04. Tree models/2.4 Gradient Boosting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center> [Adult salaries](https://archive.ics.uci.edu/ml/datasets/adult): Gradient boosting\n",
    "### <center>Predict whether income exceeds $50K/yr based on census data.\n",
    "\n",
    "Link: https://www.kaggle.com/mattmet/income-prediction-xgboost-accuracy-86-02"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "from sklearn.linear_model  import LogisticRegression\n",
    "from sklearn.tree          import DecisionTreeClassifier\n",
    "from sklearn.ensemble      import RandomForestClassifier\n",
    "from sklearn.ensemble      import ExtraTreesClassifier\n",
    "from sklearn.ensemble      import GradientBoostingClassifier\n",
    "from xgboost               import XGBClassifier, plot_tree\n",
    "\n",
    "from sklearn.preprocessing   import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics         import accuracy_score, balanced_accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Leer CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "##### DOWNLOAD DATASET. ONLY IN COLAB !!!\n",
    "#!wget -P ./../data/ https://raw.githubusercontent.com/SaturdaysAI-Murcia/machine-learning/master/data/adult.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../data/adult.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>native-country</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>101320</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>1902</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;=50k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>236746</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>10520</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;=50k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>96185</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;50k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>112847</td>\n",
       "      <td>Prof-school</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;=50k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>82297</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;50k</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt     education  education-num  \\\n",
       "0   49            Private  101320    Assoc-acdm           12.0   \n",
       "1   44            Private  236746       Masters           14.0   \n",
       "2   38            Private   96185       HS-grad            NaN   \n",
       "3   38       Self-emp-inc  112847   Prof-school           15.0   \n",
       "4   42   Self-emp-not-inc   82297       7th-8th            NaN   \n",
       "\n",
       "        marital-status        occupation    relationship                 race  \\\n",
       "0   Married-civ-spouse               NaN            Wife                White   \n",
       "1             Divorced   Exec-managerial   Not-in-family                White   \n",
       "2             Divorced               NaN       Unmarried                Black   \n",
       "3   Married-civ-spouse    Prof-specialty         Husband   Asian-Pac-Islander   \n",
       "4   Married-civ-spouse     Other-service            Wife                Black   \n",
       "\n",
       "       sex  capital-gain  capital-loss  hours-per-week  native-country salary  \n",
       "0   Female             0          1902              40   United-States  >=50k  \n",
       "1     Male         10520             0              45   United-States  >=50k  \n",
       "2   Female             0             0              32   United-States   <50k  \n",
       "3     Male             0             0              40   United-States  >=50k  \n",
       "4   Female             0             0              50   United-States   <50k  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Qué queremos predecir? -> `df.salary`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "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": [
    "sns.countplot(df.salary);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": false
   },
   "source": [
    "# Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Numerical features:\n",
      " ['age' 'fnlwgt' 'education-num' 'capital-gain' 'capital-loss'\n",
      " 'hours-per-week']\n",
      "\n",
      "Categorical features:\n",
      " ['workclass' 'education' 'marital-status' 'occupation' 'relationship'\n",
      " 'race' 'sex' 'native-country' 'salary']\n",
      "\n",
      "Date/time features:\n",
      " []\n"
     ]
    }
   ],
   "source": [
    "cat  = df.select_dtypes(include=[object]).columns\n",
    "num  = df.select_dtypes(exclude=[object, 'datetime64','timedelta64']).columns\n",
    "time = df.select_dtypes(include=['datetime64']).columns\n",
    "\n",
    "print(\"\\nNumerical features:\\n\", num.values)\n",
    "print(\"\\nCategorical features:\\n\", cat.values)\n",
    "print(\"\\nDate/time features:\\n\", time.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32561, 15)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31812, 15)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna(axis='rows')\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>native-country</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>236746</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>10520</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38</td>\n",
       "      <td>5</td>\n",
       "      <td>112847</td>\n",
       "      <td>14</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>63210</td>\n",
       "      <td>11</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>46</td>\n",
       "      <td>4</td>\n",
       "      <td>328216</td>\n",
       "      <td>11</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>746432</td>\n",
       "      <td>11</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  workclass  fnlwgt  education  education-num  marital-status  \\\n",
       "1    44          4  236746         12           14.0               0   \n",
       "3    38          5  112847         14           15.0               2   \n",
       "5    20          4   63210         11            9.0               4   \n",
       "8    46          4  328216         11            9.0               2   \n",
       "21   26          4  746432         11            9.0               4   \n",
       "\n",
       "    occupation  relationship  race  sex  capital-gain  capital-loss  \\\n",
       "1            4             1     4    1         10520             0   \n",
       "3           10             0     1    1             0             0   \n",
       "5            6             3     4    1             0             0   \n",
       "8            3             0     4    1             0             0   \n",
       "21           6             3     2    1             0             0   \n",
       "\n",
       "    hours-per-week  native-country  salary  \n",
       "1               45              39       1  \n",
       "3               40              39       1  \n",
       "5               15              39       0  \n",
       "8               40              39       1  \n",
       "21              48              39       0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[cat] = df[cat].apply(LabelEncoder().fit_transform)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[['age', 'workclass', 'fnlwgt', 'education', 'education-num',\n",
    "       'marital-status', 'occupation', 'relationship', 'race', 'sex',\n",
    "       'capital-gain', 'capital-loss', 'hours-per-week', 'native-country'\n",
    "       ]]\n",
    "y = df[\"salary\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression: 78.99% (0.63%)\n",
      "Decision Tree: 81.23% (0.40%)\n",
      "Extra Trees: 84.57% (0.30%)\n",
      "Random Forest: 85.67% (0.26%)\n",
      "Gradient Boosting: 86.56% (0.35%)\n",
      "XGBoost: 87.03% (0.41%)\n"
     ]
    }
   ],
   "source": [
    "models = [\n",
    "    ('Logistic Regression', LogisticRegression(n_jobs=-1)),\n",
    "    ('Decision Tree',       DecisionTreeClassifier()),\n",
    "    ('Extra Trees',         ExtraTreesClassifier(n_jobs=-1)),\n",
    "    ('Random Forest',       RandomForestClassifier(n_jobs=-1)),\n",
    "    ('Gradient Boosting',   GradientBoostingClassifier()),\n",
    "    ('XGBoost',             XGBClassifier(n_estimators=250))\n",
    "]\n",
    "\n",
    "outcome = []\n",
    "Modelnames = []\n",
    "\n",
    "for name, model in models:\n",
    "    skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=0)\n",
    "    cv_r = cross_val_score(model, x, y, cv=skf, scoring='accuracy')\n",
    "    outcome.append(cv_r)\n",
    "    Modelnames.append(name)\n",
    "    print(\"%s: %.2f%% (%.2f%%)\" % (name, cv_r.mean()*100, cv_r.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x127024048>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YEeMurrqoV5IkSZKkpgLs/cDihukDgQcbpg8HNmfmLdX0pcAHqmWeA3yjCq8HAddExGmZmc0U512IJUmSJGn6abgL8bg0E2CvB94XEQcAm4DTgXManr8XeF5ERBVMTwVuzcx/o3wdDgAR0QO82rsQS5IkSZImYsxrYDPzAeB84EbgDuBL1VDhayLimMzcAJwNXBURdwJvBN4whTVLkiRJkmagjqGhXXKI7kJgtUOIJUmSJGn6aRhCfCjQ0/RyU1WQJEmSJEmTyQArSZIkSaoFA6wkSZIkqRYMsJIkSZKkWjDASpIkSZJqwQArSZIkSaoFA6wkSZIkqRYMsJIkSZKkWjDASpIkSZJqwQArSZIkSaoFA6wkSZIkqRYMsJIkSZKkWjDASpIkSZJqwQArSZIkSaoFA6wkSZIkqRYMsJIkSZKkWjDASpIkSZJqwQArSZIkSaoFA6wkSZIkqRYMsJIkSZKkWjDASpIkSZJqwQArSZIkSaoFA6wkSZIkqRYMsJIkSZKkWjDASpIkSZJqwQArSZIkSaoFA6wkSZIkqRYMsJIkSZKkWjDASpIkSZJqwQArSZIkSaoFA6wkSZIkqRYMsJIkSZKkWjDASpIkSZJqwQArSZIkSaoFA6wkSZIkqRYMsJIkSZKkWjDASpIkSZJqwQArSZIkSaoFA6wkSZIkqRYMsJIkSZKkWjDASpIkSZJqwQArSZIkSaoFA6wkSZIkqRYMsJIkSZKkWjDASpIkSZJqwQArSZIkSaoFA6wkSZIkqRYMsJIkSZKkWjDASpIkSZJqwQArSZIkSaoFA6wkSZIkqRYMsJIkSZKkWjDASpIkSZJqYXYzM0XEmcAFwBzg4sz89IjnjwIuBXYDfgKclZn9EfGL1eP7AJuB/5WZd0xi/ZIkSZKkGWLMHtiIOBj4ELAIOBI4JyIOGzHbJ4D3ZuZLgATOqx7/LPCRzDwSOB/4wmQVLkmSJEmaWZoZQnwKcENmrs/MTcDVwBkj5plF6WUF2IvS2wrwOeDa6uc7gfk7V64kSZIkaaZqZgjxQcDahum1wHEj5jkX+FZEXAxsAo4HyMzLG+Z5P/C1CVcqSZIkSZrRmgmwncBQw3QHMDg8ERF7ApcBp2TmLRFxLnAF8Jrq+Q7gL4ATgJePp7h58+aOZ3ZJkiRJ0jTWTIC9H1jcMH0g8GDD9OHA5sy8pZq+FPgAQETMpoTZg4GXZ+bAeIrr69vI4ODQ2DNKkiRJkmqjs7NjQh2WzVwDez1wckQcEBF7Aafz9HWtAPcCz4uIqKZPBW6tfv4Y5drYXxlveJUkSZIkqVHH0NDYPZzV1+i8m/I1OZ/LzI9GxDWUOw/fFhGvAj5MGV78EPAWYCPletnVlOtiAajuSDyWhcBqe2AlSZIkafpp6IE9FOhpdrmmAmwbLMQAK0mSJEnT0kQDbDNDiCVJkiRJajsDrCRJkiSpFpq5C7GkNli16iZWrlzR8nYHBvoB6Orqbmm7ixcvYdGik1rapiRJkurFACtpG/397QmwkiRJ0li8iZOkbSxffhEAy5Zd2OZKJEmSNF15EydJkiRJ0rRmgJUkSZIk1YIBVpIkSZJUCwZYSZIkSVItGGAlSZIkSbXgXYilMVx55eWsWdPb7jJapre3B4AFCxa2tY5WmT9/AUuXnt3uMiRJkmaUid6F2O+BlcawZk0vPff8iAPnzml3KS2xN1sBeGztvW2uZOr9dOOT7S5BkiRJ42CAlZpw4Nw5vOml+7e7DE2yy25f1+4SJEmSNA5eAytJkiRJqgUDrCRJkiSpFhxCLI1hYKCf9RufdLjpNLR245PsN9Df7jIkSZLUJHtgJUmSJEm1YA+sNIaurm52f3SdN3Gahi67fR17dHW3uwxJkiQ1yR5YSZIkSVItGGAlSZIkSbVggJUkSZIk1YIBVpIkSZJUCwZYSZIkSVItGGAlSZIkSbVggJUkSZIk1YLfAys14acbn+Sy29e1u4yW2PjEVgDm7jarzZVMvZ9ufJKF7S5CkiRJTTPASmOYP39Bu0toqZ/19gCw/3MXtrWOVljIzPt8JUmS6qxjaGio3TWMZiGwuq9vI4ODu2R90rS1fPlFACxbdmGbK5EkSdJ01dnZwbx5cwEOBXqaXm6qCpIkSZIkaTIZYCVJkiRJtWCAlSRJkiTVggFWkiRJklQLBlhJkiRJUi0YYCVJkiRJtWCAlSRJkiTVggFWkiRJklQLBlhJkiRJUi0YYCVJkiRJtdAxNDTU7hpGsxBY3de3kcHBXbI+acqtWnUTK1euaHm7vb09ACxYsLCl7S5evIRFi05qaZuSJElqj87ODubNmwtwKNDT7HKzp6ogSfXU3d3d7hIkSZKkUdkDK0mSJElqqYn2wHoNrCRJkiSpFgywkiRJkqRaMMBKkiRJkmrBACtJkiRJqgUDrCRJkiSpFgywkiRJkqRaMMBKkiRJkmrBACtJkiRJqgUDrCRJkiSpFgywkiRJkqRaMMBKkiRJkmrBACtJkiRJqoXZzcwUEWcCFwBzgIsz89Mjnj8KuBTYDfgJcFZm9kdEN3Al8HzgYeC3M/Onk1i/JEmSJGmGGLMHNiIOBj4ELAKOBM6JiMNGzPYJ4L2Z+RIggfOqxz8IrMzMXwQ+W80nSZIkSdK4NTOE+BTghsxcn5mbgKuBM0bMMwvYp/p5L2Bz9fNrKD2wAF8GXhURc3auZEmSJEnSTNRMgD0IWNswvRY4ZMQ85wKfjYi1wCuBS0Yum5lbgJ8DB+xMwZIkSZKkmamZa2A7gaGG6Q5gcHgiIvYELgNOycxbIuJc4ApK72vHiNfaZtmxzJs3t9lZJUmSJEnTXDMB9n5gccP0gcCDDdOHA5sz85Zq+lLgA9XPD1Tz3x8Rs4FnAX3NFtfXt5HBwaGxZ5QkSZIk1UZnZ8eEOiybGUJ8PXByRBwQEXsBpwPXNjx/L/C8iIhq+lTg1urna4Dfr37+HcoNnZ4cd5WSJEmSpBlvzACbmQ8A5wM3AncAX6qGCl8TEcdk5gbgbOCqiLgTeCPwhmrx9wAnRMT/A/4AeNsUvAdJkiRJ0gzQMTS0Sw7RXQisdgixJEmSJE0/DUOIDwV6ml5uqgqSJEmSJGkyNXMTJzVh1aqbWLlyRcvbHRjoB6Crq7vlbS9evIRFi05qebuSJEmSZiYDbM3197cvwEqSJElSK3kNbM0tX34RAMuWXdjmSiRJkiSpOV4DK0mSJEma1gywkiRJkqRaMMBKkiRJkmrBACtJkiRJqgUDrCRJkiSpFgywkiRJkqRaMMBKkiRJkmrBACtJkiRJqgUDrCRJkiSpFjqGhobaXcNoFgKr+/o2Mjg4vvquvPJy1qzpnYKSdk29vT0ALFiwsK11tMr8+QtYuvTsdpchSZIkaSd0dnYwb95cgEOBnmaXmz1VBbXLmjW9/Pg/72HWHvu2u5SWGNw6C4B71qxrcyVTb+tjG9pdgiRJkqQ2mnYBFmDWHvvyrOe/st1laJI9ct917S5BkiRJUht5DawkSZIkqRamXQ/swEA/Wx/bYG/dNLT1sQ0MDEy7VVaSJElSk+yBlSRJkiTVwrTrzurq6uahgS1eAzsNPXLfdXR1dbe7DEmSJEltYg+sJEmSJKkWDLCSJEmSpFowwEqSJEmSasEAK0mSJEmqBQOsJEmSJKkWDLCSJEmSpFowwEqSJEmSasEAK0mSJEmqBQOsJEmSJKkWDLCSJEmSpFowwEqSJEmSasEAK0mSJEmqBQOsJEmSJKkWDLCSJEmSpFowwEqSJEmSasEAK0mSJEmqBQOsJEmSJKkWDLCSJEmSpFowwEqSJEmSamF2uwuYClsf28Aj913X7jJaYnDLZgA6Z+/Z5kqm3tbHNgD7t7sMSZIkSW0y7QLs/PkL2l1CS/X29gCwYP5MCHb7z7jPV5IkSdLTOoaGhtpdw2gWAqv7+jYyOLhL1rfLWL78IgCWLbuwzZVIkiRJUnM6OzuYN28uwKFAT9PLTVVBkiRJkiRNJgOsJEmSJKkWDLCSJEmSpFowwEqSJEmSasEAK0mSJEmqBQOsJEmSJKkWDLCSJEmSpFowwEqSJEmSasEAK0mSJEmqBQOsJEmSJKkWDLCSJEmSpFqY3cxMEXEmcAEwB7g4Mz/d8NyRwOUNsx8AbMjMwyNiIXAFsA/QD7w+M3snp3RJkiRJ0kwyZg9sRBwMfAhYBBwJnBMRhw0/n5l3ZOaRmXkk8DJgA/DW6ukPAF+unvtK9TqSJEmSJI1bMz2wpwA3ZOZ6gIi4GjgDeP8o8y4DbsrMVdX0LErvK8DewOadK3fXtWrVTaxcuaLl7fb29gCwfPlFLW978eIlLFp0UsvblSRJkjQzNRNgDwLWNkyvBY4bOVNEdAHnAC9uePg9wM0R8XZgN+DEiZeq0XR3d7e7BEmSJElqiWYCbCcw1DDdAQyOMt9ZwNcy86GGx74AnJOZX4+I04GvRsQRmTk0yvLPMG/e3GZm2yWcdtqvcdppv9buMiRJkiRp2momwN4PLG6YPhB4cJT5fhP48+GJiDgAeFFmfh0gM78SEZcA+wMPN1NcX99GBgebyrqSJEmSpJro7OyYUIdlM1+jcz1wckQcEBF7AacD1zbOEBEdwNHAdxoeXgc8FhGLq3l+GXgkM5sKr5IkSZIkNRozwGbmA8D5wI3AHcCXMvOWiLgmIo6pZjsAeCIzH2tYbgh4LfCxiLgT+Cgl/EqSJEmSNG4dQ0O75BDdhcBqhxBLkiRJ0vTTMIT4UKCn6eWmqiBJkiRJkiaTAVaSJEmSVAsGWEmSJElSLRhgJUmSJEm1YICVJEmSJNWCAVaSJEmSVAsGWEmSJElSLRhgJUmSJEm1MLvdBWzHLChfbitJkiRJml4ast6s8Sy3qwbY5wLsu+/e7a5DkiRJkjR1ngv8V7MzdwwNDU1hLRO2O3AssBbY2uZaJEmSJEmTaxYlvN4KPN7sQrtqgJUkSZIkaRvexEmSJEmSVAsGWEmSJElSLRhgJUmSJEm1YICVJEmSJNWCAVaSJEmSVAsGWEmSJElSLRhgJUmSJEm1MLvdBewKImIJ8L7MXDIJr3VHZh65g+dvzMyXNzPviOV6gEeBJ6qHuoHbgNdn5qadKnoSRMRBwOcy89XtrqXOImIh8J/A3dVDewI3A+/KzJ9N4PXeD9yWmd/YzvOfAy7JzNsmVjFExBuA/11NHgbcS1lP/z0z3zbR19XUGWU9G/bZzPz0dpY5Djg9M985wTb/A9gd2A+YC6ypnnpdZv5wIq+pqTfKutIJ7AN8ITMvnITXPxtYkpln7+xrjXjNv+TpdQzgZ5n5PyarjRHt7dTfxnQWEbOBdwJnAUPALOALwPLMHJrgay4EVmTmwrH2cWO8zkXA9Zm5csTjK4BDgI1VvQ8DZ2fmmme8yARFxFuAjZn55Z15D5ocVQ74MvCSzHyoeuxPgRMy8/SIeAVwIXAgZZ24A/jjzLy/WvZfKMc+HcBulOOqT0xifV3A5Zl52mS9Zt0ZYCdZE4F0yTjmHenVmdkDEBG7AauA3wc+M87XmXSZ+SBgeJ0cDw6vGxHRAfw5cDWweLwvlJnvHeP5N0+owm1f42+Bv4WnTrQ8tZ5ql/bgOLdBhwHPmWhjmXk8TE1g0ZTbZl2pTljeExF/n5k/amNdO/KNFq5jO/W3Mc39DeV3c2Jm9kfEPsBXgQFg1JNl4zHWPm4MJwE3bue5N2fmCoCI+GPgY8Bv70RbI/0ysAJ2+j1oEmTmioj4IvBZ4NSIOBE4Bzg2IhYDXwRem5nfBYiIt1HW42Orl7htuBMsIp4F3B0R12XmyJPEE7Uv8NJJeq1pwQA7hoh4N+XM4VbgW8CfZebWiHg78EdAP/Bj4L8y830RMZSZHRFxMvBRyhnHDcDvAe+tXvM/MvP4hnn3Ay4DXgQ8DpybmTeMUVo30AWsr17zV4H3A3OA1cBbMrOvOjP018AW4DvAYZm5pDrDuB74JeB3KGeVRlv+Y8ArgUHga5l50Xbe21yePiP6nOr9zK/afXdmXhsR7wMOBl4ALKD02H6o+U9j5snMoYi4EPhZRByRmXdGxLsoO9JZwL8B76zm+xPgrZR19Z8z850RcTllJ/lPlLOLB1YvfVFmfqNaD95Xbbyfsa4Dz6NspO+ibDx/BvwwjqxtAAAM1UlEQVRWZq5vpv5q/ftoVetdwNsoBy2HV499pDoDPQv4C8oJnlmUM41/NYFfmXZSRBwFXAO8mLIu3A6cStk+zI2I84EHgNcD+wP/DHyJsp2ZCzyb0rtyyTjaXEFz26Njgb8C9gLWAf8zM1dHxLlVPYPALZn5P3fmd6AxPZfS0/BI1cP2Gcrf9HOAOyn7hOewnW1HRLwOuAD4OdBL6ekiIk4APgHswdOf773V+vF9YFH13Dspoz4OA/5qPNuKMdoY9z6xeq2n/jbcpz0tIg6h7FMOzsx+gMz8eXXw/0vVPJcD84BfoOxz9gDeQRl9tDvwxsy8OSJeSjmuAPhBQxuXU449Lo+I3wf+mDJK4HvA2zLzsYhYSzkJvIhyTPLblBPCxwCfi4jTxhgF0kVZf4mITuBi4GTKMdDfZeZHqudG24fuzYh9L2U03W8Ar6hq+z3KfnoF2/+b+W3KeraJsk2e7UnASXc+cEvD8f3vVydd3gN8cDi8AmTmpyNiz4jYfZTX2ZOyDgzADrc5LwT+D2VU0ibg7Zl5a0ScSVl3tlK2PWcBnwQOioiv2gtbeA3sDkTEqygbmWMoG5NfAN4aEUdQDsSPpmwEXzDK4hcAb83MY4DrgKMy8+3wdE9Egw8A92bmLwKvA7a3A7wmIu6MiJ9RDjA/BVwVEQcAHwb+R2a+lBJqPhIRc4C/A5ZWjz854vXuzMygHIyOtvwC4FWZ+RLK2cLDImKP0d7biNf9a+CGzDwCOAP4fBVqAY4AfgU4HnhXRHRv572qkplPAPcAL6pOVBxNOev3UsoJgaXVgf0fAMdRfsdHR8TRDS9zGtCTmUcDb2JEb+721vXq6ZcAf5mZh1NO2Cwd51t4IfCKzHw9Zd35XlXHfwfOj4jnA2+p3utR1Xs4tTrrqalzUETcMeLfizPz+8CllBMKfw18JjPvoJyA+0bDAfohwEsz893Amyk7+GOBl1fLjtdY26PdgM8BZ1bryceBz1YnP5ZR1t2jgd0i4uCJ/EK0XcPryo8jYh3wQeC0zLwfeBnwRGaeSNludPP0aJxnbDuq3tuPUv7+TwSeBU+NKvp74A+rfc4llAP/YR2ZeRzwFcp6+VrKdmx7vVe/MWLdfnkTbYx7nwg8xjP/NlQcB9ydmRsaH8zMH2fmVxoe6quOf/6Vst/5tep3/FHK3zbAFZSTtUcB941sKCJ+ibIfeVk1WuAh4Lzq6QOB/1t9lt+mfP5XUC7DevN2wuvnqvWmBzgX+Hz1+FspJ3aPqN7f6RHxmh3sQ5+x783M64FvAO/NzH8b0e5ofzMH8HRoPpYSeDTJqmOtpZTLD/4+M79TPXUCZb0ZOf/HMvPxavKYan25E+ihnIx4cIxtzheBT1bHyn8CXF0F4g8Cv1KtM6spnVtvp4yEMbxW7IHdsZOBL2fmowAR8XnKWf7dgX/JzJ9Xj3+Z0r3f6BvAVyPia8DXM/O6HbRzEnAmQLUhPXE78706M3si4nTKH9g/Vj1vx1N6O2+MCCg9WOspPSgPZead1fKfp5wFGvYf1f/bW/4BYHNE/DtlfP87q7OZz3hvUa5JGfYKng4k90W59m04tN9YbSQeioj1lDOb/Tv43agYAjYDp1B+l9+rHt+Tcp3XgZRe14Hq8VMAqs8TynW0f14d2P8r5aRJo+2t6/9KWYdur+a7i/HvPHNEXXtFxBur6b0pZ+JPAY6Mcp0JlJ68FwPbXJukSbWjIcQfpBzcbaacVBvN9zNzS/XzO4BfjYhllM9t7gTqGWt79ELgvwHfaFiv96lGxNwM3Ap8Hfh4Zj4wgfa1fQ9m5pFV79PHKcHtOoDM/HZE9FW9ai+inNAd/vxH23a8DLg5q2v6owzbO5ny+W7IzFur1/3HiPg/Ua79Avhm9X8v8N1qW9W7g5OgzxhCHBGHj9HGRPeJzfwOZ6qnrnONiDMoJzFnAY9VJ7yg+r1n5mBEnAb8epRf6hJga0TsDxzUcBx1OSUMNno5Zd37bvV57EbptR92bfX/XZSTJ2NpHEJ8BnBdRBxKOb65PDO3Ao9GxJWU9XeQ0feh72TH+96RRvubWQx8Z3i7FhFfoARjTb5fpvSSnhIRFzXs44bgqRNtt1SP7Qf8bvVz4xDifSjr27soI5S2t835hcz8p+rx71bHxFEt8+8R8VXgK5l5x4hjbGEP7FhG/n46KKF/6yjPbaMa0rSEclH3R6MMu9ueJ9l2I/+i6kBhe6/9Fcofx/AZwVnAqsw8sjogPRY4vYk6N+9o+eoP93jgPZQhPt+JiBc28d6293uDcrZ62FD1nHag2mAG5SYqs4CLGz6r4yk99iPXoYMaD+wy8x7KweWVlJ3hLSPWsan8zDY3/DwLOKuh/hMo6/IsyvD8xsc//8yXUot0UXrGns32T1g0fq5XUQ6o7qYMw5qIHW6Pqsfva3j8aMqQQIDfBP4XZd28NiJOmmAN2oHMHAT+lDLy4zyAiPgNynblUcq18N/m6W3EaNuOkduQ4QPE0fZVHZTPHZ6+gWHjMuM1VhsT2idOsJaZ4DbKyK19ADLz6ur3+evAAQ3zbQaIiLmUcHAoZT36JDteZxrNAq5q+MyOA/5w+MnMHF4Xx70Py8yrq9cPtr+vHPXxJva9I432NzPmMad2XkQcRhni/TLK5XwXVE/dSgm2ZOYTDevYfZQTJduoOrf+oVpme9ucru08Pjsz/zdln7cB+GJEnLUz72u68g9ix24Afq8a5z4beAPlgv//C7w6IvapwsXpNIQHeOqOm8/KzIsp12wND7PdWr1Wo29TroEgIl5EOaAf6+587wEWRcRrKGcvT2zYkb6HcsOBHwH7RsSLq8fP3M7rjrp8lGtObgK+nZnnUQ5OYwfvbdgNVGdHq+Ghv0y5/lbjVO3oLqL0OPwX5Xf7uoiYW61HX6MM015JWSeHH/8yZSjT8Ov8IeW613+kDDV+NuVOosO2t65PthsoQYOIeC7lern51eNviYg51UHMKkqIVXv8DeUShb+p/kE5aNzeqJ1XUobDfR14FUA1tHcitrc9+zGwXzw9tPyNwJeq4XV3Az/McjOUb1GG92kKVCHuPOA9EXEgZfTEVVlu5tZP6Qnb0We/ivL5Hlxt335n+KWBeVEuhyDKNX+92eT19s2W32Qb49onsuO/jRkry117/w74wvAJ1Wr/8muUUDbSCynHKH9O2f+8FpiVmX2U3vbXVPOdOcqyK4DTIuLZUW5++BnK9bA70tTnFuVynDmU9ecG4PURMSsi9qIMOb2R7exDd7DvHc86czPlZkLPrd7b7zL2MaLGIcrlcf8A/Glm3kfpPf+jKNevXgi8txrtODz/EcDzGWU9rvZ9SygjALa3zVkD3BcRr60eP4Eyku6uiLgHWJeZyylD51+K25hnMMA+bXFEbGz4d0lm/gtlmNBtwP+jDNX868y8i3Jm8DuU4PAI2/ZGALwbuDwivkcJc++qHv868IPqj2XYhcALIuIHlLN0r8sxbi+f5TbfH6Fca7aOcjB3VUT8kBIo31EN1T0LuKKq43mj1Elm/nQ7y99evce7IuL7lJ31N3fw3oa9nXJzgh9SAtabM3Ptjt6PtvHUtYmUm1UcTHWCIzP/mXIN2H9QhhfdQfk6i+9TAsd3qmW+XV1nM+wKysmHH1LW2T/N6qYa1euOuq5PwXu7CNgzIu6i7PD/rArml1Cu8729quFvh4dvacqMdg3sJ6sd7H+jXG5wMfDC6rFbgBMi4sOjvNb7gFURcTell6GH0osybjvYHj0O/Bbw8SjXGb0eeFNmPky5Ecat1TZpD+y9n1KZeS1lW/MByl07f6/6rP4R+Hd28NlXQ4f/CLiesk79vHr8cUqY/VS1ffhDng63k1V3U21MYJ+4o7+Nme4PKOvEjdXf7T2U0ROvGmXeH1D2aT+m7IceptzwEcqxzIURcTtl+7SNzPwBZf9yQ7XsLMp1zDtyLXBJRLxslOeGr4H9HmV7cmZmPkK5P8D9Va23Uy7d+eoO9qHb2/deD7w7yvDkHaq2cW+nDNu/lRKmn3Esp53yV8BdmflFgMzspZwA+SJlnfwd4INR7kNzH+WzfUc+/RVMw9fA3k5ZNx6l3KRyR9ucs4C3V+vGpyh3OX6Cck39dRFxG+VE/kcoN/RaExFT0bFQSx1DQ57EGa/qrOxrqqG0RMTXKXfU/ef2Vrat6uz2hyln/zZFuVPnwZn5jjaXJkmSpDFExDxKgL2ouk74k8A9mTkVJ5mlWrA7emJ6KcM57qIM4/g3ypm3XUq1oVtP6Zl4gtIrMvLGB5IkSdo1rafc3fuuiNhCGZr62faWJLWXPbCSJEmSpFrwGlhJkiRJUi0YYCVJkiRJtWCAlSRJkiTVggFWkiRJklQLBlhJkiRJUi0YYCVJkiRJtfD/AQJQWjrJIR0vAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results ={'Names': Modelnames,'Results': outcome}\n",
    "plt.figure(figsize=(16,6))\n",
    "sns.boxplot(x='Names',y='Results',data=results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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