yhills_project2.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"TaxiFare.csv\")\n",
        "df"
      ],
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              "      unique_id  amount      date_time_of_pickup  longitude_of_pickup  \\\n",
              "0       26:21.0     4.5  2009-06-15 17:26:21 UTC           -73.844311   \n",
              "1       52:16.0    16.9  2010-01-05 16:52:16 UTC           -74.016048   \n",
              "2       35:00.0     5.7  2011-08-18 00:35:00 UTC           -73.982738   \n",
              "3       30:42.0     7.7  2012-04-21 04:30:42 UTC           -73.987130   \n",
              "4       51:00.0     5.3  2010-03-09 07:51:00 UTC           -73.968095   \n",
              "...         ...     ...                      ...                  ...   \n",
              "49995   25:15.0    15.0  2013-06-12 23:25:15 UTC           -73.999973   \n",
              "49996   19:18.0     7.5  2015-06-22 17:19:18 UTC           -73.984756   \n",
              "49997   53:00.0     6.9  2011-01-30 04:53:00 UTC           -74.002698   \n",
              "49998   09:00.0     4.5  2012-11-06 07:09:00 UTC           -73.946062   \n",
              "49999   13:14.0    10.9  2010-01-13 08:13:14 UTC           -73.932603   \n",
              "\n",
              "       latitude_of_pickup  longitude_of_dropoff  latitude_of_dropoff  \\\n",
              "0               40.721319            -73.841610            40.712278   \n",
              "1               40.711303            -73.979268            40.782004   \n",
              "2               40.761270            -73.991242            40.750562   \n",
              "3               40.733143            -73.991567            40.758092   \n",
              "4               40.768008            -73.956655            40.783762   \n",
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              "49996           40.768211            -73.987366            40.760597   \n",
              "49997           40.739428            -73.998108            40.759483   \n",
              "49998           40.777567            -73.953450            40.779687   \n",
              "49999           40.763805            -73.932603            40.763805   \n",
              "\n",
              "       no_of_passenger  \n",
              "0                    1  \n",
              "1                    1  \n",
              "2                    2  \n",
              "3                    1  \n",
              "4                    1  \n",
              "...                ...  \n",
              "49995                1  \n",
              "49996                1  \n",
              "49997                1  \n",
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          "metadata": {},
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        }
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    {
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      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "an-CZI3-3Xf9",
        "outputId": "4a0cd01d-e5fd-4461-9d20-9292df73af9d"
      },
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "unique_id                object\n",
              "amount                  float64\n",
              "date_time_of_pickup      object\n",
              "longitude_of_pickup     float64\n",
              "latitude_of_pickup      float64\n",
              "longitude_of_dropoff    float64\n",
              "latitude_of_dropoff     float64\n",
              "no_of_passenger           int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 190
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vSGUx5Q63Xr8",
        "outputId": "53465ad1-e84c-47c0-f103-972db2b8faa6"
      },
      "execution_count": 191,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['unique_id', 'amount', 'date_time_of_pickup', 'longitude_of_pickup',\n",
              "       'latitude_of_pickup', 'longitude_of_dropoff', 'latitude_of_dropoff',\n",
              "       'no_of_passenger'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 191
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L5lttPWL3X4I",
        "outputId": "f0772d54-096a-4e7d-8e05-f8591326b5b3"
      },
      "execution_count": 192,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "unique_id               0\n",
              "amount                  0\n",
              "date_time_of_pickup     0\n",
              "longitude_of_pickup     0\n",
              "latitude_of_pickup      0\n",
              "longitude_of_dropoff    0\n",
              "latitude_of_dropoff     0\n",
              "no_of_passenger         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# There are no missing values in this dataset."
      ],
      "metadata": {
        "id": "F2hcbGfr3Yc2"
      },
      "execution_count": 193,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe(include=\"all\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "b11u2-ws3Yse",
        "outputId": "261314dd-72ff-44f1-9089-a3a6f2abfbfe"
      },
      "execution_count": 194,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       unique_id        amount      date_time_of_pickup  longitude_of_pickup  \\\n",
              "count      50000  50000.000000                    50000         50000.000000   \n",
              "unique      3597           NaN                    49555                  NaN   \n",
              "top      26:00.0           NaN  2011-09-03 01:30:00 UTC                  NaN   \n",
              "freq         457           NaN                        3                  NaN   \n",
              "mean         NaN     11.364171                      NaN           -72.509756   \n",
              "std          NaN      9.685557                      NaN            10.393860   \n",
              "min          NaN     -5.000000                      NaN           -75.423848   \n",
              "25%          NaN      6.000000                      NaN           -73.992062   \n",
              "50%          NaN      8.500000                      NaN           -73.981840   \n",
              "75%          NaN     12.500000                      NaN           -73.967148   \n",
              "max          NaN    200.000000                      NaN            40.783472   \n",
              "\n",
              "        latitude_of_pickup  longitude_of_dropoff  latitude_of_dropoff  \\\n",
              "count         50000.000000          50000.000000         50000.000000   \n",
              "unique                 NaN                   NaN                  NaN   \n",
              "top                    NaN                   NaN                  NaN   \n",
              "freq                   NaN                   NaN                  NaN   \n",
              "mean             39.933759            -72.504616            39.926251   \n",
              "std               6.224857             10.407570             6.014737   \n",
              "min             -74.006893            -84.654241           -74.006377   \n",
              "25%              40.734880            -73.991152            40.734372   \n",
              "50%              40.752678            -73.980082            40.753372   \n",
              "75%              40.767360            -73.963584            40.768167   \n",
              "max             401.083332             40.851027            43.415190   \n",
              "\n",
              "        no_of_passenger  \n",
              "count      50000.000000  \n",
              "unique              NaN  \n",
              "top                 NaN  \n",
              "freq                NaN  \n",
              "mean           1.667840  \n",
              "std            1.289195  \n",
              "min            0.000000  \n",
              "25%            1.000000  \n",
              "50%            1.000000  \n",
              "75%            2.000000  \n",
              "max            6.000000  "
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>unique_id</th>\n",
              "      <th>amount</th>\n",
              "      <th>date_time_of_pickup</th>\n",
              "      <th>longitude_of_pickup</th>\n",
              "      <th>latitude_of_pickup</th>\n",
              "      <th>longitude_of_dropoff</th>\n",
              "      <th>latitude_of_dropoff</th>\n",
              "      <th>no_of_passenger</th>\n",
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              "      <th>count</th>\n",
              "      <td>50000</td>\n",
              "      <td>50000.000000</td>\n",
              "      <td>50000</td>\n",
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              "      <td>50000.000000</td>\n",
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              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>3597</td>\n",
              "      <td>NaN</td>\n",
              "      <td>49555</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>26:00.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2011-09-03 01:30:00 UTC</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>457</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11.364171</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-72.509756</td>\n",
              "      <td>39.933759</td>\n",
              "      <td>-72.504616</td>\n",
              "      <td>39.926251</td>\n",
              "      <td>1.667840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>NaN</td>\n",
              "      <td>9.685557</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.393860</td>\n",
              "      <td>6.224857</td>\n",
              "      <td>10.407570</td>\n",
              "      <td>6.014737</td>\n",
              "      <td>1.289195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>NaN</td>\n",
              "      <td>-5.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-75.423848</td>\n",
              "      <td>-74.006893</td>\n",
              "      <td>-84.654241</td>\n",
              "      <td>-74.006377</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-73.992062</td>\n",
              "      <td>40.734880</td>\n",
              "      <td>-73.991152</td>\n",
              "      <td>40.734372</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>8.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-73.981840</td>\n",
              "      <td>40.752678</td>\n",
              "      <td>-73.980082</td>\n",
              "      <td>40.753372</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>12.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-73.967148</td>\n",
              "      <td>40.767360</td>\n",
              "      <td>-73.963584</td>\n",
              "      <td>40.768167</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>NaN</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.783472</td>\n",
              "      <td>401.083332</td>\n",
              "      <td>40.851027</td>\n",
              "      <td>43.415190</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "              title=\"Suggest charts.\"\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "    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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
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              "    domScope.querySelector('#df-f464eedf-4510-42c9-8768-9fe8b16488d5 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ed2d39fa-9523-4a5a-8a20-b6c29f5ded5d 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-ed2d39fa-9523-4a5a-8a20-b6c29f5ded5d');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 194
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#The minimal amount is negative. As this does not seem to be realistic I will drop them from the dataset."
      ],
      "metadata": {
        "id": "WthqDqqa3Y8e"
      },
      "execution_count": 195,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop('unique_id', axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "IhFR9TzsWr-o"
      },
      "execution_count": 196,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Old size: %d' % len(df))\n",
        "df = df[df.amount>=0]\n",
        "print('New size: %d' % len(df))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0bZXCAwa3ZL9",
        "outputId": "986df738-3d78-4829-933e-431500d13234"
      },
      "execution_count": 197,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Old size: 50000\n",
            "New size: 49994\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Old size: %d' % len(df))\n",
        "df = df.dropna(how = 'any', axis = 'rows')\n",
        "print('New size: %d' % len(df))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fMxgpTd13ZdE",
        "outputId": "1767c343-78ae-4b20-9cf5-4cd25e87037a"
      },
      "execution_count": 198,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Old size: 49994\n",
            "New size: 49994\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Some of the minimum and maximum longitude/lattitude coordinates are way off. These I will also remove from the dataset."
      ],
      "metadata": {
        "id": "ydHETy6H3Zq3"
      },
      "execution_count": 199,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#I define a bounding box of interest by [long_min, long_max, latt_min, latt_max] using the minimum and maximum coordinates from the testset. This way, I'm sure to train a model for the full pickup/dropoff coordinate range of the test set."
      ],
      "metadata": {
        "id": "GChYTLJr3Z4O"
      },
      "execution_count": 200,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# minimum and maximum longitude test set\n",
        "min(df.longitude_of_pickup.min(), df.longitude_of_dropoff.min()), \\\n",
        "max(df.longitude_of_pickup.max(), df.longitude_of_dropoff.max())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ijh6z2b79qQ1",
        "outputId": "dacbe45e-43e8-4f91-dba6-ab5d9031949b"
      },
      "execution_count": 201,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-84.654241, 40.851027)"
            ]
          },
          "metadata": {},
          "execution_count": 201
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# minimum and maximum latitude test\n",
        "min(df.latitude_of_pickup.min(), df.latitude_of_dropoff.min()), \\\n",
        "max(df.latitude_of_pickup.max(), df.latitude_of_dropoff.max())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jr9_eCgT9qfF",
        "outputId": "f221d9b9-ba50-47cf-e377-39b232a16692"
      },
      "execution_count": 202,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-74.006893, 401.083332)"
            ]
          },
          "metadata": {},
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# this function will also be used with the test set below\n",
        "def select_within_boundingbox(df, BB):\n",
        "    return (df.longitude_of_pickup >= BB[0]) & (df.longitude_of_pickup <= BB[1]) & \\\n",
        "           (df.latitude_of_pickup >= BB[2]) & (df.latitude_of_pickup <= BB[3]) & \\\n",
        "           (df.longitude_of_dropoff >= BB[0]) & (df.longitude_of_dropoff <= BB[1]) & \\\n",
        "           (df.latitude_of_dropoff >= BB[2]) & (df.latitude_of_dropoff <= BB[3])\n",
        "\n",
        "# load image of NYC map\n",
        "BB = (-84.6, 40.8, -74.0, 41.8)"
      ],
      "metadata": {
        "id": "rZQNWtks9qxI"
      },
      "execution_count": 203,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Old size: %d' % len(df))\n",
        "df = df[select_within_boundingbox(df, BB)]\n",
        "print('New size: %d' % len(df))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Whvs0AMO9rAj",
        "outputId": "ea6511de-a78c-451b-ccf8-21186ab9a95c"
      },
      "execution_count": 204,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Old size: 49994\n",
            "New size: 49984\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nj6uDnYaV39o",
        "outputId": "d65001d2-d871-4123-9602-d2aec91d9f41"
      },
      "execution_count": 205,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "amount                  float64\n",
              "date_time_of_pickup      object\n",
              "longitude_of_pickup     float64\n",
              "latitude_of_pickup      float64\n",
              "longitude_of_dropoff    float64\n",
              "latitude_of_dropoff     float64\n",
              "no_of_passenger           int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 205
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Distance visualisation**"
      ],
      "metadata": {
        "id": "thOWUixYDSQc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Before building a model I want to test:\n",
        "\n",
        "#The longer the distance between pickup and dropoff location, the higher the fare. To visualize the distance - fare relation we need to calculate the distance of a trip first."
      ],
      "metadata": {
        "id": "9qNmOt7n9rUp"
      },
      "execution_count": 206,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# For this plot and further analysis, we need a function to calculate the distance in miles between locations in lon,lat coordinates.\n",
        "\n",
        "def distance(lat1, lon1, lat2, lon2):\n",
        "    p = 0.017453292519943295 # Pi/180\n",
        "    a = 0.5 - np.cos((lat2 - lat1) * p)/2 + np.cos(lat1 * p) * np.cos(lat2 * p) * (1 - np.cos((lon2 - lon1) * p)) / 2\n",
        "    return 0.6213712 * 12742 * np.arcsin(np.sqrt(a))\n",
        "\n",
        "df['distance_miles'] = distance(df.latitude_of_pickup, df.longitude_of_pickup, \\\n",
        "                                      df.latitude_of_dropoff, df.longitude_of_dropoff)"
      ],
      "metadata": {
        "id": "OP2N1r6-FZ1H"
      },
      "execution_count": 207,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# add new column to dataframe with distance in miles\n",
        "fig, ax = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Plot the histogram with logarithmic scale on y-axis\n",
        "ax.hist(df['distance_miles'], bins=100, color='skyblue', alpha=0.7)\n",
        "ax.set_yscale('log')\n",
        "\n",
        "# Limit the x-axis range to exclude outliers\n",
        "ax.set_xlim(0, 150)\n",
        "\n",
        "# Set labels and title\n",
        "plt.xlabel('Distance (miles)')\n",
        "plt.ylabel('Frequency (log scale)')\n",
        "plt.title('Distribution of Ride Distances in Miles')\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "nBdSp7FD9rnY",
        "outputId": "df8421d8-945d-4bb8-a892-52671b9fb630"
      },
      "execution_count": 208,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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1a5fd86yqV6+e70NU9+/fb3fmxEy2wMBALV++XGfOnLE7C5I7I1lgYGCh93W142zZskU5OTl2f4QW93EkqWfPnurUqZNeeeUVPfTQQ6pcuXKBmaR/PvtLzy5cuHBBe/fuVVhYmG1ZSEiINm/erC5duhT72Ev//FxaLBbddtttV9yucuXKGjBggAYMGKCsrCz169dPL7/8ssaNGydPT88Cs3377bfKzMzUokWL7M7QXMtliCEhIbbyd6VtCvu5Xe13zlEGDx6s8ePH6+jRo7Z/P4oqJCREhmEoODi4wAlELtWyZUu1bNlSzz33nP73v/+pQ4cOmjlzpiZNmnRNOQA4DpfzAShzVqxYoZdeeknBwcG65557Ctzu1KlTeZblPrQ2d1rm3D/M8ys1RfHJJ5/Y3Q/z9ddf6+jRo+revbttWUhIiH799VdlZWXZln333Xd5pkY2k61Hjx7Kzs7Wu+++a7d82rRpslgsdse/Fj169NCxY8fsZka7ePGi3nnnHVWpUkWdOnUqluPkeuqpp/TXX3/pww8/LHCbiIgI1apVSzNnzrT7TOPj4/N8dnfffbcOHz6c7/7OnTunjIyMImd99dVXtXTpUg0YMCDPpYWX+uuvv+xeu7u7KzQ0VIZh6MKFC5IKHvvcsx6XnoVJTU3VrFmzipz7zjvv1ObNmzV//vw863KPU9jPrTC/c44SEhKi6dOna/LkyWrbtu017atfv35ydXXVxIkT85wRMwzDNsZpaWm6ePGi3fqWLVvKxcXF4Z8HgGvDmSgApdrixYu1bds2Xbx4UcePH9eKFSu0bNkyBQYGatGiRVd8KOaLL76oVatWqWfPngoMDFRKSoree+891atXTx07dpT0zx9W1apV08yZM+Xl5aXKlSurXbt2Cg4OLlJeHx8fdezYUcOHD9fx48c1ffp0NWzY0G4a9vvvv19ff/21unXrprvvvlu7d+/WZ599ZjfRg9lsvXr1UufOnfXss89q3759CgsL09KlS7Vw4UI9/vjjefZdVA8++KDef/99DRs2TElJSQoKCtLXX3+tNWvWaPr06Ve8F6gounfvrhYtWmjq1KmKjY3N9yHDFSpU0KRJk/TQQw/p1ltv1YABA7R3717NmjUrzz1RQ4YM0Zw5c/Twww9r5cqV6tChg7Kzs7Vt2zbNmTNHP/74o93DnfNz8eJF2/PJzp8/r/3792vRokXasmWLOnfurA8++OCK39+1a1f5+fmpQ4cO8vX11Z9//ql3331XPXv2tH1+bdq0kSQ9++yzGjhwoCpUqKBevXqpa9eucnd3V69evfTQQw8pPT1dH374oWrXrq2jR48W+nO91BNPPKGvv/5ad911l+677z61adNGp06d0qJFizRz5kyFhYUV+nMrzO+cI40aNapY9hMSEqJJkyZp3Lhx2rdvn/r06SMvLy/t3btX8+fP14MPPqixY8dqxYoVGjlypO666y41btxYFy9e1KeffipXV1fdeeedxZIFgIM4ZlJAALiy3OmNc7/c3d0NPz8/47bbbjPeeustu6m0c10+DXFCQoJxxx13GP7+/oa7u7vh7+9vDBo0yNixY4fd9y1cuNAIDQ013Nzc7KY27tSpU4FTURc0xfmXX35pjBs3zqhdu7ZRsWJFo2fPnsb+/fvzfP+UKVOMunXrGh4eHkaHDh2M9evX59nnlbJdPsW5YRjGmTNnjNGjRxv+/v5GhQoVjEaNGhlvvPGG3fTQhpF3SudcBU29frnjx48bw4cPN2rWrGm4u7sbLVu2zHcadrNTnBe0bXx8vN17v3yK81zvvfeeERwcbHh4eBgRERHGqlWr8v1Ms7KyjNdee81o3ry54eHhYVSvXt1o06aNMXHiRCM1NfWKOWNiYux+LitVqmQEBQUZd955p/H1118b2dnZeb7n8gzvv/++cfPNNxs1atQwPDw8jJCQEOOJJ57Ic+yXXnrJqFu3ruHi4mI33fmiRYuMVq1aGZ6enkZQUJDx2muvGR9//HGeKdEL+kzz+0z++usvY+TIkUbdunUNd3d3o169ekZMTIxx8uRJU59bYX/n8lPQFOeXP+agoPG/3KVTnF/J5b8PhZniPNc333xjdOzY0ahcubJRuXJlo2nTpkZsbKyxfft2wzAMY8+ePcZ9991nhISEGJ6enoaPj4/RuXNnY/ny5VfMBKD0sxiGA+8iBgAAAIAyhnuiAAAAAMAEShQAAAAAmECJAgAAAAATKFEAAAAAYAIlCgAAAABMoEQBAAAAgAlO/7DdnJwcHTlyRF5eXrJYLI6OAwAAAMBBDMPQmTNn5O/vLxeXgs83OX2JOnLkiAICAhwdAwAAAEApcfDgQdWrV6/A9U5fory8vCT980FVrVrVwWkAAAAAOEpaWpoCAgJsHaEgTluirFarrFarsrOzJUlVq1alRAEAAAC46m0+FsMwjOuUpVRKS0uTt7e3UlNTKVEAAACAEytsN2B2PgAAAAAwgRIFAAAAACZQogAAAADABEoUAAAAAJjgtCXKarUqNDRUkZGRjo4CAAAAoAxhdj5m5wMAAAAgZucDAAAAgBJBiQIAAAAAEyhRAAAAAGACJQoAAAAATHDaEsXsfAAAAACKgtn5mJ0PAAAAgJidDwAAAABKBCUKAAAAAEygRAEAAACACZQoAAAAADCBEgUAAAAAJrg5OoCjWK1WWa1WZWdnS5Lm70lVJS+nnqgQQDG6K8Tb0REAAEAJcdozUbGxsUpOTlZiYqKjowAAAAAoQ5y2RAEAAABAUVCiAAAAAMAEShQAAAAAmECJAgAAAAATKFEAAAAAYAIlCgAAAABMcNoSZbVaFRoaqsjISEdHAQAAAFCGOG2J4jlRAAAAAIrCaUsUAAAAABQFJQoAAAAATKBEAQAAAIAJlCgAAAAAMIESBQAAAAAmUKIAAAAAwARKFAAAAACYQIkCAAAAABMoUQAAAABggtOWKKvVqtDQUEVGRjo6CgAAAIAyxGlLVGxsrJKTk5WYmOjoKAAAAADKEKctUQAAAABQFJQoAAAAADCBEgUAAAAAJlCiAAAAAMAEShQAAAAAmECJAgAAAAATKFEAAAAAYAIlCgAAAABMoEQBAAAAgAmUKAAAAAAwgRIFAAAAACZQogAAAADABEoUAAAAAJhAiQIAAAAAE5y2RFmtVoWGhioyMtLRUQAAAACUIU5bomJjY5WcnKzExERHRwEAAABQhjhtiQIAAACAoqBEAQAAAIAJlCgAAAAAMIESBQAAAAAmUKIAAAAAwARKFAAAAACYQIkCAAAAABMoUQAAAABgAiUKAAAAAEygRAEAAACACZQoAAAAADCBEgUAAAAAJlCiAAAAAMAEShQAAAAAmECJAgAAAAATKFEAAAAAYAIlCgAAAABMoEQBAAAAgAmUKAAAAAAwgRIFAAAAACaUmxJ19uxZBQYGauzYsY6OAgAAAKAcKzcl6uWXX9a//vUvR8cAAAAAUM6VixK1c+dObdu2Td27d3d0FAAAAADlnMNL1KpVq9SrVy/5+/vLYrFowYIFebaxWq0KCgqSp6en2rVrp3Xr1tmtHzt2rCZPnnydEgMAAABwZg4vURkZGQoLC5PVas13/ezZsxUXF6cJEyZow4YNCgsLU3R0tFJSUiRJCxcuVOPGjdW4ceNCHS8zM1NpaWl2XwAAAABQWG6ODtC9e/crXoY3depUPfDAAxo+fLgkaebMmfr+++/18ccf6+mnn9avv/6qr776SnPnzlV6erouXLigqlWravz48fnub/LkyZo4cWKJvBcAAAAA5Z/Dz0RdSVZWlpKSkhQVFWVb5uLioqioKK1du1bSP6Xo4MGD2rdvn95880098MADBRYoSRo3bpxSU1NtXwcPHizx9wEAAACg/HD4magrOXnypLKzs+Xr62u33NfXV9u2bSvSPj08POTh4VEc8QAAAAA4oVJdoswaNmyYoyMAAAAAKOdK9eV8NWvWlKurq44fP263/Pjx4/Lz87umfVutVoWGhioyMvKa9gMAAADAuZTqEuXu7q42bdooISHBtiwnJ0cJCQlq3779Ne07NjZWycnJSkxMvNaYAAAAAJyIwy/nS09P165du2yv9+7dq02bNsnHx0f169dXXFycYmJiFBERobZt22r69OnKyMiwzdYHAAAAANeTw0vU+vXr1blzZ9vruLg4SVJMTIzi4+M1YMAAnThxQuPHj9exY8cUHh6uJUuW5JlsAgAAAACuB4thGIajQzhSWlqavL29Fb/xgCp5VXV0HADlxF0h3o6OAAAATMrtBqmpqapateBuUKrviSpJTCwBAAAAoCictkQxsQQAAACAonDaEgUAAAAARUGJAgAAAAATKFEAAAAAYILTligmlgAAAABQFE5bophYAgAAAEBROG2JAgAAAICioEQBAAAAgAmUKAAAAAAwgRIFAAAAACY4bYlidj4AAAAAReG0JYrZ+QAAAAAUhdOWKAAAAAAoCkoUAAAAAJhAiQIAAAAAEyhRAAAAAGACJQoAAAAATHDaEsUU5wAAAACKwmlLFFOcAwAAACgKpy1RAAAAAFAUlCgAAAAAMIESBQAAAAAmUKIAAAAAwARKFAAAAACYQIkCAAAAABMoUQAAAABggtOWKB62CwAAAKAonLZE8bBdAAAAAEXhtCUKAAAAAIqCEgUAAAAAJlCiAAAAAMAEShQAAAAAmECJAgAAAAATKFEAAAAAYAIlCgAAAABMoEQBAAAAgAmUKAAAAAAwwWlLlNVqVWhoqCIjIx0dBQAAAEAZ4rQlKjY2VsnJyUpMTHR0FAAAAABliNOWKAAAAAAoCkoUAAAAAJhAiQIAAAAAEyhRAAAAAGACJQoAAAAATKBEAQAAAIAJlCgAAAAAMIESBQAAAAAmUKIAAAAAwARKFAAAAACYQIkCAAAAABMoUQAAAABgAiUKAAAAAEygRAEAAACACU5boqxWq0JDQxUZGenoKAAAAADKEKctUbGxsUpOTlZiYqKjowAAAAAoQ5y2RAEAAABAUVCiAAAAAMAEShQAAAAAmECJAgAAAAATKFEAAAAAYAIlCgAAAABMoEQBAAAAgAmUKAAAAAAwgRIFAAAAACZQogAAAADABEoUAAAAAJjgVpRvunDhgo4dO6azZ8+qVq1a8vHxKe5cAAAAAFAqFfpM1JkzZzRjxgx16tRJVatWVVBQkJo1a6ZatWopMDBQDzzwgBITE0syKwAAAAA4XKFK1NSpUxUUFKRZs2YpKipKCxYs0KZNm7Rjxw6tXbtWEyZM0MWLF9W1a1d169ZNO3fuLOncAAAAAOAQhbqcLzExUatWrVLz5s3zXd+2bVvdd999mjlzpmbNmqVffvlFjRo1KtagAAAAAFAaFKpEffnll4XamYeHhx5++OFrCgQAAAAApVmRZ+fbtWuXfvzxR507d06SZBhGsYUCAAAAgNLKdIn666+/FBUVpcaNG6tHjx46evSoJGnEiBEaM2ZMsQcEAAAAgNLEdIkaPXq03NzcdODAAVWqVMm2fMCAAVqyZEmxhgMAAACA0sb0c6KWLl2qH3/8UfXq1bNb3qhRI+3fv7/YghXW6dOnFRUVpYsXL+rixYsaNWqUHnjggeueAwAAAIBzMF2iMjIy7M5A5Tp16pQ8PDyKJZQZXl5eWrVqlSpVqqSMjAy1aNFC/fr1U40aNa57FgAAAADln+nL+W666SZ98sknttcWi0U5OTl6/fXX1blz52INVxiurq62UpeZmSnDMJjkAgAAAECJMV2iXn/9dX3wwQfq3r27srKy9OSTT6pFixZatWqVXnvtNdMBVq1apV69esnf318Wi0ULFizIs43ValVQUJA8PT3Vrl07rVu3zm796dOnFRYWpnr16umJJ55QzZo1TecAAAAAgMIwXaJatGihHTt2qGPHjrrjjjuUkZGhfv36aePGjQoJCTEdICMjQ2FhYbJarfmunz17tuLi4jRhwgRt2LBBYWFhio6OVkpKim2batWqafPmzdq7d6+++OILHT9+3HQOAAAAACgMi1GKrn2zWCyaP3+++vTpY1vWrl07RUZG6t1335Uk5eTkKCAgQI8++qiefvrpPPt45JFHdOutt6p///75HiMzM1OZmZm212lpaQoICFD8xgOq5FW1eN8QAKd1V4i3oyMAAACT0tLS5O3trdTUVFWtWnA3KNTEElu2bCn0gVu1alXoba8mKytLSUlJGjdunG2Zi4uLoqKitHbtWknS8ePHValSJXl5eSk1NVWrVq3Sv//97wL3OXnyZE2cOLHYMgIAAABwLoUqUeHh4bJYLFedsMFisSg7O7tYgknSyZMnlZ2dLV9fX7vlvr6+2rZtmyRp//79evDBB20TSjz66KNq2bJlgfscN26c4uLibK9zz0QBAAAAQGEUqkTt3bu3pHMUWdu2bbVp06ZCb+/h4eGQqdgBAAAAlA+FKlGBgYElnSNfNWvWlKura56JIo4fPy4/Pz+HZAIAAADg3Ew/bDdXcnKyDhw4oKysLLvlvXv3vuZQudzd3dWmTRslJCTYJpvIyclRQkKCRo4ceU37tlqtslqtxXr5IQAAAIDyz3SJ2rNnj/r27autW7fa3SdlsVgkyXQpSU9P165du2yv9+7dq02bNsnHx0f169dXXFycYmJiFBERobZt22r69OnKyMjQ8OHDzUa3Exsbq9jYWNsMHAAAAABQGKafEzVq1CgFBwcrJSVFlSpV0h9//KFVq1YpIiJCP/30k+kA69evV+vWrdW6dWtJUlxcnFq3bq3x48dLkgYMGKA333xT48ePV3h4uDZt2qQlS5bkmWwCAAAAAK4H08+JqlmzplasWKFWrVrJ29tb69atU5MmTbRixQqNGTNGGzduLKmsJSL3TBTPiQJQnHhOFAAAZU9hnxNl+kxUdna2vLy8JP1TqI4cOSLpn8kntm/fXsS415/ValVoaKgiIyMdHQUAAABAGWL6nqgWLVpo8+bNCg4OVrt27fT666/L3d1dH3zwgRo0aFASGUsE90QBAAAAKArTJeq5555TRkaGJOnFF1/U7bffrptuukk1atTQ7Nmziz0gAAAAAJQmpktUdHS07b83bNhQ27Zt06lTp1S9enXbDH0AAAAAUF6ZvicqNTVVp06dslvm4+Ojv//+W2lpacUWDAAAAABKI9MlauDAgfrqq6/yLJ8zZ44GDhxYLKGuByaWAAAAAFAUpqc49/Hx0Zo1a9SsWTO75du2bVOHDh30119/FWvAksYU5wBKAlOcAwBQ9pTYFOeZmZm6ePFinuUXLlzQuXPnzO4OAAAAAMoU0yWqbdu2+uCDD/Isnzlzptq0aVMsoQAAAACgtDI9O9+kSZMUFRWlzZs3q0uXLpKkhIQEJSYmaunSpcUeEAAAAABKE9Nnojp06KC1a9cqICBAc+bM0bfffquGDRtqy5Ytuummm0oiIwAAAACUGqbPRElSeHi4Pv/88+LOcl1ZrVZZrVZlZ2c7OgoAAACAMsT0magNGzZo69atttcLFy5Unz599MwzzygrK6tYw5Wk2NhYJScnKzEx0dFRAAAAAJQhpkvUQw89pB07dkiS9uzZowEDBqhSpUqaO3eunnzyyWIPCAAAAACliekStWPHDoWHh0uS5s6dq06dOumLL75QfHy8vvnmm+LOBwAAAACliukSZRiGcnJyJEnLly9Xjx49JEkBAQE6efJk8aYDAAAAgFLGdImKiIjQpEmT9Omnn+rnn39Wz549JUl79+6Vr69vsQcEAAAAgNLEdImaPn26NmzYoJEjR+rZZ59Vw4YNJUlff/21brzxxmIPCAAAAACliekpzlu1amU3O1+uN954Q66ursUS6npginMAAAAARWExDMNwdAhHSktLk7e3t+I3HlAlr6qOjgOgnLgrxNvREQAAgEm53SA1NVVVqxbcDUxfzgcAAAAAzowSBQAAAAAmUKIAAAAAwARKFAAAAACYYHp2vri4uHyXWywWeXp6qmHDhrrjjjvk4+NzzeEAAAAAoLQxXaI2btyoDRs2KDs7W02aNJEk7dixQ66urmratKnee+89jRkzRqtXr1ZoaGixBwYAAAAARzJ9Od8dd9yhqKgoHTlyRElJSUpKStKhQ4d02223adCgQTp8+LBuvvlmjR49uiTyAgAAAIBDmX5OVN26dbVs2bI8Z5n++OMPde3aVYcPH9aGDRvUtWtXnTx5sljDFqdLH7a7Y8cOnhMFoFjxnCgAAMqeEntOVGpqqlJSUvIsP3HihNLS0iRJ1apVU1ZWltldX1exsbFKTk5WYmKio6MAAAAAKEOKdDnffffdp/nz5+vQoUM6dOiQ5s+frxEjRqhPnz6SpHXr1qlx48bFnRUAAAAAHM70xBLvv/++Ro8erYEDB+rixYv/7MTNTTExMZo2bZokqWnTpvroo4+KNykAAAAAlAKm74nKlZ6erj179kiSGjRooCpVqhRrsOsl97pH7okCUJy4JwoAgLKnsPdEmT4TlatKlSq2Z0GV1QIFAAAAAGaZvicqJydHL774ory9vRUYGKjAwEBVq1ZNL730knJyckoiIwAAAACUGqbPRD377LP6z3/+o1dffVUdOnSQJK1evVovvPCCzp8/r5dffrnYQwIAAABAaWG6RP33v//VRx99pN69e9uWtWrVSnXr1tUjjzxCiQIAAABQrpm+nO/UqVNq2rRpnuVNmzbVqVOniiUUAAAAAJRWpktUWFiY3n333TzL3333XYWFhRVLKAAAAAAorUxfzvf666+rZ8+eWr58udq3by9JWrt2rQ4ePKgffvih2AOWFKvVKqvVquzsbEdHAQAAAFCGFOk5UUeOHJHVatW2bdskSc2aNdMjjzwif3//Yg9Y0nhOFICSwHOiAAAoe0r0OVH+/v5MIAEAAADAKRWqRG3ZsqXQO2zVqlWRwwAAAABAaVeoEhUeHi6LxaKrXflnsVi4xwgAAABAuVaoErV3796SzgEAAAAAZUKhSlRgYGBJ5wAAAACAMqFQz4n69ddfC73Ds2fP6o8//ihyIAAAAAAozQpVooYMGaLo6GjNnTtXGRkZ+W6TnJysZ555RiEhIUpKSirWkAAAAABQWhTqcr7k5GTNmDFDzz33nAYPHqzGjRvL399fnp6e+vvvv7Vt2zalp6erb9++Wrp0qVq2bFnSuQEAAADAIUw/bHf9+vVavXq19u/fr3PnzqlmzZpq3bq1OnfuLB8fn5LKWWJ42C6AksDDdgEAKHtK7GG7ERERioiIuKZwAAAAAFBWFeqeKAAAAADAPyhRAAAAAGACJQoAAAAATKBEAQAAAIAJpkvUnj17SiLHdWe1WhUaGqrIyEhHRwEAAABQhpguUQ0bNlTnzp312Wef6fz58yWR6bqIjY1VcnKyEhMTHR0FAAAAQBliukRt2LBBrVq1UlxcnPz8/PTQQw9p3bp1JZENAAAAAEod0yUqPDxcb731lo4cOaKPP/5YR48eVceOHdWiRQtNnTpVJ06cKImcAAAAAFAqFHliCTc3N/Xr109z587Va6+9pl27dmns2LEKCAjQ0KFDdfTo0eLMCQAAAAClQpFL1Pr16/XII4+oTp06mjp1qsaOHavdu3dr2bJlOnLkiO64447izAkAAAAApYKb2W+YOnWqZs2ape3bt6tHjx765JNP1KNHD7m4/NPHgoODFR8fr6CgoOLOCgAAAAAOZ7pEzZgxQ/fdd5+GDRumOnXq5LtN7dq19Z///OeawwEAAABAaWO6RO3cufOq27i7uysmJqZIgQAAAACgNDN9T9SsWbM0d+7cPMvnzp2r//73v8USCgAAAABKK9MlavLkyapZs2ae5bVr19Yrr7xSLKEAAAAAoLQyXaIOHDig4ODgPMsDAwN14MCBYgkFAAAAAKWV6RJVu3ZtbdmyJc/yzZs3q0aNGsUSCgAAAABKK9MlatCgQXrssce0cuVKZWdnKzs7WytWrNCoUaM0cODAksgIAAAAAKWG6dn5XnrpJe3bt09dunSRm9s/356Tk6OhQ4dyTxQAAACAcs90iXJ3d9fs2bP10ksvafPmzapYsaJatmypwMDAksgHAAAAAKWK6RKVq3HjxmrcuHFxZgEAAACAUs90icrOzlZ8fLwSEhKUkpKinJwcu/UrVqwotnAAAAAAUNqYLlGjRo1SfHy8evbsqRYtWshisZRELgAAAAAolUyXqK+++kpz5sxRjx49SiIPAAAAAJRqpqc4d3d3V8OGDUsiCwAAAACUeqZL1JgxY/TWW2/JMIySyGPawYMHdcsttyg0NFStWrXS3LlzHR0JAAAAQDlm+nK+1atXa+XKlVq8eLGaN2+uChUq2K2fN29esYUrDDc3N02fPl3h4eE6duyY2rRpox49eqhy5crXNQcAAAAA52C6RFWrVk19+/YtiSxFUqdOHdWpU0eS5Ofnp5o1a+rUqVOUKAAAAAAlwnSJmjVrVrEGWLVqld544w0lJSXp6NGjmj9/vvr06WO3jdVq1RtvvKFjx44pLCxM77zzjtq2bZtnX0lJScrOzlZAQECxZgQAAACAXKbviZKkixcvavny5Xr//fd15swZSdKRI0eUnp5uel8ZGRkKCwuT1WrNd/3s2bMVFxenCRMmaMOGDQoLC1N0dLRSUlLstjt16pSGDh2qDz74wPwbAgAAAIBCshgmZ4jYv3+/unXrpgMHDigzM1M7duxQgwYNNGrUKGVmZmrmzJlFD2Ox5DkT1a5dO0VGRurdd9+VJOXk5CggIECPPvqonn76aUlSZmambrvtNj3wwAMaMmTIFY+RmZmpzMxM2+u0tDQFBAQofuMBVfKqWuTsAHCpu0K8HR0BAACYlJaWJm9vb6Wmpqpq1YK7gekzUaNGjVJERIT+/vtvVaxY0ba8b9++SkhIKFraAmRlZSkpKUlRUVG2ZS4uLoqKitLatWslSYZhaNiwYbr11luvWqAkafLkyfL29rZ9cekfAAAAADNMl6hffvlFzz33nNzd3e2WBwUF6fDhw8UWTJJOnjyp7Oxs+fr62i339fXVsWPHJElr1qzR7NmztWDBAoWHhys8PFxbt24tcJ/jxo1Tamqq7evgwYPFmhkAAABA+WZ6YomcnBxlZ2fnWX7o0CF5eXkVSygzOnbsqJycnEJv7+HhIQ8PjxJMBAAAAKA8M30mqmvXrpo+fbrttcViUXp6uiZMmKAePXoUZzbVrFlTrq6uOn78uN3y48ePy8/Pr1iPBQAAAACFYbpETZkyRWvWrFFoaKjOnz+vwYMH2y7le+2114o1nLu7u9q0aWN3r1VOTo4SEhLUvn37a9q31WpVaGioIiMjrzUmAAAAACdi+nK+evXqafPmzfrqq6+0ZcsWpaena8SIEbrnnnvsJpoorPT0dO3atcv2eu/evdq0aZN8fHxUv359xcXFKSYmRhEREWrbtq2mT5+ujIwMDR8+3PSxLhUbG6vY2FjbDBwAAAAAUBimS5Qkubm56d577y2WAOvXr1fnzp1tr+Pi4iRJMTExio+P14ABA3TixAmNHz9ex44dU3h4uJYsWZJnsgkAAAAAuB5MPyfqk08+ueL6oUOHXlOg6y33TBTPiQJQnHhOFAAAZU9hnxNl+kzUqFGj7F5fuHBBZ8+elbu7uypVqlTmShQAAAAAmGF6Yom///7b7is9PV3bt29Xx44d9eWXX5ZExhLBxBIAAAAAisL05XwFWb9+ve69915t27atOHZ33XA5H4CSwOV8AACUPYW9nM/0maiCuLm56ciRI8W1OwAAAAAolUzfE7Vo0SK714Zh6OjRo3r33XfVoUOHYgsGAAAAAKWR6RLVp08fu9cWi0W1atXSrbfeqilTphRXLgAAAAAolUyXqJycnJLIcd1ZrVZZrVZlZ2c7OgoAAACAMqTY7okqa2JjY5WcnKzExERHRwEAAABQhpg+ExUXF1fobadOnWp29wAAAABQqpkuURs3btTGjRt14cIFNWnSRJK0Y8cOubq66oYbbrBtZ7FYii8lAAAAAJQSpktUr1695OXlpf/+97+qXr26pH8ewDt8+HDddNNNGjNmTLGHBAAAAIDSwvTDduvWraulS5eqefPmdst///13de3atcw9K4qH7QIoCTxsFwCAsqfEHrablpamEydO5Fl+4sQJnTlzxuzuHMZqtSo0NFSRkZGOjgIAAACgDDFdovr27avhw4dr3rx5OnTokA4dOqRvvvlGI0aMUL9+/UoiY4lgdj4AAAAARWH6nqiZM2dq7NixGjx4sC5cuPDPTtzcNGLECL3xxhvFHhAAAAAAShPT90TlysjI0O7duyVJISEhqly5crEGu164JwpASeCeKAAAyp4Suycq19GjR3X06FE1atRIlStXVhG7GAAAAACUKaZL1F9//aUuXbqocePG6tGjh44ePSpJGjFiBNObAwAAACj3TJeo0aNHq0KFCjpw4IAqVapkWz5gwAAtWbKkWMMBAAAAQGljemKJpUuX6scff1S9evXsljdq1Ej79+8vtmAlzWq1ymq1Kjs729FRAAAAAJQhps9EZWRk2J2BynXq1Cl5eHgUS6jrgSnOAQAAABSF6RJ100036ZNPPrG9tlgsysnJ0euvv67OnTsXazgAAAAAKG1MX873+uuvq0uXLlq/fr2ysrL05JNP6o8//tCpU6e0Zs2aksgIAAAAAKWG6TNRLVq00I4dO9SxY0fdcccdysjIUL9+/bRx40aFhISUREYAAAAAKDVMnYm6cOGCunXrppkzZ+rZZ58tqUwAAAAAUGqZOhNVoUIFbdmypaSyAAAAAECpZ/pyvnvvvVf/+c9/SiILAAAAAJR6pieWuHjxoj7++GMtX75cbdq0UeXKle3WT506tdjClSSeEwUAAACgKCyGYRhmvuFK05hbLBatWLHimkNdT2lpafL29lb8xgOq5FXV0XEAlBN3hXg7OgIAADAptxukpqaqatWCu0Ghz0Tt2bNHwcHBWrlyZbEEBAAAAICyqND3RDVq1EgnTpywvR4wYICOHz9eIqEAAAAAoLQqdIm6/Kq/H374QRkZGcUeCAAAAABKM9Oz8wEAAACAMyt0ibJYLLJYLHmWAQAAAIAzKfTEEoZhaNiwYfLw8JAknT9/Xg8//HCeKc7nzZtXvAkBAAAAoBQpdImKiYmxe33vvfcWexgAAAAAKO0KXaJmzZpVkjkAAAAAoExgYgkAAAAAMIESBQAAAAAmOG2JslqtCg0NVWRkpKOjAAAAAChDnLZExcbGKjk5WYmJiY6OAgAAAKAMcdoSBQAAAABFQYkCAAAAABMoUQAAAABgAiUKAAAAAEygRAEAAACACZQoAAAAADCBEgUAAAAAJlCiAAAAAMAEN0cHAIDyaO7uVEdHAFDO3BXi7egIAP4PZ6IAAAAAwARKFAAAAACYQIkCAAAAABMoUQAAAABggtOWKKvVqtDQUEVGRjo6CgAAAIAyxGlLVGxsrJKTk5WYmOjoKAAAAADKEKctUQAAAABQFJQoAAAAADCBEgUAAAAAJlCiAAAAAMAEShQAAAAAmECJAgAAAAATKFEAAAAAYAIlCgAAAABMoEQBAAAAgAmUKAAAAAAwgRIFAAAAACZQogAAAADABEoUAAAAAJhAiQIAAAAAEyhRAAAAAGACJQoAAAAATKBEAQAAAIAJlCgAAAAAMIESBQAAAAAmUKIAAAAAwIRyUaL69u2r6tWrq3///o6OAgAAAKCcKxclatSoUfrkk08cHQMAAACAEygXJeqWW26Rl5eXo2MAAAAAcAIOL1GrVq1Sr1695O/vL4vFogULFuTZxmq1KigoSJ6enmrXrp3WrVt3/YMCAAAAgEpBicrIyFBYWJisVmu+62fPnq24uDhNmDBBGzZsUFhYmKKjo5WSklKk42VmZiotLc3uCwAAAAAKy+Elqnv37po0aZL69u2b7/qpU6fqgQce0PDhwxUaGqqZM2eqUqVK+vjjj4t0vMmTJ8vb29v2FRAQcC3xAQAAADgZh5eoK8nKylJSUpKioqJsy1xcXBQVFaW1a9cWaZ/jxo1Tamqq7evgwYPFFRcAAACAE3BzdIArOXnypLKzs+Xr62u33NfXV9u2bbO9joqK0ubNm5WRkaF69epp7ty5at++fb779PDwkIeHR4nmBgAAAFB+leoSVVjLly93dAQAAAAATqJUX85Xs2ZNubq66vjx43bLjx8/Lj8/v2vat9VqVWhoqCIjI69pPwAAAACcS6kuUe7u7mrTpo0SEhJsy3JycpSQkFDg5XqFFRsbq+TkZCUmJl5rTAAAAABOxOGX86Wnp2vXrl2213v37tWmTZvk4+Oj+vXrKy4uTjExMYqIiFDbtm01ffp0ZWRkaPjw4Q5MDQAAAMBZObxErV+/Xp07d7a9jouLkyTFxMQoPj5eAwYM0IkTJzR+/HgdO3ZM4eHhWrJkSZ7JJgAAAADgerAYhmE4OoQjpaWlydvbW/EbD6iSV1VHxwEAAMjXXSHejo4AlHu53SA1NVVVqxbcDUr1PVEliYklAAAAABSF05YoJpYAAAAAUBROW6IAAAAAoCgoUQAAAABgAiUKAAAAAExw2hLFxBIAAAAAisJpSxQTSwAAAAAoCqctUQAAAABQFJQoAAAAADCBEgUAAAAAJlCiAAAAAMAEpy1RzM4HAAAAoCictkQxOx8AAACAonDaEgUAAAAARUGJAgAAAAATKFEAAAAAYAIlCgAAAABMoEQBAAAAgAlOW6KY4hwAAABAUThtiWKKcwAAAABF4bQlCgAAAACKghIFAAAAACZQogAAAADABEoUAAAAAJhAiQIAAAAAEyhRAAAAAGACJQoAAAAATHDaEsXDdgEAAAAUhdOWKB62CwAAAKAonLZEAQAAAEBRUKIAAAAAwARKFAAAAACYQIkCAAAAABMoUQAAAABgAiUKAAAAAEygRAEAAACACZQoAAAAADCBEgUAAAAAJjhtibJarQoNDVVkZKSjowAAAAAoQ5y2RMXGxio5OVmJiYmOjgIAAACgDHHaEgUAAAAARUGJAgAAAAATKFEAAAAAYAIlCgA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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('no_of_passenger')['distance_miles', 'amount'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "K6n5nS_R9rzz",
        "outputId": "68d6d65d-8e1c-4ab1-9762-811bbab931df"
      },
      "execution_count": 209,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 distance_miles     amount\n",
              "no_of_passenger                           \n",
              "0                    132.420320   9.414545\n",
              "1                     11.573665  11.212253\n",
              "2                      8.305378  12.013336\n",
              "3                      9.519631  11.219359\n",
              "4                     23.238271  11.473360\n",
              "5                      8.319510  11.345435\n",
              "6                      7.642741  12.495820"
            ],
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              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-fba80312-547f-4a20-864d-3e0734bb4d70 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-69dcda57-22cd-4a39-bc06-ac29d5e160cc 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-69dcda57-22cd-4a39-bc06-ac29d5e160cc');\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": 209
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# remove datapoints with distance <0.05 miles\n",
        "idx = (df.distance_miles >= 0.05)\n",
        "print('Old size: %d' % len(df))\n",
        "df = df[idx]\n",
        "print('New size: %d' % len(df))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p-LRhIhf9sDZ",
        "outputId": "a9620d26-735a-4e55-e635-6ea5945237be"
      },
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Old size: 49984\n",
            "New size: 48269\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "metadata": {
        "id": "aMCSrBa1RfiE"
      },
      "execution_count": 211,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['date_time_of_pickup'] = pd.to_datetime(df['date_time_of_pickup'])\n",
        "\n",
        "# Convert the Timestamps to seconds since the reference point (for example, UNIX timestamp)\n",
        "df['date_time_of_pickup'] = df['date_time_of_pickup'].astype(int) / 10**9\n",
        "\n",
        "df.drop('distance_miles', axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "Bq9FSz_TRJoc"
      },
      "execution_count": 212,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Building Models**"
      ],
      "metadata": {
        "id": "1Zz1DMTUNvaR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Based on the analysis above, I would start with the following models:"
      ],
      "metadata": {
        "id": "qVTCMJWn9sTF"
      },
      "execution_count": 213,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics\n",
        "from sklearn.linear_model import LinearRegression"
      ],
      "metadata": {
        "id": "hkBFEWJi9sfK"
      },
      "execution_count": 214,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y=df['amount']\n",
        "x=df.drop(['amount'], axis=1)"
      ],
      "metadata": {
        "id": "zwbHs_NJO5Ie"
      },
      "execution_count": 215,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train , y_test=train_test_split(x,y, test_size=0.25, random_state=88)"
      ],
      "metadata": {
        "id": "Hf2Oen0M9srf"
      },
      "execution_count": 216,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Linear Regression**"
      ],
      "metadata": {
        "id": "Q_DD_sz1PMYN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression()\n",
        "\n",
        "model.fit(x_train,y_train)\n",
        "\n",
        "print(f\"Training Score : {model.score(x_train, y_train)}\")\n",
        "print(f\"Testing Score : {model.score(x_test, y_test)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XbUcEQFB9s3S",
        "outputId": "c9e28e87-2558-49b0-bfc6-f8084aaee701"
      },
      "execution_count": 217,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Score : 0.017729746916952616\n",
            "Testing Score : 0.01870200497239738\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "avg_fare=round(np.mean(y_train),2) #11.31\n",
        "baseline_pred=np.repeat(avg_fare,y_test.shape[0])\n",
        "baseline_rmse=np.sqrt(mean_squared_error(baseline_pred, y_test))\n",
        "print(\"Basline RMSE of Validation data :\",baseline_rmse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uhDFZu5O9tDE",
        "outputId": "3cc1757c-ea17-4613-d986-92ea9ce3221e"
      },
      "execution_count": 219,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Basline RMSE of Validation data : 9.292502589018783\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Decision Tree Regressor**"
      ],
      "metadata": {
        "id": "7S9NhPZnZbY5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "model_2=DecisionTreeRegressor(max_depth=3)\n",
        "model_2.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "pMp5dRccYoUx",
        "outputId": "d897bb94-d781-4d3d-cd7f-6d4e6810a70e"
      },
      "execution_count": 225,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(max_depth=3)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=3)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 225
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_2.score(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kuVCFN1CZqew",
        "outputId": "ead67f0f-ecd4-4497-cb83-bb62f42ab851"
      },
      "execution_count": 226,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5563335627381728"
            ]
          },
          "metadata": {},
          "execution_count": 226
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_2.score(x_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fe_X3qo8ZxOI",
        "outputId": "a0a9eafa-7de8-46b1-f928-0c2db804fab1"
      },
      "execution_count": 227,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5583545046138765"
            ]
          },
          "metadata": {},
          "execution_count": 227
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Bagging Regression Model**"
      ],
      "metadata": {
        "id": "gokWlvwwZ4V1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import BaggingRegressor\n",
        "model_3=BaggingRegressor(n_estimators=90,base_estimator=model_2)\n",
        "model_3.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "id": "O4yetZCdZzkU",
        "outputId": "4e41aec5-35d1-4457-94ea-f6ccdd175118"
      },
      "execution_count": 228,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BaggingRegressor(base_estimator=DecisionTreeRegressor(max_depth=3),\n",
              "                 n_estimators=90)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BaggingRegressor(base_estimator=DecisionTreeRegressor(max_depth=3),\n",
              "                 n_estimators=90)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BaggingRegressor</label><div class=\"sk-toggleable__content\"><pre>BaggingRegressor(base_estimator=DecisionTreeRegressor(max_depth=3),\n",
              "                 n_estimators=90)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">base_estimator: DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=3)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=3)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 228
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_3.score(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JJEEgXAHaE7E",
        "outputId": "06602720-3b62-4e79-fdfc-c3c67efd7ce9"
      },
      "execution_count": 229,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5692059269824705"
            ]
          },
          "metadata": {},
          "execution_count": 229
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_3.score(x_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RxvpK-Q-aIo9",
        "outputId": "444c9a5d-aac5-40ce-b861-e0aa36991a17"
      },
      "execution_count": 230,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5691959637669594"
            ]
          },
          "metadata": {},
          "execution_count": 230
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **AdaBoost Regression Model**"
      ],
      "metadata": {
        "id": "pBzZeGx7aPrT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import AdaBoostRegressor\n",
        "model_4=AdaBoostRegressor(n_estimators=25)\n",
        "model_4.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "hMcq7u7IaKwV",
        "outputId": "aa5877f7-c59a-4191-9a2f-5313b4d91c58"
      },
      "execution_count": 231,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdaBoostRegressor(n_estimators=25)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor(n_estimators=25)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostRegressor</label><div class=\"sk-toggleable__content\"><pre>AdaBoostRegressor(n_estimators=25)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 231
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_4.score(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aDjbyJEUaeSw",
        "outputId": "1e591892-1d76-4807-8f8d-78743a382cbc"
      },
      "execution_count": 232,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.15633307576386002"
            ]
          },
          "metadata": {},
          "execution_count": 232
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_4.score(x_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ir4hJq9AagZL",
        "outputId": "f8d533c5-2bef-4056-f609-19746f40400f"
      },
      "execution_count": 233,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.12954642859174803"
            ]
          },
          "metadata": {},
          "execution_count": 233
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **GradientBoosting Regression Model**"
      ],
      "metadata": {
        "id": "7lZbwp3Dam1F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "model_5=GradientBoostingRegressor()\n",
        "model_5.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "6jqhvxHTaiEK",
        "outputId": "2cb2075e-56d4-4ad3-bfde-eb94d9cb2c9d"
      },
      "execution_count": 234,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingRegressor()"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 234
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_5.score(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u_BTJfhSayaL",
        "outputId": "0894cfea-9d88-41f6-ef28-0b36e8859396"
      },
      "execution_count": 235,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8206672416339882"
            ]
          },
          "metadata": {},
          "execution_count": 235
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_5.score(x_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NZaMghFZa1D9",
        "outputId": "23e256a8-9217-4858-8310-d3b631f76bbd"
      },
      "execution_count": 236,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7890060636866733"
            ]
          },
          "metadata": {},
          "execution_count": 236
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **RandomForest Regression Model**"
      ],
      "metadata": {
        "id": "kr5nRQXxa_lp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "model_6=RandomForestRegressor(n_estimators=1,max_features=1)\n",
        "model_6.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "bTmSS70ra3qn",
        "outputId": "9b74f2af-b0d7-497a-b4c6-da4e5554f484"
      },
      "execution_count": 237,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(max_features=1, n_estimators=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_features=1, n_estimators=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_features=1, n_estimators=1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 237
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_6.score(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dKQPLTXObJ5G",
        "outputId": "cc1b99c3-1359-4cb5-aee1-d155f2db95dc"
      },
      "execution_count": 238,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8295950412354482"
            ]
          },
          "metadata": {},
          "execution_count": 238
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_5.score(x_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v7JuU0VtbQtt",
        "outputId": "c2c5229d-643c-4c5e-b8f3-5f865bbb00c5"
      },
      "execution_count": 239,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7890060636866733"
            ]
          },
          "metadata": {},
          "execution_count": 239
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# No overfitting in models"
      ],
      "metadata": {
        "id": "puPC6W-QbSIP"
      },
      "execution_count": 240,
      "outputs": []
    }
  ]
}