AccidentPrediction.ipynb

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      "name": "Untitled",
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      "name": "python3",
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      "metadata": {
        "id": "kBWYvDXd7m1s",
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      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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          "height": 217
        }
      },
      "source": [
        "df=pd.read_csv(\"train.csv\")\n",
        "df.head()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Severity</th>\n",
              "      <th>Safety_Score</th>\n",
              "      <th>Days_Since_Inspection</th>\n",
              "      <th>Total_Safety_Complaints</th>\n",
              "      <th>Control_Metric</th>\n",
              "      <th>Turbulence_In_gforces</th>\n",
              "      <th>Cabin_Temperature</th>\n",
              "      <th>Accident_Type_Code</th>\n",
              "      <th>Max_Elevation</th>\n",
              "      <th>Violations</th>\n",
              "      <th>Adverse_Weather_Metric</th>\n",
              "      <th>Accident_ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Minor_Damage_And_Injuries</td>\n",
              "      <td>49.223744</td>\n",
              "      <td>14</td>\n",
              "      <td>22</td>\n",
              "      <td>71.285324</td>\n",
              "      <td>0.272118</td>\n",
              "      <td>78.04</td>\n",
              "      <td>2</td>\n",
              "      <td>31335.476824</td>\n",
              "      <td>3</td>\n",
              "      <td>0.424352</td>\n",
              "      <td>7570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Minor_Damage_And_Injuries</td>\n",
              "      <td>62.465753</td>\n",
              "      <td>10</td>\n",
              "      <td>27</td>\n",
              "      <td>72.288058</td>\n",
              "      <td>0.423939</td>\n",
              "      <td>84.54</td>\n",
              "      <td>2</td>\n",
              "      <td>26024.711057</td>\n",
              "      <td>2</td>\n",
              "      <td>0.352350</td>\n",
              "      <td>12128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Significant_Damage_And_Fatalities</td>\n",
              "      <td>63.059361</td>\n",
              "      <td>13</td>\n",
              "      <td>16</td>\n",
              "      <td>66.362808</td>\n",
              "      <td>0.322604</td>\n",
              "      <td>78.86</td>\n",
              "      <td>7</td>\n",
              "      <td>39269.053927</td>\n",
              "      <td>3</td>\n",
              "      <td>0.003364</td>\n",
              "      <td>2181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Significant_Damage_And_Serious_Injuries</td>\n",
              "      <td>48.082192</td>\n",
              "      <td>11</td>\n",
              "      <td>9</td>\n",
              "      <td>74.703737</td>\n",
              "      <td>0.337029</td>\n",
              "      <td>81.79</td>\n",
              "      <td>3</td>\n",
              "      <td>42771.499200</td>\n",
              "      <td>1</td>\n",
              "      <td>0.211728</td>\n",
              "      <td>5946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Significant_Damage_And_Fatalities</td>\n",
              "      <td>26.484018</td>\n",
              "      <td>13</td>\n",
              "      <td>25</td>\n",
              "      <td>47.948952</td>\n",
              "      <td>0.541140</td>\n",
              "      <td>77.16</td>\n",
              "      <td>3</td>\n",
              "      <td>35509.228515</td>\n",
              "      <td>2</td>\n",
              "      <td>0.176883</td>\n",
              "      <td>9054</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                  Severity  ...  Accident_ID\n",
              "0                Minor_Damage_And_Injuries  ...         7570\n",
              "1                Minor_Damage_And_Injuries  ...        12128\n",
              "2        Significant_Damage_And_Fatalities  ...         2181\n",
              "3  Significant_Damage_And_Serious_Injuries  ...         5946\n",
              "4        Significant_Damage_And_Fatalities  ...         9054\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qr4-FVUl9G9T",
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        "colab": {
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          "height": 105
        }
      },
      "source": [
        "df['Severity'].value_counts()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Highly_Fatal_And_Damaging                  3049\n",
              "Significant_Damage_And_Serious_Injuries    2729\n",
              "Minor_Damage_And_Injuries                  2527\n",
              "Significant_Damage_And_Fatalities          1695\n",
              "Name: Severity, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vGy48pwBA7ck",
        "colab_type": "code",
        "outputId": "ade12d9f-afc2-42a5-cb6b-183a182d9e8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        }
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Safety_Score</th>\n",
              "      <th>Days_Since_Inspection</th>\n",
              "      <th>Total_Safety_Complaints</th>\n",
              "      <th>Control_Metric</th>\n",
              "      <th>Turbulence_In_gforces</th>\n",
              "      <th>Cabin_Temperature</th>\n",
              "      <th>Accident_Type_Code</th>\n",
              "      <th>Max_Elevation</th>\n",
              "      <th>Violations</th>\n",
              "      <th>Adverse_Weather_Metric</th>\n",
              "      <th>Accident_ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.00000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>41.876406</td>\n",
              "      <td>12.931100</td>\n",
              "      <td>6.564300</td>\n",
              "      <td>65.145324</td>\n",
              "      <td>0.381495</td>\n",
              "      <td>79.969331</td>\n",
              "      <td>3.814900</td>\n",
              "      <td>32001.803282</td>\n",
              "      <td>2.01220</td>\n",
              "      <td>0.255635</td>\n",
              "      <td>6266.554200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>16.138072</td>\n",
              "      <td>3.539803</td>\n",
              "      <td>6.971982</td>\n",
              "      <td>11.882934</td>\n",
              "      <td>0.121301</td>\n",
              "      <td>2.759739</td>\n",
              "      <td>1.902577</td>\n",
              "      <td>9431.995196</td>\n",
              "      <td>1.03998</td>\n",
              "      <td>0.381128</td>\n",
              "      <td>3610.170288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.134000</td>\n",
              "      <td>74.740000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>831.695553</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000316</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>30.593607</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>56.927985</td>\n",
              "      <td>0.293665</td>\n",
              "      <td>77.960000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>25757.636908</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.012063</td>\n",
              "      <td>3139.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>41.278539</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>65.587967</td>\n",
              "      <td>0.365879</td>\n",
              "      <td>79.540000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>32060.336419</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>0.074467</td>\n",
              "      <td>6280.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>52.511416</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>73.336372</td>\n",
              "      <td>0.451346</td>\n",
              "      <td>81.560000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>38380.641513</td>\n",
              "      <td>3.00000</td>\n",
              "      <td>0.354059</td>\n",
              "      <td>9391.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>100.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>0.882648</td>\n",
              "      <td>97.510000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>64297.651218</td>\n",
              "      <td>5.00000</td>\n",
              "      <td>2.365378</td>\n",
              "      <td>12500.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Safety_Score  ...   Accident_ID\n",
              "count  10000.000000  ...  10000.000000\n",
              "mean      41.876406  ...   6266.554200\n",
              "std       16.138072  ...   3610.170288\n",
              "min        0.000000  ...      2.000000\n",
              "25%       30.593607  ...   3139.750000\n",
              "50%       41.278539  ...   6280.500000\n",
              "75%       52.511416  ...   9391.500000\n",
              "max      100.000000  ...  12500.000000\n",
              "\n",
              "[8 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 34
        }
      ]
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "sns.barplot(y=\"Safety_Score\", x=\"Days_Since_Inspection\", hue=\"Severity\", data=df)\n"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f327341f860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
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KDYyHO82noPkUDhw4wK9+9StvF1dMTAyTJk1yVU+POnXq8MorrzB06FCSkpKI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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q4-6_-F-Dnp8",
        "colab_type": "code",
        "outputId": "9cfa80d3-9f64-41a9-ce79-28876fdc9670",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.barplot(y=\"Control_Metric\", x=\"Violations\", hue=\"Severity\", data=df)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3273265748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jT8aYKcAUsAz7JEWsKVXOXE+xcNWm5A4j0XbsePTitSPDBglp0qAOTay3qjo6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\ntPrFXQoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MgsH0vg1Wuxe",
        "colab_type": "code",
        "outputId": "4f008c3c-5e05-4f09-a058-13b22e6e8dcd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "source": [
        "sns.catplot(y=\"Cabin_Temperature\", x=\"Violations\", hue=\"Severity\",kind=\"bar\", data=df)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f32738408d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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H7mtPWGZmZhARs4D31Vm1EHh/g31+APygpmx2ve0jYsea98dXLV8EXFSzy3bAr7qO3FY2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\nzMys9P4/iVcFG1X8P28AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 604.75x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UHlmJYE4pjFR",
        "colab_type": "code",
        "outputId": "fe4e2b25-4447-4340-c9f0-66e98dcd47ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.barplot(x=\"Accident_Type_Code\", y=\"Max_Elevation\", hue=\"Severity\", data=df)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3272ffa0b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9y3MDB3I2NJQ7YWE88thj0TFHVRps2bIluXLlAmDFihUEBwfTpW0zAG7fukWe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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_tJYgTaGqqcZ",
        "colab_type": "code",
        "outputId": "0de3ea48-010d-46f6-beae-0fa78e8fc33c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.barplot(x=\"Accident_Type_Code\", y=\"Adverse_Weather_Metric\", hue=\"Severity\", data=df)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3271e775f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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nTx/v8Fr+nTqPPvooo0aNom3btoSHh3u3ee6557zHjoiI4Lrrriv2GG6ICBER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\nxmDJwBhjDJYMjDHGAP8PNWyPkCCYkewAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pdrq4r39CMA1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# d={\"Highly_Fatal_And_Damaging\":1,\"Significant_Damage_And_Serious_Injuries\":2,\"Significant_Damage_And_Fatalities\": 3,\"Minor_Damage_And_Injuries\": 4}\n",
        "# df['Severity']=df['Severity'].map(d)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x80o7J93NOPD",
        "colab_type": "code",
        "outputId": "3ecd96a7-cc24-419b-9a25-31f9e1441db9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "df['Severity'].value_counts()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Highly_Fatal_And_Damaging                  3049\n",
              "Significant_Damage_And_Serious_Injuries    2729\n",
              "Minor_Damage_And_Injuries                  2527\n",
              "Significant_Damage_And_Fatalities          1695\n",
              "Name: Severity, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X2R9tryCHuwV",
        "colab_type": "code",
        "outputId": "a787d3b1-b8be-4cfe-acfa-62db33faa87b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        }
      },
      "source": [
        "X=df.drop(['Accident_ID','Severity','Accident_Type_Code'], axis=1)\n",
        "y=df['Severity']\n",
        "X.head(n=5)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Safety_Score</th>\n",
              "      <th>Days_Since_Inspection</th>\n",
              "      <th>Total_Safety_Complaints</th>\n",
              "      <th>Control_Metric</th>\n",
              "      <th>Turbulence_In_gforces</th>\n",
              "      <th>Cabin_Temperature</th>\n",
              "      <th>Max_Elevation</th>\n",
              "      <th>Violations</th>\n",
              "      <th>Adverse_Weather_Metric</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>49.223744</td>\n",
              "      <td>14</td>\n",
              "      <td>22</td>\n",
              "      <td>71.285324</td>\n",
              "      <td>0.272118</td>\n",
              "      <td>78.04</td>\n",
              "      <td>31335.476824</td>\n",
              "      <td>3</td>\n",
              "      <td>0.424352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>62.465753</td>\n",
              "      <td>10</td>\n",
              "      <td>27</td>\n",
              "      <td>72.288058</td>\n",
              "      <td>0.423939</td>\n",
              "      <td>84.54</td>\n",
              "      <td>26024.711057</td>\n",
              "      <td>2</td>\n",
              "      <td>0.352350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>63.059361</td>\n",
              "      <td>13</td>\n",
              "      <td>16</td>\n",
              "      <td>66.362808</td>\n",
              "      <td>0.322604</td>\n",
              "      <td>78.86</td>\n",
              "      <td>39269.053927</td>\n",
              "      <td>3</td>\n",
              "      <td>0.003364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48.082192</td>\n",
              "      <td>11</td>\n",
              "      <td>9</td>\n",
              "      <td>74.703737</td>\n",
              "      <td>0.337029</td>\n",
              "      <td>81.79</td>\n",
              "      <td>42771.499200</td>\n",
              "      <td>1</td>\n",
              "      <td>0.211728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>26.484018</td>\n",
              "      <td>13</td>\n",
              "      <td>25</td>\n",
              "      <td>47.948952</td>\n",
              "      <td>0.541140</td>\n",
              "      <td>77.16</td>\n",
              "      <td>35509.228515</td>\n",
              "      <td>2</td>\n",
              "      <td>0.176883</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Safety_Score  Days_Since_Inspection  ...  Violations  Adverse_Weather_Metric\n",
              "0     49.223744                     14  ...           3                0.424352\n",
              "1     62.465753                     10  ...           2                0.352350\n",
              "2     63.059361                     13  ...           3                0.003364\n",
              "3     48.082192                     11  ...           1                0.211728\n",
              "4     26.484018                     13  ...           2                0.176883\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IDc_vUM6gjue",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# Instantiate MinMaxScaler and use it to rescale X_train and X_test\n",
        "scaler = MinMaxScaler(feature_range=(0,1))\n",
        "rescaledX=scaler.fit_transform(X)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L07WBrrzH_Al",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "xtrain,xtest,ytrain,ytest=train_test_split(rescaledX,y,test_size=0.25, random_state=2)               "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sGCCfoi28kF3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "clf2=SVC(gamma='auto')\n",
        "clf2.fit(xtrain,ytrain)\n",
        "ypred2=clf2.predict(xtest)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_vikSdaf8p4H",
        "colab_type": "code",
        "outputId": "bf02d1b7-b785-46bb-cab9-e757976c712f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.metrics import f1_score\n",
        "z=100*f1_score(ytest,ypred2, average='weighted')\n",
        "print(z)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "61.07119786096257\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H9wPrUeiIZ-y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "# from sklearn.ensemble import RandomForestClassifier\n",
        "# clf1=RandomForestClassifier(n_estimators=1000,n_jobs=-1,max_features='log2', random_state=1)\n",
        "# clf1.fit(xtrain,ytrain)\n",
        "# ypred=clf1.predict(xtest)\n",
        "# #285 "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6EyX-g7bcKWS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# from sklearn.model_selection import cross_val_score\n",
        "# scores = cross_val_score(clf1, xtrain, ytrain, cv=5)\n",
        "# scores.mean()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z12afOMKWz3i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# from sklearn.metrics import f1_score\n",
        "# z=100*f1_score(ytest,ypred, average='weighted')\n",
        "# print(z)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iK8VY4nKgdFy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# from sklearn.model_selection import GridSearchCV\n",
        "# param_grid = { \n",
        "#     'n_estimators': [1230, 1170, 1200],\n",
        "#     'max_features': ['log2'],\n",
        "#     'max_depth': [2, 3, 5, 10],\n",
        "#     'n_jobs':[-1]\n",
        "# }\n",
        "\n",
        "# grid_model = GridSearchCV(estimator=RandomForestClassifier(), param_grid=param_grid, cv= 5)\n",
        "# grid_model_result = grid_model.fit(rescaledX,y)\n",
        "\n",
        "# # Summarize results\n",
        "# best_score, best_params = grid_model_result.best_score_,grid_model_result.best_params_\n",
        "# print(\"Best: %f using %s\" % (best_score, best_params))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gvd1oeIm64zX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import make_scorer, accuracy_score,f1_score\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "# Choose the type of classifier. \n",
        "clf = RandomForestClassifier()\n",
        "\n",
        "# Choose some parameter combinations to try\n",
        "parameters = { \n",
        "    'n_estimators': [1240],\n",
        "    'max_features': ['log2'],\n",
        "     'max_depth': [15, 10],\n",
        "     'criterion': ['entropy', 'gini'],\n",
        "    'n_jobs':[-1]\n",
        "}\n",
        "\n",
        "# Type of scoring used to compare parameter combinations\n",
        "acc_scorer = make_scorer(f1_score,average='weighted')\n",
        "\n",
        "# Run the grid search\n",
        "grid_obj = GridSearchCV(clf, parameters, scoring=acc_scorer)\n",
        "grid_obj = grid_obj.fit(rescaledX,y)\n",
        "\n",
        "\n",
        "# Set the clf to the best combination of parameters\n",
        "clf = grid_obj.best_estimator_\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AbUUuMkVRTbQ",
        "colab_type": "code",
        "outputId": "f68e093d-f930-48f7-8b56-784ba656836c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "best_score, best_params = grid_obj.best_score_,grid_obj.best_params_\n",
        "print(\"Best: %f using %s\" % (best_score, best_params))"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best: 0.952300 using {'criterion': 'entropy', 'max_depth': 15, 'max_features': 'log2', 'n_estimators': 1240, 'n_jobs': -1}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UpPFwMz0-x8o",
        "colab_type": "code",
        "outputId": "32741135-4831-458c-d7b3-acb5a792f1a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Fit the best algorithm to the data. \n",
        "clf.fit(xtrain, ytrain)\n",
        "#Prediction\n",
        "predictions = clf.predict(xtest)\n",
        "print(f1_score(ytest, predictions, average='weighted'))"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9488678396153033\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mpWo1IcCBChm",
        "colab_type": "code",
        "outputId": "5206c510-ce79-4b54-887c-877d9ecfcb2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        }
      },
      "source": [
        "from sklearn.model_selection import KFold\n",
        "\n",
        "def run_kfold(clf):\n",
        "    kf = KFold(n_splits=10, shuffle=True)\n",
        "    outcomes = []\n",
        "    fold = 0\n",
        "    for train_index, test_index in kf.split(rescaledX):\n",
        "         fold+=1\n",
        "         X_train, X_test = rescaledX[train_index], rescaledX[test_index]\n",
        "         y_train, y_test = y[train_index], y[test_index]\n",
        "         clf.fit(X_train, y_train)\n",
        "         predictions = clf.predict(X_test)\n",
        "         accuracy = f1_score(y_test, predictions,average='weighted')\n",
        "         outcomes.append(accuracy)\n",
        "         print(\"Fold {0} accuracy: {1}\".format(fold, accuracy))     \n",
        "    mean_outcome = np.mean(outcomes)\n",
        "    print(\"Mean Accuracy: {0}\".format(mean_outcome)) \n",
        "\n",
        "run_kfold(clf)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fold 1 accuracy: 0.948025330854493\n",
            "Fold 2 accuracy: 0.946025209146839\n",
            "Fold 3 accuracy: 0.9629749056557274\n",
            "Fold 4 accuracy: 0.9500084749815437\n",
            "Fold 5 accuracy: 0.9550168165167677\n",
            "Fold 6 accuracy: 0.9590790681411379\n",
            "Fold 7 accuracy: 0.9610140786475213\n",
            "Fold 8 accuracy: 0.9410518251786332\n",
            "Fold 9 accuracy: 0.9530246371736312\n",
            "Fold 10 accuracy: 0.946866622005434\n",
            "Mean Accuracy: 0.9523086968301728\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "krCKHdlNHnKq",
        "colab_type": "code",
        "outputId": "7eb321df-fb46-40f7-a497-c94ac4169662",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "df1=pd.read_csv(\"test.csv\")\n",
        "ids = df1['Accident_ID']\n",
        "clf=clf.fit(rescaledX,y)\n",
        "rescaledd=scaler.fit_transform(df1.drop(['Accident_ID','Accident_Type_Code'], axis=1))\n",
        "predictions = clf.predict(rescaledd)\n",
        "output = pd.DataFrame({ 'Accident_ID' : ids, 'Severity': predictions })\n",
        "# output.to_csv('titanic-predictions.csv', index = False)\n",
        "output['Severity'].value_counts()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Minor_Damage_And_Injuries                  748\n",
              "Highly_Fatal_And_Damaging                  712\n",
              "Significant_Damage_And_Fatalities          530\n",
              "Significant_Damage_And_Serious_Injuries    510\n",
              "Name: Severity, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O_pT9EtSMntg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "output.to_csv('AccidentPrediction.csv', index = False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oNHIutFlxEth",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}