CRS_DEP_TIME,CARRIER,DEP_TIME,DEST,DISTANCE,FL_DATE,FL_NUM,ORIGIN,Weather,DAY_WEEK,DAY_OF_MONTH,TAIL...

Question

CRS_DEP_TIME,CARRIER,DEP_TIME,DEST,DISTANCE,FL_DATE,FL_NUM,ORIGIN,Weather,DAY_WEEK,DAY_OF_MONTH,TAIL_NUM,Flight Status 1455,OH,1455,JFK,184,01/01/2004,5935,BWI,0,4,1,N940CA,ontime...

1 answer below »

CRS_DEP_TIME,CARRIER,DEP_TIME,DEST,DISTANCE,FL_DATE,FL_NUM,ORIGIN,Weather,DAY_WEEK,DAY_OF_MONTH,TAIL_NUM,Flight Status
1455,OH,1455,JFK,184,01/01/2004,5935,BWI,0,4,1,N940CA,ontime
1640,DH,1640,JFK,213,01/01/2004,6155,DCA,0,4,1,N405FJ,ontime
1245,DH,1245,LGA,229,01/01/2004,7208,IAD,0,4,1,N695BR,ontime
1715,DH,1709,LGA,229,01/01/2004,7215,IAD,0,4,1,N662BR,ontime
1039,DH,1035,LGA,229,01/01/2004,7792,IAD,0,4,1,N698BR,ontime
840,DH,839,JFK,228,01/01/2004,7800,IAD,0,4,1,N687BR,ontime
1240,DH,1243,JFK,228,01/01/2004,7806,IAD,0,4,1,N321UE,ontime
1645,DH,1644,JFK,228,01/01/2004,7810,IAD,0,4,1,N301UE,ontime
1715,DH,1710,JFK,228,01/01/2004,7812,IAD,0,4,1,N328UE,ontime
2120,DH,2129,JFK,228,01/01/2004,7814,IAD,0,4,1,N685BR,ontime
2120,DH,2114,LGA,229,01/01/2004,7924,IAD,0,4,1,N645BR,ontime
1455,DL,1458,JFK,213,01/01/2004,746,DCA,0,4,1,N918DE,ontime
930,DL,932,LGA,214,01/01/2004,1746,DCA,0,4,1,N242DL,ontime
1230,DL,1228,LGA,214,01/01/2004,1752,DCA,0,4,1,N241DL,ontime
1430,DL,1429,LGA,214,01/01/2004,1756,DCA,0,4,1,N242DL,ontime
1730,DL,1728,LGA,214,01/01/2004,1762,DCA,0,4,1,N241DL,ontime
2030,DL,2029,LGA,214,01/01/2004,1768,DCA,0,4,1,N242DL,ontime
1530,MQ,1525,JFK,213,01/01/2004,4752,DCA,0,4,1,N709MQ,ontime
600,MQ,556,JFK,213,01/01/2004,4760,DCA,0,4,1,N717MQ,ontime
1830,MQ,1822,JFK,213,01/01/2004,4784,DCA,0,4,1,N707MQ,ontime
900,MQ,853,LGA,214,01/01/2004,4956,DCA,0,4,1,N737MQ,ontime
1300,MQ,1254,LGA,214,01/01/2004,4964,DCA,0,4,1,N717MQ,ontime
1400,MQ,1356,LGA,214,01/01/2004,4966,DCA,0,4,1,N726MQ,ontime
1500,MQ,1452,LGA,214,01/01/2004,4968,DCA,0,4,1,N724MQ,ontime
1900,MQ,1853,LGA,214,01/01/2004,4976,DCA,0,4,1,N724MQ,ontime
850,UA,841,LGA,229,01/01/2004,846,IAD,0,4,1,N513UA,ontime
900,US,858,LGA,214,01/01/2004,2164,DCA,0,4,1,N709UW,ontime
1100,US,1056,LGA,214,01/01/2004,2168,DCA,0,4,1,N748UW,ontime
1300,US,1253,LGA,214,01/01/2004,2172,DCA,0,4,1,N709UW,ontime
1500,US,1458,LGA,214,01/01/2004,2176,DCA,0,4,1,N748UW,ontime
1700,US,1655,LGA,214,01/01/2004,2180,DCA,0,4,1,N709UW,ontime
2100,US,2055,LGA,214,01/01/2004,2188,DCA,0,4,1,N709UW,ontime
1455,RU,1452,EWR,169,01/01/2004,2403,BWI,0,4,1,N14916,ontime
1720,RU,1710,EWR,169,01/01/2004,2675,BWI,0,4,1,N16954,ontime
1030,RU,1030,EWR,169,01/01/2004,2303,BWI,0,4,1,N26549,ontime
700,RU,656,EWR,169,01/01/2004,2703,BWI,0,4,1,N16954,ontime
1300,CO,1256,EWR,199,01/01/2004,808,DCA,0,4,1,N18611,ontime
1730,CO,1726,EWR,199,01/01/2004,814,DCA,0,4,1,N19357,ontime
840,DH,840,EWR,213,01/01/2004,7299,IAD,0,4,1,N691BR,ontime
1710,DH,1704,EWR,213,01/01/2004,7302,IAD,0,4,1,N691BR,ontime
1245,DH,1245,EWR,213,01/01/2004,7303,IAD,0,4,1,N697BR,ontime
2120,DH,2118,EWR,213,01/01/2004,7304,IAD,0,4,1,N699BR,ontime
1700,RU,1651,EWR,213,01/01/2004,2497,IAD,0,4,1,N12540,ontime
1900,RU,1850,EWR,213,01/01/2004,2385,IAD,0,4,1,N16149,ontime
1525,RU,1521,EWR,199,01/01/2004,2261,DCA,0,4,1,N12564,ontime
1900,RU,1855,EWR,199,01/01/2004,2336,DCA,0,4,1,N21537,ontime
1400,RU,1357,EWR,199,01/01/2004,2216,DCA,0,4,1,N15983,ontime
1515,RU,1508,EWR,213,01/01/2004,2156,IAD,0,4,1,N16149,ontime
1300,RU,1255,EWR,213,01/01/2004,2664,IAD,0,4,1,N12519,ontime
1630,RU,1625,EWR,199,01/01/2004,2181,DCA,0,4,1,N19966,ontime
1455,OH,1455,JFK,184,01/02/2004,5935,BWI,0,5,2,N995CA,ontime
1640,DH,1641,JFK,213,01/02/2004,6155,DCA,0,5,2,N415FJ,ontime
1245,DH,1249,LGA,229,01/02/2004,7208,IAD,0,5,2,N688BR,ontime
1455,DH,1531,LGA,229,01/02/2004,7211,IAD,0,5,2,N665BR,delayed
1715,DH,1712,LGA,229,01/02/2004,7215,IAD,0,5,2,N639BR,ontime
640,DH,645,LGA,229,01/02/2004,7790,IAD,0,5,2,N686BR,ontime
1039,DH,1236,LGA,229,01/02/2004,7792,IAD,0,5,2,N665BR,delayed
840,DH,859,JFK,228,01/02/2004,7800,IAD,0,5,2,N645BR,ontime
1240,DH,1232,JFK,228,01/02/2004,7806,IAD,0,5,2,N332UE,ontime
1455,DH,1455,JFK,228,01/02/2004,7808,IAD,0,5,2,N324UE,ontime
1645,DH,1645,JFK,228,01/02/2004,7810,IAD,0,5,2,N305UE,ontime
1715,DH,1716,JFK,228,01/02/2004,7812,IAD,0,5,2,N322UE,ontime
2120,DH,2305,JFK,228,01/02/2004,7814,IAD,0,5,2,N657BR,delayed
1610,DH,1605,JFK,228,01/02/2004,7816,IAD,0,5,2,N315UE,ontime
2120,DH,2118,LGA,229,01/02/2004,7924,IAD,0,5,2,N709BR,ontime
1455,DL,1458,JFK,213,01/02/2004,746,DCA,0,5,2,N964DL,ontime
930,DL,930,LGA,214,01/02/2004,1746,DCA,0,5,2,N241DL,ontime
1230,DL,1230,LGA,214,01/02/2004,1752,DCA,0,5,2,N225DL,ontime
1430,DL,1427,LGA,214,01/02/2004,1756,DCA,0,5,2,N241DL,ontime
1730,DL,1730,LGA,214,01/02/2004,1762,DCA,0,5,2,N225DL,ontime
2030,DL,2028,LGA,214,01/02/2004,1768,DCA,0,5,2,N241DL,ontime
1530,MQ,1522,JFK,213,01/02/2004,4752,DCA,0,5,2,N720MQ,ontime
600,MQ,552,JFK,213,01/02/2004,4760,DCA,0,5,2,N736MQ,ontime
1830,MQ,1847,JFK,213,01/02/2004,4784,DCA,0,5,2,N727MQ,ontime
900,MQ,852,LGA,214,01/02/2004,4956,DCA,0,5,2,N713MQ,ontime
1100,MQ,1053,LGA,214,01/02/2004,4960,DCA,0,5,2,N708MQ,ontime
1300,MQ,1258,LGA,214,01/02/2004,4964,DCA,0,5,2,N713MQ,ontime
1400,MQ,1402,LGA,214,01/02/2004,4966,DCA,0,5,2,N718MQ,ontime
1500,MQ,1456,LGA,214,01/02/2004,4968,DCA,0,5,2,N708MQ,ontime
850,UA,850,LGA,229,01/02/2004,846,IAD,0,5,2,N556UA,ontime
700,US,657,LGA,214,01/02/2004,2160,DCA,0,5,2,N710UW,ontime
900,US,857,LGA,214,01/02/2004,2164,DCA,0,5,2,N736UW,ontime
1100,US,1058,LGA,214,01/02/2004,2168,DCA,0,5,2,N710UW,ontime
1300,US,1258,LGA,214,01/02/2004,2172,DCA,0,5,2,N736UW,ontime
1500,US,1458,LGA,214,01/02/2004,2176,DCA,0,5,2,N710UW,ontime
1700,US,1655,LGA,214,01/02/2004,2180,DCA,0,5,2,N736UW,ontime
1900,US,1855,LGA,214,01/02/2004,2184,DCA,0,5,2,N710UW,ontime
2100,US,2056,LGA,214,01/02/2004,2188,DCA,0,5,2,N736UW,ontime
1720,RU,1715,EWR,169,01/02/2004,2675,BWI,0,5,2,N19966,ontime
1030,RU,1030,EWR,169,01/02/2004,2303,BWI,0,5,2,N12540,ontime
700,RU,656,EWR,169,01/02/2004,2703,BWI,0,5,2,N16961,ontime
1455,RU,1456,EWR,169,01/02/2004,2403,BWI,0,5,2,N12946,ontime
1730,CO,1727,EWR,199,01/02/2004,814,DCA,0,5,2,N14342,ontime
1300,CO,1301,EWR,199,01/02/2004,808,DCA,0,5,2,N14664,ontime
759,CO,754,EWR,199,01/02/2004,806,DCA,0,5,2,N11641,ontime
840,DH,837,EWR,213,01/02/2004,7299,IAD,0,5,2,N679BR,ontime
1245,DH,1350,EWR,213,01/02/2004,7303,IAD,0,5,2,N686BR,delayed
1430,DH,1512,EWR,213,01/02/2004,7307,IAD,0,5,2,N309UE,delayed
630,DH,629,EWR,213,01/02/2004,7371,IAD,0,5,2,N312UE,ontime
1630,RU,1625,EWR,199,01/02/2004,2181,DCA,0,5,2,N14977,ontime
700,RU,655,EWR,213,01/02/2004,2855,IAD,0,5,2,N13990,ontime
900,RU,858,EWR,199,01/02/2004,2582,DCA,0,5,2,N14907,ontime
700,RU,657,EWR,199,01/02/2004,2761,DCA,0,5,2,N13997,ontime
1700,RU,1650,EWR,213,01/02/2004,2497,IAD,0,5,2,N12528,ontime
1900,RU,1856,EWR,213,01/02/2004,2385,IAD,0,5,2,N11107,ontime
1300,RU,1253,EWR,213,01/02/2004,2692,IAD,0,5,2,N14505,ontime
900,RU,854,EWR,213,01/02/2004,3276,IAD,0,5,2,N16151,ontime
1900,RU,1858,EWR,199,01/02/2004,2336,DCA,0,5,2,N15985,ontime
2100,RU,2050,EWR,199,01/02/2004,2879,DCA,0,5,2,N17108,ontime
1400,RU,1358,EWR,199,01/02/2004,2216,DCA,0,5,2,N13118,ontime
1515,RU,1510,EWR,213,01/02/2004,2156,IAD,0,5,2,N11107,delayed
1525,RU,1519,EWR,199,01/02/2004,2261,DCA,0,5,2,N15574,ontime
1245,DH,1243,LGA,229,01/03/2004,7208,IAD,0,6,3,N688BR,ontime
1715,DH,1738,LGA,229,01/03/2004,7215,IAD,0,6,3,N639BR,ontime
640,DH,640,LGA,229,01/03/2004,7790,IAD,0,6,3,N696BR,ontime
1039,DH,1030,LGA,229,01/03/2004,7792,IAD,0,6,3,N696BR,ontime
840,DH,855,JFK,228,01/03/2004,7800,IAD,0,6,3,N709BR,ontime
1240,DH,1237,JFK,228,01/03/2004,7806,IAD,0,6,3,N327UE,ontime
1455,DH,1455,JFK,228,01/03/2004,7808,IAD,0,6,3,N309UE,ontime
1645,DH,1654,JFK,228,01/03/2004,7810,IAD,0,6,3,N311UE,ontime
1715,DH,1741,JFK,228,01/03/2004,7812,IAD,0,6,3,N327UE,ontime
2120,DH,2213,JFK,228,01/03/2004,7814,IAD,0,6,3,N655BR,delayed
1610,DH,1604,JFK,228,01/03/2004,7816,IAD,0,6,3,N329UE,ontime
2120,DH,2138,LGA,229,01/03/2004,7924,IAD,0,6,3,N688BR,ontime
1455,DL,1505,JFK,213,01/03/2004,746,DCA,0,6,3,N997DL,delayed
830,DL,828,LGA,214,01/03/2004,1744,DCA,0,6,3,N225DL,ontime
1030,DL,1030,LGA,214,01/03/2004,1748,DCA,0,6,3,N242DL,ontime
1230,DL,1230,LGA,214,01/03/2004,1752,DCA,0,6,3,N225DL,ontime
1430,DL,1428,LGA,214,01/03/2004,1756,DCA,0,6,3,N242DL,ontime
1630,DL,1629,LGA,214,01/03/2004,1760,DCA,0,6,3,N225DL,ontime
1830,DL,1829,LGA,214,01/03/2004,1764,DCA,0,6,3,N242DL,ontime
2030,DL,2024,LGA,214,01/03/2004,1768,DCA,0,6,3,N225DL,ontime
1530,MQ,1600,JFK,213,01/03/2004,4752,DCA,0,6,3,N734MQ,delayed
600,MQ,555,JFK,213,01/03/2004,4760,DCA,0,6,3,N712MQ,ontime
1830,MQ,1829,JFK,213,01/03/2004,4784,DCA,0,6,3,N709MQ,ontime
900,MQ,855,LGA,214,01/03/2004,4956,DCA,0,6,3,N739MQ,ontime
1300,MQ,1254,LGA,214,01/03/2004,4964,DCA,0,6,3,N739MQ,ontime
850,UA,849,LGA,229,01/03/2004,846,IAD,0,6,3,N567UA,ontime
700,US,655,LGA,214,01/03/2004,2160,DCA,0,6,3,N760UW,ontime
900,US,858,LGA,214,01/03/2004,2164,DCA,0,6,3,N710UW,ontime
1100,US,1059,LGA,214,01/03/2004,2168,DCA,0,6,3,N760UW,ontime
1300,US,1256,LGA,214,01/03/2004,2172,DCA,0,6,3,N710UW,ontime
1500,US,1500,LGA,214,01/03/2004,2176,DCA,0,6,3,N760UW,ontime
1700,US,1658,LGA,214,01/03/2004,2180,DCA,0,6,3,N710UW,ontime
1900,US,1857,LGA,214,01/03/2004,2184,DCA,0,6,3,N760UW,ontime
1720,RU,1714,EWR,169,01/03/2004,2675,BWI,0,6,3,N15574,ontime
700,RU,655,EWR,169,01/03/2004,2703,BWI,0,6,3,N11536,ontime
1030,RU,1026,EWR,169,01/03/2004,2303,BWI,0,6,3,N14907,ontime
1455,RU,1448,EWR,169,01/03/2004,2267,BWI,0,6,3,N14974,ontime
1300,CO,1255,EWR,199,01/03/2004,808,DCA,0,6,3,N11612,ontime
840,DH,857,EWR,213,01/03/2004,7299,IAD,0,6,3,N693BR,delayed
1710,DH,1705,EWR,213,01/03/2004,7302,IAD,0,6,3,N693BR,ontime
1245,DH,1329,EWR,213,01/03/2004,7303,IAD,0,6

toyotacorolla-1-sn1l5hfu.csv flightdelays-p3zebqzf.csv ebayauctions-zsnuquf0.csv screen-shot-2021-05-12-at-104430-pm-1d41pk5p.png screen-shot-2021-05-12-at-104419-pm-pcrchpxn.png

Answered 5 days After May 13, 2021

Solution

Vicky answered on May 18 2021

137 Votes

{
"cells": [
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import itertools\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.tree import DecisionTreeClassifier"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"

\n",
"

Vicky · Accepted Answer

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd
",
    "import numpy as np
",
    "import itertools
",
    "import seaborn as sns
",
    "import matplotlib.pyplot as plt
",
    "from sklearn.model_selection import train_test_split
",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
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       "    .dataframe tbody tr th:only-of-type {
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       "  
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       "    
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       "      
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       "      Category
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       "      currency
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       "      sellerRating
",
       "      Duration
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       "      endDay
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       "      ClosePrice
",
       "      OpenPrice
",
       "      Competitive?
",
       "    
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       "  
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       "  
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       "    
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       "      0
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       "      Music/Movie/Game
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       "      0.01
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      ],
      "text/plain": [
       "           Category currency  sellerRating  Duration endDay  ClosePrice  \
",
       "0  Music/Movie/Game       US          3249         5    Mon        0.01   
",
       "1  Music/Movie/Game       US          3249         5    Mon        0.01   
",
       "2  Music/Movie/Game       US          3249         5    Mon        0.01   
",
       "3  Music/Movie/Game       US          3249         5    Mon        0.01   
",
       "4  Music/Movie/Game       US          3249         5    Mon        0.01   
",
       "
",
       "   OpenPrice  Competitive?  
",
       "0       0.01             0  
",
       "1       0.01             0  
",
       "2       0.01             0  
",
       "3       0.01             0  
",
       "4       0.01             0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ebay = pd.read_csv('ebayauctions.csv')
",
    "ebay.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "
",
      "RangeIndex: 1972 entries, 0 to 1971
",
      "Data columns (total 8 columns):
",
      " #   Column        Non-Null Count  Dtype  
",
      "---  ------        --------------  -----  
",
      " 0   Category      1972 non-null   object 
",
      " 1   currency      1972 non-null   object 
",
      " 2   sellerRating  1972 non-null   int64  
",
      " 3   Duration      1972 non-null   int64  
",
      " 4   endDay        1972 non-null   object 
",
      " 5   ClosePrice    1972 non-null   float64
",
      " 6   OpenPrice     1972 non-null   float64
",
      " 7   Competitive?  1972 non-null   int64  
",
      "dtypes: float64(2), int64(3), object(3)
",
      "memory usage: 123.4+ KB
"
     ]
    }
   ],
   "source": [
    "ebay.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert variable Duration to categorical variable
",
    "ebay['Duration'] = ebay['Duration'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "
",
      "RangeIndex: 1972 entries, 0 to 1971
",
      "Data columns (total 8 columns):
",
      " #   Column        Non-Null Count  Dtype   
",
      "---  ------        --------------  -----   
",
      " 0   Category      1972 non-null   object  
",
      " 1   currency      1972 non-null   object  
",
      " 2   sellerRating  1972 non-null   int64   
",
      " 3   Duration      1972 non-null   category
",
      " 4   endDay        1972 non-null   object  
",
      " 5   ClosePrice    1972 non-null   float64 
",
      " 6   OpenPrice     1972 non-null   float64 
",
      " 7   Competitive?  1972 non-null   int64   
",
      "dtypes: category(1), float64(2), int64(2), object(3)
",
      "memory usage: 110.1+ KB
"
     ]
    }
   ],
   "source": [
    "ebay.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_var_names=[key for key in dict(ebay.dtypes) if dict(ebay.dtypes)[key] in ['float64', 'int64', 'float32', 'int32']]
",
    "cat_var_names=[key for key in dict(ebay.dtypes) if dict(ebay.dtypes)[key] in ['object', 'O']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sellerRating', 'ClosePrice', 'OpenPrice', 'Competitive?']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_var_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Category', 'currency', 'endDay']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_var_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "      STD
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       "      VAR
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       "      1972.0
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       "      3.567686e+07
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       "      0.00
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       "      4.71
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       "      50.00
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       "      112.100
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       "      3380.00
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       "      22501.0000
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       "      27132.0000
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       "      2.241
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       "      4.9075
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       "      28.00
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       "      80.999
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       "      38.856149
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       "      1.509800e+03
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       "      0.01
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       "      0.01
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       "      0.01
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       "      0.990
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       "      1.2300
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       "      4.500
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       "      9.99
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       "      24.950
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       "      49.9900
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       "      132.5800
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       "      999.0
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       "      Competitive?
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       "      1972.0
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       "                   N  NMISS        SUM         MEAN    MEDIAN          STD  \
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       "sellerRating  1972.0    0.0  7020790.0  3560.238337  1853.000  5973.011033   
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       "ClosePrice    1972.0    0.0    71877.6    36.449087     9.995    89.493858   
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       "OpenPrice     1972.0    0.0    25490.6    12.926268     4.500    38.856149   
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       "Competitive?  1972.0    0.0     1066.0     0.540568     1.000     0.498478   
",
       "
",
       "                       VAR   MIN    P1     P5      P10       P25       P50  \
",
       "sellerRating  3.567686e+07  0.00  4.71  50.00  112.100  595.0000  1853.000   
",
       "ClosePrice    8.009151e+03  0.01  0.01   1.23    2.241    4.9075     9.995   
",
       "OpenPrice     1.509800e+03  0.01  0.01   0.01    0.990    1.2300     4.500   
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       "Competitive?  2.484802e-01  0.00  0.00   0.00    0.000    0.0000     1.000   
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       "
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       "                  P75       P90         P95         P99      MAX  
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       "sellerRating  3380.00  5702.800  22501.0000  27132.0000  37727.0  
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       "ClosePrice      28.00    80.999    153.2785    507.1867    999.0  
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       "OpenPrice        9.99    24.950     49.9900    132.5800    999.0  
",
       "Competitive?     1.00     1.000      1.0000      1.0000      1.0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating Data audit Report
",
    "def var_summary(x):
",
    "    return pd.Series([x.count(), x.isnull().sum(), x.sum(), x.mean(), x.median(),  x.std(), x.var(), x.min(), x.dropna().quantile(0.01), x.dropna().quantile(0.05),x.dropna().quantile(0.10),x.dropna().quantile(0.25),x.dropna().quantile(0.50),x.dropna().quantile(0.75), x.dropna().quantile(0.90),x.dropna().quantile(0.95), x.dropna().quantile(0.99),x.max()], 
",
    "                  index=['N', 'NMISS', 'SUM', 'MEAN','MEDIAN', 'STD', 'VAR', 'MIN', 'P1' , 'P5' ,'P10' ,'P25' ,'P50' ,'P75' ,'P90' ,'P95' ,'P99' ,'MAX'])
",
    "
",
    "ebay_num=ebay[numeric_var_names]
",
    "num_summary=ebay_num.apply(lambda x: var_summary(x)).T
",
    "num_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Handling Outliers
",
    "def outlier_capping(x):
",
    "    x = x.clip(upper=x.quantile(0.99))
",
    "    x = x.clip(lower=x.quantile(0.01))
",
    "    return x
",
    "
",
    "ebay_num=ebay_num.apply(outlier_capping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# An utility function to create dummy variable
",
    "def create_dummies( df, colname ):
",
    "    col_dummies = pd.get_dummies(df[colname], prefix=colname, drop_first=True)
",
    "    df = pd.concat([df, col_dummies], axis=1)
",
    "    df.drop( colname, axis = 1, inplace = True )
",
    "    return(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
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       "   Category_Automotive  Category_Books  Category_Business/Industrial  \
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       "0                    0               0                             0   
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       "1                    0               0                             0   
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     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#for c_feature in categorical_features
",
    "ebay_cat=ebay[cat_var_names]
",
    "for c_feature in ['Category', 'currency', 'endDay']:
",
    "    ebay_cat = create_dummies(ebay_cat,c_feature)
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    "ebay_cat.head()"
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   "cell_type": "code",
   "execution_count": 34,
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       "   Category_Clothing/Accessories  Category_Coins/Stamps  \
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       "0                              0                      0   
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       "1                              0                      0   
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       "2                              0                      0   
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       "3                              0                      0   
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       "4                              0                      0   
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       "
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       "   Category_Collectibles  ...  Category_SportingGoods  Category_Toys/Hobbies  \
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       "0                      0  ...                       0                      0   
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       "1                      0  ...                       0                      0   
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       "2                      0  ...                       0                      0   
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       "3                      0  ...                       0                      0   
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       "4                      0  ...                       0                      0   
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       "
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       "   currency_GBP  currency_US  endDay_Mon  endDay_Sat  endDay_Sun  endDay_Thu  \
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       "0             0            1           1           0           0           0   
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       "1             0            1           1           0           0           0   
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       "2             0            1           1           0           0           0   
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       "3             0            1           1           0           0           0   
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       "4             0            1           1           0           0           0   
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       "
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       "   endDay_Tue  endDay_Wed  
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       "0           0           0  
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       "
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       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ebay = pd.concat([ebay_num, ebay_cat], axis=1)
",
    "ebay.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the ebay into training (60%) and testing (40%)
",
    "
",
    "X = pd.concat([ebay.iloc[:,0:3],ebay.iloc[:,4:]],axis=1)
",
    "y = ebay['Competitive?']
",
    "
",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=7, min_samples_leaf=50)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit Decision Tree Classifier
",
    "treeclf = DecisionTreeClassifier(min_samples_leaf=50, max_depth=7)
",
    "treeclf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries for graph viz
",
    "from IPython.display import Image  
",
    "from sklearn.tree import export_graphviz
",
    "import pydotplus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png":