Frequentset_Mining_US_Accidents.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "#Extracting the data"
      ],
      "metadata": {
        "id": "eaCuihSGa81Z"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        },
        "id": "wZ-cz1-erEyS",
        "outputId": "076f625a-eaf9-4c0e-86dd-b367aea92468"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from datetime import datetime "
      ],
      "metadata": {
        "id": "0adTfB9zrec7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df= pd.read_csv(\"/content/drive/MyDrive/US_Accidents_Dec21_updated.csv\")\n",
        "df.head(5)"
      ],
      "metadata": {
        "id": "i99m1dKWrf3i",
        "colab": {
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          "height": 508
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        "outputId": "f364ac9e-c001-4c53-c40f-edfccd016033"
      },
      "execution_count": null,
      "outputs": [
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              "    ID  Severity           Start_Time             End_Time  Start_Lat  \\\n",
              "0  A-1         3  2016-02-08 00:37:08  2016-02-08 06:37:08  40.108910   \n",
              "1  A-2         2  2016-02-08 05:56:20  2016-02-08 11:56:20  39.865420   \n",
              "2  A-3         2  2016-02-08 06:15:39  2016-02-08 12:15:39  39.102660   \n",
              "3  A-4         2  2016-02-08 06:51:45  2016-02-08 12:51:45  41.062130   \n",
              "4  A-5         3  2016-02-08 07:53:43  2016-02-08 13:53:43  39.172393   \n",
              "\n",
              "   Start_Lng    End_Lat    End_Lng  Distance(mi)  \\\n",
              "0 -83.092860  40.112060 -83.031870         3.230   \n",
              "1 -84.062800  39.865010 -84.048730         0.747   \n",
              "2 -84.524680  39.102090 -84.523960         0.055   \n",
              "3 -81.537840  41.062170 -81.535470         0.123   \n",
              "4 -84.492792  39.170476 -84.501798         0.500   \n",
              "\n",
              "                                         Description  ...  Roundabout Station  \\\n",
              "0  Between Sawmill Rd/Exit 20 and OH-315/Olentang...  ...       False   False   \n",
              "1                 At OH-4/OH-235/Exit 41 - Accident.  ...       False   False   \n",
              "2                   At I-71/US-50/Exit 1 - Accident.  ...       False   False   \n",
              "3                    At Dart Ave/Exit 21 - Accident.  ...       False   False   \n",
              "4                 At Mitchell Ave/Exit 6 - Accident.  ...       False   False   \n",
              "\n",
              "    Stop Traffic_Calming Traffic_Signal Turning_Loop Sunrise_Sunset  \\\n",
              "0  False           False          False        False          Night   \n",
              "1  False           False          False        False          Night   \n",
              "2  False           False          False        False          Night   \n",
              "3  False           False          False        False          Night   \n",
              "4  False           False          False        False            Day   \n",
              "\n",
              "  Civil_Twilight Nautical_Twilight Astronomical_Twilight  \n",
              "0          Night             Night                 Night  \n",
              "1          Night             Night                 Night  \n",
              "2          Night             Night                   Day  \n",
              "3          Night               Day                   Day  \n",
              "4            Day               Day                   Day  \n",
              "\n",
              "[5 rows x 47 columns]"
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              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Severity</th>\n",
              "      <th>Start_Time</th>\n",
              "      <th>End_Time</th>\n",
              "      <th>Start_Lat</th>\n",
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              "      <th>Roundabout</th>\n",
              "      <th>Station</th>\n",
              "      <th>Stop</th>\n",
              "      <th>Traffic_Calming</th>\n",
              "      <th>Traffic_Signal</th>\n",
              "      <th>Turning_Loop</th>\n",
              "      <th>Sunrise_Sunset</th>\n",
              "      <th>Civil_Twilight</th>\n",
              "      <th>Nautical_Twilight</th>\n",
              "      <th>Astronomical_Twilight</th>\n",
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              "      <th>0</th>\n",
              "      <td>A-1</td>\n",
              "      <td>3</td>\n",
              "      <td>2016-02-08 00:37:08</td>\n",
              "      <td>2016-02-08 06:37:08</td>\n",
              "      <td>40.108910</td>\n",
              "      <td>-83.092860</td>\n",
              "      <td>40.112060</td>\n",
              "      <td>-83.031870</td>\n",
              "      <td>3.230</td>\n",
              "      <td>Between Sawmill Rd/Exit 20 and OH-315/Olentang...</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>Night</td>\n",
              "      <td>Night</td>\n",
              "      <td>Night</td>\n",
              "      <td>Night</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A-2</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-02-08 05:56:20</td>\n",
              "      <td>2016-02-08 11:56:20</td>\n",
              "      <td>39.865420</td>\n",
              "      <td>-84.062800</td>\n",
              "      <td>39.865010</td>\n",
              "      <td>-84.048730</td>\n",
              "      <td>0.747</td>\n",
              "      <td>At OH-4/OH-235/Exit 41 - Accident.</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>Night</td>\n",
              "      <td>Night</td>\n",
              "      <td>Night</td>\n",
              "      <td>Night</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A-3</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-02-08 06:15:39</td>\n",
              "      <td>2016-02-08 12:15:39</td>\n",
              "      <td>39.102660</td>\n",
              "      <td>-84.524680</td>\n",
              "      <td>39.102090</td>\n",
              "      <td>-84.523960</td>\n",
              "      <td>0.055</td>\n",
              "      <td>At I-71/US-50/Exit 1 - Accident.</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>Night</td>\n",
              "      <td>Night</td>\n",
              "      <td>Night</td>\n",
              "      <td>Day</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A-4</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-02-08 06:51:45</td>\n",
              "      <td>2016-02-08 12:51:45</td>\n",
              "      <td>41.062130</td>\n",
              "      <td>-81.537840</td>\n",
              "      <td>41.062170</td>\n",
              "      <td>-81.535470</td>\n",
              "      <td>0.123</td>\n",
              "      <td>At Dart Ave/Exit 21 - Accident.</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>Night</td>\n",
              "      <td>Night</td>\n",
              "      <td>Day</td>\n",
              "      <td>Day</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A-5</td>\n",
              "      <td>3</td>\n",
              "      <td>2016-02-08 07:53:43</td>\n",
              "      <td>2016-02-08 13:53:43</td>\n",
              "      <td>39.172393</td>\n",
              "      <td>-84.492792</td>\n",
              "      <td>39.170476</td>\n",
              "      <td>-84.501798</td>\n",
              "      <td>0.500</td>\n",
              "      <td>At Mitchell Ave/Exit 6 - Accident.</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>Day</td>\n",
              "      <td>Day</td>\n",
              "      <td>Day</td>\n",
              "      <td>Day</td>\n",
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              "<p>5 rows × 47 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Summary of the Dataset"
      ],
      "metadata": {
        "id": "maOKvsSgR6r5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
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        },
        "outputId": "67b4e9e1-5c16-49e7-8d1c-6ec0ed52ae6c"
      },
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'Severity', 'Start_Time', 'End_Time', 'Start_Lat', 'Start_Lng',\n",
              "       'End_Lat', 'End_Lng', 'Distance(mi)', 'Description', 'Number', 'Street',\n",
              "       'Side', 'City', 'County', 'State', 'Zipcode', 'Country', 'Timezone',\n",
              "       'Airport_Code', 'Weather_Timestamp', 'Temperature(F)', 'Wind_Chill(F)',\n",
              "       'Humidity(%)', 'Pressure(in)', 'Visibility(mi)', 'Wind_Direction',\n",
              "       'Wind_Speed(mph)', 'Precipitation(in)', 'Weather_Condition', 'Amenity',\n",
              "       'Bump', 'Crossing', 'Give_Way', 'Junction', 'No_Exit', 'Railway',\n",
              "       'Roundabout', 'Station', 'Stop', 'Traffic_Calming', 'Traffic_Signal',\n",
              "       'Turning_Loop', 'Sunrise_Sunset', 'Civil_Twilight', 'Nautical_Twilight',\n",
              "       'Astronomical_Twilight'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are 47 columns in the dataset."
      ],
      "metadata": {
        "id": "EkKD8Ba0SBKT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "id": "L-OaqgKcrnsg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "8242a1eb-7821-4053-f2e6-e4f9e3106a8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Severity     Start_Lat     Start_Lng       End_Lat       End_Lng  \\\n",
              "count  2.845342e+06  2.845342e+06  2.845342e+06  2.845342e+06  2.845342e+06   \n",
              "mean   2.137572e+00  3.624520e+01 -9.711463e+01  3.624532e+01 -9.711439e+01   \n",
              "std    4.787216e-01  5.363797e+00  1.831782e+01  5.363873e+00  1.831763e+01   \n",
              "min    1.000000e+00  2.456603e+01 -1.245481e+02  2.456601e+01 -1.245457e+02   \n",
              "25%    2.000000e+00  3.344517e+01 -1.180331e+02  3.344628e+01 -1.180333e+02   \n",
              "50%    2.000000e+00  3.609861e+01 -9.241808e+01  3.609799e+01 -9.241772e+01   \n",
              "75%    2.000000e+00  4.016024e+01 -8.037243e+01  4.016105e+01 -8.037338e+01   \n",
              "max    4.000000e+00  4.900058e+01 -6.711317e+01  4.907500e+01 -6.710924e+01   \n",
              "\n",
              "       Distance(mi)        Number  Temperature(F)  Wind_Chill(F)  \\\n",
              "count  2.845342e+06  1.101431e+06    2.776068e+06   2.375699e+06   \n",
              "mean   7.026779e-01  8.089408e+03    6.179356e+01   5.965823e+01   \n",
              "std    1.560361e+00  1.836009e+04    1.862263e+01   2.116097e+01   \n",
              "min    0.000000e+00  0.000000e+00   -8.900000e+01  -8.900000e+01   \n",
              "25%    5.200000e-02  1.270000e+03    5.000000e+01   4.600000e+01   \n",
              "50%    2.440000e-01  4.007000e+03    6.400000e+01   6.300000e+01   \n",
              "75%    7.640000e-01  9.567000e+03    7.600000e+01   7.600000e+01   \n",
              "max    1.551860e+02  9.999997e+06    1.960000e+02   1.960000e+02   \n",
              "\n",
              "        Humidity(%)  Pressure(in)  Visibility(mi)  Wind_Speed(mph)  \\\n",
              "count  2.772250e+06  2.786142e+06    2.774796e+06     2.687398e+06   \n",
              "mean   6.436545e+01  2.947234e+01    9.099391e+00     7.395044e+00   \n",
              "std    2.287457e+01  1.045286e+00    2.717546e+00     5.527454e+00   \n",
              "min    1.000000e+00  0.000000e+00    0.000000e+00     0.000000e+00   \n",
              "25%    4.800000e+01  2.931000e+01    1.000000e+01     3.500000e+00   \n",
              "50%    6.700000e+01  2.982000e+01    1.000000e+01     7.000000e+00   \n",
              "75%    8.300000e+01  3.001000e+01    1.000000e+01     1.000000e+01   \n",
              "max    1.000000e+02  5.890000e+01    1.400000e+02     1.087000e+03   \n",
              "\n",
              "       Precipitation(in)  \n",
              "count       2.295884e+06  \n",
              "mean        7.016940e-03  \n",
              "std         9.348831e-02  \n",
              "min         0.000000e+00  \n",
              "25%         0.000000e+00  \n",
              "50%         0.000000e+00  \n",
              "75%         0.000000e+00  \n",
              "max         2.400000e+01  "
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              "\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>Severity</th>\n",
              "      <th>Start_Lat</th>\n",
              "      <th>Start_Lng</th>\n",
              "      <th>End_Lat</th>\n",
              "      <th>End_Lng</th>\n",
              "      <th>Distance(mi)</th>\n",
              "      <th>Number</th>\n",
              "      <th>Temperature(F)</th>\n",
              "      <th>Wind_Chill(F)</th>\n",
              "      <th>Humidity(%)</th>\n",
              "      <th>Pressure(in)</th>\n",
              "      <th>Visibility(mi)</th>\n",
              "      <th>Wind_Speed(mph)</th>\n",
              "      <th>Precipitation(in)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2.845342e+06</td>\n",
              "      <td>2.845342e+06</td>\n",
              "      <td>2.845342e+06</td>\n",
              "      <td>2.845342e+06</td>\n",
              "      <td>2.845342e+06</td>\n",
              "      <td>2.845342e+06</td>\n",
              "      <td>1.101431e+06</td>\n",
              "      <td>2.776068e+06</td>\n",
              "      <td>2.375699e+06</td>\n",
              "      <td>2.772250e+06</td>\n",
              "      <td>2.786142e+06</td>\n",
              "      <td>2.774796e+06</td>\n",
              "      <td>2.687398e+06</td>\n",
              "      <td>2.295884e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.137572e+00</td>\n",
              "      <td>3.624520e+01</td>\n",
              "      <td>-9.711463e+01</td>\n",
              "      <td>3.624532e+01</td>\n",
              "      <td>-9.711439e+01</td>\n",
              "      <td>7.026779e-01</td>\n",
              "      <td>8.089408e+03</td>\n",
              "      <td>6.179356e+01</td>\n",
              "      <td>5.965823e+01</td>\n",
              "      <td>6.436545e+01</td>\n",
              "      <td>2.947234e+01</td>\n",
              "      <td>9.099391e+00</td>\n",
              "      <td>7.395044e+00</td>\n",
              "      <td>7.016940e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.787216e-01</td>\n",
              "      <td>5.363797e+00</td>\n",
              "      <td>1.831782e+01</td>\n",
              "      <td>5.363873e+00</td>\n",
              "      <td>1.831763e+01</td>\n",
              "      <td>1.560361e+00</td>\n",
              "      <td>1.836009e+04</td>\n",
              "      <td>1.862263e+01</td>\n",
              "      <td>2.116097e+01</td>\n",
              "      <td>2.287457e+01</td>\n",
              "      <td>1.045286e+00</td>\n",
              "      <td>2.717546e+00</td>\n",
              "      <td>5.527454e+00</td>\n",
              "      <td>9.348831e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.456603e+01</td>\n",
              "      <td>-1.245481e+02</td>\n",
              "      <td>2.456601e+01</td>\n",
              "      <td>-1.245457e+02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>-8.900000e+01</td>\n",
              "      <td>-8.900000e+01</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>3.344517e+01</td>\n",
              "      <td>-1.180331e+02</td>\n",
              "      <td>3.344628e+01</td>\n",
              "      <td>-1.180333e+02</td>\n",
              "      <td>5.200000e-02</td>\n",
              "      <td>1.270000e+03</td>\n",
              "      <td>5.000000e+01</td>\n",
              "      <td>4.600000e+01</td>\n",
              "      <td>4.800000e+01</td>\n",
              "      <td>2.931000e+01</td>\n",
              "      <td>1.000000e+01</td>\n",
              "      <td>3.500000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>3.609861e+01</td>\n",
              "      <td>-9.241808e+01</td>\n",
              "      <td>3.609799e+01</td>\n",
              "      <td>-9.241772e+01</td>\n",
              "      <td>2.440000e-01</td>\n",
              "      <td>4.007000e+03</td>\n",
              "      <td>6.400000e+01</td>\n",
              "      <td>6.300000e+01</td>\n",
              "      <td>6.700000e+01</td>\n",
              "      <td>2.982000e+01</td>\n",
              "      <td>1.000000e+01</td>\n",
              "      <td>7.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>4.016024e+01</td>\n",
              "      <td>-8.037243e+01</td>\n",
              "      <td>4.016105e+01</td>\n",
              "      <td>-8.037338e+01</td>\n",
              "      <td>7.640000e-01</td>\n",
              "      <td>9.567000e+03</td>\n",
              "      <td>7.600000e+01</td>\n",
              "      <td>7.600000e+01</td>\n",
              "      <td>8.300000e+01</td>\n",
              "      <td>3.001000e+01</td>\n",
              "      <td>1.000000e+01</td>\n",
              "      <td>1.000000e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>4.900058e+01</td>\n",
              "      <td>-6.711317e+01</td>\n",
              "      <td>4.907500e+01</td>\n",
              "      <td>-6.710924e+01</td>\n",
              "      <td>1.551860e+02</td>\n",
              "      <td>9.999997e+06</td>\n",
              "      <td>1.960000e+02</td>\n",
              "      <td>1.960000e+02</td>\n",
              "      <td>1.000000e+02</td>\n",
              "      <td>5.890000e+01</td>\n",
              "      <td>1.400000e+02</td>\n",
              "      <td>1.087000e+03</td>\n",
              "      <td>2.400000e+01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b6455dea-c656-4057-b6a7-b440b51b1150')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "id": "yByiiTZArp0n",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9eb2d9d2-52ce-4bfd-dc85-1165f02b7d32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2845342, 47)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "id": "SL0jmdTMrr4l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "18caeb76-7bc5-40e2-fad3-c3966995a758"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                        object\n",
              "Severity                   int64\n",
              "Start_Time                object\n",
              "End_Time                  object\n",
              "Start_Lat                float64\n",
              "Start_Lng                float64\n",
              "End_Lat                  float64\n",
              "End_Lng                  float64\n",
              "Distance(mi)             float64\n",
              "Description               object\n",
              "Number                   float64\n",
              "Street                    object\n",
              "Side                      object\n",
              "City                      object\n",
              "County                    object\n",
              "State                     object\n",
              "Zipcode                   object\n",
              "Country                   object\n",
              "Timezone                  object\n",
              "Airport_Code              object\n",
              "Weather_Timestamp         object\n",
              "Temperature(F)           float64\n",
              "Wind_Chill(F)            float64\n",
              "Humidity(%)              float64\n",
              "Pressure(in)             float64\n",
              "Visibility(mi)           float64\n",
              "Wind_Direction            object\n",
              "Wind_Speed(mph)          float64\n",
              "Precipitation(in)        float64\n",
              "Weather_Condition         object\n",
              "Amenity                     bool\n",
              "Bump                        bool\n",
              "Crossing                    bool\n",
              "Give_Way                    bool\n",
              "Junction                    bool\n",
              "No_Exit                     bool\n",
              "Railway                     bool\n",
              "Roundabout                  bool\n",
              "Station                     bool\n",
              "Stop                        bool\n",
              "Traffic_Calming             bool\n",
              "Traffic_Signal              bool\n",
              "Turning_Loop                bool\n",
              "Sunrise_Sunset            object\n",
              "Civil_Twilight            object\n",
              "Nautical_Twilight         object\n",
              "Astronomical_Twilight     object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Summary of each attribute"
      ],
      "metadata": {
        "id": "jyZaQRD2Vdn0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info(null_counts=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XuRZ1_LsUEIN",
        "outputId": "831d8764-a343-4478-a905-76e33097d2d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: null_counts is deprecated. Use show_counts instead\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2845342 entries, 0 to 2845341\n",
            "Data columns (total 47 columns):\n",
            " #   Column                 Non-Null Count    Dtype  \n",
            "---  ------                 --------------    -----  \n",
            " 0   ID                     2845342 non-null  object \n",
            " 1   Severity               2845342 non-null  int64  \n",
            " 2   Start_Time             2845342 non-null  object \n",
            " 3   End_Time               2845342 non-null  object \n",
            " 4   Start_Lat              2845342 non-null  float64\n",
            " 5   Start_Lng              2845342 non-null  float64\n",
            " 6   End_Lat                2845342 non-null  float64\n",
            " 7   End_Lng                2845342 non-null  float64\n",
            " 8   Distance(mi)           2845342 non-null  float64\n",
            " 9   Description            2845342 non-null  object \n",
            " 10  Number                 1101431 non-null  float64\n",
            " 11  Street                 2845340 non-null  object \n",
            " 12  Side                   2845342 non-null  object \n",
            " 13  City                   2845205 non-null  object \n",
            " 14  County                 2845342 non-null  object \n",
            " 15  State                  2845342 non-null  object \n",
            " 16  Zipcode                2844023 non-null  object \n",
            " 17  Country                2845342 non-null  object \n",
            " 18  Timezone               2841683 non-null  object \n",
            " 19  Airport_Code           2835793 non-null  object \n",
            " 20  Weather_Timestamp      2794606 non-null  object \n",
            " 21  Temperature(F)         2776068 non-null  float64\n",
            " 22  Wind_Chill(F)          2375699 non-null  float64\n",
            " 23  Humidity(%)            2772250 non-null  float64\n",
            " 24  Pressure(in)           2786142 non-null  float64\n",
            " 25  Visibility(mi)         2774796 non-null  float64\n",
            " 26  Wind_Direction         2771567 non-null  object \n",
            " 27  Wind_Speed(mph)        2687398 non-null  float64\n",
            " 28  Precipitation(in)      2295884 non-null  float64\n",
            " 29  Weather_Condition      2774706 non-null  object \n",
            " 30  Amenity                2845342 non-null  bool   \n",
            " 31  Bump                   2845342 non-null  bool   \n",
            " 32  Crossing               2845342 non-null  bool   \n",
            " 33  Give_Way               2845342 non-null  bool   \n",
            " 34  Junction               2845342 non-null  bool   \n",
            " 35  No_Exit                2845342 non-null  bool   \n",
            " 36  Railway                2845342 non-null  bool   \n",
            " 37  Roundabout             2845342 non-null  bool   \n",
            " 38  Station                2845342 non-null  bool   \n",
            " 39  Stop                   2845342 non-null  bool   \n",
            " 40  Traffic_Calming        2845342 non-null  bool   \n",
            " 41  Traffic_Signal         2845342 non-null  bool   \n",
            " 42  Turning_Loop           2845342 non-null  bool   \n",
            " 43  Sunrise_Sunset         2842475 non-null  object \n",
            " 44  Civil_Twilight         2842475 non-null  object \n",
            " 45  Nautical_Twilight      2842475 non-null  object \n",
            " 46  Astronomical_Twilight  2842475 non-null  object \n",
            "dtypes: bool(13), float64(13), int64(1), object(20)\n",
            "memory usage: 773.4+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "G4IM8CnnYNyf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(25,25)) \n",
        "sns.heatmap(df.corr(),annot=True, linewidths=.5, ax=ax)"
      ],
      "metadata": {
        "id": "0WjOOCV011gM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "523ea04e-c5e0-4f3f-8921-24baf5f52f03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fde5a01c890>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### List of Correlated Attributes\n",
        "\n",
        "*   Bump with Traffic_Calming (0.77)\n",
        "*   Traffic_Signal with Distance (-0.11)\n",
        "*   Pressure with Latitude and Longitude (-0.23 and 0.21)\n",
        "*   Traffic_Signal with Crossing (0.42)\n",
        "*   Temperature with Humidity (-0.37)\n",
        "*   Percipitation with Visibility (-0.12)\n",
        "\n"
      ],
      "metadata": {
        "id": "CfaEWgGA1QTA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "city=df[\"City\"]\n",
        "#Preparing data for Geographical plotting\n",
        "data_update = df.groupby('State').count()\n",
        "data_update['Cases'] = data_update['Severity']\n",
        "data_update['Percentage'] = (data_update['Severity'] /2845342) * 100\n",
        "# data_update['Percentage']\n",
        "\n",
        "fig = px.choropleth( data_update,\n",
        "                     locations = data_update.index,\n",
        "                     locationmode = 'USA-states',\n",
        "                     scope = 'usa',\n",
        "                     color = 'Cases',\n",
        "                     title = 'US Accidents',\n",
        "                     hover_name = data_update.index,\n",
        "                     hover_data = ['Cases','Percentage'],\n",
        "                     range_color = [1,800000],\n",
        "                     color_continuous_scale = 'earth')\n",
        "\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "cMeGwQ6H_FKh",
        "outputId": "c583823d-fe2b-4080-cdbd-5e76be3ff028"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1) California has recorded the highest number of accidental cases over the span of five years.\n",
        "\n",
        "2) Florida at second position in total no of accidental cases, which along with California accounts for almost 42% of total number of cases in the United States."
      ],
      "metadata": {
        "id": "F0frwMEhBu9P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Start_Time']= pd.to_datetime(df.Start_Time)\n",
        "df['Start_Time'].dtype\n",
        "df['year'] = df['Start_Time'].dt.year\n",
        "df_years = df.groupby('year',as_index=False).size()\n",
        "df_years_sorted = df_years.sort_values('size',ascending=False)\n",
        "df_years_sorted[\"Percentage of Cases\"] = (df_years_sorted['size']/df_years_sorted['size'].sum())*100 \n",
        "fig, ax = plt.subplots(figsize=(10,8))\n",
        "plt.pie(df_years_sorted[\"Percentage of Cases\"],labels=df_years_sorted[\"year\"], autopct='%.0f%%')\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "neH9M19cGmlJ",
        "outputId": "92a2e908-0aca-4c6c-cf2a-5bdd2ee4d9d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3) Majority of Cases in US (53%) were reported in 2021 only.\n",
        "\n",
        "4)Between the years 2016 and 2019 the growth in number of accidental cases was increasing marginally. However, in the years 2020 and 2021, it escalated at very high rate comparatively."
      ],
      "metadata": {
        "id": "Xl84JVy9WDbR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['month'] = df['Start_Time'].dt.month\n",
        "df_month = df.groupby('month',as_index=False).count()\n",
        "fig = px.bar(df_month, x='month', y='Severity', height=400,labels=dict(size=\"No of Cases\"))\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "8cwt0dfHN_LT",
        "outputId": "de360a2f-0c29-4621-f31d-2d36f2921d39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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      "source": [
        "5) The month of December accounts for the highest number of accidents annually.\n",
        "\n",
        "6) In the first half of every year, the number of cases remains stagnant, whereas it increases steadily in another half of the year."
      ],
      "metadata": {
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      "source": [
        "df['hour'] = df['Start_Time'].dt.hour\n",
        "df_hour = df.groupby('hour',as_index=False).size()\n",
        "df_hour_sorted = df_hour.sort_values('size',ascending=False)\n",
        "fig = px.bar(df_hour_sorted, x='hour', y='size', height=400,labels=dict(size=\"No of Cases\"))\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
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          "height": 417
        },
        "id": "D8-j7lM_LB8n",
        "outputId": "bc46b774-0197-43db-9d7a-34b93ee27153"
      },
      "execution_count": null,
      "outputs": [
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    {
      "cell_type": "markdown",
      "source": [
        "7) From the above bargraph, there is a high probability that if an accident occurred, it would be between 12:00 hours and 18:00 hours of the day.\n",
        "\n",
        "8) Frequency of accidents reaches a pinnacle of 220k at 17:00 hours of the day. "
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      "metadata": {
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      "source": [
        "df_severity_again = df.groupby('Severity',as_index=False).size()\n",
        "df_severity_again_sorted = df_severity_again.sort_values('size',ascending=False)\n",
        "fig1 = px.bar(df_severity_again_sorted, x='Severity', y='size', height=400,labels=dict(size=\"No of Cases\"))\n",
        "fig1.show()"
      ],
      "metadata": {
        "id": "dlGYl5RyFt0R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "015a198c-abbd-4272-a494-f455276e69e9"
      },
      "execution_count": null,
      "outputs": [
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      "source": [
        "9) Almost 90 percent of accident cases (2.53M)  are of class 2 Severity. \n",
        "(It implies that there is a class imbalance in the given data)"
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        "fig, ax = plt.subplots(figsize=(10,6)) \n",
        "sns.distplot(df[\"Wind_Speed(mph)\"])"
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        "id": "ltUH10QVEL8U",
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      "execution_count": null,
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning:\n",
            "\n",
            "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
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            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdda1ab33d0>"
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          "metadata": {},
          "execution_count": 15
        },
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "10) From the above density plot, it can be deduced that approximately 99% of Wind_speed values lie nearby 0, and there are some outliers whose values are above 300 mph."
      ],
      "metadata": {
        "id": "NHPspDwrDYF5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(10,6)) \n",
        "sns.boxplot(df[\"Temperature(F)\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "HnteyD_CKF6v",
        "outputId": "81866322-ce05-4430-f82b-d71bf75750c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning:\n",
            "\n",
            "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdda1a51e50>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "11) Most of the Temperature(F) values lie between 50F-100F, with the median around 60F. However, there are a significant amount of outliers in this attribute."
      ],
      "metadata": {
        "id": "Z0Y-MeclEKTP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(10,6)) \n",
        "sns.distplot(df[\"Visibility(mi)\"])\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "vrvEzrQQKuTK",
        "outputId": "f5c0304a-cf04-48fd-8eea-9019356751ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning:\n",
            "\n",
            "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdda18c9e50>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "12) Vast amount of data points of the Visibility(mi) attribute is concentrated at 10, whereas most data points are between 0 and 20."
      ],
      "metadata": {
        "id": "V-OH__NrFNaf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(10,6)) \n",
        "sns.boxplot(df[\"Humidity(%)\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "lwtY6KbXLBfl",
        "outputId": "971919fa-837f-4399-f163-773d20106cb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning:\n",
            "\n",
            "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdda1758dd0>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "13) In most accident cases, the Humidity attribute value is likely between 50 and 85. The median of this attribute is nearby 65."
      ],
      "metadata": {
        "id": "wi_bGCJSFtZ0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_timezone = df.groupby('Timezone',as_index=False).size()\n",
        "df_timezone_sorted = df_timezone.sort_values('size',ascending=False)\n",
        "df_timezone_sorted[\"Percentage\"]= df_timezone_sorted[\"size\"]/df_timezone_sorted[\"size\"].sum() \n",
        "fig, ax = plt.subplots(figsize=(10,8)) \n",
        "plt.pie(df_timezone_sorted[\"Percentage\"],labels=df_timezone_sorted['Timezone'],autopct='%.0f%%')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "PTGDlGpKLLPq",
        "outputId": "49153828-71d3-47fc-ae03-6d76ab77cf12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "14) In case of accidents in the US during 2016-2021, there is a very high chance that it has occurred either in US/Eastern or in US/Pacific timezone. However, there is a minute probability that it has occurred in US/Mountain timezone.    "
      ],
      "metadata": {
        "id": "DnZyOL9QGh88"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_weather_condition = df.groupby('Weather_Condition',as_index=False).size()\n",
        "df_weather_condition\n",
        "df_weather_condition_sorted = df_weather_condition.sort_values('size',ascending=False)\n",
        "fig = px.bar(df_weather_condition_sorted, x='Weather_Condition', y='size', height=400,labels=dict(size=\"No of Cases\"))\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "qnqvfQrIM7bZ",
        "outputId": "a9283690-5d46-4e56-d7d0-4481c0bd30db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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of Cases\"}},\"legend\":{\"tracegroupgap\":0},\"margin\":{\"t\":60},\"barmode\":\"relative\",\"height\":400},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
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              "            Plotly.purge(gd);\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "15) The number of accident cases during fair weather conditions outweighs all other weather conditions with a count of 1.1 M, followed by mostly cloudy and cloudy with 363.9k and 348.76k, respectively. "
      ],
      "metadata": {
        "id": "HxLGKuYHHd4h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n",
        "\n",
        "df_sunrise = df.groupby('Sunrise_Sunset', as_index=False).size()\n",
        "df_sunrise\n",
        "df_astronomical_twilight = df.groupby('Astronomical_Twilight', as_index=False).size()\n",
        "df_astronomical_twilight\n",
        "df_Civil_Twilight = df.groupby('Civil_Twilight', as_index=False).size()\n",
        "df_Civil_Twilight\n",
        "df_Nautical_Twilight = df.groupby('Nautical_Twilight', as_index=False).size()\n",
        "df_Nautical_Twilight\n",
        "\n",
        "fig1 = px.bar(df_sunrise, x='Sunrise_Sunset', y='size',color=\"size\")\n",
        "fig2 = px.bar(df_astronomical_twilight, x='Astronomical_Twilight', y='size',color=\"size\")\n",
        "fig3 = px.bar(df_Civil_Twilight, x='Civil_Twilight', y='size',color=\"size\")\n",
        "fig4 = px.bar(df_Nautical_Twilight, x='Nautical_Twilight', y='size',color=\"size\")\n",
        "\n",
        "fig = go.Figure(data= fig1.data + fig2.data + fig3.data + fig4.data)\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "6ZOmA2XDKt9T",
        "outputId": "aa390af0-794f-4146-8ac5-53136a4f84aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "16) In the attributes Sunrise_sunset, Astronomical_Twilight, Civil_Twilight and Nautical_Twilight the count of attribute value \"day\" out shadows \"Night.\"\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "YG4OzRgJIHkx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_bump = df.groupby(\"Bump\",as_index=False).size()\n",
        "df_bump[\"Percentage\"]= df_bump[\"size\"]/df_bump[\"size\"].sum() \n",
        "print(df_bump)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10,8)) \n",
        "plt.pie(df_bump[\"Percentage\"],labels=df_bump['Bump'],autopct='%.f%%')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "vOnuE9m8wUrd",
        "outputId": "c32483ae-59ba-4955-a3bb-3f9831fdd3c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    Bump     size  Percentage\n",
            "0  False  2844321    0.999641\n",
            "1   True     1021    0.000359\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_crossing = df.groupby(\"Crossing\",as_index=False).size()\n",
        "df_crossing[\"Percentage\"]= df_crossing[\"size\"]/df_crossing[\"size\"].sum() \n",
        "print(df_crossing)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10,8)) \n",
        "plt.pie(df_crossing[\"Percentage\"],labels=df_crossing['Crossing'],autopct='%.f%%')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "6UfC_HwFyysz",
        "outputId": "0e804ca6-f5a2-428c-aa76-7347476adce5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Crossing     size  Percentage\n",
            "0     False  2645130    0.929635\n",
            "1      True   200212    0.070365\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "17) From the above two pie charts, in most of the accident cases nearby crossing, the bumps were not there to prevent accidents. Therefore, the US Government can install bumps near crossings to prevent accidents nearby."
      ],
      "metadata": {
        "id": "5xa7tviGylf7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(df[\"Side\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "9SvU7TdOz_BU",
        "outputId": "a3b42fde-64f4-45c8-b045-1a5f2d492f6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdda1495950>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "18) Majority of accidents occur on the right side."
      ],
      "metadata": {
        "id": "JljbUvZg01lz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preprocessing"
      ],
      "metadata": {
        "id": "SiPoBpoXw4p2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Checking for null values"
      ],
      "metadata": {
        "id": "My8oG5zwsyPM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "id": "y3d_vhXKtGOx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e51c4cf-9b85-4b85-bb61-17d6a2a3b5e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                             0\n",
              "Severity                       0\n",
              "Start_Time                     0\n",
              "End_Time                       0\n",
              "Start_Lat                      0\n",
              "Start_Lng                      0\n",
              "End_Lat                        0\n",
              "End_Lng                        0\n",
              "Distance(mi)                   0\n",
              "Description                    0\n",
              "Number                   1743911\n",
              "Street                         2\n",
              "Side                           0\n",
              "City                         137\n",
              "County                         0\n",
              "State                          0\n",
              "Zipcode                     1319\n",
              "Country                        0\n",
              "Timezone                    3659\n",
              "Airport_Code                9549\n",
              "Weather_Timestamp          50736\n",
              "Temperature(F)             69274\n",
              "Wind_Chill(F)             469643\n",
              "Humidity(%)                73092\n",
              "Pressure(in)               59200\n",
              "Visibility(mi)             70546\n",
              "Wind_Direction             73775\n",
              "Wind_Speed(mph)           157944\n",
              "Precipitation(in)         549458\n",
              "Weather_Condition          70636\n",
              "Amenity                        0\n",
              "Bump                           0\n",
              "Crossing                       0\n",
              "Give_Way                       0\n",
              "Junction                       0\n",
              "No_Exit                        0\n",
              "Railway                        0\n",
              "Roundabout                     0\n",
              "Station                        0\n",
              "Stop                           0\n",
              "Traffic_Calming                0\n",
              "Traffic_Signal                 0\n",
              "Turning_Loop                   0\n",
              "Sunrise_Sunset              2867\n",
              "Civil_Twilight              2867\n",
              "Nautical_Twilight           2867\n",
              "Astronomical_Twilight       2867\n",
              "year                           0\n",
              "month                          0\n",
              "hour                           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dropping Attributes that are irrelavant or have significantly high number of null values "
      ],
      "metadata": {
        "id": "MgVKGs9oUoa-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop([\"Number\",\"Precipitation(in)\",\"Country\",\"Zipcode\",\"Airport_Code\",\"Street\",'Description','Wind_Chill(F)','Wind_Speed(mph)',\"Weather_Timestamp\",\"Wind_Direction\",\"Weather_Condition\",\"Start_Time\",\"End_Time\",\n",
        "\"Start_Lat\",\"End_Lat\",\"Start_Lng\",\"End_Lng\",\"Turning_Loop\"],axis=1,inplace=True)\n"
      ],
      "metadata": {
        "id": "EGEHMPPTvSH-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Reasons for dropping the attributes\n",
        "* High number of Null values:- Wind_Speed(mph), Percipitation(in), Wind_Chill, Number\n",
        "* Irrelavant Information :- Weather_Timestamp`, Wind_Direction (too many unique values), Airport_Code, Longitude/Latitude, Description, Zipcode \n",
        "* One Unique Value- Country (US), Turning_loop (False)"
      ],
      "metadata": {
        "id": "Eh6R0EKoYDZQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "id": "gvl5qTC_yTtm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8fb2d01e-0300-48de-b28c-671757b407fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                           0\n",
              "Severity                     0\n",
              "Distance(mi)                 0\n",
              "Side                         0\n",
              "City                       137\n",
              "County                       0\n",
              "State                        0\n",
              "Timezone                  3659\n",
              "Temperature(F)           69274\n",
              "Humidity(%)              73092\n",
              "Pressure(in)             59200\n",
              "Visibility(mi)           70546\n",
              "Amenity                      0\n",
              "Bump                         0\n",
              "Crossing                     0\n",
              "Give_Way                     0\n",
              "Junction                     0\n",
              "No_Exit                      0\n",
              "Railway                      0\n",
              "Roundabout                   0\n",
              "Station                      0\n",
              "Stop                         0\n",
              "Traffic_Calming              0\n",
              "Traffic_Signal               0\n",
              "Sunrise_Sunset            2867\n",
              "Civil_Twilight            2867\n",
              "Nautical_Twilight         2867\n",
              "Astronomical_Twilight     2867\n",
              "year                         0\n",
              "month                        0\n",
              "hour                         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Filling the missing values "
      ],
      "metadata": {
        "id": "SJ6OWQwlr8S4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Nautical_Twilight'].value_counts()"
      ],
      "metadata": {
        "id": "3xUgFMZdsMC8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a72df7ac-083a-4f04-c8f2-e17b96e2b9ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Day      2063472\n",
              "Night     779003\n",
              "Name: Nautical_Twilight, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Astronomical_Twilight'].value_counts()"
      ],
      "metadata": {
        "id": "i-91qPF_sPOC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e686c9f9-9062-4292-970c-b9ba4727c0ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Day      2176983\n",
              "Night     665492\n",
              "Name: Astronomical_Twilight, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Civil_Twilight'].value_counts()"
      ],
      "metadata": {
        "id": "_Sj5_83csguG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "848bdfb1-43be-4e7e-eddb-655375c58562"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Day      1929103\n",
              "Night     913372\n",
              "Name: Civil_Twilight, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Sunrise_Sunset'].value_counts()"
      ],
      "metadata": {
        "id": "WO2786lisg1g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "834451e0-7c81-42f4-abc8-2bc78caf1c3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Day      1811935\n",
              "Night    1030540\n",
              "Name: Sunrise_Sunset, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Visibility(mi)'].value_counts()"
      ],
      "metadata": {
        "id": "-aNrySPGhdfH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6626ab4e-c4b0-4902-f4c3-92512b587267"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10.0    2230276\n",
              "7.0       79649\n",
              "9.0       68817\n",
              "8.0       55955\n",
              "5.0       53933\n",
              "         ...   \n",
              "6.2           1\n",
              "63.0          1\n",
              "43.0          1\n",
              "36.0          1\n",
              "19.0          1\n",
              "Name: Visibility(mi), Length: 76, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Pressure(in)'].value_counts()"
      ],
      "metadata": {
        "id": "eu_Op82OiL0Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "30fa7e0d-8488-4874-8346-e8195581f072"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29.96    43668\n",
              "29.94    43165\n",
              "29.99    42978\n",
              "30.01    41736\n",
              "30.03    41477\n",
              "         ...  \n",
              "20.15        1\n",
              "19.48        1\n",
              "21.84        1\n",
              "56.54        1\n",
              "21.83        1\n",
              "Name: Pressure(in), Length: 1068, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Temperature(F)'].value_counts()"
      ],
      "metadata": {
        "id": "4FGltfnMiR1j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6086b4ff-7896-4e73-c5d5-b4c8a3f86f04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " 73.0     64505\n",
              " 77.0     63575\n",
              " 75.0     60534\n",
              " 72.0     59681\n",
              " 68.0     58557\n",
              "          ...  \n",
              " 109.8        1\n",
              "-9.8          1\n",
              " 170.6        1\n",
              " 107.2        1\n",
              " 99.1         1\n",
              "Name: Temperature(F), Length: 788, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "H_med=df[\"Humidity(%)\"].median()\n",
        "H_med"
      ],
      "metadata": {
        "id": "Gkp5X_vwiULp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cfd6d1cd-b05a-4572-d943-c0ec22273990"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "67.0"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Temp_mean = df[\"Temperature(F)\"].mean()\n",
        "Temp_mean"
      ],
      "metadata": {
        "id": "HrRCoZQuj5Jh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "550666ab-0ae7-43d0-d5c1-cf4785964453"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "61.793555921540836"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Nautical_Twilight\"] = df[\"Nautical_Twilight\"].fillna('Day')\n",
        "df[\"Sunrise_Sunset\"] = df[\"Sunrise_Sunset\"].fillna('Day')\n",
        "df[\"Astronomical_Twilight\"] = df[\"Astronomical_Twilight\"].fillna('Day')\n",
        "df[\"Civil_Twilight\"] = df[\"Civil_Twilight\"].fillna('Day')\n",
        "df[\"Visibility(mi)\"] = df[\"Visibility(mi)\"].fillna(10)\n",
        "df[\"Pressure(in)\"] = df[\"Pressure(in)\"].fillna(30)\n",
        "df[\"Humidity(%)\"] = df[\"Humidity(%)\"].fillna(H_med)\n",
        "df[\"Temperature(F)\"]=df[\"Temperature(F)\"].fillna(Temp_mean)\n"
      ],
      "metadata": {
        "id": "wBLsP3y-r-jP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "_bigUpG2kUYH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "id": "3ePPwoLXtdBv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "911bfd10-408c-4b58-cbfb-84d6c31c344f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                       0\n",
              "Severity                 0\n",
              "Distance(mi)             0\n",
              "Side                     0\n",
              "City                     0\n",
              "County                   0\n",
              "State                    0\n",
              "Timezone                 0\n",
              "Temperature(F)           0\n",
              "Humidity(%)              0\n",
              "Pressure(in)             0\n",
              "Visibility(mi)           0\n",
              "Amenity                  0\n",
              "Bump                     0\n",
              "Crossing                 0\n",
              "Give_Way                 0\n",
              "Junction                 0\n",
              "No_Exit                  0\n",
              "Railway                  0\n",
              "Roundabout               0\n",
              "Station                  0\n",
              "Stop                     0\n",
              "Traffic_Calming          0\n",
              "Traffic_Signal           0\n",
              "Sunrise_Sunset           0\n",
              "Civil_Twilight           0\n",
              "Nautical_Twilight        0\n",
              "Astronomical_Twilight    0\n",
              "year                     0\n",
              "month                    0\n",
              "hour                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Binning the Attributes"
      ],
      "metadata": {
        "id": "-wx1HpeDqw4_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ae1cf389-02f9-4a02-bc00-823b7922de8f",
        "outputId": "acacda56-1d55-41b7-e084-f84b991ee536"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "US/Eastern     1221807\n",
              "US/Pacific      967084\n",
              "US/Central      488060\n",
              "US/Mountain     164595\n",
              "Name: Timezone, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "df['Timezone'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Side'].value_counts()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U3oAI18xsWcZ",
        "outputId": "f37e9a9d-547c-47ff-ae66-6f10b191226c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "R    2350231\n",
              "L     491315\n",
              "Name: Side, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(df.index[(df[\"Side\"] == \"N\")],axis=0,inplace=True)\n"
      ],
      "metadata": {
        "id": "rp6IRR-EsbWh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bins1 = [-1,5,10,160]\n",
        "labels1 = [\"0-5\",'5-10','10+']\n",
        "distance = pd.cut(df['Distance(mi)'],bins=bins1,labels=labels1) \n",
        "distance_df = pd.get_dummies(distance, prefix='Distance')\n",
        "distance_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
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        "id": "Zym4awezxlz8",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "timezone_df = pd.get_dummies(df['Timezone'], prefix='Timezone')\n",
        "severity_df = pd.get_dummies(df['Severity'], prefix='Severity')\n",
        "sunrise_sunset_df =pd.get_dummies(df[\"Sunrise_Sunset\"], prefix='Sunrise_Sunset')\n",
        "Civil_Twilight_df =pd.get_dummies(df[\"Civil_Twilight\"], prefix='Civil_Twilight')\n",
        "Astronomical_Twilight_df =pd.get_dummies(df[\"Astronomical_Twilight\"], prefix='Astronomical_Twilight')\n",
        "Nautical_Twilight_df =pd.get_dummies(df[\"Nautical_Twilight\"], prefix='Nautical_Twilight')\n",
        "Side=pd.get_dummies(df[\"Side\"],prefix='Side')"
      ],
      "metadata": {
        "id": "z-fQwCQisNcy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JnogtHdKYdH4",
        "outputId": "69f72d1a-835f-410e-d694-4ceeb1b08744"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                        object\n",
              "Severity                   int64\n",
              "Distance(mi)             float64\n",
              "Side                      object\n",
              "City                      object\n",
              "County                    object\n",
              "State                     object\n",
              "Timezone                  object\n",
              "Temperature(F)           float64\n",
              "Humidity(%)              float64\n",
              "Pressure(in)             float64\n",
              "Visibility(mi)           float64\n",
              "Amenity                     bool\n",
              "Bump                        bool\n",
              "Crossing                    bool\n",
              "Give_Way                    bool\n",
              "Junction                    bool\n",
              "No_Exit                     bool\n",
              "Railway                     bool\n",
              "Roundabout                  bool\n",
              "Station                     bool\n",
              "Stop                        bool\n",
              "Traffic_Calming             bool\n",
              "Traffic_Signal              bool\n",
              "Sunrise_Sunset            object\n",
              "Civil_Twilight            object\n",
              "Nautical_Twilight         object\n",
              "Astronomical_Twilight     object\n",
              "year                       int64\n",
              "month                      int64\n",
              "hour                       int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "apriori_df=df.iloc[:,12:24]\n",
        "apriori_df = apriori_df.join(severity_df)\n",
        "apriori_df = apriori_df.join(distance_df)\n",
        "apriori_df = apriori_df.join(timezone_df)\n",
        "apriori_df = apriori_df.join(sunrise_sunset_df)\n",
        "apriori_df = apriori_df.join(Civil_Twilight_df)\n",
        "apriori_df = apriori_df.join(Astronomical_Twilight_df)\n",
        "apriori_df = apriori_df.join(Nautical_Twilight_df)\n",
        "\n"
      ],
      "metadata": {
        "id": "rZW8hHZavAlj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "apriori_df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g2V66ylP7T65",
        "outputId": "5d35998c-ad3f-433e-844f-d370b06d858b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Amenity                         bool\n",
              "Bump                            bool\n",
              "Crossing                        bool\n",
              "Give_Way                        bool\n",
              "Junction                        bool\n",
              "No_Exit                         bool\n",
              "Railway                         bool\n",
              "Roundabout                      bool\n",
              "Station                         bool\n",
              "Stop                            bool\n",
              "Traffic_Calming                 bool\n",
              "Traffic_Signal                  bool\n",
              "Severity_1                     uint8\n",
              "Severity_2                     uint8\n",
              "Severity_3                     uint8\n",
              "Severity_4                     uint8\n",
              "Distance_0-5                   uint8\n",
              "Distance_5-10                  uint8\n",
              "Distance_10+                   uint8\n",
              "Timezone_US/Central            uint8\n",
              "Timezone_US/Eastern            uint8\n",
              "Timezone_US/Mountain           uint8\n",
              "Timezone_US/Pacific            uint8\n",
              "Sunrise_Sunset_Day             uint8\n",
              "Sunrise_Sunset_Night           uint8\n",
              "Civil_Twilight_Day             uint8\n",
              "Civil_Twilight_Night           uint8\n",
              "Astronomical_Twilight_Day      uint8\n",
              "Astronomical_Twilight_Night    uint8\n",
              "Nautical_Twilight_Day          uint8\n",
              "Nautical_Twilight_Night        uint8\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e711034e"
      },
      "source": [
        "# Label Encoding"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "lbl_enc = LabelEncoder()\n",
        "apriori_df[list(apriori_df.columns)] = apriori_df[list(apriori_df.columns)].apply(lbl_enc.fit_transform)"
      ],
      "metadata": {
        "id": "TotVJUz27f6c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "apriori_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "PsiAVphU7mPc",
        "outputId": "1e6a85a6-6c04-4281-c147-daf1dd4db967"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Amenity  Bump  Crossing  Give_Way  Junction  No_Exit  Railway  \\\n",
              "0              0     0         0         0         0        0        0   \n",
              "1              0     0         0         0         0        0        0   \n",
              "2              0     0         0         0         1        0        0   \n",
              "3              0     0         0         0         0        0        0   \n",
              "4              0     0         0         0         0        0        0   \n",
              "...          ...   ...       ...       ...       ...      ...      ...   \n",
              "2845337        0     0         0         0         0        0        0   \n",
              "2845338        0     0         0         0         0        0        0   \n",
              "2845339        0     0         0         0         1        0        0   \n",
              "2845340        0     0         0         0         0        0        0   \n",
              "2845341        0     0         0         0         0        0        0   \n",
              "\n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "apriori_df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pf9wUZZmFmpH",
        "outputId": "d239eca5-5ed5-4e45-e3a2-169ec2477735"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Amenity                        int64\n",
              "Bump                           int64\n",
              "Crossing                       int64\n",
              "Give_Way                       int64\n",
              "Junction                       int64\n",
              "No_Exit                        int64\n",
              "Railway                        int64\n",
              "Roundabout                     int64\n",
              "Station                        int64\n",
              "Stop                           int64\n",
              "Traffic_Calming                int64\n",
              "Traffic_Signal                 int64\n",
              "Severity_1                     int64\n",
              "Severity_2                     int64\n",
              "Severity_3                     int64\n",
              "Severity_4                     int64\n",
              "Distance_0-5                   int64\n",
              "Distance_5-10                  int64\n",
              "Distance_10+                   int64\n",
              "Timezone_US/Central            int64\n",
              "Timezone_US/Eastern            int64\n",
              "Timezone_US/Mountain           int64\n",
              "Timezone_US/Pacific            int64\n",
              "Sunrise_Sunset_Day             int64\n",
              "Sunrise_Sunset_Night           int64\n",
              "Civil_Twilight_Day             int64\n",
              "Civil_Twilight_Night           int64\n",
              "Astronomical_Twilight_Day      int64\n",
              "Astronomical_Twilight_Night    int64\n",
              "Nautical_Twilight_Day          int64\n",
              "Nautical_Twilight_Night        int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x3QU2byz7ygT"
      },
      "source": [
        "# Implementing Apriori Algorithm "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from mlxtend.frequent_patterns import apriori, association_rules"
      ],
      "metadata": {
        "id": "0G9Wa3Zt70YI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Frequent Item sets for min sup = 0.01, "
      ],
      "metadata": {
        "id": "sQig3H3ERj9u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "frequent_itemsets = apriori(apriori_df, min_support = 0.01, use_colnames = True)\n",
        "frequent_itemsets.to_csv(\"f1.csv\")\n",
        "frequent_itemsets"
      ],
      "metadata": {
        "id": "uZ6sg2gv8Gsf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "6f197d7a-cec7-4b9f-ac7d-64fb71d63316"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       support                                           itemsets\n",
              "0     0.070436                                         (Crossing)\n",
              "1     0.102164                                         (Junction)\n",
              "2     0.023926                                          (Station)\n",
              "3     0.017715                                             (Stop)\n",
              "4     0.093273                                   (Traffic_Signal)\n",
              "...        ...                                                ...\n",
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            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "[a] {'Sunrise_Sunset_Day', 'Astronomical_Twilight_Day', 'Crossing', 'Civil_Twilight_Day', 'Distance_0-5', 'Timezone_US/Eastern', 'Traffic_Signal', 'Severity_2'},\n",
        "\n",
        "[b] {'Sunrise_Sunset_Day', 'Nautical_Twilight_Day', 'Crossing', 'Civil_Twilight_Day', 'Distance_0-5', 'Timezone_US/Eastern', 'Traffic_Signal', 'Severity_2'}\n",
        "\n",
        "[c] {'Sunrise_Sunset_Day', 'Nautical_Twilight_Day', 'Astronomical_Twilight_Day', 'Crossing', 'Distance_0-5', 'Timezone_US/Eastern', 'Traffic_Signal', 'Severity_2'}, \n",
        "\n",
        "[d] {'Nautical_Twilight_Day', 'Astronomical_Twilight_Day', 'Crossing', 'Civil_Twilight_Day', 'Distance_0-5', 'Timezone_US/Eastern', 'Traffic_Signal', 'Severity_2'},\n",
        "\n",
        "[e] {'Sunrise_Sunset_Day', 'Nautical_Twilight_Day', 'Astronomical_Twilight_Day', 'Crossing', 'Civil_Twilight_Day', 'Distance_0-5', 'Traffic_Signal', 'Severity_2'}, \n",
        "\n",
        "[f] {'Sunrise_Sunset_Day', 'Nautical_Twilight_Day', 'Astronomical_Twilight_Day', 'Crossing', 'Civil_Twilight_Day', 'Timezone_US/Eastern', 'Traffic_Signal', 'Severity_2'}, \n",
        "\n",
        "[g] {'Sunrise_Sunset_Day', 'Nautical_Twilight_Day', 'Astronomical_Twilight_Day', 'Crossing', 'Civil_Twilight_Day', 'Distance_0-5', 'Timezone_US/Eastern', 'Traffic_Signal'}, \n",
        "\n",
        "[h] {'Sunrise_Sunset_Day', 'Nautical_Twilight_Day', 'Astronomical_Twilight_Day', 'Crossing', 'Civil_Twilight_Day', 'Distance_0-5', 'Timezone_US/Eastern', 'Severity_2'}, \n",
        "\n",
        "[i] {'Sunrise_Sunset_Day', 'Nautical_Twilight_Day', 'Astronomical_Twilight_Day', 'Civil_Twilight_Day', 'Junction', 'Distance_0-5', 'Timezone_US/Eastern', 'Severity_2'}, \n",
        "\n",
        "[j] {'Sunrise_Sunset_Day', 'Timezone_US/Pacific', 'Nautical_Twilight_Day', 'Astronomical_Twilight_Day', 'Civil_Twilight_Day', 'Junction', 'Distance_0-5', 'Severity_2'}, \n",
        "\n",
        "[k] {'Sunrise_Sunset_Day', 'Nautical_Twilight_Day', 'Astronomical_Twilight_Day', 'Civil_Twilight_Day', 'Distance_0-5', 'Timezone_US/Eastern', 'Station', 'Severity_2'}, \n",
        "\n",
        "[l] {'Sunrise_Sunset_Day', 'Nautical_Twilight_Day', 'Astronomical_Twilight_Day', 'Civil_Twilight_Day', 'Distance_0-5', 'Timezone_US/Eastern', 'Traffic_Signal', 'Severity_2'},\n",
        "\n",
        "[m] {'Sunrise_Sunset_Day', 'Timezone_US/Pacific', 'Nautical_Twilight_Day', 'Astronomical_Twilight_Day', 'Civil_Twilight_Day', 'Distance_0-5', 'Traffic_Signal', 'Severity_2'},\n",
        "\n",
        "[n] {'Sunrise_Sunset_Day', 'Nautical_Twilight_Day', 'Astronomical_Twilight_Day', 'Crossing', 'Civil_Twilight_Day', 'Distance_0-5', 'Timezone_US/Eastern', 'Traffic_Signal', 'Severity_2'}\n"
      ],
      "metadata": {
        "id": "YsjRhU6sbdug"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Frequent Item sets for min sup = 0.02, "
      ],
      "metadata": {
        "id": "YWJGOQT6RsOF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "frequent_itemsets2 = apriori(apriori_df, min_support = 0.02, use_colnames = True)\n",
        "frequent_itemsets2.to_csv(\"f2.csv\")\n",
        "frequent_itemsets2"
      ],
      "metadata": {
        "id": "Ce11AnOcFIfH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "9c95520a-9b1b-4bed-c031-3f1240ea4ccf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       support                                           itemsets\n",
              "0     0.070436                                         (Crossing)\n",
              "1     0.102164                                         (Junction)\n",
              "2     0.023926                                          (Station)\n",
              "3     0.093273                                   (Traffic_Signal)\n",
              "4     0.890182                                       (Severity_2)\n",
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              "1356  0.022389  (Sunrise_Sunset_Day, Timezone_US/Pacific, Naut...\n",
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              "                                                     [key], {});\n",
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              "\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",
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          "metadata": {},
          "execution_count": 58
        }
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      "source": [
        "[a] {'Sunrise_Sunset_Day', 'Nautical_Twilight_Day', 'Astronomical_Twilight_Day', 'Crossing', 'Civil_Twilight_Day', 'Distance_0-5', 'Traffic_Signal', 'Severity_2'},\n",
        "\n",
        "[b] {'Sunrise_Sunset_Day', 'Nautical_Twilight_Day', 'Astronomical_Twilight_Day', 'Crossing', 'Civil_Twilight_Day', 'Distance_0-5', 'Timezone_US/Eastern', 'Severity_2'},\n",
        "\n",
        "[c] {'Sunrise_Sunset_Day', 'Nautical_Twilight_Day', 'Astronomical_Twilight_Day', 'Civil_Twilight_Day', 'Junction', 'Distance_0-5', 'Timezone_US/Eastern', 'Severity_2'},\n",
        "\n",
        "[d] {'Sunrise_Sunset_Day', 'Timezone_US/Pacific', 'Nautical_Twilight_Day', 'Astronomical_Twilight_Day', 'Civil_Twilight_Day', 'Junction', 'Distance_0-5', 'Severity_2'},\n",
        "\n",
        "[e] {'Sunrise_Sunset_Day', 'Nautical_Twilight_Day', 'Astronomical_Twilight_Day', 'Civil_Twilight_Day', 'Distance_0-5', 'Timezone_US/Eastern', 'Traffic_Signal', 'Severity_2'}"
      ],
      "metadata": {
        "id": "UKB76UngUo-R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Frequent Item sets for min sup = 0.05, "
      ],
      "metadata": {
        "id": "bwklHp4abfVE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "frequent_itemsets3 = apriori(apriori_df, min_support = 0.05, use_colnames = True)\n",
        "frequent_itemsets3.to_csv(\"f3.csv\")\n",
        "frequent_itemsets3\n"
      ],
      "metadata": {
        "id": "6ilC1hODF7t0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "dab89248-a7a6-48e3-c83c-72e03ab469ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      support                                           itemsets\n",
              "0    0.070436                                         (Crossing)\n",
              "1    0.102164                                         (Junction)\n",
              "2    0.093273                                   (Traffic_Signal)\n",
              "3    0.890182                                       (Severity_2)\n",
              "4    0.054540                                       (Severity_3)\n",
              "..        ...                                                ...\n",
              "608  0.088387  (Sunrise_Sunset_Day, Timezone_US/Central, Naut...\n",
              "609  0.243024  (Sunrise_Sunset_Day, Nautical_Twilight_Day, As...\n",
              "610  0.083564  (Sunrise_Sunset_Night, Astronomical_Twilight_N...\n",
              "611  0.197035  (Sunrise_Sunset_Day, Timezone_US/Pacific, Naut...\n",
              "612  0.077559  (Sunrise_Sunset_Night, Astronomical_Twilight_N...\n",
              "\n",
              "[613 rows x 2 columns]"
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              "      <td>0.093273</td>\n",
              "      <td>(Traffic_Signal)</td>\n",
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              "      <th>3</th>\n",
              "      <td>0.890182</td>\n",
              "      <td>(Severity_2)</td>\n",
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              "      <th>4</th>\n",
              "      <td>0.054540</td>\n",
              "      <td>(Severity_3)</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>608</th>\n",
              "      <td>0.088387</td>\n",
              "      <td>(Sunrise_Sunset_Day, Timezone_US/Central, Naut...</td>\n",
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              "      <th>609</th>\n",
              "      <td>0.243024</td>\n",
              "      <td>(Sunrise_Sunset_Day, Nautical_Twilight_Day, As...</td>\n",
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              "    <tr>\n",
              "      <th>610</th>\n",
              "      <td>0.083564</td>\n",
              "      <td>(Sunrise_Sunset_Night, Astronomical_Twilight_N...</td>\n",
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              "      <th>611</th>\n",
              "      <td>0.197035</td>\n",
              "      <td>(Sunrise_Sunset_Day, Timezone_US/Pacific, Naut...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>612</th>\n",
              "      <td>0.077559</td>\n",
              "      <td>(Sunrise_Sunset_Night, Astronomical_Twilight_N...</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-aaddd015-a517-49c0-8ffe-4c46993287b7');\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",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "[a] {'Sunrise_Sunset_Day', 'Nautical_Twilight_Day', 'Astronomical_Twilight_Day', 'Civil_Twilight_Day', 'Junction', 'Distance_0-5', 'Severity_2'}\n",
        "\n",
        "[b] {'Sunrise_Sunset_Day', 'Nautical_Twilight_Day', 'Astronomical_Twilight_Day', 'Civil_Twilight_Day', 'Distance_0-5', 'Traffic_Signal', 'Severity_2'},\n",
        "\n",
        "[c] {'Sunrise_Sunset_Day', 'Timezone_US/Central', 'Nautical_Twilight_Day', 'Astronomical_Twilight_Day', 'Civil_Twilight_Day', 'Distance_0-5', 'Severity_2'}\n",
        "\n",
        "[d] {'Sunrise_Sunset_Day', 'Nautical_Twilight_Day', 'Astronomical_Twilight_Day', 'Civil_Twilight_Day', 'Distance_0-5', 'Timezone_US/Eastern', 'Severity_2'},\n",
        "\n",
        "[e] {'Sunrise_Sunset_Night', 'Astronomical_Twilight_Night', 'Nautical_Twilight_Night', 'Civil_Twilight_Night', 'Distance_0-5', 'Timezone_US/Eastern', 'Severity_2'},\n",
        "\n",
        "[f] {'Sunrise_Sunset_Day', 'Timezone_US/Pacific', 'Nautical_Twilight_Day', 'Astronomical_Twilight_Day', 'Civil_Twilight_Day', 'Distance_0-5', 'Severity_2'\n",
        "\n",
        "[g] {'Sunrise_Sunset_Night', 'Astronomical_Twilight_Night', 'Timezone_US/Pacific', 'Nautical_Twilight_Night', 'Civil_Twilight_Night', 'Distance_0-5', 'Severity_2'}"
      ],
      "metadata": {
        "id": "XdXjk6XJUq7a"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Frequent Item sets for min sup = 0.08, "
      ],
      "metadata": {
        "id": "8b4O__83bgJi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "frequent_itemsets4 = apriori(apriori_df, min_support = 0.08, use_colnames = True)\n",
        "frequent_itemsets4.to_csv(\"f4.csv\")\n",
        "frequent_itemsets4"
      ],
      "metadata": {
        "id": "YE3UrkL5F_7u",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "3a9f8d79-9c2b-4e32-8601-e0ddc5d73ba4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      support                                           itemsets\n",
              "0    0.102164                                         (Junction)\n",
              "1    0.093273                                   (Traffic_Signal)\n",
              "2    0.890182                                       (Severity_2)\n",
              "3    0.983160                                     (Distance_0-5)\n",
              "4    0.171752                              (Timezone_US/Central)\n",
              "..        ...                                                ...\n",
              "432  0.081554  (Sunrise_Sunset_Night, Astronomical_Twilight_N...\n",
              "433  0.088387  (Sunrise_Sunset_Day, Timezone_US/Central, Naut...\n",
              "434  0.243024  (Sunrise_Sunset_Day, Nautical_Twilight_Day, As...\n",
              "435  0.083564  (Sunrise_Sunset_Night, Astronomical_Twilight_N...\n",
              "436  0.197035  (Sunrise_Sunset_Day, Timezone_US/Pacific, Naut...\n",
              "\n",
              "[437 rows x 2 columns]"
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              "      <td>0.093273</td>\n",
              "      <td>(Traffic_Signal)</td>\n",
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              "      <td>(Severity_2)</td>\n",
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              "      <th>3</th>\n",
              "      <td>0.983160</td>\n",
              "      <td>(Distance_0-5)</td>\n",
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              "      <td>0.171752</td>\n",
              "      <td>(Timezone_US/Central)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>432</th>\n",
              "      <td>0.081554</td>\n",
              "      <td>(Sunrise_Sunset_Night, Astronomical_Twilight_N...</td>\n",
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              "    <tr>\n",
              "      <th>433</th>\n",
              "      <td>0.088387</td>\n",
              "      <td>(Sunrise_Sunset_Day, Timezone_US/Central, Naut...</td>\n",
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              "    <tr>\n",
              "      <th>434</th>\n",
              "      <td>0.243024</td>\n",
              "      <td>(Sunrise_Sunset_Day, Nautical_Twilight_Day, As...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>435</th>\n",
              "      <td>0.083564</td>\n",
              "      <td>(Sunrise_Sunset_Night, Astronomical_Twilight_N...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>436</th>\n",
              "      <td>0.197035</td>\n",
              "      <td>(Sunrise_Sunset_Day, Timezone_US/Pacific, Naut...</td>\n",
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              "  </tbody>\n",
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              "<p>437 rows × 2 columns</p>\n",
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              "\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1dcf7ecf-5dc0-4335-a226-79d3ebdf23d1 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-1dcf7ecf-5dc0-4335-a226-79d3ebdf23d1');\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": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "[a] {'Sunrise_Sunset_Day', 'Timezone_US/Central', 'Nautical_Twilight_Day', 'Astronomical_Twilight_Day', 'Civil_Twilight_Day', 'Distance_0-5', 'Severity_2'}\n",
        "\n",
        "[b] {'Sunrise_Sunset_Day', 'Nautical_Twilight_Day', 'Astronomical_Twilight_Day', 'Civil_Twilight_Day', 'Distance_0-5', 'Timezone_US/Eastern', 'Severity_2'}\n",
        "\n",
        "[c] {'Sunrise_Sunset_Night', 'Astronomical_Twilight_Night', 'Nautical_Twilight_Night', 'Civil_Twilight_Night', 'Distance_0-5', 'Timezone_US/Eastern', 'Severity_2'}\n",
        "\n",
        "[d] {'Sunrise_Sunset_Day', 'Timezone_US/Pacific', 'Nautical_Twilight_Day', 'Astronomical_Twilight_Day', 'Civil_Twilight_Day', 'Distance_0-5', 'Severity_2'}"
      ],
      "metadata": {
        "id": "ebNH_DuhUtyx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Generating Association Rules "
      ],
      "metadata": {
        "id": "d-R-jaAk1afN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####For Frequent Itemset 1"
      ],
      "metadata": {
        "id": "SY1E-ikAbjl8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ass_rules1 = association_rules(frequent_itemsets, metric=\"confidence\", min_threshold=0.8)\n",
        "ass_rules1 = ass_rules1.sort_values(by='confidence', ascending =False)\n",
        "ass_rules1.to_csv(\"AR1.csv\")\n",
        "ass_rules1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "5BjlMEBW1Zpt",
        "outputId": "ae6fde21-3409-4309-cc8f-a0300d5fa246"
      },
      "execution_count": null,
      "outputs": [
        {
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              "                                             antecedents  \\\n",
              "6496   (Nautical_Twilight_Night, Astronomical_Twiligh...   \n",
              "1535   (Civil_Twilight_Day, Traffic_Signal, Timezone_...   \n",
              "1557   (Civil_Twilight_Night, Astronomical_Twilight_N...   \n",
              "13472  (Sunrise_Sunset_Day, Nautical_Twilight_Day, Cr...   \n",
              "8054   (Junction, Sunrise_Sunset_Day, Timezone_US/Eas...   \n",
              "...                                                  ...   \n",
              "13806  (Timezone_US/Pacific, Nautical_Twilight_Day, T...   \n",
              "776        (Distance_0-5, Timezone_US/Eastern, Crossing)   \n",
              "87                       (Timezone_US/Eastern, Crossing)   \n",
              "719          (Crossing, Timezone_US/Eastern, Severity_2)   \n",
              "3013   (Severity_2, Distance_0-5, Timezone_US/Eastern...   \n",
              "\n",
              "                                             consequents  antecedent support  \\\n",
              "6496                              (Sunrise_Sunset_Night)            0.039912   \n",
              "1535                             (Nautical_Twilight_Day)            0.014308   \n",
              "1557                              (Sunrise_Sunset_Night)            0.020736   \n",
              "13472                        (Astronomical_Twilight_Day)            0.012632   \n",
              "8054         (Civil_Twilight_Day, Nautical_Twilight_Day)            0.020492   \n",
              "...                                                  ...                 ...   \n",
              "13806  (Civil_Twilight_Day, Distance_0-5, Sunrise_Sun...            0.015321   \n",
              "776                          (Astronomical_Twilight_Day)            0.038245   \n",
              "87                           (Astronomical_Twilight_Day)            0.038279   \n",
              "719                          (Astronomical_Twilight_Day)            0.035027   \n",
              "3013                         (Astronomical_Twilight_Day)            0.035006   \n",
              "\n",
              "       consequent support   support  confidence      lift  leverage  \\\n",
              "6496             0.361891  0.039912    1.000000  2.763264  0.025468   \n",
              "1535             0.726510  0.014308    1.000000  1.376443  0.003913   \n",
              "1557             0.361891  0.020736    1.000000  2.763264  0.013232   \n",
              "13472            0.766386  0.012632    1.000000  1.304826  0.002951   \n",
              "8054             0.679292  0.020492    1.000000  1.472121  0.006572   \n",
              "...                   ...       ...         ...       ...       ...   \n",
              "13806            0.557071  0.012266    0.800583  1.437131  0.003731   \n",
              "776              0.766386  0.030617    0.800559  1.044590  0.001307   \n",
              "87               0.766386  0.030644    0.800544  1.044571  0.001308   \n",
              "719              0.766386  0.028024    0.800062  1.043942  0.001180   \n",
              "3013             0.766386  0.028007    0.800054  1.043931  0.001179   \n",
              "\n",
              "       conviction  \n",
              "6496          inf  \n",
              "1535          inf  \n",
              "1557          inf  \n",
              "13472         inf  \n",
              "8054          inf  \n",
              "...           ...  \n",
              "13806    2.221126  \n",
              "776      1.171347  \n",
              "87       1.171257  \n",
              "719      1.168434  \n",
              "3013     1.168387  \n",
              "\n",
              "[13868 rows x 9 columns]"
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              "      <th>6496</th>\n",
              "      <td>(Nautical_Twilight_Night, Astronomical_Twiligh...</td>\n",
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              "      <td>(Sunrise_Sunset_Day, Nautical_Twilight_Day, Cr...</td>\n",
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              "      <td>0.012632</td>\n",
              "      <td>0.766386</td>\n",
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              "      <td>1.304826</td>\n",
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              "      <th>8054</th>\n",
              "      <td>(Junction, Sunrise_Sunset_Day, Timezone_US/Eas...</td>\n",
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              "      <td>0.020492</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>13806</th>\n",
              "      <td>(Timezone_US/Pacific, Nautical_Twilight_Day, T...</td>\n",
              "      <td>(Civil_Twilight_Day, Distance_0-5, Sunrise_Sun...</td>\n",
              "      <td>0.015321</td>\n",
              "      <td>0.557071</td>\n",
              "      <td>0.012266</td>\n",
              "      <td>0.800583</td>\n",
              "      <td>1.437131</td>\n",
              "      <td>0.003731</td>\n",
              "      <td>2.221126</td>\n",
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              "    <tr>\n",
              "      <th>776</th>\n",
              "      <td>(Distance_0-5, Timezone_US/Eastern, Crossing)</td>\n",
              "      <td>(Astronomical_Twilight_Day)</td>\n",
              "      <td>0.038245</td>\n",
              "      <td>0.766386</td>\n",
              "      <td>0.030617</td>\n",
              "      <td>0.800559</td>\n",
              "      <td>1.044590</td>\n",
              "      <td>0.001307</td>\n",
              "      <td>1.171347</td>\n",
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              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>(Timezone_US/Eastern, Crossing)</td>\n",
              "      <td>(Astronomical_Twilight_Day)</td>\n",
              "      <td>0.038279</td>\n",
              "      <td>0.766386</td>\n",
              "      <td>0.030644</td>\n",
              "      <td>0.800544</td>\n",
              "      <td>1.044571</td>\n",
              "      <td>0.001308</td>\n",
              "      <td>1.171257</td>\n",
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              "    <tr>\n",
              "      <th>719</th>\n",
              "      <td>(Crossing, Timezone_US/Eastern, Severity_2)</td>\n",
              "      <td>(Astronomical_Twilight_Day)</td>\n",
              "      <td>0.035027</td>\n",
              "      <td>0.766386</td>\n",
              "      <td>0.028024</td>\n",
              "      <td>0.800062</td>\n",
              "      <td>1.043942</td>\n",
              "      <td>0.001180</td>\n",
              "      <td>1.168434</td>\n",
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              "      <th>3013</th>\n",
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              "      <td>0.035006</td>\n",
              "      <td>0.766386</td>\n",
              "      <td>0.028007</td>\n",
              "      <td>0.800054</td>\n",
              "      <td>1.043931</td>\n",
              "      <td>0.001179</td>\n",
              "      <td>1.168387</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####For Frequent Itemset 2"
      ],
      "metadata": {
        "id": "cads54cobkVI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ass_rules2 = association_rules(frequent_itemsets2, metric=\"confidence\", min_threshold=0.8)\n",
        "ass_rules2 = ass_rules2.sort_values(by='confidence', ascending =False)\n",
        "ass_rules2.to_csv(\"AR2.csv\")\n",
        "ass_rules2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "yDtMUkKt2yha",
        "outputId": "7f4ef6ab-a79d-4afd-c149-d308e8a5e06c"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "                                            antecedents  \\\n",
              "6082  (Sunrise_Sunset_Day, Astronomical_Twilight_Day...   \n",
              "4147  (Civil_Twilight_Day, Sunrise_Sunset_Day, Astro...   \n",
              "6935  (Sunrise_Sunset_Day, Nautical_Twilight_Day, As...   \n",
              "7632  (Sunrise_Sunset_Day, Civil_Twilight_Day, Junct...   \n",
              "6937  (Sunrise_Sunset_Day, Astronomical_Twilight_Day...   \n",
              "...                                                 ...   \n",
              "2172     (Civil_Twilight_Day, Traffic_Signal, Crossing)   \n",
              "619       (Distance_0-5, Timezone_US/Eastern, Crossing)   \n",
              "76                      (Timezone_US/Eastern, Crossing)   \n",
              "587         (Crossing, Timezone_US/Eastern, Severity_2)   \n",
              "2243  (Severity_2, Distance_0-5, Timezone_US/Eastern...   \n",
              "\n",
              "                                  consequents  antecedent support  \\\n",
              "6082                  (Nautical_Twilight_Day)            0.031627   \n",
              "4147                  (Nautical_Twilight_Day)            0.565150   \n",
              "6935                     (Civil_Twilight_Day)            0.247054   \n",
              "7632                  (Nautical_Twilight_Day)            0.020067   \n",
              "6937                  (Nautical_Twilight_Day)            0.247054   \n",
              "...                                       ...                 ...   \n",
              "2172  (Astronomical_Twilight_Day, Severity_2)            0.026801   \n",
              "619               (Astronomical_Twilight_Day)            0.038245   \n",
              "76                (Astronomical_Twilight_Day)            0.038279   \n",
              "587               (Astronomical_Twilight_Day)            0.035027   \n",
              "2243              (Astronomical_Twilight_Day)            0.035006   \n",
              "\n",
              "      consequent support   support  confidence      lift  leverage  conviction  \n",
              "6082            0.726510  0.031627    1.000000  1.376443  0.008650         inf  \n",
              "4147            0.726510  0.565150    1.000000  1.376443  0.154563         inf  \n",
              "6935            0.679292  0.247054    1.000000  1.472121  0.079232         inf  \n",
              "7632            0.726510  0.020067    1.000000  1.376443  0.005488         inf  \n",
              "6937            0.726510  0.247054    1.000000  1.376443  0.067567         inf  \n",
              "...                  ...       ...         ...       ...       ...         ...  \n",
              "2172            0.680448  0.021466    0.800935  1.177070  0.003229    1.605264  \n",
              "619             0.766386  0.030617    0.800559  1.044590  0.001307    1.171347  \n",
              "76              0.766386  0.030644    0.800544  1.044571  0.001308    1.171257  \n",
              "587             0.766386  0.028024    0.800062  1.043942  0.001180    1.168434  \n",
              "2243            0.766386  0.028007    0.800054  1.043931  0.001179    1.168387  \n",
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              "[8669 rows x 9 columns]"
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              "      <th>6082</th>\n",
              "      <td>(Sunrise_Sunset_Day, Astronomical_Twilight_Day...</td>\n",
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              "      <td>1.000000</td>\n",
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              "      <td>0.079232</td>\n",
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              "      <th>7632</th>\n",
              "      <td>(Sunrise_Sunset_Day, Civil_Twilight_Day, Junct...</td>\n",
              "      <td>(Nautical_Twilight_Day)</td>\n",
              "      <td>0.020067</td>\n",
              "      <td>0.726510</td>\n",
              "      <td>0.020067</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.376443</td>\n",
              "      <td>0.005488</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6937</th>\n",
              "      <td>(Sunrise_Sunset_Day, Astronomical_Twilight_Day...</td>\n",
              "      <td>(Nautical_Twilight_Day)</td>\n",
              "      <td>0.247054</td>\n",
              "      <td>0.726510</td>\n",
              "      <td>0.247054</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.376443</td>\n",
              "      <td>0.067567</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2172</th>\n",
              "      <td>(Civil_Twilight_Day, Traffic_Signal, Crossing)</td>\n",
              "      <td>(Astronomical_Twilight_Day, Severity_2)</td>\n",
              "      <td>0.026801</td>\n",
              "      <td>0.680448</td>\n",
              "      <td>0.021466</td>\n",
              "      <td>0.800935</td>\n",
              "      <td>1.177070</td>\n",
              "      <td>0.003229</td>\n",
              "      <td>1.605264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>619</th>\n",
              "      <td>(Distance_0-5, Timezone_US/Eastern, Crossing)</td>\n",
              "      <td>(Astronomical_Twilight_Day)</td>\n",
              "      <td>0.038245</td>\n",
              "      <td>0.766386</td>\n",
              "      <td>0.030617</td>\n",
              "      <td>0.800559</td>\n",
              "      <td>1.044590</td>\n",
              "      <td>0.001307</td>\n",
              "      <td>1.171347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>(Timezone_US/Eastern, Crossing)</td>\n",
              "      <td>(Astronomical_Twilight_Day)</td>\n",
              "      <td>0.038279</td>\n",
              "      <td>0.766386</td>\n",
              "      <td>0.030644</td>\n",
              "      <td>0.800544</td>\n",
              "      <td>1.044571</td>\n",
              "      <td>0.001308</td>\n",
              "      <td>1.171257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>587</th>\n",
              "      <td>(Crossing, Timezone_US/Eastern, Severity_2)</td>\n",
              "      <td>(Astronomical_Twilight_Day)</td>\n",
              "      <td>0.035027</td>\n",
              "      <td>0.766386</td>\n",
              "      <td>0.028024</td>\n",
              "      <td>0.800062</td>\n",
              "      <td>1.043942</td>\n",
              "      <td>0.001180</td>\n",
              "      <td>1.168434</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2243</th>\n",
              "      <td>(Severity_2, Distance_0-5, Timezone_US/Eastern...</td>\n",
              "      <td>(Astronomical_Twilight_Day)</td>\n",
              "      <td>0.035006</td>\n",
              "      <td>0.766386</td>\n",
              "      <td>0.028007</td>\n",
              "      <td>0.800054</td>\n",
              "      <td>1.043931</td>\n",
              "      <td>0.001179</td>\n",
              "      <td>1.168387</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8669 rows × 9 columns</p>\n",
              "</div>\n",
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              "                                                     [key], {});\n",
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              "\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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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####For Frequent Itemset 3"
      ],
      "metadata": {
        "id": "HBEq542zbk7Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ass_rules3 = association_rules(frequent_itemsets3, metric=\"confidence\", min_threshold=0.8)\n",
        "ass_rules3 = ass_rules3.sort_values(by='confidence', ascending =False)\n",
        "ass_rules3.to_csv(\"AR3.csv\")\n",
        "ass_rules3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "tQi6u7-d28ZE",
        "outputId": "45ee2848-83db-4f8c-89cf-3f16edfabcd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            antecedents  \\\n",
              "2059  (Nautical_Twilight_Night, Distance_0-5, Severi...   \n",
              "745   (Distance_0-5, Nautical_Twilight_Night, Severi...   \n",
              "910   (Civil_Twilight_Day, Sunrise_Sunset_Day, Sever...   \n",
              "3294  (Sunrise_Sunset_Day, Timezone_US/Pacific, Civi...   \n",
              "923   (Sunrise_Sunset_Day, Astronomical_Twilight_Day...   \n",
              "...                                                 ...   \n",
              "2346     (Timezone_US/Pacific, Nautical_Twilight_Night)   \n",
              "2322     (Timezone_US/Pacific, Nautical_Twilight_Night)   \n",
              "2317  (Timezone_US/Pacific, Sunrise_Sunset_Night, Na...   \n",
              "894      (Timezone_US/Pacific, Nautical_Twilight_Night)   \n",
              "2343  (Timezone_US/Pacific, Civil_Twilight_Night, Na...   \n",
              "\n",
              "                                            consequents  antecedent support  \\\n",
              "2059       (Civil_Twilight_Night, Sunrise_Sunset_Night)            0.241817   \n",
              "745                              (Civil_Twilight_Night)            0.241817   \n",
              "910                             (Nautical_Twilight_Day)            0.565682   \n",
              "3294                            (Nautical_Twilight_Day)            0.197434   \n",
              "923                             (Nautical_Twilight_Day)            0.565155   \n",
              "...                                                 ...                 ...   \n",
              "2346  (Civil_Twilight_Night, Astronomical_Twilight_N...            0.097260   \n",
              "2322  (Sunrise_Sunset_Night, Astronomical_Twilight_N...            0.097260   \n",
              "2317          (Astronomical_Twilight_Night, Severity_2)            0.097260   \n",
              "894           (Astronomical_Twilight_Night, Severity_2)            0.097260   \n",
              "2343          (Astronomical_Twilight_Night, Severity_2)            0.097260   \n",
              "\n",
              "      consequent support   support  confidence      lift  leverage  conviction  \n",
              "2059            0.320700  0.241817    1.000000  3.118182  0.164266         inf  \n",
              "745             0.320708  0.241817    1.000000  3.118099  0.164264         inf  \n",
              "910             0.726510  0.565682    1.000000  1.376443  0.154708         inf  \n",
              "3294            0.726510  0.197434    1.000000  1.376443  0.053996         inf  \n",
              "923             0.726510  0.565155    1.000000  1.376443  0.154564         inf  \n",
              "...                  ...       ...         ...       ...       ...         ...  \n",
              "2346            0.209183  0.078241    0.804458  3.845714  0.057896    4.044237  \n",
              "2322            0.209201  0.078241    0.804458  3.845384  0.057894    4.044146  \n",
              "2317            0.209734  0.078241    0.804458  3.835609  0.057843    4.041419  \n",
              "894             0.209734  0.078241    0.804458  3.835609  0.057843    4.041419  \n",
              "2343            0.209734  0.078241    0.804458  3.835609  0.057843    4.041419  \n",
              "\n",
              "[4118 rows x 9 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
              "      <th>confidence</th>\n",
              "      <th>lift</th>\n",
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              "      <th>conviction</th>\n",
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              "    <tr>\n",
              "      <th>2059</th>\n",
              "      <td>(Nautical_Twilight_Night, Distance_0-5, Severi...</td>\n",
              "      <td>(Civil_Twilight_Night, Sunrise_Sunset_Night)</td>\n",
              "      <td>0.241817</td>\n",
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              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>745</th>\n",
              "      <td>(Distance_0-5, Nautical_Twilight_Night, Severi...</td>\n",
              "      <td>(Civil_Twilight_Night)</td>\n",
              "      <td>0.241817</td>\n",
              "      <td>0.320708</td>\n",
              "      <td>0.241817</td>\n",
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              "      <th>910</th>\n",
              "      <td>(Civil_Twilight_Day, Sunrise_Sunset_Day, Sever...</td>\n",
              "      <td>(Nautical_Twilight_Day)</td>\n",
              "      <td>0.565682</td>\n",
              "      <td>0.726510</td>\n",
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              "      <td>0.154708</td>\n",
              "      <td>inf</td>\n",
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              "    <tr>\n",
              "      <th>3294</th>\n",
              "      <td>(Sunrise_Sunset_Day, Timezone_US/Pacific, Civi...</td>\n",
              "      <td>(Nautical_Twilight_Day)</td>\n",
              "      <td>0.197434</td>\n",
              "      <td>0.726510</td>\n",
              "      <td>0.197434</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.376443</td>\n",
              "      <td>0.053996</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>923</th>\n",
              "      <td>(Sunrise_Sunset_Day, Astronomical_Twilight_Day...</td>\n",
              "      <td>(Nautical_Twilight_Day)</td>\n",
              "      <td>0.565155</td>\n",
              "      <td>0.726510</td>\n",
              "      <td>0.565155</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2346</th>\n",
              "      <td>(Timezone_US/Pacific, Nautical_Twilight_Night)</td>\n",
              "      <td>(Civil_Twilight_Night, Astronomical_Twilight_N...</td>\n",
              "      <td>0.097260</td>\n",
              "      <td>0.209183</td>\n",
              "      <td>0.078241</td>\n",
              "      <td>0.804458</td>\n",
              "      <td>3.845714</td>\n",
              "      <td>0.057896</td>\n",
              "      <td>4.044237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2322</th>\n",
              "      <td>(Timezone_US/Pacific, Nautical_Twilight_Night)</td>\n",
              "      <td>(Sunrise_Sunset_Night, Astronomical_Twilight_N...</td>\n",
              "      <td>0.097260</td>\n",
              "      <td>0.209201</td>\n",
              "      <td>0.078241</td>\n",
              "      <td>0.804458</td>\n",
              "      <td>3.845384</td>\n",
              "      <td>0.057894</td>\n",
              "      <td>4.044146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2317</th>\n",
              "      <td>(Timezone_US/Pacific, Sunrise_Sunset_Night, Na...</td>\n",
              "      <td>(Astronomical_Twilight_Night, Severity_2)</td>\n",
              "      <td>0.097260</td>\n",
              "      <td>0.209734</td>\n",
              "      <td>0.078241</td>\n",
              "      <td>0.804458</td>\n",
              "      <td>3.835609</td>\n",
              "      <td>0.057843</td>\n",
              "      <td>4.041419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>894</th>\n",
              "      <td>(Timezone_US/Pacific, Nautical_Twilight_Night)</td>\n",
              "      <td>(Astronomical_Twilight_Night, Severity_2)</td>\n",
              "      <td>0.097260</td>\n",
              "      <td>0.209734</td>\n",
              "      <td>0.078241</td>\n",
              "      <td>0.804458</td>\n",
              "      <td>3.835609</td>\n",
              "      <td>0.057843</td>\n",
              "      <td>4.041419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2343</th>\n",
              "      <td>(Timezone_US/Pacific, Civil_Twilight_Night, Na...</td>\n",
              "      <td>(Astronomical_Twilight_Night, Severity_2)</td>\n",
              "      <td>0.097260</td>\n",
              "      <td>0.209734</td>\n",
              "      <td>0.078241</td>\n",
              "      <td>0.804458</td>\n",
              "      <td>3.835609</td>\n",
              "      <td>0.057843</td>\n",
              "      <td>4.041419</td>\n",
              "    </tr>\n",
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              "<p>4118 rows × 9 columns</p>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
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              "    }\n",
              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-9d2af9e2-113a-4da8-8533-779c61609b54 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-9d2af9e2-113a-4da8-8533-779c61609b54');\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": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####For Frequent Itemset 4"
      ],
      "metadata": {
        "id": "i6KHSCbe1z2t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ass_rules4 = association_rules(frequent_itemsets4, metric=\"confidence\", min_threshold=0.8)\n",
        "ass_rules4 = ass_rules4.sort_values(by='confidence', ascending =False)\n",
        "ass_rules4.to_csv(\"AR4.csv\")\n",
        "ass_rules4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "H6vNxYPo3Abn",
        "outputId": "843f0ece-813c-4c8e-8fd5-75ffd9da13b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            antecedents  \\\n",
              "951   (Sunrise_Sunset_Day, Timezone_US/Eastern, Astr...   \n",
              "290            (Civil_Twilight_Day, Sunrise_Sunset_Day)   \n",
              "2896  (Sunrise_Sunset_Day, Timezone_US/Pacific, Astr...   \n",
              "307     (Sunrise_Sunset_Night, Nautical_Twilight_Night)   \n",
              "306     (Civil_Twilight_Night, Nautical_Twilight_Night)   \n",
              "...                                                 ...   \n",
              "2405  (Timezone_US/Pacific, Civil_Twilight_Night, Di...   \n",
              "1298  (Timezone_US/Pacific, Civil_Twilight_Night, Di...   \n",
              "1642  (Timezone_US/Pacific, Civil_Twilight_Night, Su...   \n",
              "602         (Timezone_US/Pacific, Civil_Twilight_Night)   \n",
              "1647        (Timezone_US/Pacific, Civil_Twilight_Night)   \n",
              "\n",
              "                                            consequents  antecedent support  \\\n",
              "951                                (Civil_Twilight_Day)            0.281709   \n",
              "290                             (Nautical_Twilight_Day)            0.638101   \n",
              "2896                            (Nautical_Twilight_Day)            0.197035   \n",
              "307                              (Civil_Twilight_Night)            0.273490   \n",
              "306                              (Sunrise_Sunset_Night)            0.273490   \n",
              "...                                                 ...                 ...   \n",
              "2405  (Sunrise_Sunset_Night, Nautical_Twilight_Night...            0.113419   \n",
              "1298              (Nautical_Twilight_Night, Severity_2)            0.113419   \n",
              "1642              (Nautical_Twilight_Night, Severity_2)            0.114649   \n",
              "602               (Nautical_Twilight_Night, Severity_2)            0.114655   \n",
              "1647  (Sunrise_Sunset_Night, Nautical_Twilight_Night...            0.114655   \n",
              "\n",
              "      consequent support   support  confidence      lift  leverage  conviction  \n",
              "951             0.679292  0.281709    1.000000  1.472121  0.090346         inf  \n",
              "290             0.726510  0.638101    1.000000  1.376443  0.174514         inf  \n",
              "2896            0.726510  0.197035    1.000000  1.376443  0.053887         inf  \n",
              "307             0.320708  0.273490    1.000000  3.118099  0.185779         inf  \n",
              "306             0.361891  0.273490    1.000000  2.763264  0.174516         inf  \n",
              "...                  ...       ...         ...       ...       ...         ...  \n",
              "2405            0.245274  0.091455    0.806351  3.287557  0.063637    3.897398  \n",
              "1298            0.245274  0.091455    0.806351  3.287557  0.063637    3.897398  \n",
              "1642            0.245274  0.092323    0.805270  3.283150  0.064203    3.875762  \n",
              "602             0.245274  0.092323    0.805228  3.282979  0.064201    3.874925  \n",
              "1647            0.245274  0.092323    0.805228  3.282979  0.064201    3.874925  \n",
              "\n",
              "[2949 rows x 9 columns]"
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              "      <th></th>\n",
              "      <th>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
              "      <th>confidence</th>\n",
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              "      <th>conviction</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>951</th>\n",
              "      <td>(Sunrise_Sunset_Day, Timezone_US/Eastern, Astr...</td>\n",
              "      <td>(Civil_Twilight_Day)</td>\n",
              "      <td>0.281709</td>\n",
              "      <td>0.679292</td>\n",
              "      <td>0.281709</td>\n",
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              "      <td>1.472121</td>\n",
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              "      <td>(Civil_Twilight_Day, Sunrise_Sunset_Day)</td>\n",
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              "      <td>0.638101</td>\n",
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              "      <td>(Sunrise_Sunset_Day, Timezone_US/Pacific, Astr...</td>\n",
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              "      <td>0.273490</td>\n",
              "      <td>0.320708</td>\n",
              "      <td>0.273490</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>0.185779</td>\n",
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              "      <th>306</th>\n",
              "      <td>(Civil_Twilight_Night, Nautical_Twilight_Night)</td>\n",
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              "      <td>0.273490</td>\n",
              "      <td>0.361891</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2405</th>\n",
              "      <td>(Timezone_US/Pacific, Civil_Twilight_Night, Di...</td>\n",
              "      <td>(Sunrise_Sunset_Night, Nautical_Twilight_Night...</td>\n",
              "      <td>0.113419</td>\n",
              "      <td>0.245274</td>\n",
              "      <td>0.091455</td>\n",
              "      <td>0.806351</td>\n",
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              "      <td>0.063637</td>\n",
              "      <td>3.897398</td>\n",
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              "    <tr>\n",
              "      <th>1298</th>\n",
              "      <td>(Timezone_US/Pacific, Civil_Twilight_Night, Di...</td>\n",
              "      <td>(Nautical_Twilight_Night, Severity_2)</td>\n",
              "      <td>0.113419</td>\n",
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              "      <td>0.063637</td>\n",
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              "    <tr>\n",
              "      <th>1642</th>\n",
              "      <td>(Timezone_US/Pacific, Civil_Twilight_Night, Su...</td>\n",
              "      <td>(Nautical_Twilight_Night, Severity_2)</td>\n",
              "      <td>0.114649</td>\n",
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              "      <td>0.064203</td>\n",
              "      <td>3.875762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>602</th>\n",
              "      <td>(Timezone_US/Pacific, Civil_Twilight_Night)</td>\n",
              "      <td>(Nautical_Twilight_Night, Severity_2)</td>\n",
              "      <td>0.114655</td>\n",
              "      <td>0.245274</td>\n",
              "      <td>0.092323</td>\n",
              "      <td>0.805228</td>\n",
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              "      <td>0.064201</td>\n",
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              "    <tr>\n",
              "      <th>1647</th>\n",
              "      <td>(Timezone_US/Pacific, Civil_Twilight_Night)</td>\n",
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              "      <td>0.092323</td>\n",
              "      <td>0.805228</td>\n",
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              "      <td>0.064201</td>\n",
              "      <td>3.874925</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2949 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-105ce0c6-95ec-4d72-8609-f7bdc19c46d7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-105ce0c6-95ec-4d72-8609-f7bdc19c46d7');\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",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Formulating an Machine Learning Problem"
      ],
      "metadata": {
        "id": "ft8OZbW7bleu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Classification of accidents into different Severity Classes\n",
        "\n",
        "\n",
        "In this model, we can use the dataset to predict the severity class of the accident in a particular city in the United States.\n",
        "The government/insurance company can provide financial help/insurance claims to the Citizens/Customers. Furthermore, from the data, the insurance companies can fix the premium of their policy state-wise to gain better profits, and the government can formulate strict rules and fines based on region to control accidental cases.\n",
        "\n",
        "Useful Algorithms for the problem: Logistic Regression, State Vector Machine, and K Nearest Neighbours\n"
      ],
      "metadata": {
        "id": "4aucrBY5gpT3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Stakeholders : US Government, Insurance Companies and US Citizens"
      ],
      "metadata": {
        "id": "_69EG9cgYKpa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.iloc[:700000,2:5]\n",
        "X = X.join(df.iloc[:700000,12:27])\n",
        "X = X.join(df.iloc[:700000,7])\n",
        "y = df.iloc[:700000,1:2]"
      ],
      "metadata": {
        "id": "pCpype-sltBW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "OHP35WOOqK1p",
        "outputId": "56fd71bf-aa74-4f6d-b8b5-7cb32a90a628"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Distance(mi) Side        City  Amenity   Bump  Crossing  Give_Way  \\\n",
              "0         3.230    R      Dublin    False  False     False     False   \n",
              "1         0.747    R      Dayton    False  False     False     False   \n",
              "2         0.055    R  Cincinnati    False  False     False     False   \n",
              "3         0.123    R       Akron    False  False     False     False   \n",
              "4         0.500    R  Cincinnati    False  False     False     False   \n",
              "\n",
              "   Junction  No_Exit  Railway  Roundabout  Station   Stop  Traffic_Calming  \\\n",
              "0     False    False    False       False    False  False            False   \n",
              "1     False    False    False       False    False  False            False   \n",
              "2      True    False    False       False    False  False            False   \n",
              "3     False    False    False       False    False  False            False   \n",
              "4     False    False    False       False    False  False            False   \n",
              "\n",
              "   Traffic_Signal Sunrise_Sunset Civil_Twilight Nautical_Twilight    Timezone  \n",
              "0           False          Night          Night             Night  US/Eastern  \n",
              "1           False          Night          Night             Night  US/Eastern  \n",
              "2           False          Night          Night             Night  US/Eastern  \n",
              "3           False          Night          Night               Day  US/Eastern  \n",
              "4           False            Day            Day               Day  US/Eastern  "
            ],
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              "      <th>Distance(mi)</th>\n",
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              "      <th>Junction</th>\n",
              "      <th>No_Exit</th>\n",
              "      <th>Railway</th>\n",
              "      <th>Roundabout</th>\n",
              "      <th>Station</th>\n",
              "      <th>Stop</th>\n",
              "      <th>Traffic_Calming</th>\n",
              "      <th>Traffic_Signal</th>\n",
              "      <th>Sunrise_Sunset</th>\n",
              "      <th>Civil_Twilight</th>\n",
              "      <th>Nautical_Twilight</th>\n",
              "      <th>Timezone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3.230</td>\n",
              "      <td>R</td>\n",
              "      <td>Dublin</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>Night</td>\n",
              "      <td>Night</td>\n",
              "      <td>Night</td>\n",
              "      <td>US/Eastern</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.747</td>\n",
              "      <td>R</td>\n",
              "      <td>Dayton</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>Night</td>\n",
              "      <td>Night</td>\n",
              "      <td>Night</td>\n",
              "      <td>US/Eastern</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.055</td>\n",
              "      <td>R</td>\n",
              "      <td>Cincinnati</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>Night</td>\n",
              "      <td>Night</td>\n",
              "      <td>Night</td>\n",
              "      <td>US/Eastern</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.123</td>\n",
              "      <td>R</td>\n",
              "      <td>Akron</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>Night</td>\n",
              "      <td>Night</td>\n",
              "      <td>Day</td>\n",
              "      <td>US/Eastern</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.500</td>\n",
              "      <td>R</td>\n",
              "      <td>Cincinnati</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>Day</td>\n",
              "      <td>Day</td>\n",
              "      <td>Day</td>\n",
              "      <td>US/Eastern</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-60d22437-14aa-4891-a9ce-e8fd438c0db9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-60d22437-14aa-4891-a9ce-e8fd438c0db9 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-60d22437-14aa-4891-a9ce-e8fd438c0db9');\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": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "lbl_enc = LabelEncoder()\n",
        "X[list(X.columns)] = X[list(X.columns)].apply(lbl_enc.fit_transform)"
      ],
      "metadata": {
        "id": "eiXV3WC-q8JF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "Bur58UM9rJ4P",
        "outputId": "4246eaa4-ab34-4b0a-b27a-bf9cd25d060f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Distance(mi)  Side  City  Amenity  Bump  Crossing  Give_Way  Junction  \\\n",
              "0          3230     1  2364        0     0         0         0         0   \n",
              "1           747     1  2136        0     0         0         0         0   \n",
              "2            55     1  1594        0     0         0         0         1   \n",
              "3           123     1    55        0     0         0         0         0   \n",
              "4           500     1  1594        0     0         0         0         0   \n",
              "\n",
              "   No_Exit  Railway  Roundabout  Station  Stop  Traffic_Calming  \\\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",
              "   Traffic_Signal  Sunrise_Sunset  Civil_Twilight  Nautical_Twilight  Timezone  \n",
              "0               0               1               1                  1         1  \n",
              "1               0               1               1                  1         1  \n",
              "2               0               1               1                  1         1  \n",
              "3               0               1               1                  0         1  \n",
              "4               0               0               0                  0         1  "
            ],
            "text/html": [
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              "      <th></th>\n",
              "      <th>Distance(mi)</th>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>123</td>\n",
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              "      <td>55</td>\n",
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              "      <td>500</td>\n",
              "      <td>1</td>\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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-132a954e-8d1b-4247-a6e5-2722fe5db898 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-132a954e-8d1b-4247-a6e5-2722fe5db898');\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": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.20, random_state=42,stratify=y)\n"
      ],
      "metadata": {
        "id": "cnzTTfn2YQ-X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TK-iYJ5qzQ2R",
        "outputId": "e39a9c16-05dc-4522-b619-ca2b8b8d73cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Severity\n",
              "2           124713\n",
              "3             7993\n",
              "4             7294\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "reg = LogisticRegression().fit(X_train, y_train)\n",
        "\n"
      ],
      "metadata": {
        "id": "QRd3tHj_jRxO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "110c3d24-baf7-4c6f-ef16-69abc7b23cd2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred1=reg.predict(X_test)\n",
        "from sklearn.metrics import accuracy_score\n",
        "acc1=accuracy_score(y_pred1,y_test)\n",
        "acc1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KWaNE4tLwRmR",
        "outputId": "5b492505-2cc4-4e7d-88b8-5487fbc8d3ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8907714285714285"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    }
  ]
}