learning_rate': 0.05, 'max_depth': 4, 'max_features': 5, 'n_estimators': 200}\n",
      "0.860912 (0.011899) with: {'learning_rate': 0.05, 'max_depth': 5, 'max_features': 3, 'n_estimators': 100}\n",
      "0.861789 (0.010912) with: {'learning_rate': 0.05, 'max_depth': 5, 'max_features': 3, 'n_estimators': 150}\n",
      "0.861186 (0.010495) with: {'learning_rate': 0.05, 'max_depth': 5, 'max_features': 3, 'n_estimators': 200}\n",
      "0.861570 (0.011395) with: {'learning_rate': 0.05, 'max_depth': 5, 'max_features': 4, 'n_estimators': 100}\n",
      "0.861570 (0.010564) with: {'learning_rate': 0.05, 'max_depth': 5, 'max_features': 4, 'n_estimators': 150}\n",
      "0.862173 (0.010041) with: {'learning_rate': 0.05, 'max_depth': 5, 'max_features': 4, 'n_estimators': 200}\n",
      "0.861899 (0.010870) with: {'learning_rate': 0.05, 'max_depth': 5, 'max_features': 5, 'n_estimators': 100}\n",
      "0.862283 (0.010676) with: {'learning_rate': 0.05, 'max_depth': 5, 'max_features': 5, 'n_estimators': 150}\n",
      "0.861954 (0.010061) with: {'learning_rate': 0.05, 'max_depth': 5, 'max_features': 5, 'n_estimators': 200}\n",
      "0.861515 (0.009823) with: {'learning_rate': 0.05, 'max_depth': 6, 'max_features': 3, 'n_estimators': 100}\n",
      "0.862447 (0.009683) with: {'learning_rate': 0.05, 'max_depth': 6, 'max_features': 3, 'n_estimators': 150}\n",
      "0.862228 (0.010153) with: {'learning_rate': 0.05, 'max_depth': 6, 'max_features': 3, 'n_estimators': 200}\n",
      "0.861899 (0.010780) with: {'learning_rate': 0.05, 'max_depth': 6, 'max_features': 4, 'n_estimators': 100}\n",
      "0.861734 (0.010934) with: {'learning_rate': 0.05, 'max_depth': 6, 'max_features': 4, 'n_estimators': 150}\n",
      "0.861350 (0.009896) with: {'learning_rate': 0.05, 'max_depth': 6, 'max_features': 4, 'n_estimators': 200}\n",
      "0.861789 (0.011192) with: {'learning_rate': 0.05, 'max_depth': 6, 'max_features': 5, 'n_estimators': 100}\n",
      "0.862502 (0.010080) with: {'learning_rate': 0.05, 'max_depth': 6, 'max_features': 5, 'n_estimators': 150}\n",
      "0.862283 (0.009598) with: {'learning_rate': 0.05, 'max_depth': 6, 'max_features': 5, 'n_estimators': 200}\n"
     ]
    }
   ],
   "source": [
    "# grid search for Gradient Boosting model parameters\n",
    "max_depth = np.array([4, 5, 6])\n",
    "n_estimators = np.array([100, 150, 200])\n",
    "learning_rate = np.array([0.1, 0.075, 0.05])\n",
    "max_features = np.array([3, 4, 5])\n",
    "param_grid = dict(max_depth=max_depth,\n",
    "                  n_estimators=n_estimators,\n",
    "                  learning_rate=learning_rate,\n",
    "                  max_features=max_features)\n",
    "\n",
    "classifier = GradientBoostingClassifier()\n",
    "kfold = KFold(n_splits=num_folds, random_state=seed)\n",
    "\n",
    "grid = GridSearchCV(classifier, param_grid=param_grid, cv=kfold, n_jobs=30)\n",
    "grid_result = grid.fit(X_train, Y_train)\n",
    "\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.843087 using {'max_features': 4, 'n_estimators': 150}\n",
      "0.841825 (0.007815) with: {'max_features': 1, 'n_estimators': 50}\n",
      "0.841935 (0.008379) with: {'max_features': 1, 'n_estimators': 100}\n",
      "0.841990 (0.008704) with: {'max_features': 1, 'n_estimators': 150}\n",
      "0.842264 (0.009286) with: {'max_features': 1, 'n_estimators': 200}\n",
      "0.843032 (0.010177) with: {'max_features': 1, 'n_estimators': 250}\n",
      "0.840125 (0.007245) with: {'max_features': 2, 'n_estimators': 50}\n",
      "0.842483 (0.010143) with: {'max_features': 2, 'n_estimators': 100}\n",
      "0.840070 (0.009433) with: {'max_features': 2, 'n_estimators': 150}\n",
      "0.842483 (0.009482) with: {'max_features': 2, 'n_estimators': 200}\n",
      "0.841112 (0.009648) with: {'max_features': 2, 'n_estimators': 250}\n",
      "0.841935 (0.009112) with: {'max_features': 3, 'n_estimators': 50}\n",
      "0.841496 (0.009560) with: {'max_features': 3, 'n_estimators': 100}\n",
      "0.842483 (0.009718) with: {'max_features': 3, 'n_estimators': 150}\n",
      "0.841386 (0.008696) with: {'max_features': 3, 'n_estimators': 200}\n",
      "0.842264 (0.008727) with: {'max_features': 3, 'n_estimators': 250}\n",
      "0.840125 (0.008406) with: {'max_features': 4, 'n_estimators': 50}\n",
      "0.841057 (0.009374) with: {'max_features': 4, 'n_estimators': 100}\n",
      "0.843087 (0.009779) with: {'max_features': 4, 'n_estimators': 150}\n",
      "0.841990 (0.007903) with: {'max_features': 4, 'n_estimators': 200}\n",
      "0.840509 (0.008973) with: {'max_features': 4, 'n_estimators': 250}\n",
      "0.841332 (0.008798) with: {'max_features': 5, 'n_estimators': 50}\n",
      "0.841716 (0.008245) with: {'max_features': 5, 'n_estimators': 100}\n",
      "0.840015 (0.007984) with: {'max_features': 5, 'n_estimators': 150}\n",
      "0.840509 (0.008548) with: {'max_features': 5, 'n_estimators': 200}\n",
      "0.840783 (0.009425) with: {'max_features': 5, 'n_estimators': 250}\n"
     ]
    }
   ],
   "source": [
    "# grid search for Random Forest model parameters\n",
    "# Tune Random Forest\n",
    "n_estimators = np.array([50,100,150,200,250])\n",
    "max_features = np.array([1,2,3,4,5])\n",
    "param_grid = dict(n_estimators=n_estimators,\n",
    "                  max_features=max_features)\n",
    "model = RandomForestClassifier()\n",
    "kfold = KFold(n_splits=num_folds, random_state=seed)\n",
    "grid = GridSearchCV(estimator=model, param_grid=param_grid, scoring=scoring, cv=kfold)\n",
    "grid_result = grid.fit(X_train, Y_train)\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['age', 'fnlwgt', 'education-num', 'marital-status', 'sex', 'capital-gain', 'capital-loss', 'hours-per-week']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features=4, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=150,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create final model\n",
    "random_forest = RandomForestClassifier(n_estimators=150, max_features=4)\n",
    "random_forest.fit(train_data[features], train_data[target])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VPozpbOzsdPM"
   },
   "source": [
    "# Make Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iPlLK2SfsdPO"
   },
   "outputs": [],
   "source": [
    "test_data = pd.read_csv('income_test.csv')\n",
    "\n",
    "# encode sex and marial-status\n",
    "test_data[\"sex\"] = test_data[\"sex\"].map({\"Male\": 0, \"Female\":1})\n",
    "\n",
    "# Create Married Column - Binary Yes(1) or No(0)\n",
    "test_data[\"marital-status\"] = test_data[\"marital-status\"].replace(['Never-married','Divorced','Separated','Widowed'], 'Single')\n",
    "test_data[\"marital-status\"] = test_data[\"marital-status\"].replace(['Married-civ-spouse','Married-spouse-absent','Married-AF-spouse'], 'Married')\n",
    "test_data[\"marital-status\"] = test_data[\"marital-status\"].map({\"Married\":1, \"Single\":0})\n",
    "test_data[\"marital-status\"] = test_data[\"marital-status\"].astype(int)\n",
    "\n",
    "predictions = random_forest.predict(test_data[features])\n",
    "\n",
    "to_save = test_data[['Id']].copy()\n",
    "to_save.loc[:, 'Category'] = predictions\n",
    "to_save.loc[to_save['Category'] == 0, 'Category'] = \"<=50K\"\n",
    "to_save.loc[to_save['Category'] == 1, 'Category'] = \">50K\"\n",
    "to_save.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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