Flight_Delay.ipynb

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        "Importing data and creating dataset"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np"
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      "outputs": []
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      "source": [
        "url= 'https://raw.githubusercontent.com/mehdy28/Fligh_Delay/main/data/test.csv'\n",
        "test= pd.read_csv(url)\n",
        "test.head()"
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              "  Month DayofMonth DayOfWeek  DepTime UniqueCarrier Origin Dest  Distance\n",
              "0   c-7       c-25       c-3      615            YV    MRY  PHX       598\n",
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        "url= 'https://raw.githubusercontent.com/mehdy28/Fligh_Delay/main/data/train.csv'\n",
        "train= pd.read_csv(url)\n",
        "train.head()"
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              "  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  "
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    {
      "cell_type": "markdown",
      "source": [
        "## Exploratory Data Analysis"
      ],
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        "id": "l9Z09NGrhCwl"
      }
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      "source": [
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        "outputId": "763714cd-afdb-4044-9c12-dba28ad3de21"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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  object\n",
            " 1   DayofMonth         100000 non-null  object\n",
            " 2   DayOfWeek          100000 non-null  object\n",
            " 3   DepTime            100000 non-null  int64 \n",
            " 4   UniqueCarrier      100000 non-null  object\n",
            " 5   Origin             100000 non-null  object\n",
            " 6   Dest               100000 non-null  object\n",
            " 7   Distance           100000 non-null  int64 \n",
            " 8   dep_delayed_15min  100000 non-null  object\n",
            "dtypes: int64(2), object(7)\n",
            "memory usage: 6.9+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test.info()"
      ],
      "metadata": {
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        "id": "nEzMZPWslK3k",
        "outputId": "62ab9119-0ad2-4bd0-8c2c-a0f6d0757cb6"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 100000 entries, 0 to 99999\n",
            "Data columns (total 8 columns):\n",
            " #   Column         Non-Null Count   Dtype \n",
            "---  ------         --------------   ----- \n",
            " 0   Month          100000 non-null  object\n",
            " 1   DayofMonth     100000 non-null  object\n",
            " 2   DayOfWeek      100000 non-null  object\n",
            " 3   DepTime        100000 non-null  int64 \n",
            " 4   UniqueCarrier  100000 non-null  object\n",
            " 5   Origin         100000 non-null  object\n",
            " 6   Dest           100000 non-null  object\n",
            " 7   Distance       100000 non-null  int64 \n",
            "dtypes: int64(2), object(6)\n",
            "memory usage: 6.1+ MB\n"
          ]
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    {
      "cell_type": "markdown",
      "source": [
        "We do not have any null columns in our data"
      ],
      "metadata": {
        "id": "-aXWljqIkxbD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"The unique values of 'Distance' in the: \")\n",
        "print(\"Train dataset: \",train.Distance.unique())\n",
        "print(\"Test dataset: \",test.Distance.unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rtpYNGqHzeV_",
        "outputId": "9a2b9980-506d-48c7-9c16-432e3d55afd8"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The unique values of 'Distance' in the: \n",
            "Train dataset:  [ 732  834  416 ... 2242  536 1198]\n",
            "Test dataset:  [ 598 1235  577 ...   96 1367   91]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"The unique values of 'UniqueCarrier' in the: \")\n",
        "print(\"Train dataset: \",train.UniqueCarrier.unique())\n",
        "print(\"Test dataset: \",test.UniqueCarrier.unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oh7o9PzlmgvZ",
        "outputId": "a9f12056-c22e-4897-a516-51fc459efcfc"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The unique values of 'UniqueCarrier' in the: \n",
            "Train dataset:  ['AA' 'US' 'XE' 'OO' 'WN' 'NW' 'DL' 'OH' 'AS' 'UA' 'MQ' 'CO' 'EV' 'DH'\n",
            " 'YV' 'F9' 'AQ' 'TZ' 'HP' 'B6' 'FL' 'HA']\n",
            "Test dataset:  ['YV' 'WN' 'MQ' 'UA' 'NW' 'B6' 'US' 'AA' 'OH' 'OO' 'FL' 'DL' 'EV' 'CO'\n",
            " 'XE' '9E' 'HA' 'AS' 'AQ' 'F9']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"The unique values of 'Origin' in the: \")\n",
        "print(\"Train dataset: \",train.Origin.unique())\n",
        "print(\"Test dataset: \",test.Origin.unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kpnwFG5rybWY",
        "outputId": "283cd08f-51a1-4abc-8f5d-b7f5510eb44d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The unique values of 'Origin' in the: \n",
            "Train dataset:  ['ATL' 'PIT' 'RDU' 'DEN' 'MDW' 'MEM' 'PBI' 'MSP' 'ONT' 'BDL' 'PHX' 'LAS'\n",
            " 'DFW' 'DSM' 'CMH' 'ORF' 'SLC' 'CLT' 'GSO' 'IAD' 'SMF' 'FLL' 'DAL' 'ORD'\n",
            " 'ITO' 'SAN' 'ROA' 'LGA' 'SFO' 'GSP' 'SEA' 'DAB' 'SJC' 'LIT' 'LAX' 'OAK'\n",
            " 'COS' 'OKC' 'GRR' 'JFK' 'BOI' 'MCI' 'BWI' 'BHM' 'CRP' 'BOS' 'SAT' 'PHL'\n",
            " 'STL' 'CIC' 'AUS' 'IAH' 'COD' 'HNL' 'RNO' 'BNA' 'TPA' 'MIA' 'EVV' 'PNS'\n",
            " 'EWR' 'RSW' 'ANC' 'SNA' 'AMA' 'CID' 'DTW' 'DCA' 'LGB' 'MAF' 'MFE' 'BMI'\n",
            " 'PDX' 'IPL' 'GRB' 'FAR' 'HOU' 'MTJ' 'DRO' 'MLU' 'VPS' 'TUL' 'CVG' 'SBA'\n",
            " 'PWM' 'IDA' 'MCO' 'ACV' 'CHS' 'BGM' 'MSY' 'OGG' 'CLE' 'MOB' 'CAK' 'FAY'\n",
            " 'SHV' 'TUS' 'IND' 'CAE' 'PVD' 'ROC' 'MFR' 'VLD' 'ELP' 'RIC' 'MKE' 'SGF'\n",
            " 'TYS' 'CHO' 'EGE' 'BIS' 'JAN' 'JAX' 'BUF' 'MSO' 'BGR' 'CEC' 'ICT' 'MYR'\n",
            " 'ALB' 'LIH' 'SBP' 'AEX' 'GNV' 'SAV' 'BTM' 'BRO' 'SJU' 'XNA' 'CPR' 'SDF'\n",
            " 'JAC' 'AVL' 'PHF' 'GPT' 'SYR' 'PSP' 'MHT' 'MRY' 'CLD' 'FAT' 'MSN' 'ISP'\n",
            " 'BUR' 'PSC' 'MEI' 'LEX' 'LBB' 'GEG' 'LFT' 'OMA' 'ISO' 'MGM' 'GRK' 'AVP'\n",
            " 'ABQ' 'SRQ' 'BTV' 'FLG' 'BTR' 'MDT' 'ABI' 'TRI' 'ADQ' 'FSM' 'SMX' 'RST'\n",
            " 'RAP' 'ILM' 'SIT' 'EKO' 'DBQ' 'CHA' 'BQK' 'BZN' 'MOD' 'MOT' 'MLB' 'TVC'\n",
            " 'LAN' 'DAY' 'HSV' 'EUG' 'SGU' 'ACT' 'AGS' 'CLL' 'HLN' 'LNK' 'ASE' 'HRL'\n",
            " 'ATW' 'CMI' 'LWS' 'DHN' 'FNT' 'FLO' 'RDM' 'TYR' 'KOA' 'FAI' 'OME' 'RDD'\n",
            " 'MCN' 'TLH' 'MQT' 'AZO' 'FCA' 'CRW' 'TOL' 'HPN' 'FSD' 'FWA' 'SUN' 'LAW'\n",
            " 'YUM' 'PIA' 'GTF' 'ACY' 'PIH' 'SPS' 'MLI' 'BIL' 'TWF' 'HTS' 'SBN' 'PFN'\n",
            " 'GJT' 'CSG' 'JNU' 'TXK' 'LRD' 'BQN' 'CWA' 'SWF' 'GTR' 'BFL' 'OXR' 'KTN'\n",
            " 'PIE' 'SCE' 'PSG' 'DLH' 'SJT' 'GUC' 'SPI' 'IYK' 'ABY' 'STT' 'ABE' 'GFK'\n",
            " 'HDN' 'CDV' 'MBS' 'TUP' 'LCH' 'EYW' 'OTZ' 'ADK' 'GGG' 'VIS' 'GST' 'LYH'\n",
            " 'HVN' 'BRW' 'LSE' 'ERI' 'HKY' 'BET' 'CDC' 'OAJ' 'WRG' 'ACK' 'DLG' 'YAK'\n",
            " 'AKN' 'TEX' 'STX' 'SCC' 'APF' 'BPT' 'WYS' 'RFD' 'BLI' 'ILG' 'VCT' 'LWB'\n",
            " 'PSE']\n",
            "Test dataset:  ['MRY' 'LAS' 'GSP' 'BWI' 'ORD' 'ORF' 'IAD' 'MSP' 'PIT' 'DFW' 'JFK' 'ASE'\n",
            " 'GRR' 'DEN' 'DAB' 'ATL' 'CMH' 'MDT' 'IAH' 'MIA' 'LAX' 'TYS' 'MCI' 'STL'\n",
            " 'LGA' 'DTW' 'CLT' 'DCA' 'SFO' 'MSN' 'IND' 'ALB' 'HNL' 'PWM' 'MDW' 'PHX'\n",
            " 'BOS' 'GEG' 'ABE' 'DAL' 'EWR' 'LEX' 'OAK' 'SAN' 'SGF' 'OMA' 'MAF' 'CLD'\n",
            " 'SNA' 'BUR' 'PHL' 'PDX' 'BDL' 'ANC' 'JAN' 'OKC' 'MLU' 'RDU' 'ONT' 'SLC'\n",
            " 'TWF' 'FLL' 'ABI' 'CLE' 'CVG' 'AZO' 'SWF' 'SJU' 'COS' 'GSO' 'MEM' 'MCO'\n",
            " 'RSW' 'TPA' 'FAT' 'BUF' 'JAX' 'ITO' 'ERI' 'PIA' 'LIT' 'SBA' 'CID' 'BHM'\n",
            " 'BTM' 'CAE' 'GTF' 'BNA' 'PIH' 'LIH' 'XNA' 'MKE' 'BMI' 'RIC' 'SAT' 'SJC'\n",
            " 'SMF' 'ABQ' 'PVD' 'BOI' 'SYR' 'SGU' 'ISP' 'MGM' 'AGS' 'PSP' 'ICT' 'LFT'\n",
            " 'SEA' 'STT' 'DRO' 'EYW' 'GPT' 'AUS' 'MYR' 'SAV' 'KOA' 'SHV' 'PFN' 'GJT'\n",
            " 'PBI' 'MHT' 'BTR' 'BGR' 'DSM' 'ELP' 'MSY' 'HOU' 'JAC' 'TUL' 'ROA' 'RNO'\n",
            " 'CRW' 'MFR' 'IDA' 'MLI' 'DAY' 'PNS' 'ROC' 'HSV' 'AVL' 'CWA' 'ACT' 'BRO'\n",
            " 'TUS' 'LYH' 'FSD' 'SIT' 'BIS' 'GRK' 'LBB' 'FAR' 'OGG' 'SRQ' 'SDF' 'PHF'\n",
            " 'MTJ' 'GTR' 'CHS' 'VPS' 'HPN' 'ACV' 'RAP' 'HRL' 'LRD' 'LAN' 'EUG' 'CPR'\n",
            " 'CAK' 'BZN' 'IPL' 'CHO' 'BRW' 'AMA' 'EVV' 'FWA' 'MOD' 'SUN' 'SBP' 'BTV'\n",
            " 'MOB' 'CRP' 'HDN' 'SBN' 'BGM' 'CMI' 'LAW' 'TVC' 'CSG' 'FAI' 'CDV' 'DLH'\n",
            " 'YAK' 'AVP' 'BET' 'AEX' 'RST' 'FNT' 'BIL' 'LGB' 'ILM' 'JNU' 'TYR' 'SMX'\n",
            " 'MFE' 'DLG' 'MBS' 'DHN' 'MQT' 'RDM' 'ATW' 'BFL' 'CLL' 'FAY' 'SCE' 'KTN'\n",
            " 'CHA' 'TXK' 'GNV' 'FCA' 'BQK' 'TLH' 'EWN' 'ADQ' 'LNK' 'CIC' 'MLB' 'TRI'\n",
            " 'FLG' 'PSG' 'SPI' 'HLN' 'ILG' 'GRB' 'MCN' 'ADK' 'ELM' 'OAJ' 'TOL' 'GGG'\n",
            " 'CMX' 'FSM' 'ACK' 'LSE' 'GFK' 'PLN' 'SPS' 'YKM' 'LWS' 'YUM' 'RDD' 'DBQ'\n",
            " 'ACY' 'EKO' 'PSE' 'MSO' 'OME' 'ABY' 'SCC' 'IYK' 'EGE' 'SJT' 'HHH' 'VLD'\n",
            " 'INL' 'BQN' 'APF' 'FLO' 'RFD' 'PSC' 'GUC' 'LCH' 'EAU' 'MOT' 'BPT' 'COD'\n",
            " 'SUX' 'MEI' 'OXR' 'SLE' 'TEX' 'ROW' 'WRG' 'BLI' 'TTN' 'CEC' 'MTH' 'STX'\n",
            " 'OTZ' 'PMD' 'SOP' 'HTS' 'GST' 'BJI' 'WYS' 'LWB' 'ALO' 'AKN' 'TUP' 'RHI']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"The unique values of 'DayofMonth' in the: \")\n",
        "print(\"Train dataset: \",train.DayofMonth.unique())\n",
        "print(\"Test dataset: \",test.DayofMonth.unique(),'\\n')\n",
        "print(\"The unique values of 'Month' in the: \")\n",
        "print(\"Train dataset: \",train.Month.unique())\n",
        "print(\"Test dataset: \",test.Month.unique(),'\\n')\n",
        "print(\"The unique values of 'DayOfWeek' in the: \")\n",
        "print(\"Train dataset: \",train.DayOfWeek.unique())\n",
        "print(\"Test dataset: \",test.DayOfWeek.unique(),'\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2XASFuocyf82",
        "outputId": "9e18ca04-d1ff-442c-e39c-6accea295fe8"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The unique values of 'DayofMonth' in the: \n",
            "Train dataset:  ['c-21' 'c-20' 'c-2' 'c-25' 'c-7' 'c-3' 'c-27' 'c-29' 'c-28' 'c-5' 'c-6'\n",
            " 'c-10' 'c-19' 'c-26' 'c-14' 'c-22' 'c-9' 'c-31' 'c-4' 'c-16' 'c-18'\n",
            " 'c-30' 'c-1' 'c-24' 'c-15' 'c-17' 'c-8' 'c-12' 'c-13' 'c-11' 'c-23']\n",
            "Test dataset:  ['c-25' 'c-17' 'c-2' 'c-6' 'c-22' 'c-30' 'c-11' 'c-14' 'c-23' 'c-18'\n",
            " 'c-12' 'c-31' 'c-9' 'c-10' 'c-7' 'c-27' 'c-20' 'c-13' 'c-3' 'c-16' 'c-15'\n",
            " 'c-5' 'c-8' 'c-26' 'c-1' 'c-29' 'c-21' 'c-28' 'c-4' 'c-24' 'c-19'] \n",
            "\n",
            "The unique values of 'Month' in the: \n",
            "Train dataset:  ['c-8' 'c-4' 'c-9' 'c-11' 'c-10' 'c-1' 'c-7' 'c-6' 'c-5' 'c-3' 'c-12'\n",
            " 'c-2']\n",
            "Test dataset:  ['c-7' 'c-4' 'c-12' 'c-3' 'c-6' 'c-2' 'c-10' 'c-1' 'c-9' 'c-8' 'c-11'\n",
            " 'c-5'] \n",
            "\n",
            "The unique values of 'DayOfWeek' in the: \n",
            "Train dataset:  ['c-7' 'c-3' 'c-5' 'c-6' 'c-4' 'c-2' 'c-1']\n",
            "Test dataset:  ['c-3' 'c-2' 'c-7' 'c-4' 'c-1' 'c-5' 'c-6'] \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will be removing the \"c-\" in the 'Month', 'DayofMonth', 'DayOFWeek' columns in both train and test datasets."
      ],
      "metadata": {
        "id": "lq6BYsG04d8D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_C(data):\n",
        "  return data[2:]\n",
        "\n",
        "train['Month'] = train.apply(lambda row : remove_C(row['Month']), axis=1)\n",
        "train['DayofMonth'] = train.apply(lambda row : remove_C(row['DayofMonth']), axis=1)\n",
        "train['DayOfWeek'] = train.apply(lambda row : remove_C(row['DayOfWeek']), axis=1)\n",
        "\n",
        "test['Month'] = test.apply(lambda row : remove_C(row['Month']), axis=1)\n",
        "test['DayofMonth'] = test.apply(lambda row : remove_C(row['DayofMonth']), axis=1)\n",
        "test['DayOfWeek'] = test.apply(lambda row : remove_C(row['DayOfWeek']), axis=1)\n"
      ],
      "metadata": {
        "id": "oiFy96Tn4Bdm"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#We will be replacing the 'dep_delayed_15min' with 0-1;\n",
        "train['dep_delayed_15min'].replace({\"Y\": 1, \"N\": 0}, inplace=True)"
      ],
      "metadata": {
        "id": "SNobqUoK5JJx"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "hb6afXzA5D05",
        "outputId": "d34bc2b0-f4d6-4cd5-94ea-98d2a9b95a13"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Month DayofMonth DayOfWeek  DepTime UniqueCarrier Origin Dest  Distance  \\\n",
              "0     8         21         7     1934            AA    ATL  DFW       732   \n",
              "1     4         20         3     1548            US    PIT  MCO       834   \n",
              "2     9          2         5     1422            XE    RDU  CLE       416   \n",
              "3    11         25         6     1015            OO    DEN  MEM       872   \n",
              "4    10          7         6     1828            WN    MDW  OMA       423   \n",
              "\n",
              "   dep_delayed_15min  \n",
              "0                  0  \n",
              "1                  0  \n",
              "2                  0  \n",
              "3                  0  \n",
              "4                  1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2fbcd2e9-8b06-4686-85bc-ebccbd9398cb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>8</td>\n",
              "      <td>21</td>\n",
              "      <td>7</td>\n",
              "      <td>1934</td>\n",
              "      <td>AA</td>\n",
              "      <td>ATL</td>\n",
              "      <td>DFW</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>US</td>\n",
              "      <td>PIT</td>\n",
              "      <td>MCO</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>XE</td>\n",
              "      <td>RDU</td>\n",
              "      <td>CLE</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>OO</td>\n",
              "      <td>DEN</td>\n",
              "      <td>MEM</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>WN</td>\n",
              "      <td>MDW</td>\n",
              "      <td>OMA</td>\n",
              "      <td>423</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2fbcd2e9-8b06-4686-85bc-ebccbd9398cb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2fbcd2e9-8b06-4686-85bc-ebccbd9398cb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2fbcd2e9-8b06-4686-85bc-ebccbd9398cb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "      </script>\n",
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              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 12
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      ]
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    {
      "cell_type": "code",
      "source": [
        "test.head()"
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      "metadata": {
        "colab": {
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          "height": 206
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        "id": "kiGlSpXY5uFf",
        "outputId": "3df73af9-3558-46ec-97a2-26f64d30d737"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Month DayofMonth DayOfWeek  DepTime UniqueCarrier Origin Dest  Distance\n",
              "0     7         25         3      615            YV    MRY  PHX       598\n",
              "1     4         17         2      739            WN    LAS  HOU      1235\n",
              "2    12          2         7      651            MQ    GSP  ORD       577\n",
              "3     3         25         7     1614            WN    BWI  MHT       377\n",
              "4     6          6         3     1505            UA    ORD  STL       258"
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              "          const element = document.querySelector('#df-efd536e5-152a-4a0a-b755-0aafdd84769a');\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Changing the type of the supposedly numeric columns into int64\n",
        "train.DayofMonth=train.DayofMonth.astype(np.int64)\n",
        "train.Month=train.DayofMonth.astype(np.int64)\n",
        "train.DayOfWeek=train.DayofMonth.astype(np.int64)\n",
        "\n",
        "test.DayofMonth=test.DayofMonth.astype(np.int64)\n",
        "test.Month=test.DayofMonth.astype(np.int64)\n",
        "test.DayOfWeek=test.DayofMonth.astype(np.int64)"
      ],
      "metadata": {
        "id": "VcRQBEEnZetL"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sqIm-I1AZAXN",
        "outputId": "1a62e6e9-eb22-427b-c098-34539a3cd638"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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  object\n",
            " 5   Origin             100000 non-null  object\n",
            " 6   Dest               100000 non-null  object\n",
            " 7   Distance           100000 non-null  int64 \n",
            " 8   dep_delayed_15min  100000 non-null  int64 \n",
            "dtypes: int64(6), object(3)\n",
            "memory usage: 6.9+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x7bgSKb5ce8X",
        "outputId": "eb0c3e0a-6ec6-4ac8-8fa6-269852b61e1f"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 100000 entries, 0 to 99999\n",
            "Data columns (total 8 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  object\n",
            " 5   Origin         100000 non-null  object\n",
            " 6   Dest           100000 non-null  object\n",
            " 7   Distance       100000 non-null  int64 \n",
            "dtypes: int64(5), object(3)\n",
            "memory usage: 6.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## One Hot Encoder: For both train and test data"
      ],
      "metadata": {
        "id": "1rGSoK4_eDC6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Create a list of culumns that u wish to perform encoding on\n",
        "column_list = train.loc[:, (train.dtypes == \"object\")].columns.values.tolist()\n",
        "print(column_list)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v5OcsWcFYSgG",
        "outputId": "39cc5da2-71e5-46bf-c7c3-685379c5bcc5"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['UniqueCarrier', 'Origin', 'Dest']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### One Hot Encoder: Train dataset"
      ],
      "metadata": {
        "id": "x1O-lkiVebZJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "# Apply one-hot encoder to each column with categorical data\n",
        "OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)\n",
        "OH_cols = pd.DataFrame(OH_encoder.fit_transform(train[column_list]),columns= (OH_encoder.get_feature_names_out() ))\n",
        "\n",
        "# One-hot encoding removed index; put it back\n",
        "OH_cols.index = train.index\n",
        "\n",
        "# Remove categorical columns (will replace with one-hot encoding)\n",
        "train = train.drop(column_list, axis=1)\n",
        "\n",
        "# Add one-hot encoded columns to numerical features\n",
        "train = pd.concat([train, OH_cols], axis=1)"
      ],
      "metadata": {
        "id": "kMTf2bOOYD2U"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "VbmJJn7i7E_W",
        "outputId": "840a95ff-06e9-4b55-cf53-26db4a4f63bc"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Month  DayofMonth  DayOfWeek  DepTime  Distance  dep_delayed_15min  \\\n",
              "0     21          21         21     1934       732                  0   \n",
              "1     20          20         20     1548       834                  0   \n",
              "2      2           2          2     1422       416                  0   \n",
              "3     25          25         25     1015       872                  0   \n",
              "4      7           7          7     1828       423                  1   \n",
              "\n",
              "   UniqueCarrier_AA  UniqueCarrier_AQ  UniqueCarrier_AS  UniqueCarrier_B6  \\\n",
              "0               1.0               0.0               0.0               0.0   \n",
              "1               0.0               0.0               0.0               0.0   \n",
              "2               0.0               0.0               0.0               0.0   \n",
              "3               0.0               0.0               0.0               0.0   \n",
              "4               0.0               0.0               0.0               0.0   \n",
              "\n",
              "   ...  Dest_TYS  Dest_VCT  Dest_VIS  Dest_VLD  Dest_VPS  Dest_WRG  Dest_WYS  \\\n",
              "0  ...       0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
              "1  ...       0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
              "2  ...       0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
              "3  ...       0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
              "4  ...       0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
              "\n",
              "   Dest_XNA  Dest_YAK  Dest_YUM  \n",
              "0       0.0       0.0       0.0  \n",
              "1       0.0       0.0       0.0  \n",
              "2       0.0       0.0       0.0  \n",
              "3       0.0       0.0       0.0  \n",
              "4       0.0       0.0       0.0  \n",
              "\n",
              "[5 rows x 606 columns]"
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>1828</td>\n",
              "      <td>423</td>\n",
              "      <td>1</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 606 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f2473e18-a54c-484b-99a9-206423e6e90d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f2473e18-a54c-484b-99a9-206423e6e90d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f2473e18-a54c-484b-99a9-206423e6e90d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### One Hot Encoder: Test dataset"
      ],
      "metadata": {
        "id": "EFEf8x_XetkS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "# Apply one-hot encoder to each column with categorical data\n",
        "OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)\n",
        "OH_cols = pd.DataFrame(OH_encoder.fit_transform(test[column_list]),columns= (OH_encoder.get_feature_names_out() ))\n",
        "\n",
        "# One-hot encoding removed index; put it back\n",
        "OH_cols.index = test.index\n",
        "\n",
        "# Remove categorical columns (will replace with one-hot encoding)\n",
        "test = test.drop(column_list, axis=1)\n",
        "\n",
        "# Add one-hot encoded columns to numerical features\n",
        "test = pd.concat([test, OH_cols], axis=1)"
      ],
      "metadata": {
        "id": "xwOAHgQzeX1P"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "lwJxZ6SN7HQR",
        "outputId": "709ac399-85dc-4e68-8ce1-dd1659a6c36b"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Month  DayofMonth  DayOfWeek  DepTime  Distance  UniqueCarrier_9E  \\\n",
              "0     25          25         25      615       598               0.0   \n",
              "1     17          17         17      739      1235               0.0   \n",
              "2      2           2          2      651       577               0.0   \n",
              "3     25          25         25     1614       377               0.0   \n",
              "4      6           6          6     1505       258               0.0   \n",
              "\n",
              "   UniqueCarrier_AA  UniqueCarrier_AQ  UniqueCarrier_AS  UniqueCarrier_B6  \\\n",
              "0               0.0               0.0               0.0               0.0   \n",
              "1               0.0               0.0               0.0               0.0   \n",
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              "3               0.0               0.0               0.0               0.0   \n",
              "4               0.0               0.0               0.0               0.0   \n",
              "\n",
              "   ...  Dest_TYR  Dest_TYS  Dest_VLD  Dest_VPS  Dest_WRG  Dest_WYS  Dest_XNA  \\\n",
              "0  ...       0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
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              "4  ...       0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
              "\n",
              "   Dest_YAK  Dest_YKM  Dest_YUM  \n",
              "0       0.0       0.0       0.0  \n",
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              "\n",
              "[5 rows x 623 columns]"
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              "      <div>\n",
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              "<p>5 rows × 623 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dee41ce5-ca6a-4aec-a51c-a548782a6cb2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dee41ce5-ca6a-4aec-a51c-a548782a6cb2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dee41ce5-ca6a-4aec-a51c-a548782a6cb2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Applying Machine learning models and testing"
      ],
      "metadata": {
        "id": "aw7PG3NYfe84"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Splitting the dataset int test and train subsets\n",
        "from sklearn.model_selection import train_test_split\n",
        "X, y= train.drop(['dep_delayed_15min'], axis=1) ,train.dep_delayed_15min\n",
        "# Divide data into training and validation subsets\n",
        "x_train, x_test, y_train, y_test = train_test_split(X, y, train_size=0.20,random_state=0)"
      ],
      "metadata": {
        "id": "QQLSuvgffIUT"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a={'Model Name':[],'True Positive':[],'False Negative':[],'False Positive':[],'True Negative':[],'Accuracy':[],'Precision':[],'Recall':[],'F1 Score':[],'Specificity':[],'MCC':[],'Balanced Accuracy':[]}\n",
        "result=pd.DataFrame(a)\n",
        "result"
      ],
      "metadata": {
        "id": "56C5d4JkgGWU",
        "outputId": "a200cf41-2ccb-4411-caca-255c4618ee32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        }
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
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    {
      "cell_type": "code",
      "source": [
        "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.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import BaggingClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "import lightgbm as lgb\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "ModelLR = LogisticRegression(penalty='l2', max_iter=5000)\n",
        "ModelDC = DecisionTreeClassifier()\n",
        "ModelRF = RandomForestClassifier()\n",
        "ModelET = ExtraTreesClassifier()\n",
        "ModelKNN = KNeighborsClassifier(n_neighbors=5)\n",
        "ModelSVM = SVC(probability=True)\n",
        "modelBAG = BaggingClassifier(base_estimator=None, n_estimators=100, max_samples=1.0, max_features=1.0,bootstrap=True, bootstrap_features=False, oob_score=False, warm_start=False,n_jobs=None, random_state=None, verbose=0)\n",
        "ModelGB = GradientBoostingClassifier(loss='deviance', learning_rate=0.1,n_estimators=100, subsample=1.0,criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1,min_weight_fraction_leaf=0.0, max_depth=3, min_impurity_decrease=0.0, init=None,random_state=None,max_features=None, verbose=0, max_leaf_nodes=None, warm_start=False,validation_fraction=0.1, n_iter_no_change=None, tol=0.0001, ccp_alpha=0.0)\n",
        "ModelLGB = lgb.LGBMClassifier()\n",
        "ModelGNB = GaussianNB()\n",
        "\n",
        " \n",
        "MM = [ModelLR, ModelDC, ModelRF, ModelET,ModelKNN, ModelSVM, modelBAG,ModelGB, ModelLGB, ModelGNB]\n",
        "for models in MM:\n",
        "    \n",
        "    # Fit the model\n",
        "    \n",
        "    models.fit(x_train, y_train)\n",
        "    \n",
        "    # Prediction\n",
        "    \n",
        "    y_pred = models.predict(x_test)\n",
        "    y_pred_prob = models.predict_proba(x_test)\n",
        "    \n",
        "    # Print the model name\n",
        "    \n",
        "    print('Model Name: ', models)\n",
        "    \n",
        "    # confusion matrix in sklearn\n",
        "\n",
        "    from sklearn.metrics import confusion_matrix\n",
        "    from sklearn.metrics import classification_report\n",
        "\n",
        "    # actual values\n",
        "\n",
        "    actual = y_test\n",
        "\n",
        "    # predicted values\n",
        "\n",
        "    predicted = y_pred\n",
        "\n",
        "    # confusion matrix\n",
        "\n",
        "    matrix = confusion_matrix(actual,predicted, labels=[1,0],sample_weight=None, normalize=None)\n",
        "    print('Confusion matrix : \\n', matrix)\n",
        "\n",
        "    # outcome values order in sklearn\n",
        "\n",
        "    tp, fn, fp, tn = confusion_matrix(actual,predicted,labels=[1,0]).reshape(-1)\n",
        "    print('Outcome values : \\n', tp, fn, fp, tn)\n",
        "\n",
        "    # classification report for precision, recall f1-score and accuracy\n",
        "\n",
        "    C_Report = classification_report(actual,predicted,labels=[1,0])\n",
        "\n",
        "    print('Classification report : \\n', C_Report)\n",
        "\n",
        "    # calculating the metrics\n",
        "\n",
        "    sensitivity = round(tp/(tp+fn), 3);\n",
        "    specificity = round(tn/(tn+fp), 3);\n",
        "    accuracy = round((tp+tn)/(tp+fp+tn+fn), 3);\n",
        "    balanced_accuracy = round((sensitivity+specificity)/2, 3);\n",
        "    \n",
        "    precision = round(tp/(tp+fp), 3);\n",
        "    f1Score = round((2*tp/(2*tp + fp + fn)), 3);\n",
        "\n",
        "    # Matthews Correlation Coefficient (MCC). Range of values of MCC lie between -1 to +1. \n",
        "    # A model with a score of +1 is a perfect model and -1 is a poor model\n",
        "    import math\n",
        "    from math import sqrt\n",
        "\n",
        "    mx = (tp+fp) * (tp+fn) * (tn+fp) * (tn+fn)\n",
        "    MCC = round(((tp * tn) - (fp * fn)) / sqrt(mx), 3)\n",
        "\n",
        "    print('Accuracy :', round(accuracy*100, 2),'%')\n",
        "    print('Precision :', round(precision*100, 2),'%')\n",
        "    print('Recall :', round(sensitivity*100,2), '%')\n",
        "    print('F1 Score :', f1Score)\n",
        "    print('Specificity or True Negative Rate :', round(specificity*100,2), '%'  )\n",
        "    print('Balanced Accuracy :', round(balanced_accuracy*100, 2),'%')\n",
        "    print('MCC :', 'MCC')\n",
        "\n",
        "\n",
        "    print('-----------------------------------------------------------------------------------------------------')\n",
        "    #----------------------------------------------------------------------------------------------------------\n",
        "    new_row = {'Model Name' : models,\n",
        "               'True Positive' : tp, \n",
        "               'False Negative' : fn, \n",
        "               'False Positive' : fp,\n",
        "               'True Negative' : tn,\n",
        "               'Accuracy' : accuracy,\n",
        "               'Precision' : precision,\n",
        "               'Recall' : sensitivity,\n",
        "               'F1 Score' : f1Score,\n",
        "               'Specificity' : specificity,\n",
        "               'MCC':MCC,\n",
        "               'Balanced Accuracy':balanced_accuracy}\n",
        "    result = result.append(new_row, ignore_index=True)"
      ],
      "metadata": {
        "id": "pKxoB9Maf9U2",
        "outputId": "2c9222bb-e8c9-4f22-ea8b-fc3e4c9d0b57",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Name:  LogisticRegression(max_iter=5000)\n",
            "Confusion matrix : \n",
            " [[  598 14622]\n",
            " [  560 64220]]\n",
            "Outcome values : \n",
            " 598 14622 560 64220\n",
            "Classification report : \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           1       0.52      0.04      0.07     15220\n",
            "           0       0.81      0.99      0.89     64780\n",
            "\n",
            "    accuracy                           0.81     80000\n",
            "   macro avg       0.67      0.52      0.48     80000\n",
            "weighted avg       0.76      0.81      0.74     80000\n",
            "\n",
            "Accuracy : 81.0 %\n",
            "Precision : 51.6 %\n",
            "Recall : 3.9 %\n",
            "F1 Score : 0.073\n",
            "Specificity or True Negative Rate : 99.1 %\n",
            "Balanced Accuracy : 51.5 %\n",
            "MCC : MCC\n",
            "-----------------------------------------------------------------------------------------------------\n",
            "Model Name:  DecisionTreeClassifier()\n",
            "Confusion matrix : \n",
            " [[ 4363 10857]\n",
            " [ 9682 55098]]\n",
            "Outcome values : \n",
            " 4363 10857 9682 55098\n",
            "Classification report : \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           1       0.31      0.29      0.30     15220\n",
            "           0       0.84      0.85      0.84     64780\n",
            "\n",
            "    accuracy                           0.74     80000\n",
            "   macro avg       0.57      0.57      0.57     80000\n",
            "weighted avg       0.74      0.74      0.74     80000\n",
            "\n",
            "Accuracy : 74.3 %\n",
            "Precision : 31.1 %\n",
            "Recall : 28.7 %\n",
            "F1 Score : 0.298\n",
            "Specificity or True Negative Rate : 85.1 %\n",
            "Balanced Accuracy : 56.9 %\n",
            "MCC : MCC\n",
            "-----------------------------------------------------------------------------------------------------\n",
            "Model Name:  RandomForestClassifier()\n",
            "Confusion matrix : \n",
            " [[ 1279 13941]\n",
            " [ 1752 63028]]\n",
            "Outcome values : \n",
            " 1279 13941 1752 63028\n",
            "Classification report : \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           1       0.42      0.08      0.14     15220\n",
            "           0       0.82      0.97      0.89     64780\n",
            "\n",
            "    accuracy                           0.80     80000\n",
            "   macro avg       0.62      0.53      0.51     80000\n",
            "weighted avg       0.74      0.80      0.75     80000\n",
            "\n",
            "Accuracy : 80.4 %\n",
            "Precision : 42.2 %\n",
            "Recall : 8.4 %\n",
            "F1 Score : 0.14\n",
            "Specificity or True Negative Rate : 97.3 %\n",
            "Balanced Accuracy : 52.8 %\n",
            "MCC : MCC\n",
            "-----------------------------------------------------------------------------------------------------\n",
            "Model Name:  ExtraTreesClassifier()\n",
            "Confusion matrix : \n",
            " [[ 2041 13179]\n",
            " [ 4217 60563]]\n",
            "Outcome values : \n",
            " 2041 13179 4217 60563\n",
            "Classification report : \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           1       0.33      0.13      0.19     15220\n",
            "           0       0.82      0.93      0.87     64780\n",
            "\n",
            "    accuracy                           0.78     80000\n",
            "   macro avg       0.57      0.53      0.53     80000\n",
            "weighted avg       0.73      0.78      0.74     80000\n",
            "\n",
            "Accuracy : 78.3 %\n",
            "Precision : 32.6 %\n",
            "Recall : 13.4 %\n",
            "F1 Score : 0.19\n",
            "Specificity or True Negative Rate : 93.5 %\n",
            "Balanced Accuracy : 53.4 %\n",
            "MCC : MCC\n",
            "-----------------------------------------------------------------------------------------------------\n",
            "Model Name:  KNeighborsClassifier()\n",
            "Confusion matrix : \n",
            " [[ 2329 12891]\n",
            " [ 4235 60545]]\n",
            "Outcome values : \n",
            " 2329 12891 4235 60545\n",
            "Classification report : \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           1       0.35      0.15      0.21     15220\n",
            "           0       0.82      0.93      0.88     64780\n",
            "\n",
            "    accuracy                           0.79     80000\n",
            "   macro avg       0.59      0.54      0.54     80000\n",
            "weighted avg       0.74      0.79      0.75     80000\n",
            "\n",
            "Accuracy : 78.6 %\n",
            "Precision : 35.5 %\n",
            "Recall : 15.3 %\n",
            "F1 Score : 0.214\n",
            "Specificity or True Negative Rate : 93.5 %\n",
            "Balanced Accuracy : 54.4 %\n",
            "MCC : MCC\n",
            "-----------------------------------------------------------------------------------------------------\n",
            "Model Name:  SVC(probability=True)\n",
            "Confusion matrix : \n",
            " [[    0 15220]\n",
            " [    0 64780]]\n",
            "Outcome values : \n",
            " 0 15220 0 64780\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "<ipython-input-32-434a4d205f63>:76: RuntimeWarning: invalid value encountered in long_scalars\n",
            "  precision = round(tp/(tp+fp), 3);\n",
            "<ipython-input-32-434a4d205f63>:85: RuntimeWarning: invalid value encountered in true_divide\n",
            "  MCC = round(((tp * tn) - (fp * fn)) / sqrt(mx), 3)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification report : \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           1       0.00      0.00      0.00     15220\n",
            "           0       0.81      1.00      0.89     64780\n",
            "\n",
            "    accuracy                           0.81     80000\n",
            "   macro avg       0.40      0.50      0.45     80000\n",
            "weighted avg       0.66      0.81      0.72     80000\n",
            "\n",
            "Accuracy : 81.0 %\n",
            "Precision : nan %\n",
            "Recall : 0.0 %\n",
            "F1 Score : 0.0\n",
            "Specificity or True Negative Rate : 100.0 %\n",
            "Balanced Accuracy : 50.0 %\n",
            "MCC : MCC\n",
            "-----------------------------------------------------------------------------------------------------\n",
            "Model Name:  BaggingClassifier(n_estimators=100)\n",
            "Confusion matrix : \n",
            " [[ 2317 12903]\n",
            " [ 2667 62113]]\n",
            "Outcome values : \n",
            " 2317 12903 2667 62113\n",
            "Classification report : \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           1       0.46      0.15      0.23     15220\n",
            "           0       0.83      0.96      0.89     64780\n",
            "\n",
            "    accuracy                           0.81     80000\n",
            "   macro avg       0.65      0.56      0.56     80000\n",
            "weighted avg       0.76      0.81      0.76     80000\n",
            "\n",
            "Accuracy : 80.5 %\n",
            "Precision : 46.5 %\n",
            "Recall : 15.2 %\n",
            "F1 Score : 0.229\n",
            "Specificity or True Negative Rate : 95.9 %\n",
            "Balanced Accuracy : 55.6 %\n",
            "MCC : MCC\n",
            "-----------------------------------------------------------------------------------------------------\n",
            "Model Name:  GradientBoostingClassifier()\n",
            "Confusion matrix : \n",
            " [[  834 14386]\n",
            " [  376 64404]]\n",
            "Outcome values : \n",
            " 834 14386 376 64404\n",
            "Classification report : \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           1       0.69      0.05      0.10     15220\n",
            "           0       0.82      0.99      0.90     64780\n",
            "\n",
            "    accuracy                           0.82     80000\n",
            "   macro avg       0.75      0.52      0.50     80000\n",
            "weighted avg       0.79      0.82      0.75     80000\n",
            "\n",
            "Accuracy : 81.5 %\n",
            "Precision : 68.9 %\n",
            "Recall : 5.5 %\n",
            "F1 Score : 0.102\n",
            "Specificity or True Negative Rate : 99.4 %\n",
            "Balanced Accuracy : 52.4 %\n",
            "MCC : MCC\n",
            "-----------------------------------------------------------------------------------------------------\n",
            "Model Name:  LGBMClassifier()\n",
            "Confusion matrix : \n",
            " [[ 1541 13679]\n",
            " [  991 63789]]\n",
            "Outcome values : \n",
            " 1541 13679 991 63789\n",
            "Classification report : \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           1       0.61      0.10      0.17     15220\n",
            "           0       0.82      0.98      0.90     64780\n",
            "\n",
            "    accuracy                           0.82     80000\n",
            "   macro avg       0.72      0.54      0.54     80000\n",
            "weighted avg       0.78      0.82      0.76     80000\n",
            "\n",
            "Accuracy : 81.7 %\n",
            "Precision : 60.9 %\n",
            "Recall : 10.1 %\n",
            "F1 Score : 0.174\n",
            "Specificity or True Negative Rate : 98.5 %\n",
            "Balanced Accuracy : 54.3 %\n",
            "MCC : MCC\n",
            "-----------------------------------------------------------------------------------------------------\n",
            "Model Name:  GaussianNB()\n",
            "Confusion matrix : \n",
            " [[12262  2958]\n",
            " [48720 16060]]\n",
            "Outcome values : \n",
            " 12262 2958 48720 16060\n",
            "Classification report : \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           1       0.20      0.81      0.32     15220\n",
            "           0       0.84      0.25      0.38     64780\n",
            "\n",
            "    accuracy                           0.35     80000\n",
            "   macro avg       0.52      0.53      0.35     80000\n",
            "weighted avg       0.72      0.35      0.37     80000\n",
            "\n",
            "Accuracy : 35.4 %\n",
            "Precision : 20.1 %\n",
            "Recall : 80.6 %\n",
            "F1 Score : 0.322\n",
            "Specificity or True Negative Rate : 24.8 %\n",
            "Balanced Accuracy : 52.7 %\n",
            "MCC : MCC\n",
            "-----------------------------------------------------------------------------------------------------\n"
          ]
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            "text/plain": [
              "                                          Model Name  True Positive  \\\n",
              "0                  LogisticRegression(max_iter=5000)          598.0   \n",
              "1                           DecisionTreeClassifier()         4363.0   \n",
              "2  (DecisionTreeClassifier(max_features='auto', r...         1279.0   \n",
              "3  (ExtraTreeClassifier(random_state=167135386), ...         2041.0   \n",
              "4                             KNeighborsClassifier()         2329.0   \n",
              "5                              SVC(probability=True)            0.0   \n",
              "6  (DecisionTreeClassifier(random_state=124551168...         2317.0   \n",
              "7  ([DecisionTreeRegressor(criterion='friedman_ms...          834.0   \n",
              "8                                   LGBMClassifier()         1541.0   \n",
              "9                                       GaussianNB()        12262.0   \n",
              "\n",
              "   False Negative  False Positive  True Negative  Accuracy  Precision  Recall  \\\n",
              "0         14622.0           560.0        64220.0     0.810      0.516   0.039   \n",
              "1         10857.0          9682.0        55098.0     0.743      0.311   0.287   \n",
              "2         13941.0          1752.0        63028.0     0.804      0.422   0.084   \n",
              "3         13179.0          4217.0        60563.0     0.783      0.326   0.134   \n",
              "4         12891.0          4235.0        60545.0     0.786      0.355   0.153   \n",
              "5         15220.0             0.0        64780.0     0.810        NaN   0.000   \n",
              "6         12903.0          2667.0        62113.0     0.805      0.465   0.152   \n",
              "7         14386.0           376.0        64404.0     0.815      0.689   0.055   \n",
              "8         13679.0           991.0        63789.0     0.817      0.609   0.101   \n",
              "9          2958.0         48720.0        16060.0     0.354      0.201   0.806   \n",
              "\n",
              "   F1 Score  Specificity    MCC  Balanced Accuracy  \n",
              "0     0.073        0.991  0.101              0.515  \n",
              "1     0.298        0.851  0.142              0.569  \n",
              "2     0.140        0.973  0.117              0.528  \n",
              "3     0.190        0.935  0.101              0.534  \n",
              "4     0.214        0.935  0.125              0.544  \n",
              "5     0.000        1.000    NaN              0.500  \n",
              "6     0.229        0.959  0.180              0.556  \n",
              "7     0.102        0.994  0.158              0.524  \n",
              "8     0.174        0.985  0.193              0.543  \n",
              "9     0.322        0.248  0.049              0.527  "
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              "      <th>Model Name</th>\n",
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              "      <th>0</th>\n",
              "      <td>LogisticRegression(max_iter=5000)</td>\n",
              "      <td>598.0</td>\n",
              "      <td>14622.0</td>\n",
              "      <td>560.0</td>\n",
              "      <td>64220.0</td>\n",
              "      <td>0.810</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>DecisionTreeClassifier()</td>\n",
              "      <td>4363.0</td>\n",
              "      <td>10857.0</td>\n",
              "      <td>9682.0</td>\n",
              "      <td>55098.0</td>\n",
              "      <td>0.743</td>\n",
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              "      <td>0.287</td>\n",
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              "      <td>0.851</td>\n",
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              "      <th>2</th>\n",
              "      <td>(DecisionTreeClassifier(max_features='auto', r...</td>\n",
              "      <td>1279.0</td>\n",
              "      <td>13941.0</td>\n",
              "      <td>1752.0</td>\n",
              "      <td>63028.0</td>\n",
              "      <td>0.804</td>\n",
              "      <td>0.422</td>\n",
              "      <td>0.084</td>\n",
              "      <td>0.140</td>\n",
              "      <td>0.973</td>\n",
              "      <td>0.117</td>\n",
              "      <td>0.528</td>\n",
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              "      <th>3</th>\n",
              "      <td>(ExtraTreeClassifier(random_state=167135386), ...</td>\n",
              "      <td>2041.0</td>\n",
              "      <td>13179.0</td>\n",
              "      <td>4217.0</td>\n",
              "      <td>60563.0</td>\n",
              "      <td>0.783</td>\n",
              "      <td>0.326</td>\n",
              "      <td>0.134</td>\n",
              "      <td>0.190</td>\n",
              "      <td>0.935</td>\n",
              "      <td>0.101</td>\n",
              "      <td>0.534</td>\n",
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              "      <th>4</th>\n",
              "      <td>KNeighborsClassifier()</td>\n",
              "      <td>2329.0</td>\n",
              "      <td>12891.0</td>\n",
              "      <td>4235.0</td>\n",
              "      <td>60545.0</td>\n",
              "      <td>0.786</td>\n",
              "      <td>0.355</td>\n",
              "      <td>0.153</td>\n",
              "      <td>0.214</td>\n",
              "      <td>0.935</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.544</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>SVC(probability=True)</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
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              "      <td>0.500</td>\n",
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              "      <th>6</th>\n",
              "      <td>(DecisionTreeClassifier(random_state=124551168...</td>\n",
              "      <td>2317.0</td>\n",
              "      <td>12903.0</td>\n",
              "      <td>2667.0</td>\n",
              "      <td>62113.0</td>\n",
              "      <td>0.805</td>\n",
              "      <td>0.465</td>\n",
              "      <td>0.152</td>\n",
              "      <td>0.229</td>\n",
              "      <td>0.959</td>\n",
              "      <td>0.180</td>\n",
              "      <td>0.556</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>([DecisionTreeRegressor(criterion='friedman_ms...</td>\n",
              "      <td>834.0</td>\n",
              "      <td>14386.0</td>\n",
              "      <td>376.0</td>\n",
              "      <td>64404.0</td>\n",
              "      <td>0.815</td>\n",
              "      <td>0.689</td>\n",
              "      <td>0.055</td>\n",
              "      <td>0.102</td>\n",
              "      <td>0.994</td>\n",
              "      <td>0.158</td>\n",
              "      <td>0.524</td>\n",
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              "      <th>8</th>\n",
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    {
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      "source": [
        "models={'models':['LogisticRegression','DecisionTreeClassifier','RandomForestClassifier','ExtraTreesClassifier','KNeighborsClassifier','SVC','BaggingClassifier','GradientBoostingClassifier','LGBMClassifier','GaussianNB']}\n",
        "out=pd.DataFrame(models)\n",
        "out['Accuracy']=result['Accuracy']\n",
        "out['Precision']=result['Precision']\n",
        "out['Recall']=result['Recall']\n",
        "out['F1 Score']=result['F1 Score']\n",
        "out['True Positive']=result['True Positive']\n",
        "out['False Negative']=result['False Negative']\n",
        "out['False Positive']=result['False Positive']\n",
        "out['True Negative']=result['True Negative']\n",
        "out.sort_values(by=['Accuracy','F1 Score','Recall','Precision'],ascending=False,inplace=True)\n",
        "out.reset_index(drop=True,inplace=True)\n",
        "out"
      ],
      "metadata": {
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        "colab": {
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        }
      },
      "execution_count": 34,
      "outputs": [
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          "data": {
            "text/plain": [
              "                       models  Accuracy  Precision  Recall  F1 Score  \\\n",
              "0              LGBMClassifier     0.817      0.609   0.101     0.174   \n",
              "1  GradientBoostingClassifier     0.815      0.689   0.055     0.102   \n",
              "2          LogisticRegression     0.810      0.516   0.039     0.073   \n",
              "3                         SVC     0.810        NaN   0.000     0.000   \n",
              "4           BaggingClassifier     0.805      0.465   0.152     0.229   \n",
              "5      RandomForestClassifier     0.804      0.422   0.084     0.140   \n",
              "6        KNeighborsClassifier     0.786      0.355   0.153     0.214   \n",
              "7        ExtraTreesClassifier     0.783      0.326   0.134     0.190   \n",
              "8      DecisionTreeClassifier     0.743      0.311   0.287     0.298   \n",
              "9                  GaussianNB     0.354      0.201   0.806     0.322   \n",
              "\n",
              "   True Positive  False Negative  False Positive  True Negative  \n",
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              "6         2329.0         12891.0          4235.0        60545.0  \n",
              "7         2041.0         13179.0          4217.0        60563.0  \n",
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              "      <th>1</th>\n",
              "      <td>GradientBoostingClassifier</td>\n",
              "      <td>0.815</td>\n",
              "      <td>0.689</td>\n",
              "      <td>0.055</td>\n",
              "      <td>0.102</td>\n",
              "      <td>834.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LogisticRegression</td>\n",
              "      <td>0.810</td>\n",
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              "      <th>3</th>\n",
              "      <td>SVC</td>\n",
              "      <td>0.810</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15220.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>64780.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BaggingClassifier</td>\n",
              "      <td>0.805</td>\n",
              "      <td>0.465</td>\n",
              "      <td>0.152</td>\n",
              "      <td>0.229</td>\n",
              "      <td>2317.0</td>\n",
              "      <td>12903.0</td>\n",
              "      <td>2667.0</td>\n",
              "      <td>62113.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>RandomForestClassifier</td>\n",
              "      <td>0.804</td>\n",
              "      <td>0.422</td>\n",
              "      <td>0.084</td>\n",
              "      <td>0.140</td>\n",
              "      <td>1279.0</td>\n",
              "      <td>13941.0</td>\n",
              "      <td>1752.0</td>\n",
              "      <td>63028.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>KNeighborsClassifier</td>\n",
              "      <td>0.786</td>\n",
              "      <td>0.355</td>\n",
              "      <td>0.153</td>\n",
              "      <td>0.214</td>\n",
              "      <td>2329.0</td>\n",
              "      <td>12891.0</td>\n",
              "      <td>4235.0</td>\n",
              "      <td>60545.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>ExtraTreesClassifier</td>\n",
              "      <td>0.783</td>\n",
              "      <td>0.326</td>\n",
              "      <td>0.134</td>\n",
              "      <td>0.190</td>\n",
              "      <td>2041.0</td>\n",
              "      <td>13179.0</td>\n",
              "      <td>4217.0</td>\n",
              "      <td>60563.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>DecisionTreeClassifier</td>\n",
              "      <td>0.743</td>\n",
              "      <td>0.311</td>\n",
              "      <td>0.287</td>\n",
              "      <td>0.298</td>\n",
              "      <td>4363.0</td>\n",
              "      <td>10857.0</td>\n",
              "      <td>9682.0</td>\n",
              "      <td>55098.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>GaussianNB</td>\n",
              "      <td>0.354</td>\n",
              "      <td>0.201</td>\n",
              "      <td>0.806</td>\n",
              "      <td>0.322</td>\n",
              "      <td>12262.0</td>\n",
              "      <td>2958.0</td>\n",
              "      <td>48720.0</td>\n",
              "      <td>16060.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e965e6fe-acd9-4e00-8047-fcda55088aec')\"\n",
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              "\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-e965e6fe-acd9-4e00-8047-fcda55088aec button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e965e6fe-acd9-4e00-8047-fcda55088aec');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=ModelGB.predict(x_test)\n",
        "out=pd.DataFrame({'actual':y_test,'predicted':y_pred})\n",
        "out=test.merge(out,left_index=True,right_index=True)\n",
        "out['actual'].replace(1,'Delay',inplace=True)\n",
        "out['actual'].replace(0,'No Delay',inplace=True)\n",
        "out['predicted'].replace(1,'Delay',inplace=True)\n",
        "out['predicted'].replace(0,'No Delay',inplace=True)\n",
        "out[['actual','predicted']].sample(10)"
      ],
      "metadata": {
        "id": "JyYPi2ZYqyy7",
        "outputId": "feed7111-61dd-43e1-f9ae-80d674ef760f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         actual predicted\n",
              "71179  No Delay  No Delay\n",
              "35161  No Delay  No Delay\n",
              "16587  No Delay     Delay\n",
              "45583  No Delay  No Delay\n",
              "94783  No Delay  No Delay\n",
              "56864  No Delay  No Delay\n",
              "3175   No Delay  No Delay\n",
              "69186  No Delay  No Delay\n",
              "82060     Delay  No Delay\n",
              "98512     Delay  No Delay"
            ],
            "text/html": [
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>actual</th>\n",
              "      <th>predicted</th>\n",
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              "      <th>71179</th>\n",
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              "      <th>16587</th>\n",
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              "      <td>Delay</td>\n",
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              "      <th>45583</th>\n",
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              "      <td>No Delay</td>\n",
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              "      <th>94783</th>\n",
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              "      <td>No Delay</td>\n",
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              "      <th>56864</th>\n",
              "      <td>No Delay</td>\n",
              "      <td>No Delay</td>\n",
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              "      <th>3175</th>\n",
              "      <td>No Delay</td>\n",
              "      <td>No Delay</td>\n",
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              "      <th>69186</th>\n",
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              "      <td>No Delay</td>\n",
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              "      <th>82060</th>\n",
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              "      <th>98512</th>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-97f2f9ec-5239-4539-81a3-343f679be678 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-97f2f9ec-5239-4539-81a3-343f679be678');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 38
        }
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    }
  ]
}