gradient_boosting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objs as go\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, iplot\n",
    "import plotly.figure_factory as ff\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame\n",
    "import cufflinks as cf\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import ensemble\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tentativa inicial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        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>Month</th>\n",
       "      <th>DayofMonth</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>DepTime</th>\n",
       "      <th>UniqueCarrier</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Dest</th>\n",
       "      <th>Distance</th>\n",
       "      <th>dep_delayed_15min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c-8</td>\n",
       "      <td>c-21</td>\n",
       "      <td>c-7</td>\n",
       "      <td>1934</td>\n",
       "      <td>AA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>DFW</td>\n",
       "      <td>732</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>c-4</td>\n",
       "      <td>c-20</td>\n",
       "      <td>c-3</td>\n",
       "      <td>1548</td>\n",
       "      <td>US</td>\n",
       "      <td>PIT</td>\n",
       "      <td>MCO</td>\n",
       "      <td>834</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c-9</td>\n",
       "      <td>c-2</td>\n",
       "      <td>c-5</td>\n",
       "      <td>1422</td>\n",
       "      <td>XE</td>\n",
       "      <td>RDU</td>\n",
       "      <td>CLE</td>\n",
       "      <td>416</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c-11</td>\n",
       "      <td>c-25</td>\n",
       "      <td>c-6</td>\n",
       "      <td>1015</td>\n",
       "      <td>OO</td>\n",
       "      <td>DEN</td>\n",
       "      <td>MEM</td>\n",
       "      <td>872</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c-10</td>\n",
       "      <td>c-7</td>\n",
       "      <td>c-6</td>\n",
       "      <td>1828</td>\n",
       "      <td>WN</td>\n",
       "      <td>MDW</td>\n",
       "      <td>OMA</td>\n",
       "      <td>423</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Month DayofMonth DayOfWeek  DepTime UniqueCarrier Origin Dest  Distance  \\\n",
       "0   c-8       c-21       c-7     1934            AA    ATL  DFW       732   \n",
       "1   c-4       c-20       c-3     1548            US    PIT  MCO       834   \n",
       "2   c-9        c-2       c-5     1422            XE    RDU  CLE       416   \n",
       "3  c-11       c-25       c-6     1015            OO    DEN  MEM       872   \n",
       "4  c-10        c-7       c-6     1828            WN    MDW  OMA       423   \n",
       "\n",
       "  dep_delayed_15min  \n",
       "0                 N  \n",
       "1                 N  \n",
       "2                 N  \n",
       "3                 N  \n",
       "4                 Y  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"flight-delays-spring-2018/flight_delays_train.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [],
   "source": [
    "def normalize(df):\n",
    "    for idx,value in enumerate(df['Month']):\n",
    "        df['Month'][idx] = int(value.split('-')[1])\n",
    "        df['DayofMonth'][idx] = int(df['DayofMonth'][idx].split('-')[1])\n",
    "        df['DayOfWeek'][idx] = int(df['DayOfWeek'][idx].split('-')[1])\n",
    "        df['dep_delayed_15min'][idx] = 1 if  df['dep_delayed_15min'][idx] == 'Y' else 0\n",
    "    df.head()\n",
    "    df[\"Month\"]=pd.to_numeric(df[\"Month\"])\n",
    "    df[\"DayofMonth\"]=pd.to_numeric(df[\"DayofMonth\"])\n",
    "    df[\"DayOfWeek\"]=pd.to_numeric(df[\"DayOfWeek\"])\n",
    "    df[\"dep_delayed_15min\"]=pd.to_numeric(df[\"dep_delayed_15min\"])\n",
    "    df = pd.get_dummies(df)\n",
    "    df.info()\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Columns: 606 entries, Month to Dest_YUM\n",
      "dtypes: int64(6), uint8(600)\n",
      "memory usage: 61.8 MB\n"
     ]
    }
   ],
   "source": [
    "df = normalize(df)\n",
    "Y_train = df[\"dep_delayed_15min\"]\n",
    "X_train = df.drop(\"dep_delayed_15min\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
       "                           learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "                           max_features=None, 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=100,\n",
       "                           n_iter_no_change=None, presort='deprecated',\n",
       "                           random_state=None, subsample=1.0, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ensemble.GradientBoostingClassifier(n_estimators=100)\n",
    "model.fit(X_train,Y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Columns: 606 entries, Month to Dest_YUM\n",
      "dtypes: int64(6), uint8(600)\n",
      "memory usage: 61.8 MB\n"
     ]
    }
   ],
   "source": [
    "df_test = pd.read_csv(\"flight-delays-spring-2018/flight_delays_train.csv\")\n",
    "df_test.head()\n",
    "df_test = normalize(df_test)\n",
    "Y_test = df_test[\"dep_delayed_15min\"]\n",
    "X_test = df_test.drop(\"dep_delayed_15min\",axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5294230337407073"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "predictions = model.predict(X_test)\n",
    "auc = roc_auc_score(Y_test,predictions)\n",
    "auc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tentativa 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>DayofMonth</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>DepTime</th>\n",
       "      <th>UniqueCarrier</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Dest</th>\n",
       "      <th>Distance</th>\n",
       "      <th>dep_delayed_15min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c-8</td>\n",
       "      <td>c-21</td>\n",
       "      <td>c-7</td>\n",
       "      <td>1934</td>\n",
       "      <td>AA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>DFW</td>\n",
       "      <td>732</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>c-4</td>\n",
       "      <td>c-20</td>\n",
       "      <td>c-3</td>\n",
       "      <td>1548</td>\n",
       "      <td>US</td>\n",
       "      <td>PIT</td>\n",
       "      <td>MCO</td>\n",
       "      <td>834</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c-9</td>\n",
       "      <td>c-2</td>\n",
       "      <td>c-5</td>\n",
       "      <td>1422</td>\n",
       "      <td>XE</td>\n",
       "      <td>RDU</td>\n",
       "      <td>CLE</td>\n",
       "      <td>416</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c-11</td>\n",
       "      <td>c-25</td>\n",
       "      <td>c-6</td>\n",
       "      <td>1015</td>\n",
       "      <td>OO</td>\n",
       "      <td>DEN</td>\n",
       "      <td>MEM</td>\n",
       "      <td>872</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c-10</td>\n",
       "      <td>c-7</td>\n",
       "      <td>c-6</td>\n",
       "      <td>1828</td>\n",
       "      <td>WN</td>\n",
       "      <td>MDW</td>\n",
       "      <td>OMA</td>\n",
       "      <td>423</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Month DayofMonth DayOfWeek  DepTime UniqueCarrier Origin Dest  Distance  \\\n",
       "0   c-8       c-21       c-7     1934            AA    ATL  DFW       732   \n",
       "1   c-4       c-20       c-3     1548            US    PIT  MCO       834   \n",
       "2   c-9        c-2       c-5     1422            XE    RDU  CLE       416   \n",
       "3  c-11       c-25       c-6     1015            OO    DEN  MEM       872   \n",
       "4  c-10        c-7       c-6     1828            WN    MDW  OMA       423   \n",
       "\n",
       "  dep_delayed_15min  \n",
       "0                 N  \n",
       "1                 N  \n",
       "2                 N  \n",
       "3                 N  \n",
       "4                 Y  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"flight-delays-spring-2018/flight_delays_train.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>DayofMonth</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>DepTime</th>\n",
       "      <th>UniqueCarrier</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Dest</th>\n",
       "      <th>Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c-7</td>\n",
       "      <td>c-25</td>\n",
       "      <td>c-3</td>\n",
       "      <td>615</td>\n",
       "      <td>YV</td>\n",
       "      <td>MRY</td>\n",
       "      <td>PHX</td>\n",
       "      <td>598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>c-4</td>\n",
       "      <td>c-17</td>\n",
       "      <td>c-2</td>\n",
       "      <td>739</td>\n",
       "      <td>WN</td>\n",
       "      <td>LAS</td>\n",
       "      <td>HOU</td>\n",
       "      <td>1235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c-12</td>\n",
       "      <td>c-2</td>\n",
       "      <td>c-7</td>\n",
       "      <td>651</td>\n",
       "      <td>MQ</td>\n",
       "      <td>GSP</td>\n",
       "      <td>ORD</td>\n",
       "      <td>577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c-3</td>\n",
       "      <td>c-25</td>\n",
       "      <td>c-7</td>\n",
       "      <td>1614</td>\n",
       "      <td>WN</td>\n",
       "      <td>BWI</td>\n",
       "      <td>MHT</td>\n",
       "      <td>377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c-6</td>\n",
       "      <td>c-6</td>\n",
       "      <td>c-3</td>\n",
       "      <td>1505</td>\n",
       "      <td>UA</td>\n",
       "      <td>ORD</td>\n",
       "      <td>STL</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Month DayofMonth DayOfWeek  DepTime UniqueCarrier Origin Dest  Distance\n",
       "0   c-7       c-25       c-3      615            YV    MRY  PHX       598\n",
       "1   c-4       c-17       c-2      739            WN    LAS  HOU      1235\n",
       "2  c-12        c-2       c-7      651            MQ    GSP  ORD       577\n",
       "3   c-3       c-25       c-7     1614            WN    BWI  MHT       377\n",
       "4   c-6        c-6       c-3     1505            UA    ORD  STL       258"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv(\"flight-delays-spring-2018/flight_delays_test.csv\")\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "def normalize(df):\n",
    "    for idx,value in enumerate(df['Month']):\n",
    "        df['Month'][idx] = int(value.split('-')[1])\n",
    "        df['DayofMonth'][idx] = int(df['DayofMonth'][idx].split('-')[1])\n",
    "        df['DayOfWeek'][idx] = int(df['DayOfWeek'][idx].split('-')[1])\n",
    "        df['dep_delayed_15min'][idx] = 1 if  df['dep_delayed_15min'][idx] == 'Y' else 0\n",
    "\n",
    "    df[\"Month\"]=pd.to_numeric(df[\"Month\"])\n",
    "    df[\"DayofMonth\"]=pd.to_numeric(df[\"DayofMonth\"])\n",
    "    df[\"DayOfWeek\"]=pd.to_numeric(df[\"DayOfWeek\"])\n",
    "    df[\"dep_delayed_15min\"]=pd.to_numeric(df[\"dep_delayed_15min\"])\n",
    "      \n",
    "    cols = [\"UniqueCarrier\",\"Origin\",\"Dest\"]\n",
    "    \n",
    "    df[cols] = df[cols].apply(lambda x: pd.factorize(x)[0])\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>DayofMonth</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>DepTime</th>\n",
       "      <th>UniqueCarrier</th>\n",
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       "      <th>dep_delayed_15min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>7</td>\n",
       "      <td>1934</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>732</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>1548</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>834</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1422</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>416</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>1015</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>872</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1828</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>423</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month  DayofMonth  DayOfWeek  DepTime  UniqueCarrier  Origin  Dest  \\\n",
       "0      8          21          7     1934              0       0     0   \n",
       "1      4          20          3     1548              1       1     1   \n",
       "2      9           2          5     1422              2       2     2   \n",
       "3     11          25          6     1015              3       3     3   \n",
       "4     10           7          6     1828              4       4     4   \n",
       "\n",
       "   Distance  dep_delayed_15min  \n",
       "0       732                  0  \n",
       "1       834                  0  \n",
       "2       416                  0  \n",
       "3       872                  0  \n",
       "4       423                  1  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = normalize(df)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 9 columns):\n",
      " #   Column             Non-Null Count   Dtype\n",
      "---  ------             --------------   -----\n",
      " 0   Month              100000 non-null  int64\n",
      " 1   DayofMonth         100000 non-null  int64\n",
      " 2   DayOfWeek          100000 non-null  int64\n",
      " 3   DepTime            100000 non-null  int64\n",
      " 4   UniqueCarrier      100000 non-null  int64\n",
      " 5   Origin             100000 non-null  int64\n",
      " 6   Dest               100000 non-null  int64\n",
      " 7   Distance           100000 non-null  int64\n",
      " 8   dep_delayed_15min  100000 non-null  int64\n",
      "dtypes: int64(9)\n",
      "memory usage: 6.9 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
       "                           learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "                           max_features=None, 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=100,\n",
       "                           n_iter_no_change=None, presort='deprecated',\n",
       "                           random_state=None, subsample=1.0, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Testando com a base de treinamento\n",
    "\n",
    "Y = df[\"dep_delayed_15min\"]\n",
    "X = df.drop(\"dep_delayed_15min\",axis=1)\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.25)\n",
    "\n",
    "model = ensemble.GradientBoostingClassifier(n_estimators=100)\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5294831019931291"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(x_test)\n",
    "\n",
    "auc = roc_auc_score(y_test, y_pred)\n",
    "auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8102133333333333\n",
      "{'criterion': 'friedman_mse', 'learning_rate': 0.01, 'loss': 'deviance', 'max_depth': 3, 'max_features': 'log2', 'min_samples_leaf': 0.1, 'min_samples_split': 0.1, 'n_estimators': 10, 'subsample': 0.618}\n"
     ]
    }
   ],
   "source": [
    "# Usando GridSearchCV para encontrar os melhores Hiperparametros\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "parameters = {\n",
    "    \"loss\":[\"deviance\"],\n",
    "    \"learning_rate\": [0.01, 0.1, 0.2],\n",
    "    \"min_samples_split\": np.linspace(0.1, 0.5, 3),\n",
    "    \"min_samples_leaf\": np.linspace(0.1, 0.5, 3),\n",
    "    \"max_depth\":[3,5,8],\n",
    "    \"max_features\":[\"log2\",\"sqrt\"],\n",
    "    \"criterion\": [\"friedman_mse\",  \"mae\"],\n",
    "    \"subsample\":[0.618, 0.8, 1.0],\n",
    "    \"n_estimators\":[10]\n",
    "    }\n",
    "\n",
    "clf = GridSearchCV(ensemble.GradientBoostingClassifier(), parameters, cv=10, n_jobs=-3)\n",
    "\n",
    "clf.fit(x_train, y_train)\n",
    "print(clf.score(x_train, y_train))\n",
    "print(clf.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
       "                           learning_rate=0.01, loss='deviance', max_depth=3,\n",
       "                           max_features='log2', max_leaf_nodes=None,\n",
       "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                           min_samples_leaf=0.1, min_samples_split=0.1,\n",
       "                           min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                           n_iter_no_change=None, presort='deprecated',\n",
       "                           random_state=None, subsample=0.618, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Testando com a base de treinamento usando os hiperparametros encontrados acima\n",
    "\n",
    "Y = df[\"dep_delayed_15min\"]\n",
    "X = df.drop(\"dep_delayed_15min\",axis=1)\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.25)\n",
    "\n",
    "model = ensemble.GradientBoostingClassifier(criterion='friedman_mse', learning_rate = 0.01, \n",
    "                                            max_depth = 3, max_features = 'log2', min_samples_leaf = 0.1,\n",
    "                                            min_samples_split = 0.1, n_estimators=100, subsample = 0.618)\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(x_train)\n",
    "\n",
    "auc = roc_auc_score(y_train, y_pred)\n",
    "auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "def normalizeTest(df):\n",
    "    for idx,value in enumerate(df['Month']):\n",
    "        df['Month'][idx] = int(value.split('-')[1])\n",
    "        df['DayofMonth'][idx] = int(df['DayofMonth'][idx].split('-')[1])\n",
    "        df['DayOfWeek'][idx] = int(df['DayOfWeek'][idx].split('-')[1])\n",
    "\n",
    "    df[\"Month\"]=pd.to_numeric(df[\"Month\"])\n",
    "    df[\"DayofMonth\"]=pd.to_numeric(df[\"DayofMonth\"])\n",
    "    df[\"DayOfWeek\"]=pd.to_numeric(df[\"DayOfWeek\"])\n",
    "\n",
    "    cols = [\"UniqueCarrier\",\"Origin\",\"Dest\"]\n",
    "        \n",
    "    df[cols] = df[cols].apply(lambda x: pd.factorize(x)[0])\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "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>Month</th>\n",
       "      <th>DayofMonth</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>DepTime</th>\n",
       "      <th>UniqueCarrier</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Dest</th>\n",
       "      <th>Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>615</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>739</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>651</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>1614</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1505</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month  DayofMonth  DayOfWeek  DepTime  UniqueCarrier  Origin  Dest  \\\n",
       "0      7          25          3      615              0       0     0   \n",
       "1      4          17          2      739              1       1     1   \n",
       "2     12           2          7      651              2       2     2   \n",
       "3      3          25          7     1614              1       3     3   \n",
       "4      6           6          3     1505              3       4     4   \n",
       "\n",
       "   Distance  \n",
       "0       598  \n",
       "1      1235  \n",
       "2       577  \n",
       "3       377  \n",
       "4       258  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = normalizeTest(test)\n",
    "\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "predictions = pd.DataFrame(\n",
    "    model.predict(test),\n",
    "    columns=['dep_delayed_15min'])\n",
    "\n",
    "submission = pd.DataFrame({'id': test.index, 'dep_delayed_15min': model.predict(test)})\n",
    "\n",
    "submission.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tentativa 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Month</th>\n",
       "      <th>DayofMonth</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>DepTime</th>\n",
       "      <th>UniqueCarrier</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Dest</th>\n",
       "      <th>Distance</th>\n",
       "      <th>dep_delayed_15min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c-8</td>\n",
       "      <td>c-21</td>\n",
       "      <td>c-7</td>\n",
       "      <td>1934</td>\n",
       "      <td>AA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>DFW</td>\n",
       "      <td>732</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>c-4</td>\n",
       "      <td>c-20</td>\n",
       "      <td>c-3</td>\n",
       "      <td>1548</td>\n",
       "      <td>US</td>\n",
       "      <td>PIT</td>\n",
       "      <td>MCO</td>\n",
       "      <td>834</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c-9</td>\n",
       "      <td>c-2</td>\n",
       "      <td>c-5</td>\n",
       "      <td>1422</td>\n",
       "      <td>XE</td>\n",
       "      <td>RDU</td>\n",
       "      <td>CLE</td>\n",
       "      <td>416</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c-11</td>\n",
       "      <td>c-25</td>\n",
       "      <td>c-6</td>\n",
       "      <td>1015</td>\n",
       "      <td>OO</td>\n",
       "      <td>DEN</td>\n",
       "      <td>MEM</td>\n",
       "      <td>872</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c-10</td>\n",
       "      <td>c-7</td>\n",
       "      <td>c-6</td>\n",
       "      <td>1828</td>\n",
       "      <td>WN</td>\n",
       "      <td>MDW</td>\n",
       "      <td>OMA</td>\n",
       "      <td>423</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Month DayofMonth DayOfWeek  DepTime UniqueCarrier Origin Dest  Distance  \\\n",
       "0   c-8       c-21       c-7     1934            AA    ATL  DFW       732   \n",
       "1   c-4       c-20       c-3     1548            US    PIT  MCO       834   \n",
       "2   c-9        c-2       c-5     1422            XE    RDU  CLE       416   \n",
       "3  c-11       c-25       c-6     1015            OO    DEN  MEM       872   \n",
       "4  c-10        c-7       c-6     1828            WN    MDW  OMA       423   \n",
       "\n",
       "  dep_delayed_15min  \n",
       "0                 N  \n",
       "1                 N  \n",
       "2                 N  \n",
       "3                 N  \n",
       "4                 Y  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"flight-delays-spring-2018/flight_delays_train.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Month</th>\n",
       "      <th>DayofMonth</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>DepTime</th>\n",
       "      <th>UniqueCarrier</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Dest</th>\n",
       "      <th>Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c-7</td>\n",
       "      <td>c-25</td>\n",
       "      <td>c-3</td>\n",
       "      <td>615</td>\n",
       "      <td>YV</td>\n",
       "      <td>MRY</td>\n",
       "      <td>PHX</td>\n",
       "      <td>598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>c-4</td>\n",
       "      <td>c-17</td>\n",
       "      <td>c-2</td>\n",
       "      <td>739</td>\n",
       "      <td>WN</td>\n",
       "      <td>LAS</td>\n",
       "      <td>HOU</td>\n",
       "      <td>1235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c-12</td>\n",
       "      <td>c-2</td>\n",
       "      <td>c-7</td>\n",
       "      <td>651</td>\n",
       "      <td>MQ</td>\n",
       "      <td>GSP</td>\n",
       "      <td>ORD</td>\n",
       "      <td>577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c-3</td>\n",
       "      <td>c-25</td>\n",
       "      <td>c-7</td>\n",
       "      <td>1614</td>\n",
       "      <td>WN</td>\n",
       "      <td>BWI</td>\n",
       "      <td>MHT</td>\n",
       "      <td>377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c-6</td>\n",
       "      <td>c-6</td>\n",
       "      <td>c-3</td>\n",
       "      <td>1505</td>\n",
       "      <td>UA</td>\n",
       "      <td>ORD</td>\n",
       "      <td>STL</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Month DayofMonth DayOfWeek  DepTime UniqueCarrier Origin Dest  Distance\n",
       "0   c-7       c-25       c-3      615            YV    MRY  PHX       598\n",
       "1   c-4       c-17       c-2      739            WN    LAS  HOU      1235\n",
       "2  c-12        c-2       c-7      651            MQ    GSP  ORD       577\n",
       "3   c-3       c-25       c-7     1614            WN    BWI  MHT       377\n",
       "4   c-6        c-6       c-3     1505            UA    ORD  STL       258"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv(\"flight-delays-spring-2018/flight_delays_test.csv\")\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "places = pd.Series((test['Origin'].append(test['Dest']).append(train['Origin']).append(train['Dest'])).unique()).to_dict()\n",
    "places_map = {v: k for k, v in places.items()}\n",
    "\n",
    "carriers = pd.Series((test['UniqueCarrier'].append(test['UniqueCarrier']).append(train['UniqueCarrier']).append(train['UniqueCarrier'])).unique()).to_dict()\n",
    "carriers_map = {v: k for k, v in carriers.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(df):\n",
    "    df_copy = df.copy()\n",
    "    df_copy['Dest'] = df_copy['Dest'].map(places_map)\n",
    "    df_copy['Origin'] = df_copy['Origin'].map(places_map)\n",
    "    df_copy['UniqueCarrier'] = df_copy['UniqueCarrier'].map(carriers_map)\n",
    "    df_copy['Month'] = df_copy['Month'].str.replace('c-', '').astype(int)\n",
    "    df_copy['DayofMonth'] = df_copy['DayofMonth'].str.replace('c-', '').astype(int)\n",
    "    df_copy['DayOfWeek'] = df_copy['DayOfWeek'].str.replace('c-', '').astype(int)\n",
    "    return df_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = normalize(train).drop('dep_delayed_15min',axis=1)\n",
    "Y = train['dep_delayed_15min'].map({'Y': 1, 'N': 0}).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
       "                           learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "                           max_features=None, 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=1000,\n",
       "                           n_iter_no_change=None, presort='deprecated',\n",
       "                           random_state=None, subsample=1.0, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.25)\n",
    "\n",
    "model = ensemble.GradientBoostingClassifier(n_estimators=1000,learning_rate=0.1)\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5688739947266108"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(x_train)\n",
    "\n",
    "auc = roc_auc_score(y_train, y_pred)\n",
    "auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 24 candidates, totalling 120 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "[Parallel(n_jobs=-3)]: Done 120 out of 120 | elapsed: 32.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6005049091522655\n",
      "{'learning_rate': 0.1, 'loss': 'deviance', 'max_depth': 8, 'min_samples_leaf': 20, 'n_estimators': 100}\n"
     ]
    }
   ],
   "source": [
    "parameters = {\n",
    "    \"loss\":[\"deviance\"],\n",
    "    \"learning_rate\": [0.01, 0.1, 0.2,0.5],\n",
    "    \"min_samples_leaf\": [20, 50],\n",
    "    \"max_depth\":[3,5,8],\n",
    "    \"n_estimators\":[100]\n",
    "    }\n",
    "\n",
    "clf = GridSearchCV(ensemble.GradientBoostingClassifier(), parameters, scoring='roc_auc',\n",
    "                               verbose = 20, cv=5, n_jobs=-3)\n",
    "\n",
    "clf.fit(X, Y)\n",
    "\n",
    "y_pred = clf.predict(X)\n",
    "\n",
    "auc = roc_auc_score(Y, y_pred)\n",
    "print(auc)\n",
    "\n",
    "print(clf.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 1080 candidates, totalling 5400 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Using backend LokyBackend with 2 concurrent workers.\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 266 tasks      | elapsed:  2.2min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 399 tasks      | elapsed:  3.3min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 532 tasks      | elapsed:  5.0min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 665 tasks      | elapsed:  6.3min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 798 tasks      | elapsed:  8.4min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 931 tasks      | elapsed: 10.9min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 1064 tasks      | elapsed: 13.0min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 1195 tasks      | elapsed: 14.2min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 1326 tasks      | elapsed: 15.2min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 1457 tasks      | elapsed: 16.2min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 1588 tasks      | elapsed: 17.8min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
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     "name": "stderr",
     "output_type": "stream",
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     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 2244 tasks      | elapsed: 27.0min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 2376 tasks      | elapsed: 28.1min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 2507 tasks      | elapsed: 28.9min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 2638 tasks      | elapsed: 30.5min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 2769 tasks      | elapsed: 31.9min\n",
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      "[Parallel(n_jobs=-3)]: Done 2771 tasks      | elapsed: 32.0min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 2900 tasks      | elapsed: 33.4min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 3031 tasks      | elapsed: 35.9min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 3162 tasks      | elapsed: 38.3min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 3293 tasks      | elapsed: 40.2min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 3424 tasks      | elapsed: 41.5min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 3555 tasks      | elapsed: 42.6min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 3686 tasks      | elapsed: 44.2min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 3817 tasks      | elapsed: 46.0min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 3948 tasks      | elapsed: 47.5min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 4079 tasks      | elapsed: 50.5min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 4341 tasks      | elapsed: 55.4min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 4472 tasks      | elapsed: 56.7min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 4603 tasks      | elapsed: 57.7min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 4734 tasks      | elapsed: 59.2min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 4865 tasks      | elapsed: 61.0min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 4996 tasks      | elapsed: 62.6min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 5128 tasks      | elapsed: 65.1min\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 5259 tasks      | elapsed: 67.9min\n",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-3)]: Done 5390 tasks      | elapsed: 70.4min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5355434232316384\n",
      "{'learning_rate': 0.2, 'loss': 'deviance', 'max_depth': 8, 'max_features': 'log2', 'min_samples_leaf': 10, 'min_samples_split': 5, 'n_estimators': 10, 'subsample': 1.0}\n"
     ]
    }
   ],
   "source": [
    "parameters = {\n",
    "    \"loss\":[\"deviance\"],\n",
    "    \"learning_rate\": [0.01, 0.05, 0.075, 0.1, 0.2],\n",
    "    \"min_samples_split\": [5,10,20],\n",
    "    \"min_samples_leaf\": [5,10,20],\n",
    "    \"max_depth\":[3,5,8],\n",
    "    \"max_features\":[\"log2\",\"sqrt\"],\n",
    "    \"subsample\":[0.618, 0.8, 0.9, 1.0],\n",
    "    \"n_estimators\":[10]\n",
    "    }\n",
    "\n",
    "clf = GridSearchCV(ensemble.GradientBoostingClassifier(), parameters, scoring='roc_auc',\n",
    "                               verbose = 20, cv=5, n_jobs=-3)\n",
    "\n",
    "clf.fit(X, Y)\n",
    "\n",
    "y_pred = clf.predict(X)\n",
    "\n",
    "auc = roc_auc_score(Y, y_pred)\n",
    "print(auc)\n",
    "\n",
    "print(clf.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7254967153608991"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.25)\n",
    "\n",
    "model = ensemble.GradientBoostingClassifier(n_estimators=300, learning_rate=0.5, \n",
    "                                            max_depth = 5)\n",
    "\n",
    "model.fit(x_train,y_train)\n",
    "\n",
    "y_pred = model.predict(x_train)\n",
    "\n",
    "auc = roc_auc_score(y_train, y_pred)\n",
    "auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5846882126086936"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(x_test)\n",
    "\n",
    "auc = roc_auc_score(y_test, y_pred)\n",
    "auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Month</th>\n",
       "      <th>DayofMonth</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>DepTime</th>\n",
       "      <th>UniqueCarrier</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Dest</th>\n",
       "      <th>Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>615</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>739</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>127</td>\n",
       "      <td>1235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>651</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>1614</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>121</td>\n",
       "      <td>377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1505</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month  DayofMonth  DayOfWeek  DepTime  UniqueCarrier  Origin  Dest  \\\n",
       "0      7          25          3      615              0       0    35   \n",
       "1      4          17          2      739              1       1   127   \n",
       "2     12           2          7      651              2       2     4   \n",
       "3      3          25          7     1614              1       3   121   \n",
       "4      6           6          3     1505              3       4    23   \n",
       "\n",
       "   Distance  \n",
       "0       598  \n",
       "1      1235  \n",
       "2       577  \n",
       "3       377  \n",
       "4       258  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = normalize(test)\n",
    "\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = pd.DataFrame(\n",
    "    model.predict(test),\n",
    "    columns=['dep_delayed_15min'])\n",
    "\n",
    "submission = pd.DataFrame({'id': test.index, 'dep_delayed_15min': model.predict(test)})\n",
    "\n",
    "submission.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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