NYC_Fare_Prediction.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import dask.dataframe as dd\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NxBL3HRclCdz"
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      "source": [
        "df1 = pd.DataFrame()\n",
        "df1 = pd.read_csv('yellow_tripdata_2015-01.csv')\n",
        "\n",
        "# Reinitialize the DataFrame and load the data\n",
        "df2 = pd.DataFrame()\n",
        "df2 = pd.read_csv('yellow_tripdata_2016-01.csv')\n",
        "\n",
        "# Reinitialize the DataFrame and load the data\n",
        "df3 = pd.DataFrame()\n",
        "df3 = pd.read_csv('yellow_tripdata_2016-02.csv')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-wKvZS2VlnDC"
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      "source": [
        "df=pd.concat([df1,df2,df3],axis=0)"
      ]
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            "text/plain": [
              "(8685530, 20)"
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      "source": [
        "df.shape"
      ]
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              "20"
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          "execution_count": 173
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      "source": [
        "len(df.columns)"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VendorID                       0\n",
              "tpep_pickup_datetime           0\n",
              "tpep_dropoff_datetime          0\n",
              "passenger_count                0\n",
              "trip_distance                  0\n",
              "pickup_longitude               1\n",
              "pickup_latitude                2\n",
              "RateCodeID               5791111\n",
              "store_and_fwd_flag             2\n",
              "dropoff_longitude              2\n",
              "dropoff_latitude               2\n",
              "payment_type                   3\n",
              "fare_amount                    3\n",
              "extra                          3\n",
              "mta_tax                        3\n",
              "tip_amount                     3\n",
              "tolls_amount                   3\n",
              "improvement_surcharge          3\n",
              "total_amount                   3\n",
              "RatecodeID               2894421\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 175
        }
      ],
      "source": [
        "df.isna().sum()"
      ]
    },
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      "cell_type": "code",
      "execution_count": null,
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        "id": "hNF-kEUUnzmL"
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      "source": [
        "df=df.drop(['RateCodeID','RatecodeID'],axis=1)"
      ]
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          "data": {
            "text/plain": [
              "   VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
              "0         2  2015-01-15 19:05:39   2015-01-15 19:23:42                1   \n",
              "1         1  2015-01-10 20:33:38   2015-01-10 20:53:28                1   \n",
              "2         1  2015-01-10 20:33:38   2015-01-10 20:43:41                1   \n",
              "3         1  2015-01-10 20:33:39   2015-01-10 20:35:31                1   \n",
              "4         1  2015-01-10 20:33:39   2015-01-10 20:52:58                1   \n",
              "\n",
              "   trip_distance  pickup_longitude  pickup_latitude store_and_fwd_flag  \\\n",
              "0           1.59        -73.993896        40.750111                  N   \n",
              "1           3.30        -74.001648        40.724243                  N   \n",
              "2           1.80        -73.963341        40.802788                  N   \n",
              "3           0.50        -74.009087        40.713818                  N   \n",
              "4           3.00        -73.971176        40.762428                  N   \n",
              "\n",
              "   dropoff_longitude  dropoff_latitude  payment_type  fare_amount  extra  \\\n",
              "0         -73.974785         40.750618           1.0         12.0    1.0   \n",
              "1         -73.994415         40.759109           1.0         14.5    0.5   \n",
              "2         -73.951820         40.824413           2.0          9.5    0.5   \n",
              "3         -74.004326         40.719986           2.0          3.5    0.5   \n",
              "4         -74.004181         40.742653           2.0         15.0    0.5   \n",
              "\n",
              "   mta_tax  tip_amount  tolls_amount  improvement_surcharge  total_amount  \n",
              "0      0.5        3.25           0.0                    0.3         17.05  \n",
              "1      0.5        2.00           0.0                    0.3         17.80  \n",
              "2      0.5        0.00           0.0                    0.3         10.80  \n",
              "3      0.5        0.00           0.0                    0.3          4.80  \n",
              "4      0.5        0.00           0.0                    0.3         16.30  "
            ],
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              "      <th></th>\n",
              "      <th>VendorID</th>\n",
              "      <th>tpep_pickup_datetime</th>\n",
              "      <th>tpep_dropoff_datetime</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>2015-01-15 19:05:39</td>\n",
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              "      <td>3.30</td>\n",
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              "      <th>2</th>\n",
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              "      <td>2015-01-10 20:33:38</td>\n",
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              "      <th>3</th>\n",
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              "      <td>2015-01-10 20:33:39</td>\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4bea725a-385b-4a89-be91-dafb3f13f342 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 177
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VTXXfEu5obrV",
        "outputId": "ed384572-f2cb-46fe-bac4-acdde6c6bb50"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VendorID                   int64\n",
              "tpep_pickup_datetime      object\n",
              "tpep_dropoff_datetime     object\n",
              "passenger_count            int64\n",
              "trip_distance            float64\n",
              "pickup_longitude         float64\n",
              "pickup_latitude          float64\n",
              "store_and_fwd_flag        object\n",
              "dropoff_longitude        float64\n",
              "dropoff_latitude         float64\n",
              "payment_type             float64\n",
              "fare_amount              float64\n",
              "extra                    float64\n",
              "mta_tax                  float64\n",
              "tip_amount               float64\n",
              "tolls_amount             float64\n",
              "improvement_surcharge    float64\n",
              "total_amount             float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 178
        }
      ],
      "source": [
        "df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qo9w_RoPwsIj"
      },
      "outputs": [],
      "source": [
        "df['pickup_latitude'] = pd.to_numeric(df['pickup_latitude'])\n",
        "df['pickup_longitude'] = pd.to_numeric(df['pickup_longitude'])\n",
        "df['dropoff_latitude'] = pd.to_numeric(df['dropoff_latitude'])\n",
        "df['dropoff_longitude'] = pd.to_numeric(df['dropoff_longitude'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y-Et0TZ-pW-l"
      },
      "outputs": [],
      "source": [
        "def ft_haversine_distance(lat1, lng1, lat2, lng2):\n",
        "    \"\"\"\n",
        "    Calculate the Haversine distance between two points on the earth specified by latitude and longitude.\n",
        "\n",
        "    Parameters:\n",
        "    lat1, lng1: Latitude and longitude of the first point in decimal degrees.\n",
        "    lat2, lng2: Latitude and longitude of the second point in decimal degrees.\n",
        "\n",
        "    Returns:\n",
        "    The distance between the two points in kilometers.\n",
        "    \"\"\"\n",
        "    AVG_EARTH_RADIUS = 6371  # Average radius of the earth in km\n",
        "    # Convert latitude and longitude from degrees to radians\n",
        "    lat1, lng1, lat2, lng2 = map(np.radians, (lat1, lng1, lat2, lng2))\n",
        "\n",
        "    # Compute differences in coordinates\n",
        "    dlat = lat2 - lat1\n",
        "    dlng = lng2 - lng1\n",
        "\n",
        "    # Haversine formula to calculate the distance\n",
        "    a = np.sin(dlat / 2) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlng / 2) ** 2\n",
        "    c = 2 * np.arcsin(np.sqrt(a))\n",
        "    h = AVG_EARTH_RADIUS * c\n",
        "\n",
        "    return h\n",
        "\n",
        "def add_distance_feature(df):\n",
        "    \"\"\"\n",
        "    Adds a new column 'distance' to the dataframe which is the Haversine distance between pickup and dropoff locations.\n",
        "\n",
        "    Parameters:\n",
        "    df: The dataframe to which the distance feature should be added. Assumes the dataframe has 'pickup_latitude',\n",
        "    'pickup_longitude', 'dropoff_latitude', 'dropoff_longitude' columns.\n",
        "    \"\"\"\n",
        "    # Calculate distance and assign to 'distance' column\n",
        "    df['haversine_distance'] = ft_haversine_distance(df['pickup_latitude'],\n",
        "                                           df['pickup_longitude'],\n",
        "                                           df['dropoff_latitude'],\n",
        "                                           df['dropoff_longitude'])\n",
        "\n",
        "\n",
        "add_distance_feature(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "us7kP5mNvtzK",
        "outputId": "50135cd6-ed94-42a8-ec45-b545316523ec"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
              "0         2  2015-01-15 19:05:39   2015-01-15 19:23:42                1   \n",
              "1         1  2015-01-10 20:33:38   2015-01-10 20:53:28                1   \n",
              "2         1  2015-01-10 20:33:38   2015-01-10 20:43:41                1   \n",
              "3         1  2015-01-10 20:33:39   2015-01-10 20:35:31                1   \n",
              "4         1  2015-01-10 20:33:39   2015-01-10 20:52:58                1   \n",
              "\n",
              "   trip_distance  pickup_longitude  pickup_latitude store_and_fwd_flag  \\\n",
              "0           1.59        -73.993896        40.750111                  N   \n",
              "1           3.30        -74.001648        40.724243                  N   \n",
              "2           1.80        -73.963341        40.802788                  N   \n",
              "3           0.50        -74.009087        40.713818                  N   \n",
              "4           3.00        -73.971176        40.762428                  N   \n",
              "\n",
              "   dropoff_longitude  dropoff_latitude  payment_type  fare_amount  extra  \\\n",
              "0         -73.974785         40.750618           1.0         12.0    1.0   \n",
              "1         -73.994415         40.759109           1.0         14.5    0.5   \n",
              "2         -73.951820         40.824413           2.0          9.5    0.5   \n",
              "3         -74.004326         40.719986           2.0          3.5    0.5   \n",
              "4         -74.004181         40.742653           2.0         15.0    0.5   \n",
              "\n",
              "   mta_tax  tip_amount  tolls_amount  improvement_surcharge  total_amount  \\\n",
              "0      0.5        3.25           0.0                    0.3         17.05   \n",
              "1      0.5        2.00           0.0                    0.3         17.80   \n",
              "2      0.5        0.00           0.0                    0.3         10.80   \n",
              "3      0.5        0.00           0.0                    0.3          4.80   \n",
              "4      0.5        0.00           0.0                    0.3         16.30   \n",
              "\n",
              "   haversine_distance  \n",
              "0            1.610893  \n",
              "1            3.924552  \n",
              "2            2.592739  \n",
              "3            0.794628  \n",
              "4            3.544626  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9d9f5f3f-0748-4cc8-8f8f-a0b7cc7adc11\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>VendorID</th>\n",
              "      <th>tpep_pickup_datetime</th>\n",
              "      <th>tpep_dropoff_datetime</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>trip_distance</th>\n",
              "      <th>pickup_longitude</th>\n",
              "      <th>pickup_latitude</th>\n",
              "      <th>store_and_fwd_flag</th>\n",
              "      <th>dropoff_longitude</th>\n",
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              "      <th>fare_amount</th>\n",
              "      <th>extra</th>\n",
              "      <th>mta_tax</th>\n",
              "      <th>tip_amount</th>\n",
              "      <th>tolls_amount</th>\n",
              "      <th>improvement_surcharge</th>\n",
              "      <th>total_amount</th>\n",
              "      <th>haversine_distance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>2015-01-15 19:05:39</td>\n",
              "      <td>2015-01-15 19:23:42</td>\n",
              "      <td>1</td>\n",
              "      <td>1.59</td>\n",
              "      <td>-73.993896</td>\n",
              "      <td>40.750111</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2015-01-10 20:33:38</td>\n",
              "      <td>2015-01-10 20:53:28</td>\n",
              "      <td>1</td>\n",
              "      <td>3.30</td>\n",
              "      <td>-74.001648</td>\n",
              "      <td>40.724243</td>\n",
              "      <td>N</td>\n",
              "      <td>-73.994415</td>\n",
              "      <td>40.759109</td>\n",
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              "      <td>14.5</td>\n",
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              "      <td>2.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>17.80</td>\n",
              "      <td>3.924552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>2015-01-10 20:33:38</td>\n",
              "      <td>2015-01-10 20:43:41</td>\n",
              "      <td>1</td>\n",
              "      <td>1.80</td>\n",
              "      <td>-73.963341</td>\n",
              "      <td>40.802788</td>\n",
              "      <td>N</td>\n",
              "      <td>-73.951820</td>\n",
              "      <td>40.824413</td>\n",
              "      <td>2.0</td>\n",
              "      <td>9.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>10.80</td>\n",
              "      <td>2.592739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>2015-01-10 20:33:39</td>\n",
              "      <td>2015-01-10 20:35:31</td>\n",
              "      <td>1</td>\n",
              "      <td>0.50</td>\n",
              "      <td>-74.009087</td>\n",
              "      <td>40.713818</td>\n",
              "      <td>N</td>\n",
              "      <td>-74.004326</td>\n",
              "      <td>40.719986</td>\n",
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              "      <td>4.80</td>\n",
              "      <td>0.794628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>2015-01-10 20:33:39</td>\n",
              "      <td>2015-01-10 20:52:58</td>\n",
              "      <td>1</td>\n",
              "      <td>3.00</td>\n",
              "      <td>-73.971176</td>\n",
              "      <td>40.762428</td>\n",
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              "      <td>-74.004181</td>\n",
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              "      <td>3.544626</td>\n",
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              "    }\n",
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              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 181
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zv3bcabsw6AZ"
      },
      "outputs": [],
      "source": [
        "df['dropoff_latitude'] = pd.to_numeric(df['dropoff_latitude'])\n",
        "df['dropoff_longitude'] = pd.to_numeric(df['dropoff_longitude'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wC1bjJOcxXKO"
      },
      "outputs": [],
      "source": [
        "def ft_degree(lat1, lng1, lat2, lng2):\n",
        "    \"\"\"\n",
        "    Calculate the direction of travel from one point to another using latitude and longitude.\n",
        "\n",
        "    Parameters:\n",
        "    lat1, lng1: Latitude and longitude of the starting point.\n",
        "    lat2, lng2: Latitude and longitude of the ending point.\n",
        "\n",
        "    Returns:\n",
        "    The travel direction in degrees from the starting point to the ending point.\n",
        "    \"\"\"\n",
        "    AVG_EARTH_RADIUS = 6371  # km, average radius of Earth\n",
        "    lng_delta_rad = np.radians(lng2 - lng1)  # Difference in longitude, converted to radians\n",
        "    # Convert all latitudes and longitudes to radians\n",
        "    lat1, lng1, lat2, lng2 = map(np.radians, (lat1, lng1, lat2, lng2))\n",
        "\n",
        "    # Calculate the direction\n",
        "    y = np.sin(lng_delta_rad) * np.cos(lat2)\n",
        "    x = np.cos(lat1) * np.sin(lat2) - np.sin(lat1) * np.cos(lat2) * np.cos(lng_delta_rad)\n",
        "    return np.degrees(np.arctan2(y, x))  # Convert the result to degrees\n",
        "\n",
        "def add_direction_feature(df):\n",
        "    \"\"\"\n",
        "    Adds a 'direction' feature to the dataframe, indicating the travel direction from pickup to dropoff.\n",
        "\n",
        "    Parameters:\n",
        "    df: The dataframe to which the direction feature should be added. Assumes the dataframe has\n",
        "    'pickup_latitude', 'pickup_longitude', 'dropoff_latitude', 'dropoff_longitude' columns.\n",
        "    \"\"\"\n",
        "    # Calculate direction and assign to 'direction' column\n",
        "    df['direction'] = ft_degree(df['pickup_latitude'].values,\n",
        "                                df['pickup_longitude'].values,\n",
        "                                df['dropoff_latitude'].values,\n",
        "                                df['dropoff_longitude'].values)\n",
        "\n",
        "# Apply the function to add a 'direction' feature to both train and test datasets\n",
        "add_direction_feature(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "id": "W5btbHtSxAug",
        "outputId": "25a82fb8-cdbf-47ad-e02f-15cdd6998b81"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
              "0         2  2015-01-15 19:05:39   2015-01-15 19:23:42                1   \n",
              "1         1  2015-01-10 20:33:38   2015-01-10 20:53:28                1   \n",
              "2         1  2015-01-10 20:33:38   2015-01-10 20:43:41                1   \n",
              "3         1  2015-01-10 20:33:39   2015-01-10 20:35:31                1   \n",
              "4         1  2015-01-10 20:33:39   2015-01-10 20:52:58                1   \n",
              "5         1  2015-01-10 20:33:39   2015-01-10 20:53:52                1   \n",
              "6         1  2015-01-10 20:33:39   2015-01-10 20:58:31                1   \n",
              "7         1  2015-01-10 20:33:39   2015-01-10 20:42:20                3   \n",
              "8         1  2015-01-10 20:33:39   2015-01-10 21:11:35                3   \n",
              "9         1  2015-01-10 20:33:40   2015-01-10 20:40:44                2   \n",
              "\n",
              "   trip_distance  pickup_longitude  pickup_latitude store_and_fwd_flag  \\\n",
              "0           1.59        -73.993896        40.750111                  N   \n",
              "1           3.30        -74.001648        40.724243                  N   \n",
              "2           1.80        -73.963341        40.802788                  N   \n",
              "3           0.50        -74.009087        40.713818                  N   \n",
              "4           3.00        -73.971176        40.762428                  N   \n",
              "5           9.00        -73.874374        40.774048                  N   \n",
              "6           2.20        -73.983276        40.726009                  N   \n",
              "7           0.80        -74.002663        40.734142                  N   \n",
              "8          18.20        -73.783043        40.644356                  N   \n",
              "9           0.90        -73.985588        40.767948                  N   \n",
              "\n",
              "   dropoff_longitude  dropoff_latitude  payment_type  fare_amount  extra  \\\n",
              "0         -73.974785         40.750618           1.0         12.0    1.0   \n",
              "1         -73.994415         40.759109           1.0         14.5    0.5   \n",
              "2         -73.951820         40.824413           2.0          9.5    0.5   \n",
              "3         -74.004326         40.719986           2.0          3.5    0.5   \n",
              "4         -74.004181         40.742653           2.0         15.0    0.5   \n",
              "5         -73.986977         40.758194           1.0         27.0    0.5   \n",
              "6         -73.992470         40.749634           2.0         14.0    0.5   \n",
              "7         -73.995010         40.726326           1.0          7.0    0.5   \n",
              "8         -73.987595         40.759357           2.0         52.0    0.0   \n",
              "9         -73.985916         40.759365           1.0          6.5    0.5   \n",
              "\n",
              "   mta_tax  tip_amount  tolls_amount  improvement_surcharge  total_amount  \\\n",
              "0      0.5        3.25          0.00                    0.3         17.05   \n",
              "1      0.5        2.00          0.00                    0.3         17.80   \n",
              "2      0.5        0.00          0.00                    0.3         10.80   \n",
              "3      0.5        0.00          0.00                    0.3          4.80   \n",
              "4      0.5        0.00          0.00                    0.3         16.30   \n",
              "5      0.5        6.70          5.33                    0.3         40.33   \n",
              "6      0.5        0.00          0.00                    0.3         15.30   \n",
              "7      0.5        1.66          0.00                    0.3          9.96   \n",
              "8      0.5        0.00          5.33                    0.3         58.13   \n",
              "9      0.5        1.55          0.00                    0.3          9.35   \n",
              "\n",
              "   haversine_distance   direction  \n",
              "0            1.610893   87.986790  \n",
              "1            3.924552    8.929682  \n",
              "2            2.592739   21.954795  \n",
              "3            0.794628   30.325198  \n",
              "4            3.544626 -128.331214  \n",
              "5            9.645482 -100.494177  \n",
              "6            2.738731  -16.425663  \n",
              "7            1.082202  143.426254  \n",
              "8           21.467560  -53.372875  \n",
              "9            0.954794 -178.341651  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-70b6fe47-d25b-4f93-a189-8778de9535be\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>VendorID</th>\n",
              "      <th>tpep_pickup_datetime</th>\n",
              "      <th>tpep_dropoff_datetime</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>trip_distance</th>\n",
              "      <th>pickup_longitude</th>\n",
              "      <th>pickup_latitude</th>\n",
              "      <th>store_and_fwd_flag</th>\n",
              "      <th>dropoff_longitude</th>\n",
              "      <th>dropoff_latitude</th>\n",
              "      <th>payment_type</th>\n",
              "      <th>fare_amount</th>\n",
              "      <th>extra</th>\n",
              "      <th>mta_tax</th>\n",
              "      <th>tip_amount</th>\n",
              "      <th>tolls_amount</th>\n",
              "      <th>improvement_surcharge</th>\n",
              "      <th>total_amount</th>\n",
              "      <th>haversine_distance</th>\n",
              "      <th>direction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>2015-01-15 19:05:39</td>\n",
              "      <td>2015-01-15 19:23:42</td>\n",
              "      <td>1</td>\n",
              "      <td>1.59</td>\n",
              "      <td>-73.993896</td>\n",
              "      <td>40.750111</td>\n",
              "      <td>N</td>\n",
              "      <td>-73.974785</td>\n",
              "      <td>40.750618</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>3.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.3</td>\n",
              "      <td>17.05</td>\n",
              "      <td>1.610893</td>\n",
              "      <td>87.986790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2015-01-10 20:33:38</td>\n",
              "      <td>2015-01-10 20:53:28</td>\n",
              "      <td>1</td>\n",
              "      <td>3.30</td>\n",
              "      <td>-74.001648</td>\n",
              "      <td>40.724243</td>\n",
              "      <td>N</td>\n",
              "      <td>-73.994415</td>\n",
              "      <td>40.759109</td>\n",
              "      <td>1.0</td>\n",
              "      <td>14.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.3</td>\n",
              "      <td>17.80</td>\n",
              "      <td>3.924552</td>\n",
              "      <td>8.929682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>2015-01-10 20:33:38</td>\n",
              "      <td>2015-01-10 20:43:41</td>\n",
              "      <td>1</td>\n",
              "      <td>1.80</td>\n",
              "      <td>-73.963341</td>\n",
              "      <td>40.802788</td>\n",
              "      <td>N</td>\n",
              "      <td>-73.951820</td>\n",
              "      <td>40.824413</td>\n",
              "      <td>2.0</td>\n",
              "      <td>9.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.3</td>\n",
              "      <td>10.80</td>\n",
              "      <td>2.592739</td>\n",
              "      <td>21.954795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>2015-01-10 20:33:39</td>\n",
              "      <td>2015-01-10 20:35:31</td>\n",
              "      <td>1</td>\n",
              "      <td>0.50</td>\n",
              "      <td>-74.009087</td>\n",
              "      <td>40.713818</td>\n",
              "      <td>N</td>\n",
              "      <td>-74.004326</td>\n",
              "      <td>40.719986</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.3</td>\n",
              "      <td>4.80</td>\n",
              "      <td>0.794628</td>\n",
              "      <td>30.325198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>2015-01-10 20:33:39</td>\n",
              "      <td>2015-01-10 20:52:58</td>\n",
              "      <td>1</td>\n",
              "      <td>3.00</td>\n",
              "      <td>-73.971176</td>\n",
              "      <td>40.762428</td>\n",
              "      <td>N</td>\n",
              "      <td>-74.004181</td>\n",
              "      <td>40.742653</td>\n",
              "      <td>2.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.3</td>\n",
              "      <td>16.30</td>\n",
              "      <td>3.544626</td>\n",
              "      <td>-128.331214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>2015-01-10 20:33:39</td>\n",
              "      <td>2015-01-10 20:53:52</td>\n",
              "      <td>1</td>\n",
              "      <td>9.00</td>\n",
              "      <td>-73.874374</td>\n",
              "      <td>40.774048</td>\n",
              "      <td>N</td>\n",
              "      <td>-73.986977</td>\n",
              "      <td>40.758194</td>\n",
              "      <td>1.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>6.70</td>\n",
              "      <td>5.33</td>\n",
              "      <td>0.3</td>\n",
              "      <td>40.33</td>\n",
              "      <td>9.645482</td>\n",
              "      <td>-100.494177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>2015-01-10 20:33:39</td>\n",
              "      <td>2015-01-10 20:58:31</td>\n",
              "      <td>1</td>\n",
              "      <td>2.20</td>\n",
              "      <td>-73.983276</td>\n",
              "      <td>40.726009</td>\n",
              "      <td>N</td>\n",
              "      <td>-73.992470</td>\n",
              "      <td>40.749634</td>\n",
              "      <td>2.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.3</td>\n",
              "      <td>15.30</td>\n",
              "      <td>2.738731</td>\n",
              "      <td>-16.425663</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>2015-01-10 20:33:39</td>\n",
              "      <td>2015-01-10 20:42:20</td>\n",
              "      <td>3</td>\n",
              "      <td>0.80</td>\n",
              "      <td>-74.002663</td>\n",
              "      <td>40.734142</td>\n",
              "      <td>N</td>\n",
              "      <td>-73.995010</td>\n",
              "      <td>40.726326</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.66</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.3</td>\n",
              "      <td>9.96</td>\n",
              "      <td>1.082202</td>\n",
              "      <td>143.426254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>2015-01-10 20:33:39</td>\n",
              "      <td>2015-01-10 21:11:35</td>\n",
              "      <td>3</td>\n",
              "      <td>18.20</td>\n",
              "      <td>-73.783043</td>\n",
              "      <td>40.644356</td>\n",
              "      <td>N</td>\n",
              "      <td>-73.987595</td>\n",
              "      <td>40.759357</td>\n",
              "      <td>2.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>5.33</td>\n",
              "      <td>0.3</td>\n",
              "      <td>58.13</td>\n",
              "      <td>21.467560</td>\n",
              "      <td>-53.372875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>2015-01-10 20:33:40</td>\n",
              "      <td>2015-01-10 20:40:44</td>\n",
              "      <td>2</td>\n",
              "      <td>0.90</td>\n",
              "      <td>-73.985588</td>\n",
              "      <td>40.767948</td>\n",
              "      <td>N</td>\n",
              "      <td>-73.985916</td>\n",
              "      <td>40.759365</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.55</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.3</td>\n",
              "      <td>9.35</td>\n",
              "      <td>0.954794</td>\n",
              "      <td>-178.341651</td>\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 184
        }
      ],
      "source": [
        "df.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pt1u70rLxj0A",
        "outputId": "4bed27f9-d6e6-40f2-aef1-2ef69a907b7b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VendorID                   int64\n",
              "tpep_pickup_datetime      object\n",
              "tpep_dropoff_datetime     object\n",
              "passenger_count            int64\n",
              "trip_distance            float64\n",
              "pickup_longitude         float64\n",
              "pickup_latitude          float64\n",
              "store_and_fwd_flag        object\n",
              "dropoff_longitude        float64\n",
              "dropoff_latitude         float64\n",
              "payment_type             float64\n",
              "fare_amount              float64\n",
              "extra                    float64\n",
              "mta_tax                  float64\n",
              "tip_amount               float64\n",
              "tolls_amount             float64\n",
              "improvement_surcharge    float64\n",
              "total_amount             float64\n",
              "haversine_distance       float64\n",
              "direction                float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 185
        }
      ],
      "source": [
        "df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_iAoCHtUyJh0"
      },
      "outputs": [],
      "source": [
        "df=df.dropna(subset=['passenger_count','trip_distance','pickup_longitude','pickup_latitude','store_and_fwd_flag','dropoff_longitude','dropoff_latitude','payment_type','fare_amount','extra','mta_tax','tip_amount','tolls_amount','tolls_amount','improvement_surcharge','total_amount','haversine_distance','direction'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lfey17it0HiS",
        "outputId": "2194a678-992a-408b-b3b9-718e95bbe1ab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VendorID                 0\n",
              "tpep_pickup_datetime     0\n",
              "tpep_dropoff_datetime    0\n",
              "passenger_count          0\n",
              "trip_distance            0\n",
              "pickup_longitude         0\n",
              "pickup_latitude          0\n",
              "store_and_fwd_flag       0\n",
              "dropoff_longitude        0\n",
              "dropoff_latitude         0\n",
              "payment_type             0\n",
              "fare_amount              0\n",
              "extra                    0\n",
              "mta_tax                  0\n",
              "tip_amount               0\n",
              "tolls_amount             0\n",
              "improvement_surcharge    0\n",
              "total_amount             0\n",
              "haversine_distance       0\n",
              "direction                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 187
        }
      ],
      "source": [
        "df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UJVOGZqL10_a"
      },
      "outputs": [],
      "source": [
        "df=df.drop('VendorID',axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TO-zEfK_2X19"
      },
      "outputs": [],
      "source": [
        "# We tried building models form this dataset and this point and we got a an accuracy of 0.17% .we now intend to improve it by exploring the dataset further."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "-f3AC3xjMTkX",
        "outputId": "a8569ea3-41b6-4455-857a-ea8d1c8f7b5e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  trip_distance  \\\n",
              "0  2015-01-15 19:05:39   2015-01-15 19:23:42                1           1.59   \n",
              "1  2015-01-10 20:33:38   2015-01-10 20:53:28                1           3.30   \n",
              "2  2015-01-10 20:33:38   2015-01-10 20:43:41                1           1.80   \n",
              "3  2015-01-10 20:33:39   2015-01-10 20:35:31                1           0.50   \n",
              "4  2015-01-10 20:33:39   2015-01-10 20:52:58                1           3.00   \n",
              "\n",
              "   pickup_longitude  pickup_latitude store_and_fwd_flag  dropoff_longitude  \\\n",
              "0        -73.993896        40.750111                  N         -73.974785   \n",
              "1        -74.001648        40.724243                  N         -73.994415   \n",
              "2        -73.963341        40.802788                  N         -73.951820   \n",
              "3        -74.009087        40.713818                  N         -74.004326   \n",
              "4        -73.971176        40.762428                  N         -74.004181   \n",
              "\n",
              "   dropoff_latitude  payment_type  fare_amount  extra  mta_tax  tip_amount  \\\n",
              "0         40.750618           1.0         12.0    1.0      0.5        3.25   \n",
              "1         40.759109           1.0         14.5    0.5      0.5        2.00   \n",
              "2         40.824413           2.0          9.5    0.5      0.5        0.00   \n",
              "3         40.719986           2.0          3.5    0.5      0.5        0.00   \n",
              "4         40.742653           2.0         15.0    0.5      0.5        0.00   \n",
              "\n",
              "   tolls_amount  improvement_surcharge  total_amount  haversine_distance  \\\n",
              "0           0.0                    0.3         17.05            1.610893   \n",
              "1           0.0                    0.3         17.80            3.924552   \n",
              "2           0.0                    0.3         10.80            2.592739   \n",
              "3           0.0                    0.3          4.80            0.794628   \n",
              "4           0.0                    0.3         16.30            3.544626   \n",
              "\n",
              "    direction  \n",
              "0   87.986790  \n",
              "1    8.929682  \n",
              "2   21.954795  \n",
              "3   30.325198  \n",
              "4 -128.331214  "
            ],
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              "      <th>tpep_pickup_datetime</th>\n",
              "      <th>tpep_dropoff_datetime</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>trip_distance</th>\n",
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              "      <th>improvement_surcharge</th>\n",
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              "      <th>direction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-01-15 19:05:39</td>\n",
              "      <td>2015-01-15 19:23:42</td>\n",
              "      <td>1</td>\n",
              "      <td>1.59</td>\n",
              "      <td>-73.993896</td>\n",
              "      <td>40.750111</td>\n",
              "      <td>N</td>\n",
              "      <td>-73.974785</td>\n",
              "      <td>40.750618</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.0</td>\n",
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              "      <td>1.610893</td>\n",
              "      <td>87.986790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-01-10 20:33:38</td>\n",
              "      <td>2015-01-10 20:53:28</td>\n",
              "      <td>1</td>\n",
              "      <td>3.30</td>\n",
              "      <td>-74.001648</td>\n",
              "      <td>40.724243</td>\n",
              "      <td>N</td>\n",
              "      <td>-73.994415</td>\n",
              "      <td>40.759109</td>\n",
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              "      <td>3.924552</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-01-10 20:33:38</td>\n",
              "      <td>2015-01-10 20:43:41</td>\n",
              "      <td>1</td>\n",
              "      <td>1.80</td>\n",
              "      <td>-73.963341</td>\n",
              "      <td>40.802788</td>\n",
              "      <td>N</td>\n",
              "      <td>-73.951820</td>\n",
              "      <td>40.824413</td>\n",
              "      <td>2.0</td>\n",
              "      <td>9.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>10.80</td>\n",
              "      <td>2.592739</td>\n",
              "      <td>21.954795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-01-10 20:33:39</td>\n",
              "      <td>2015-01-10 20:35:31</td>\n",
              "      <td>1</td>\n",
              "      <td>0.50</td>\n",
              "      <td>-74.009087</td>\n",
              "      <td>40.713818</td>\n",
              "      <td>N</td>\n",
              "      <td>-74.004326</td>\n",
              "      <td>40.719986</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>4.80</td>\n",
              "      <td>0.794628</td>\n",
              "      <td>30.325198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-01-10 20:33:39</td>\n",
              "      <td>2015-01-10 20:52:58</td>\n",
              "      <td>1</td>\n",
              "      <td>3.00</td>\n",
              "      <td>-73.971176</td>\n",
              "      <td>40.762428</td>\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 190
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ysHR13RW0Fj5"
      },
      "outputs": [],
      "source": [
        "df['tpep_pickup_datetime']=dd.to_datetime(df['tpep_pickup_datetime'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "SGSH_h-6yAhn",
        "outputId": "a9b93b9d-cbcb-42b4-8701-8022ae939b6c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  trip_distance  \\\n",
              "0  2015-01-15 19:05:39   2015-01-15 19:23:42                1           1.59   \n",
              "1  2016-01-01 00:00:00   2015-01-10 20:53:28                1           3.30   \n",
              "2  2016-02-25 17:24:20   2015-01-10 20:43:41                1           1.80   \n",
              "3  2016-02-25 23:10:50   2015-01-10 20:35:31                1           0.50   \n",
              "4  2015-01-10 20:33:38   2015-01-10 20:52:58                1           3.00   \n",
              "\n",
              "   pickup_longitude  pickup_latitude store_and_fwd_flag  dropoff_longitude  \\\n",
              "0        -73.993896        40.750111                  N         -73.974785   \n",
              "1        -74.001648        40.724243                  N         -73.994415   \n",
              "2        -73.963341        40.802788                  N         -73.951820   \n",
              "3        -74.009087        40.713818                  N         -74.004326   \n",
              "4        -73.971176        40.762428                  N         -74.004181   \n",
              "\n",
              "   dropoff_latitude  payment_type  fare_amount  extra  mta_tax  tip_amount  \\\n",
              "0         40.750618           1.0         12.0    1.0      0.5        3.25   \n",
              "1         40.759109           1.0         14.5    0.5      0.5        2.00   \n",
              "2         40.824413           2.0          9.5    0.5      0.5        0.00   \n",
              "3         40.719986           2.0          3.5    0.5      0.5        0.00   \n",
              "4         40.742653           2.0         15.0    0.5      0.5        0.00   \n",
              "\n",
              "   tolls_amount  improvement_surcharge  total_amount  haversine_distance  \\\n",
              "0           0.0                    0.3         17.05            1.610893   \n",
              "1           0.0                    0.3         17.80            3.924552   \n",
              "2           0.0                    0.3         10.80            2.592739   \n",
              "3           0.0                    0.3          4.80            0.794628   \n",
              "4           0.0                    0.3         16.30            3.544626   \n",
              "\n",
              "    direction  \n",
              "0   87.986790  \n",
              "1    8.929682  \n",
              "2   21.954795  \n",
              "3   30.325198  \n",
              "4 -128.331214  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tpep_pickup_datetime</th>\n",
              "      <th>tpep_dropoff_datetime</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>trip_distance</th>\n",
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              "      <th>improvement_surcharge</th>\n",
              "      <th>total_amount</th>\n",
              "      <th>haversine_distance</th>\n",
              "      <th>direction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-01-15 19:05:39</td>\n",
              "      <td>2015-01-15 19:23:42</td>\n",
              "      <td>1</td>\n",
              "      <td>1.59</td>\n",
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              "      <td>40.750111</td>\n",
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              "      <td>-73.974785</td>\n",
              "      <td>40.750618</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.0</td>\n",
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              "      <td>1.610893</td>\n",
              "      <td>87.986790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-01-01 00:00:00</td>\n",
              "      <td>2015-01-10 20:53:28</td>\n",
              "      <td>1</td>\n",
              "      <td>3.30</td>\n",
              "      <td>-74.001648</td>\n",
              "      <td>40.724243</td>\n",
              "      <td>N</td>\n",
              "      <td>-73.994415</td>\n",
              "      <td>40.759109</td>\n",
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              "      <td>3.924552</td>\n",
              "      <td>8.929682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-02-25 17:24:20</td>\n",
              "      <td>2015-01-10 20:43:41</td>\n",
              "      <td>1</td>\n",
              "      <td>1.80</td>\n",
              "      <td>-73.963341</td>\n",
              "      <td>40.802788</td>\n",
              "      <td>N</td>\n",
              "      <td>-73.951820</td>\n",
              "      <td>40.824413</td>\n",
              "      <td>2.0</td>\n",
              "      <td>9.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>10.80</td>\n",
              "      <td>2.592739</td>\n",
              "      <td>21.954795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-02-25 23:10:50</td>\n",
              "      <td>2015-01-10 20:35:31</td>\n",
              "      <td>1</td>\n",
              "      <td>0.50</td>\n",
              "      <td>-74.009087</td>\n",
              "      <td>40.713818</td>\n",
              "      <td>N</td>\n",
              "      <td>-74.004326</td>\n",
              "      <td>40.719986</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
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              "      <td>0.794628</td>\n",
              "      <td>30.325198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-01-10 20:33:38</td>\n",
              "      <td>2015-01-10 20:52:58</td>\n",
              "      <td>1</td>\n",
              "      <td>3.00</td>\n",
              "      <td>-73.971176</td>\n",
              "      <td>40.762428</td>\n",
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              "      <td>-128.331214</td>\n",
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              "        const element = document.querySelector('#df-f2072506-1efe-4296-998e-5eec56869d67');\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",
              "\n",
              "\n",
              "<div id=\"df-fc0fce6b-b087-4582-a8de-e38ce47e5ef9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fc0fce6b-b087-4582-a8de-e38ce47e5ef9')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fc0fce6b-b087-4582-a8de-e38ce47e5ef9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 192
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eAh49sSGAHTv"
      },
      "outputs": [],
      "source": [
        "\n",
        "# Extract month, day, and hour\n",
        "df['month'] = df['tpep_pickup_datetime'].dt.month\n",
        "df['day'] = df['tpep_pickup_datetime'].dt.day\n",
        "df['hour'] = df['tpep_pickup_datetime'].dt.hour"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ofoMMSYMyBUk"
      },
      "outputs": [],
      "source": [
        "df = df.drop(['tpep_pickup_datetime', 'tpep_dropoff_datetime'], axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R6nsjo0TyMFX"
      },
      "outputs": [],
      "source": [
        "features=['passenger_count','trip_distance','pickup_longitude','pickup_latitude','dropoff_longitude','dropoff_latitude','haversine_distance','month','day','hour','fare_amount']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AKKsOQkWy4_o"
      },
      "outputs": [],
      "source": [
        "df_original=df[features]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "UxC7Qy4ry9be",
        "outputId": "05adc263-7fa6-40f7-c6dc-5304a9b7f120"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   passenger_count  trip_distance  pickup_longitude  pickup_latitude  \\\n",
              "0                1           1.59        -73.993896        40.750111   \n",
              "1                1           3.30        -74.001648        40.724243   \n",
              "2                1           1.80        -73.963341        40.802788   \n",
              "3                1           0.50        -74.009087        40.713818   \n",
              "4                1           3.00        -73.971176        40.762428   \n",
              "\n",
              "   dropoff_longitude  dropoff_latitude  haversine_distance  month  day  hour  \\\n",
              "0         -73.974785         40.750618            1.610893      1   15    19   \n",
              "1         -73.994415         40.759109            3.924552      1    1     0   \n",
              "2         -73.951820         40.824413            2.592739      2   25    17   \n",
              "3         -74.004326         40.719986            0.794628      2   25    23   \n",
              "4         -74.004181         40.742653            3.544626      1   10    20   \n",
              "\n",
              "   fare_amount  \n",
              "0         12.0  \n",
              "1         14.5  \n",
              "2          9.5  \n",
              "3          3.5  \n",
              "4         15.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d30570e6-c066-42ce-9260-5d542b769123\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>trip_distance</th>\n",
              "      <th>pickup_longitude</th>\n",
              "      <th>pickup_latitude</th>\n",
              "      <th>dropoff_longitude</th>\n",
              "      <th>dropoff_latitude</th>\n",
              "      <th>haversine_distance</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>hour</th>\n",
              "      <th>fare_amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1.59</td>\n",
              "      <td>-73.993896</td>\n",
              "      <td>40.750111</td>\n",
              "      <td>-73.974785</td>\n",
              "      <td>40.750618</td>\n",
              "      <td>1.610893</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "      <td>19</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>3.30</td>\n",
              "      <td>-74.001648</td>\n",
              "      <td>40.724243</td>\n",
              "      <td>-73.994415</td>\n",
              "      <td>40.759109</td>\n",
              "      <td>3.924552</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>14.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>1.80</td>\n",
              "      <td>-73.963341</td>\n",
              "      <td>40.802788</td>\n",
              "      <td>-73.951820</td>\n",
              "      <td>40.824413</td>\n",
              "      <td>2.592739</td>\n",
              "      <td>2</td>\n",
              "      <td>25</td>\n",
              "      <td>17</td>\n",
              "      <td>9.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0.50</td>\n",
              "      <td>-74.009087</td>\n",
              "      <td>40.713818</td>\n",
              "      <td>-74.004326</td>\n",
              "      <td>40.719986</td>\n",
              "      <td>0.794628</td>\n",
              "      <td>2</td>\n",
              "      <td>25</td>\n",
              "      <td>23</td>\n",
              "      <td>3.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>3.00</td>\n",
              "      <td>-73.971176</td>\n",
              "      <td>40.762428</td>\n",
              "      <td>-74.004181</td>\n",
              "      <td>40.742653</td>\n",
              "      <td>3.544626</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>20</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d30570e6-c066-42ce-9260-5d542b769123')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-d30570e6-c066-42ce-9260-5d542b769123 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-d30570e6-c066-42ce-9260-5d542b769123');\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",
              "\n",
              "\n",
              "<div id=\"df-4fff26e6-0f72-498c-a47f-c56b080afe45\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4fff26e6-0f72-498c-a47f-c56b080afe45')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4fff26e6-0f72-498c-a47f-c56b080afe45 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_original"
            }
          },
          "metadata": {},
          "execution_count": 197
        }
      ],
      "source": [
        "df_original.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iPNMpHZOygCm"
      },
      "outputs": [],
      "source": [
        "\n",
        "def remove_outliers_all_columns(df,col):\n",
        "    numeric_columns = df[col]\n",
        "    if col in numeric_columns:\n",
        "        Q1 = df[col].quantile(0.25).compute()\n",
        "        Q3 = df[col].quantile(0.75).compute()\n",
        "        IQR = Q3 - Q1\n",
        "\n",
        "        # Define the acceptable range\n",
        "        lower_bound = Q1 - 1.5 * IQR\n",
        "        upper_bound = Q3 + 1.5 * IQR\n",
        "\n",
        "        # Remove the outliers\n",
        "        df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)]\n",
        "\n",
        "    return df\n",
        "\n",
        "# Example usage:\n",
        "df_pandas= remove_outliers_all_columns(df_original, 'fare_amount')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ulbNuFZ91t43"
      },
      "outputs": [],
      "source": [
        "df_taxi=df_pandas.sample(80000)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Darq-t_n2MOL"
      },
      "source": [
        " ## Visualizing the columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3clD8isB5OeJ",
        "outputId": "edf7bfd2-0e04-46a1-934b-514c968dbd79"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.2346337054274845\n"
          ]
        }
      ],
      "source": [
        "skew_passenger=df_taxi['passenger_count'].skew()\n",
        "print(skew_passenger)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "vRFLxRVIJ6re",
        "outputId": "82fe8b6a-e970-41b8-ea74-9fe61c3f594b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 201
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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8nk6v6TYB9qabbkp4/6677tLLL7+sxYsXa/r06aaew+12q7y8PBPL61Gam5tVVVWlkSNHKj8/3+7l9Brd4b47XtssqVWD+pdq/PjxHV7nrt0nLdooSSobOkzjxw7pohUmrzvc956I+24P7rs9uO/mrV+/3tR13SbA7s/lcmnIkCHauXOn6cc4HA4VFBRkcFU9S35+PvfLBtl83/2RY2EL83IPusa+rXHHx7rcWfv9xMvm+96Tcd/twX23B/e9c2baB6QsGKNlhmEYmjdvntasWRP7mM/n0+bNmzV8+HAbVwb0LrE5sEmN0WIKAQDAWlkbYHfs2KFzzz1XW7ZskcPhUHV1te69917t2LFDjY2Neuihh+R2u/WFL3zB7qUCvUbbSVwcJQsAsI+tLQSTJk2SJAUCAUnS4sWLJUkVFRXy+/3atGmTfD6fJOknP/mJHnjgAV1yySVqaGjQkUceqaeffppSPNCFzJ/ERQUWAJA5tgbYioqKDj83bNgwrV27NvZ+3759NW/evK5YFoAOxA4y6KyFwNUWcH1UYAEAFsvaFgIA2SUUMhSIbOLqrALrdDqU4ww34lOBBQBYjQALwJT4I2E9OZ3/6IiG3NYgFVgAgLUIsABMid+M1VkFNnyNM/I4KrAAAGsRYAGY0uJvC6K5yVRg6YEFAFiMAAvAlPhKqpkKrMdFBRYAkBkEWACmxPeymmshoAILAMgMAiwAUxIqsJ2M0ZLogQUAZA4BFoApyW/iilZgCbAAAGsRYAGYEh9EOztKVmqr0jJGCwBgNQIsAFMSN3ElM4WACiwAwFoEWACmJNtC4In1wFKBBQBYiwALwJTkN3GFQ66PCiwAwGIEWACmpH4SFxVYAIC1CLAATGkNptYD62MTFwDAYgRYAKYkHiWbTAWWFgIAgLUIsABM8SW0EJiowLo4iQsAkBkEWACmRCupLqdDLidjtAAA9iHAAjAleiCBmepr/HXxvbMAAFiBAAvAlGglNc9E/6sUX4GlhQAAYC0CLABTokHUzAau8HVs4gIAZAYBFoAp0SBqvoWgrQJrGEbG1gUA6H0IsABMiVVgXeYqsJ64oOtnFiwAwEIEWACmtFVgTbYQxAVd+mABAFYiwAIwJfkWgrbr6IMFAFiJAAvAlLYxWslNIYh/LAAAViDAAjAlepSs2Qqsx0UFFgCQGQRYAKZEQ6gnlQosPbAAAAsRYAGY4kvxJC6JCiwAwFoEWACmxDZxmRyjlViBJcACAKxDgAVgSrQNIM+d3Elc8Y8FAMAKBFgApqR6Elf8YwEAsAIBFoApsZO4TG/iiqvAMkYLAGAhAiwAU9p6YE1WYF1UYAEAmUGABWBKWhVYemABABYiwAIwpTWYeg+sjwALALAQARaAKW0ncaVylCwtBAAA6xBgAXQqFDIUCBmSUjtK1kcPLADAQgRYAJ2Kr6CaPUrW6XTIHQmx9MACAKxEgAXQqfgAavYkLqmtWssUAgCAlQiwADoVH0Dz3OZ/bETDLhVYAICVCLAAOpVQgTXZQhC+lgosAMB6BFgAnYoPoMkF2EgFlpO4AAAWIsAC6FRCgDV5EpdEBRYAkBkEWACdSr2FgB5YAID1UgqwDz/8sGpqaqxeC4AsldhCQAUWAGCvlALswoULdfbZZ+sb3/iGFi1aJL/fb/W6AGSR+B7WZCqwHqYQAAAyIKUA+/rrr+v555/X6NGjdf/99+vUU0/VvHnztH79eqvXByALRI+RlajAAgDsl3IP7NFHH6177rlHb775pn7xi1+orq5O06dP14wZM7Rw4UKFQlRcgJ4i1SkEnlgPLAEWAGCdtDdxBQIBeb1e7du3T4FAQK2trXrooYf0P//zP6qurrZijQBsFt8CkJfCHFgfY7QAABbKSfWBn332mV544QW99NJL8vv9Ou+88/T8889r0qRJ8vv9+vGPf6w5c+bo2WeftXK9AGzQGkyxhcBFBRYAYL2UAuz06dO1atUqjRkzRrfeeqsuvPBCFRUVxT7vdrt1991368QTT7RsoQDsk/5JXFRgAQDWSSnAlpeX6/vf/76OPvroAz4XCASUk5OjvLw83X///emuD0AW8KU8RosKLADAein1wC5fvrzd8FpfX69TTz019v5XvvKVlBcGIHtEpxC4nA65nKlMIaACCwCwTlIV2NWrV2vVqlXasWOHXnjhBRmGkfD5qqoqNTc3W7pAAPaLzoFNpvoavp4KLADAekkF2F27dmn+/PkKBoP6wQ9+cMDn8/PzdfXVV1u2OADZIRpAk5lAIDGFAACQGUkF2DPOOENnnHGGpk6dqrfffjtTawKQZaItAMls4Iq/nhYCAICVUuqBJbwCvUu0Apt0CwFjtAAAGWC6Ajtt2jQtWbJEkjR16tSDXkvABXqWWAXWlXoLgWEYcjgclq8NAND7mA6wl156aeztyy67jH+IgF6kJVaBTS7AeuKu9wVDST8eAID2mA6wN954Y+ztW265JSOLAZCdYpu43MlOIWi7vjUQJMACACyRUg/szp07ddddd8Xef/jhh3X88cfrsssu05YtWyxbHIDskO4mrvjnAAAgXSkF2B//+MdqbW2VJK1cuVJ/+MMfNHv2bI0fP14/+9nPLF0gAPtFK7AeV3oVWAAArJDSUbLLly/Xa6+9Jkn617/+pS984Qu69NJL9aUvfUlnn322pQsEYL/WFHtgqcACADIhpQqs3+9XSUmJJOm9997TaaedJkkqLCxUU1OTdasDkBVSbiFwUYEFAFgvpQrs8OHD9fbbbysvL0+fffZZbKzWypUr1b9/f0sXCMB+Kc+BpQILAMiAlALs9ddfr+uvv16hUEhf//rXNXDgQO3du1c33XSTrrzySqvXCMBm0fCZ6lGyktQapAILALBGSgH2vPPO03HHHafGxkaNGjVKklRcXKzvfe97Ov/88y1dIAD7pdoD63FRgQUAWC+lACtJgwcPTnjf4XAQXoEeKlo9TbaFIM8dH2CpwAIArJFSgF21apXuvfderVu3LjZOK15lZWXaCwOQPVKfA8smLgCA9VIKsD/4wQ+Ul5enW2+9VQUFBVavCUCWafGnWIGNC7wttBAAACySUoDduHGj3nnnHRUWFlq9HgBZqK2FIJ05sFRgAQDWSGkO7NChQ2UYhtVrAZClUp1CkBdXsY1WcQEASFdKAfaOO+7QvHnz1NDQYPV6AGSZYCikYCj8C2s6c2B9tBAAACySUgvBb37zG1VXV+vvf/+7+vXrJ4fDkfD5t99+25LFAbBf/PgrT5IV2ByXUy6nQ8GQoRZaCAAAFkkpwJ511llWrwNAlorvXU22Aht9TJMvSA8sAMAyKQXYm2++2ep1AMhS8ZXTXFdyFVgp3Dfb5AtSgQUAWCalHlhJWrZsmWbPnq2rrrpKkhQKhbRo0SLLFgYgO8S3EKRWgXUd8DwAAKQjpQC7aNEiXXvttfJ6vfroo48kSdu3b9cPfvAD/fWvf03qud566y1NmTJFt99++0GvC4VC+uUvf6lp06bphBNO0Le+9S1t2bIlleUDSEL8S//xJ2uZFZ1cQAUWAGCVlALs448/rgcffFCPP/54bAPXIYccokceeURPPvmk6ed54okndN9992nEiBGdXvvcc8/ppZde0u9+9zu98cYbGjlypG666SbGeQEZlliBTT7ARqu29MACAKySUoDdvHmzzjnnHElKmEBw8sknq7q62vTz5Obm6m9/+5upADt//nxdc801Gj16tIqKinT77bdrw4YN+uSTT5L/BgCYlrCJy5X8j4xYBZY5sAAAi6QUYPv166fa2toDPr5p06akTue66qqr1KdPn06va2lp0fr16zVhwoTYx4qKijRixAhVVFSY/noAkpd+BZYeWACAtVKaQjBlyhTNmTNHs2fPliR5vV6tWrVKDzzwgM4880xLFyhJe/fulWEYKikpSfh4SUmJ6urqTD+PYRhqamqyenk9TnNzc8L/o2tk633f29gYe9sI+JL+O+SO/Jrc2Jr8Y7tCtt73no77bg/uuz247+YZhnHA+QLtSSnAzpo1S9/5znd0/vnnSwq3DhiGodNPPz0WajMh3X5Xv9+vyspKi1bT81VVVdm9hF4p2+77+pp9sberP6+SY48nqcf7W8I/sPd467P671+23ffegvtuD+67Pbjv5ng8nf87k1KALS4u1i9/+UtVVVXpv//9r4YPH64jjzxShx12WCpP16m+ffvK6XTK6/UmfNzr9ap///6mn8ftdqu8vNzi1fU8zc3Nqqqq0siRI5Wfn2/3cnqNbL3vn4VqJIUnfkwYN0bDSgqSenz/D+uk7Y3KycvX+PHjM7DC9GTrfe/puO/24L7bg/tu3vr1601dl3SA3bp1q+bOnatly5bJMAwZhiGXy6Vp06bphz/8YVKB0qzc3FyNGTNGq1ev1oknnihJqq+v1+bNm3XkkUeafh6Hw6GCguT+8e3N8vPzuV82yLr77mr7MdG3qFAFBcn98C3IDf8mHTCUXd/XfrLuvvcS3Hd7cN/twX3vnJn2ASnJTVwNDQ268sor1dDQoIcfflgLFy7UggULdO+992rTpk26/PLL1dDQkNKC97djxw6de+65sVmvl19+uZ555hlt2LBBDQ0NeuihhzR+/HhNmjTJkq8HoH3pbuJiDiwAwGpJVWCffvppDRs2TE899ZRccUdKjh07VhdccIG+8Y1v6Pe//71uu+02U88XDZ+BQECStHjxYklSRUWF/H6/Nm3aJJ/PJ0maMWOGdu3apa9//etqbGzU5MmT9etf/zqZ5QNIQcJRsmnMgWWMFgDAKkkF2H//+9+66667EsJrlMfj0cyZMzV37lzTAfZgI7CGDRumtWvXxt53OBy69dZbdeuttyazZABp8sUFWE8ac2AZowUAsEpS/xpt2rRJ48aN6/DzRx11lGpqatJeFIDsEQ2eHpdTTqe53qR4ubQQAAAsllSA9fv9B8xijedyuTjaFehhWoPh4JlK+4Ak5bk5ShYAYK2UTuIC0HtEg2e0lzVZbOICAFgtqR5Yv9+vmTNnHvSa6IYsAD1DtIUg1QosR8kCAKyWVIA97rjjtHPnzoNec+yxx6a1IADZJTo9IN0KrD8YUihkpNRHCwBAvKQC7LPPPpupdQDIUun2wHrigm9rMKh8Z0oHAAIAEEMPLICDir70n5fyJq62xzELFgBgBQIsgINKdxNXbtzcaPpgAQBWIMACOKh0N3ElVGCZRAAAsAABFsBBRSuwqZzCJSVWbpkFCwCwAgEWwEG1tRCkWIHNoQILALAWARbAQbW1EKRagaUHFgBgLQIsgIOKVmDje1mTkVCBZQoBAMACBFgABxWrwLpSPYmLHlgAgLUIsAAOKt0xWkwhAABYjQAL4KBag+mN0aIHFgBgNQIsgIOK9q2mXIFlCgEAwGIEWAAHFdvElXIFlh5YAIC1CLAADirtk7jiWwj8tBAAANJHgAXQIcMwYi/7pzpGK8fllNPhkCS1BqnAAgDSR4AF0CFfsK1immoFVpLy3OEfNcyBBQBYgQALoEPxgTPVHlipbYYsUwgAAFYgwALoUPzUgPwUWwiktvYDphAAAKxAgAXQoYQKbBoBNjqJgCkEAAArEGABdKjZohaC6GOpwAIArECABdCh+MCZXgWWHlgAgHUIsAA6lBBgUzyJK/zYSAWWKQQAAAsQYAF0yPIeWObAAgAsQIAF0KHEKQQ5KT9PLhVYAICFCLAAOmTVJq5ogPXRAwsAsAABFkCHEg8ySKMHljmwAAALEWABdMi6KQTMgQUAWIcAC6BDiVMImAMLAMgOBFgAHWqNtBA4HQ65Xan/uGAOLADASgRYAB2KbuLKczvlcDhSfh7mwAIArESABdCh6Ev+6bQPSMyBBQBYiwALoEPRimm6ATY2hcBPCwEAIH0EWAAdilVg05hAILGJCwBgLQIsgA5Z1UKQHwnA/mBIwRBVWABAegiwADoUfck/P80KbG5cAG5mIxcAIE0EWAAdavYHJFlXgQ0/JwEWAJAeAiyADlnVA5vvzml7TgIsACBNBFgAHYqGzVwrK7Bs5AIApIkAC6BD0ZOz0q/AxrcQBNJ6LgAACLAAOhRtIUh3Exc9sAAAKxFgAXTIqk1ceQRYAICFCLAAOhQdo5X+FAI2cQEArEOABdAhy6YQMAcWAGAhAiyADkWrpdbOgWUTFwAgPQRYAB2ybg4sFVgAgHUIsAA6ZNUUgvgATA8sACBdBFgA7fIHQwqGDEnptxC4nE65XeEfNy0cZAAASBMBFkC74iul6Z7EJbVVcWkhAACkiwALoF3xldJ0e2Cl+ADLJi4AQHoIsADaFV8pTbeFQKICCwCwDgEWQLuafG2V0kJPzkGuNCd6mAEBFgCQLgIsgHbFB810pxBIbVVcNnEBANJFgAXQrqa4XtUCSyqwtBAAAKxBgAXQLqsrsGziAgBYhQALoF3xPbAFVrQQRJ6DgwwAAOkiwAJol/UtBOHnIMACANJFgAXQrkxt4mpmExcAIE0EWADtavbFB1g2cQEAsgcBFkC7oput3C6n3K70f1SwiQsAYBUCLIB2NUUqpVa0D8Q/DxVYAEC6CLAA2hWdQlBgQfuAxCYuAIB1CLAA2hXdbEUFFgCQbQiwANoVq8B6rAmw0SkEvmBIwVDIkucEAPROBFgA7YpWSq1qIciLq+TSRgAASAcBFkC7ogcZWN1CINFGAABIDwEWQLuaInNg8y04hUtKPM2riQALAEgDARZAu1osrsAWxgdYH7NgAQCpI8ACaFeTxT2w8c/TSIAFAKSBAAugXdETs6yaQhBfgSXAAgDSQYAF0K5YD6xFFdjC3PgeWAIsACB1BFgA7YqGzAKLemDjn4cKLAAgHQRYAO2KzYG1aAoBLQQAAKsQYAG0KzopID8nE1MIGKMFAEidNaWVFNXU1Ojee+/VJ598ooKCAp133nmaOXOmnM7EXP3oo4/qt7/9rXJyEpf7xhtvaMCAAV25ZKBX8AdDCoQMSdZVYN0up1xOh4IhgzFaAIC02Bpgb7nlFh1xxBFavHixamtrdf3112vAgAH6xje+ccC1F154oX7605/asEqg92mO22SVZ1EPrMPhUKEnR/UtfjZxAQDSYlsLQUVFhdasWaM777xTffr00ciRI3XNNddo/vz5di0JQET8Ua9WzYGNf67GVgIsACB1tlVgV69eraFDh6qkpCT2sSOOOEKbNm1SQ0ODioqKEq5fu3atZsyYoc8++0xDhgzRnDlzNHXq1KS+pmEYampqsmT9PVlzc3PC/6NrZNN9r61vjL3tMgKW/b0pcId/Z97b1JI1fxez6b73Jtx3e3Df7cF9N88wDDkcjk6vsy3Aer1eFRcXJ3wsGmbr6uoSAmxZWZmGDx+umTNnatCgQZo/f75uuOEGLViwQKNGjTL9Nf1+vyorK635BnqBqqoqu5fQK2XDfd/gbYm9vWtbjSqNvZY8rzMUrrxu3VWbdX8Xs+G+90bcd3tw3+3BfTfH4/F0eo2tPbCGYZi6bvr06Zo+fXrs/WuuuUYLFy7UggULdNttt5n+em63W+Xl5ckus9dpbm5WVVWVRo4cqfz8fLuX02tk031vrqmTtFGSNG70KI0/tL8lz9vvre2St1WegiKNHz/ekudMVzbd996E+24P7rs9uO/mrV+/3tR1tgXY0tJSeb3ehI95vV45HA6VlpZ2+vihQ4dq586dSX1Nh8OhgoKCpB7Tm+Xn53O/bJAN9z3o3Bd7u7RPoWXrKc4L/1bdGpLt3+P+suG+90bcd3tw3+3Bfe+cmfYBycZNXBMnTtS2bdu0Z8+e2McqKipUXl6uwsLChGt/+9vfatmyZQkf27Bhg4YPH94lawV6m4ZWf+ztolzrfs+NHkvLGC0AQDpsC7ATJkzQpEmT9POf/1wNDQ3asGGDnnrqKV1++eWSpHPPPVcrVqyQFK7M3nvvvdq4caNaW1v15JNPavPmzbr44ovtWj7QozXETQko8rgte97oYQacxAUASIetPbC/+tWvNHfuXJ1yyikqKirSjBkz9LWvfU2StGnTptgu5ZkzZ0oK9756vV6Vl5frj3/8o8rKymxbO9CTxQdMKyuw0QDLHFgAQDpsDbBlZWV64okn2v3c2rVrY2/n5ubq7rvv1t13391VSwN6tUZfWwuBpXNgPa7I8xNgAQCps62FAED2irYQFHpy5HSaa6g3gxYCAIAVCLAADtDgawuwViqIthD4gp1cCQBAxwiwAA4QnUJgZf+rlFiBNTsHGgCA/RFgARwg2kJg5QQCqa0CGzIM+YIhS58bANB7EGABHCDao2p1BTZ+Qxh9sACAVBFgARwgUz2w8c/X2Gp/gN1Yu0//u3i13t26r/OLAQBZw9YxWgCyU2OsB9bqFgJX7G27Z8HW7G3SSQ//S7VNrZKknL4DdeOpR9i6JgCAOVRgARygSyqwNrcQ3Pf6ylh4laS5r65SMwcsAEC3QIAFcIDYJi6Le2DjN4U12NhC0OwP6PkPN0mSBhflSZLqmn16ceVm29YEADCPAAvgALExWhZXYPvktT1ffav/IFdm1qtrtsYC9JPTT9CQwnCwXrC62rY1AQDMI8ACOEDbFAJre2CL455vX4t9AXbJuu2SpH75Hp0ycqCmHlIkSXp97VYFQ4z3AoBsR4AFkMAwjFgPrNUtBH3y4gKsjRXYN9aHA+wZ5WVyOR06oaxQkrS3xa81O+ttWxcAwBwCLIAErYGQgqHwKVlWb+LKy3HJ5XRIsq8CW9/iU+WOvZKkU0cNkiQd0T8/9vnlm3fbsi4AgHkEWAAJGuIqo1YHWIfDEWsj2GfTJq6Pa+pibx87rFSSNLDArUOKwyH2/c21tqwLAGAeARZAgoa48VZW98BKbW0EdrUQfFSzJ/b20YeUxt4+dmg/SdL7W6jAAkC2I8ACSBBfgbV6CoEk9Yn01dbb1EIQDbBjBxYn9OQeFwmwK7fWMQ8WALIcARZAgsaECqz1AbY41yPJxgpsdTjAHjO0NOHj0QpsIGRo9fa9Xb4uAIB5BFgACeIPGMhEC0E0FNsRYFv8QVXuDIfTaP9r1PhBxbG3o5u8AADZiQALIEF8D6zVm7ikuB5YG1oI1u+uj01YmFDWN+FzZX3yVBJZW+UObxevDACQDAIsgAT7MtwDa+cUgvW798XeHjOgT8LnHA6HJgzuK0n6lAosAGQ1AiyABPXNbQG2JN9j+fPbOYUgGmBdTodGlhYd8Pnxg0skSWsIsACQ1QiwABLsbfFJkvLdLrld1v+IsHMKQTTAjuxX1O73NqEsHGA31DaoxR/s0rUBAMwjwAJIsDcSLEvyrK++SvZOIVi/O3xMbPnAPu1+/vBB4QAbMgyt3UUVFgCyFQEWQIJoBbZvvvUTCCSpKC9cgfUFQ2oNdG2Vc92ucAW2vH/7ATbaQhB/LQAg+xBgASTwNocDbKYqsH3iRnN15SSCZn9A1XubJEljOqjADu9bEGst2LCbAAsA2YoACyBBtIWgOC8zFdiEANuFbQTxgXT0gOJ2r3E5nRoV2dy1ngALAFmLAAsgQXQKQSYmEEiJwbgrR2mtO8gIrXijIp/bUEuABYBsRYAFkCDaA1vSBRXYrpxEsKGTEVpR5dEASwUWALIWARZAgkxPIYiO0ZK6toVgXWQCQUcjtKKiG7yq9zap2d/1hy0AADpHgAWQILqJK1NTCOJbCOyowHY0QitqVFx7wcbahoyuCQCQGgIsgJhAMKRGX7jqmKkKbN+43tq6SFjuCp2N0IoqjwuwtBEAQHYiwAKIqY97Sb84QxXYfHeO8nJckiRvc2tGvsb+mnydj9CKGtmvUE6HQxIbuQAgWxFgAcTsjauIZqoCK0n9CsLPXdfUNRXYjXFBtLyDEVpRnhyXDu1XIIlRWgCQrQiwAGL2xvWkZmoKgSSVRgLsni4KsPEjtMoPMkIranR/JhEAQDYjwAKI8XZVBTY/V1LX9cCaHaEVFa3S0kIAANmJAAsgJr4Cm6kpBFJ8C0HX9MCaHaEVNbp/OORW7WmULxDM6NoAAMkjwAKIiR5iIGXuJC5J6hd57q6qwK7fZW6EVtToSJtByDD0eV1jxtYFAEgNARZATPQYWUkqzs1kD2y4hWBPF1Vgo5uxDnaEbLyEUVq0EQBA1iHAAojxRiqwhZ4c5Zh4qT1VsRaCLqjAxo/QMrOBS5JG9WcWLABkMwIsgJjdjeGK6IDC3Ix+ndLIJq6G1oD8wVBGv9aGJEZoRRV4cnRIcb6kxAkGAIDsQIAFELO7oUWSNLAoL6Nfp29B3GlcGW4jiJ/l2tkhBvHGDIxMIiDAAkDWIcACiNkVqcD2z3AFtl8XHicb3cDlcjo0ol/nI7SiorNgOcwAALIPARZATG1XtRDEVWAzfZjB+trwCK3DSs2N0IqK9stu2tOgQIbbHAAAySHAAojZ3RhuIch0gO1X0Pb8XVWBHW1yA1dUdOSWPxjSFi+jtAAgmxBgAUiSDMPQroZwBXZgYWZ7YEvz4yuwme2BXZfkCK2o+IkFbOQCgOxCgAUgKTxuqiVy6lSme2D7xgVYbwZbCJp8AdUkOUIrajSjtAAgaxFgAUhqG6ElSQMyXIHNcTljIXZXpG0hEz7bVR97e+zAkqQeW5Tr1pDIKC02cgFAdiHAApCUGGAHFmW2AitJgyOjunbs65oAO26QuRmw8aJV23W76zu5EgDQlQiwACQlVkIzXYGVpLJIdXP7vuaMfY1ogM3NcerQvoVJPz7aRkALAQBkFwIsAEn7txB0QQW2T7QCm7kAu3ZnOMCOGVAsp9OR9OOjBx9srG1QMMQoLQDIFgRYAJLaZsA6HIlzWjOlrE+0Apu5FoJ1kQrs2BTaByRpdOToWV8wpC3eJsvWBQBIDwEWgCRpV+QY2dL8XLmcmf/REAuw9c0yDMPy5zcMQ2sjAXbcwNQCbPzorWg1FwBgPwIsAEltPbCZHqEVNTgSYH3BkPa2+C1//h37WlQfed5UK7DjBhXL6Qi3Hny6w2vV0gAAaSLAApAkbd0b7kUdWpLfJV+vrLhto9j2euv7YNcmjNBKLcDmu3M0qn+RJOnT7XstWRcAIH0EWACSpOpIj+fQkuR366dicFFbUM7EJIKEEVopBlhJGj84PD+WCiwAZA8CLABJUvXeRknSsL4FXfL1EiqwmQiwkZ7VgUW56leQelvEEWV9JUmf7tibkV5ddB8NrX7d9/pKffmJJbrjn+9r61429gF2ybF7AQDs1+QLaE/kSNdhJV0TYAcW5snhkAwjM4cZrNkZfsl/XJIncO0vWoGtb/GrZm+ThqUwTxbdX11Tq8767etaua1OkvTKmq3684dVWvqdc2J/RgB0HSqwAFQdV0nqqgpsjsupgZEDEzLRA7tqu1dSWwU1VUcMbnv8avpgeyXDMHT9X9+LhddoYN3Z0KLLn31TLf6gncsDeiUCLABVextjbw/roh5YSTokchpXtcUvxXqbfdpcF/6eJh3SN63nOnxwsSKDCFSZRX2we5t9enPDDlXtabB7KT3e/I+r9P9WbpYkXX3CaFXcdb5+ceHxkqSKbV79+PWVdi4P6JUIsABsqcBK0sjIDn+rQ1hFpFImSUcO6ZfWc+W7czSqNDwPNlsqsL95e42G3vs3nfnb1zT6J3/X1559Sw2t1o8ig+QPhjT3Xx9Lkkb1L9KjF58gh8OhW089XGePHSJJeuTNSm2rpx8W6EoEWACqiUwgyM1xdskxslHRYLix1uIAu9Ube3timi0EkjShLPySceUO+wPsb95eo1v//r6a4162nv9xlS556t8cd5sBT7+/Ifbn83/PPVqFuW5JksPh0IMXHCeHQ2r2B/XTJavsXCbQ6xBgAcSOSR1WUihH9PXyLnBYabgCu31fs5p8AcueN9qrOLK0UCX56R+LG+2jrdhep1DIvkkEyzfv1m3/WCEp/L29ct00XXb0SEnSknXbdd/rFbatrScyDEOPvrVGknREWUnsXkdNGtJPXz0q/LEnl6+Xt9nXxSsEei8CLIAuH6EVdVikhUCSNlnYRrByazjATkqzfSDqmKGlkqSG1kDCAQldyR8M6boXlilkGOqT69ar139BZ487RE9/7RSdPGKgJOkniyuyokrcU7y1cWdsM+Atp46X03ngL3e3nz5ektTkC+qPy9d35fKAXo0ACyD2EumIfl07ImpUXIDdWLvPkuf0BYL6eOseSW3BM10nDO8fe3vFllpLnjNZz6zYoIptXknST79yrMoHhA9ncLucevaKU5Sb41QwZOiulz6wZX090WPvrpUkleS59bVjRrZ7zQmHDtBJIwZIkn7zzlraOIAuQoAFerlgKKQNu8PhcUwaJ1alYkS/otgOf6s2cq3c5lVrIBwiJkeCRboO7VeogUXh3uAVW3Zb8pzJCARDemDJaknhl7KvO2lMwucP699Hd5w+QZL0r8oavbtpZ5evsafZXt+sF+MmD0R7X9tz89TDJYV/EVxUWdMl6wN6OwIs0MttrmuULxgOfF0dYPPcLg0tDrctWLWR67+f74q9feKh1gRYh8Oh44aFq7B2VGDnf1ylDZEK9Zxpk9p9Kft7Zx2hkrxwyLqfDUVp+/1/1ykQ6Xe+YcrYg177P0ceqrI+4ZFwj737WcbXlizDMPTXTz7XyY8s0pAf/1OXLdygJ9/faGs/N5AuAizQy63b3fbS/ZgBfbr860fbCNbvtqaF4L3PwxXSsQOLVZrGEbL7O2F4OAx/VLNHvkDXDa4PhQzNiwTSMQP66KtHj2j3uuI8T6wS+K/KGn1cs6fL1tjTBIIh/W7ZOknStDFlGjfo4CdteXJcujZSFX91zdbYKxrZIBAM6doXlmnGM29q+eZaNfgC2rS3Vd9d8JHOe2KJpZsnga5EgAV6udWRTSpOh0Nju7gCK0njIyddxc9uTcfyzeEAa1X7QNRJI8PP1xoI6f0urMK+WLE5tjFr1rSJcjk7/rF966mHq8DjkqRY6EXyXvq0WjWR2cg3njLO1GO+fVK5XJHK+P8ty54q7MwFK/TU8g2SwpMr7jxtnMr7hn+xe/2zbbr4qX8rEKRvF90PARbo5aI79ssH9FGBJ6fLv/7RQ8OTArZ4m7SroSWt59pS1xir5J4U2ZlvlamHDVJOJKC8sX67pc/dEcMwdP/i8GisEf0KdeVxow56/YCiPF13Uvjl7v+38nOtYSJBSh57J7x5a1hJgc6fMMzUY4b1LdQFRwyXJD21fL2a/fZXNv9VWaNfvx3+Xk4fPVgfzfyKfnj2RD1z7ihdMjH8fS3+bJu+v+gjO5cJpIQAC/Ry0TFBk4b0teXrHzusbYf/R2m+7P3aZ1tjb58zbkhaz7W/olx3LBS/sa5rAuzCyhp9EvkF43tnTZTb1fmP7JlnTJDH5ZRhUIVNxaptdVoS+e977cljlGPinkfdGOmV3dPk018/+Twj6zNrd0OLvj1/mSRpcJ88zb/qNBXnhWci5zgd+v2lJ+j00YMlSQ/9+1Mt/mybbWsFUkGABXqxFn8wNprpaItGTiVrYlnfWGXzo+o0A+za8D/Co/v30aj+1vfznlleJkl6t2pXxnsHDcPQT15fKUk6pDhf15ww2tTjDikp0DdOLJck/fmjTVq/2565te0JhQy9/Gm1rnr+bX3x9//RrLe26Mn3N2ZFtTLqkTfDBxfk5jgPmPbQmbPGlGlcpA3n8XfsayMwDEPX/+09bd/XLEl64qsna2BRXsI1bpdTz195amy6xnUvLNO+Fo4jRvdBgAV6sY9q9sgf6X+zasd+svLcrthJVx+mUYENhkJaEqkinW1x9TXqrDHhAOsLhvR6hitWr6zZquWbw722d555hPLcLtOPnXXWEcpxOhQMGVlzxOmG3ft0xm9e1YV/eEPPfbBJ736+W29s2afvLvhIh8/7p5aus78CuHNfs577cKMk6YpjR2lQZLKAWQ6HIzax4L+bdydMxOhKf3x/g/5RsUWSdP3JY/XlDtogyorz9eglkyVJn9c1atbLH3bZGoF0EWCBXiw6L9ThSBzW39WiBw68V7VLhpHaaJ+l67arLnKU5xfHHWLZ2uJNGTlQgyKVrL9l8CXiUMjQ3H99LEkq65Mf2+Fu1ojSIl11fLhi++yKjZbN2E3Vyq11OuXRf+mdqnCgG9wnT18aN0SHFYdf0q7e26Rz/m+xHrd5BNXj734WmyH83dMOT+k5rjphtPpEZsbacbTvxtp9uu0f70sKT6148PxjD3r99KNG6NKjwpMt/m/ZZ3p97daDXg9kCwIs0ItFq4jHDC1VSb7HtnVEK5vVe5tiLQ3Jeu7DTZKkvvkeffHwzATYHJdTlxx5qCRpweotGXvp+/9VbI71A99z9qSUNtfNnjZRLqdDgZChe1/9xOolmvZhda2mPfaadjW0yuGQfvTFo1R1zyV64cop+suXR+vZyyartMAjw5Bu+n//1U+XdH3ok6S6plY9/GalJOnssUM0McVjiPvme3TrqeHwu6iyRu9v7rqDLwLBkK5+/h01tAbkcjr0zBVTD3oAQ9SvLzkx1krwrfnLVNfUmumlAmkjwAK9VGOrX29tDFdgvzAmMy+5m3Xu4UPljBzJ9fKn1Uk/vrHVr79XhE9Nmn7UCOXmmH+5PVnTI9WqhtZA7KQmK7X4g7onsiv8sNIifWtyeUrPM3pAn1jf7DMrNuodG07nWr55t85+fLH2NPnkdDj0x8tP0dxzjpQn8t/H4XDooonD9M6tX9KhkWOMv7/oY9376icpV+JT9dC/P9XeSA/oj849Kq3nuu308bEq7I+68JeHny5dpXcjVe4fnHOk6baggUV5evzSkyRJNXubdMuLyzO2RsAqBFigl1pYWaOWyED+rxxhblRQpvQvzNWUkeEd/i+vTj7A/v6/69XQGq6GdjZqKl2njhqk8siBD7/496eWB615Sypio8D+90tHx8JeKn5y3jHqG6ms3/Li8i6d9/nOpp065/HF8jb75HI69NyVUzv8bzN2YLHevOmLsYM0/ve1lfrBKx93WYhdu3OvfvHvTyVJX54wNO0RbKUFubEq7CtrtmrBqi1pr7EzSz7bpntfDW/6O2nEAM0+a2JSj79o0qG6OvILz58/qtJzH2y0fI2AlQiwQC/15H/XS5KGlhToZItnpqbi/EiI/u/m3Vq70/z80mZ/QD9bulqSNPnQATrlsMx+Ly6nU7edPl6S9PHWOr2yxrqewXc27YyNvpo2pkyXHzMyrecbWJSn+847WpL0ydY6/e9rK9NcoTmvr92qL/1uifa1+uV2OfXCVafpq0ePPOhjhvcr1Bs3naPDB4V38d+/eJXmLPwo4yE2EAzpuhfeky8Yksfl1IPnH2fJ89555gQNLQkfk3zLi8tV25i5l+U31zXqiufeUsgw1C/fo+euPDWp8V9RD190vEZEKuHXvfCe3rNpExpgBgEW6IU+qt4T63/99uRyOSNjrOx0xXGHxeac/jiJoPWjVz6JjQuae86Rcjgy/71cc8Lo2GauW/++XI2t6Y8fqtrToBnPvKlgyFCfXLceu/QkS76X604ao6mHDZIk/WRxRcbnk/7pg406/w9vqNEXUG6OUy9+4wxdNOlQU48dUlygpd85RxMGh49uffCN1fru39+PTcrIhFkvf6i3I+0Vc6ZN7PTYWLOK8zz61cUnSAr3dl/53NsZ+T621TfpnMdf166GcEB+5oqpGllalNJzFed59MLVpyvf7VJLIKiLnnxDn0bmRAPZxtYAW1NTo+uuu06TJ0/WmWeeqQcffFChUPt/wZ955hl98Ytf1LHHHqvLL79cq1Zlx2gYoLvxB0OxHrdCT05s7I/dhhQX6Bsntr2EaWas0j8qNuvn/wm/9DttTJnOzdDmrf3lu3P0iwuPlyRtrG3Qt+YvU7CDn11mrN7u1bTHXtPW+nAQ//1lJ2v0AGvm2LqcTj3/9bZ5n1f+6S39KQMvD+9r8evb89/V1c+/I38wpOI8txZeO03njR+a1PMM7pOvpd85J3awxm/eWatpv31NG2v3WbreUMjQ9xd9FNu4dcbowbr7C5Ms/RoXTTpU34kcRfva2q26/Nm3LJ0f/MGWWk351StaF2k5eeArxyZ9v/d3/PD+evaKqXI4pF0NrTr9N6/qNSYTIAvZGmBvueUWDR48WIsXL9ZTTz2lxYsX6+mnnz7guqVLl+rRRx/Vz372M7377rs688wzdcMNN6ipqcmGVQPd195mny575k0ti7w0ePcXJiY96zKT7vvSMepfEA5al/7xP7G5rvtrDQT14NLV+uozb8owpNICj566/JQuqb5GzThmpC6aFD469K+ffK4L/vCGttQ1JvUcuxpa9JPXV+qkRxapak/4sT+/4LjYWCOrDC0p0MJvT1NxnluBkKGrn39Hlz/7plZbUF3bXt+sB5euVvn9f9dTyzdIkob3LdAb3zkndvBDsgYW5WnJjefojMhJUe9U7dKEBxbo1heXW7Lm9zfv1tmPvx6bkXv4oGL9+eupvezemZ9fcJzOHhveJPn3is068eFFWvhptUKh1FsjoqOyTv7Vv7Q58mfuh+ccqTvPPMKSNV886VA98dWT5XQ4tKfJpy/9bomufv4dfbJ1T5dvrgM64jBs+tNYUVGhyy67TMuWLVNJSfglmz//+c96+umn9corryRce/3112vkyJGaM2eOJCkUCum0007TnDlz9OUvf9n015OkSZOs/Q27I7sbWrTFmxiwDbV/q9v7L9Dele39p0rnuo6+dnNLiz6vqtKIkSOVl5t7kK/TzsfaubKjP2GWf48m72NyX6e950z/a4cMQ4YhhQxDISP8nC0trdpSXa1DDhkqt8cjh0NyOhxx/4u879zv/cjbjv2uc0hq8AVU3+LXjn3N+rB6j/6xaou8kVmpXxg7RAu/fVZG/tFOx4JVWzT96f8oEPkH/uQRA3XCof01sChPjb6ANtbu05LPtqs2Muqnb75HC689K+WNN01NTaqsrNT48eNVUFCQ3GN9AX3l90v1nw07JEkup0NnjB6sk0cO1LC+hRpUlKfcHKcccihkGPI2+7SnqVVbvE36sLpW/9mwI/Z9elxOPXLxCbru5MxVxD+srtWlf/yPPo8L2pOG9NWUkYM0blCxBhXlqV+BRx6XUzlOp3KcDjkcDrUGgvIFQ2oNBLW3xa/t9c3atKdBH2yp1Yrq2oQ//5ceNUKPXzpZ/SK/iHTEzH0PhkL68WsVmrekInafpPB808kjBqp8QB8N61ugvvke5btdynfnyO10KGgYCoQMBUOGAqGQahtbtbOhRZ9u36tln+9S5Y62HuvjhpXqpW+fpcEZ/EWuxR/U159/O2FqxeA+efrC2CE6YnBflRXnqyTPreI8t5wOhwyFf04YhtQcCKq2sVV7mlr12a56Lf98tz6OHC8shV9FefSSE2MbsDqTzJ/3f1XW6Jo/v6Pdcf274wYW6+SRA3X4oBINLs5TaUGucl1O5ea45MkJ/7np6NfIjn6/dHT4iI4f0920tLRo06ZNOuyww5SXl9f5A7JIcZ47IycbdsRsXrMtwP7lL3/RH/7wB73++uuxj61cuVLTp0/XBx98oKKith6eqVOn6q677tKFF14Y+9i1116r0aNHa/bs2aa+XkVFhQzDUHl5aiNpklG5o15TH1siXxfu+AWSccUxI/SLrxyd0nzRrrBozVbd8OIHsYMJOnLC8FL99qLjYht/UtHc3KyqqiqNHDlS+fnJh5hmf1D3vr5Kv31vfYe/rHXm1MMG6oEvHRl72TyT9jT59JOlq/Xk+5sSQmG6Tjq0v+6ZNkGnjxpk6vpk7vuG2gbdt2S1/rG6xrI1F3lydMspY3TnaYfLk5P5X+IMw9AzH1bpviWfavu+lrSfz+GQLjlimH50zkSNjGy8MiPZP+87G1p0/9JP9cyHVfIHqb72VvefO0m3nNI17Wbr16+Xw+HoNMDa9q+X1+tVcXHiPzrRSmxdXV1CgPV6vbHPxV9bV1enZPj9flVWVqa4YvM+q2tRII1+OCATDil068iBBbpodF8dO7hQn29YZ/eSOnSYpL9++TD9fV2d3t3WoK0NPnlbg8rPcap/Xo4m9M/X2SOKdWJZoYzaGlXW1qT9NauqqlJ+7NWHeTRtQLkWbPTqo52NWrOnRa0H+ce+2ONUed88HTmwQF8cUazRffMk7zZVervmONVvj87T/wwdo9c/r9d/tzdobV2LdjUFOnzVYH8uhzQw360x/XI1aUCBzhzeRyOKc6XWWlVW1ia1FrP3/XuTinXtmAIt3VKvj3c2aU1di7Y3+g96n+M5HdLgArcOL83T5LIiTTu0WCW5Dm1Ytzap9abjxALpr+eN1Ds1DXqzZp8qa5u1eZ9PZr4Fp0MaFLnnJ5YV6ozhxRpc4Fbz9s2q3J78WpL58379mHzNOHSslm6p1/LtjVq7p1nbGv2m1o2eYfPW7aqsDHbZ1/N4Oj9Yx9bySzLFXysKxW63u0sqsOMlrT5inD6va79Ht72XRNp7laS9fr72r2vv+dp5bIcv3yRqbW3Vtm3bdMghQ5Sbm9fhdZn42uFru+b7bnc9aTyn2cdKSnj5P9oq0NrSourqah06fLgK8sP3PRRrMzA6fDu+FWH/zxe6c1Scl6O++R4VZmm19WAmpzdP3pR0K7BR4yWdfWL4bcMw1OALaHdjq/xBI9Z20jfPo375ni6p+Jkx5Zi2twPBkPY0++Rt9isQCikQMuQPhmRIynU55clxyuNyqsiTo/4FuWlPrkj1vsev2TAMeVv82tfqV4s/pJZAUP5gSDlOp1xOh3Ii7Tb98t0qLciVKwumbUjS0ROlmyJvB0OG9rb4Vd/q174Wvwy1tQA5HJInx6nS/Fz1zXNbMi0knT/vJx3d9nYoZKiuxae6Zp8CQUOtgZB8wVCHxZtk2tjaHtNzErKv1adt27dpSNkQeXLtO/UwFSW5bk0sK+myPQbr1683dZ1t/6KVlpbK6/UmfMzr9crhcKi0tDTh4/369Wv32jFjkjsf3OFwJN3jlqqxBQUaa+/hRilrampSpc+r8aOGdNn9Qvi+t9Ru14iBfbnvNsjPz7f0vhcWSoNTO43UNsVd1+YWk+59LzT/6nnW6pPa1Ku0WPHnvaioUMMtWk9P19TUpMpQvcaPG8rP906YDcq2lQEmTpyobdu2ac+ePbGPVVRUqLy8XIX7/USaOHGiVq9eHXs/GAzq008/1VFHdUF5BgAAAFnFtgA7YcIETZo0ST//+c/V0NCgDRs26KmnntLll18uSTr33HO1YsUKSdLll1+uf/zjH/r444/V3Nysxx57TB6PR2eccYZdywcAAIBNbG2K+9WvfqW5c+fqlFNOUVFRkWbMmKGvfe1rkqRNmzbF5ryedtppuuOOO3TbbbeptrZWkyZN0u9+97tuN4oCAAAA6bM1wJaVlemJJ55o93Nr1ybuDP3a174WC7cAAADovbJjKywAAABgEgEWAAAA3QoBFgAAAN0KARYAAADdCgEWAAAA3QoBFgAAAN0KARYAAADdCgEWAAAA3QoBFgAAAN0KARYAAADdCgEWAAAA3YrDMAzD7kV0hQ8//FCGYcjj8di9lKxnGIb8fr/cbrccDofdy+k1uO/24L7bg/tuD+67Pbjv5vl8PjkcDh177LEHvS6ni9ZjO/7AmOdwOAj6NuC+24P7bg/uuz247/bgvpvncDhMZbZeU4EFAABAz0APLAAAALoVAiwAAAC6FQIsAAAAuhUCLAAAALoVAiwAAAC6FQIsAAAAuhUCLAAAALoVAiwAAAC6FQIsAAAAuhUCLNpVV1enWbNm6ZRTTtHkyZN18803a9u2bXYvq0eqqanRddddp8mTJ+vMM8/Ugw8+qFAoZPeyeryamhrddNNNmjx5sqZMmaLZs2ervr7e7mX1Kvfff7/GjRtn9zJ6jccee0xTp07V0UcfrWuuuUbV1dV2L6nH+/TTT3XVVVfp+OOP1ymnnKI777xTe/bssXtZPQIBFu2aM2eOdu/erZdeekmvvvqq/H6/5syZY/eyeqRbbrlFgwcP1uLFi/XUU09p8eLFevrpp+1eVo93ww03qLi4WEuXLtWLL76odevW6YEHHrB7Wb1GZWWl/vnPf9q9jF7jueee04IFC/TMM8/o7bffVnl5uf74xz/avaweLRAI6LrrrtPRRx+td999Vy+//LL27NmjH/3oR3YvrUcgwOIAhmFo8ODBmjVrlkpLS9W3b1/NmDFDH3zwgQzDsHt5PUpFRYXWrFmjO++8U3369NHIkSN1zTXXaP78+XYvrUerr6/XxIkTNXPmTBUWFqqsrEwXX3yxVqxYYffSeoVQKKQf/vCHuuaaa+xeSq/x5JNP6vbbb9eoUaNUVFSke+65R/fcc4/dy+rRdu3apV27dunCCy+Ux+NRv379dPbZZ6uystLupfUIBFgcwOFw6N5779XYsWNjH9u2bZsGDhwoh8Nh48p6ntWrV2vo0KEqKSmJfeyII47Qpk2b1NDQYOPKerbi4mLNmzdPAwYMiH1s27ZtGjRokI2r6j3+8pe/KDc3V+eff77dS+kVduzYoerqau3du1fnnXeeJk+erFtvvZWXsjNs8ODBGj9+vObPn6/GxkbV1tbqtdde0xlnnGH30noEAiw6VV1drUceeUQ33nij3Uvpcbxer4qLixM+Fg2zdXV1diypV6qoqNCf/vQn/ox3gd27d+vRRx/VD3/4Q7uX0mts375dkvTKK6/oqaee0j//+U9t376dCmyGOZ1OPfroo1qyZImOPfZYTZkyRYFAQDNnzrR7aT0CAbaX+uc//6lx48a1+78XX3wxdt2GDRt05ZVX6uKLL9b06dNtXHHPRVuGvT744AN961vf0syZMzVlyhS7l9PjzZs3T5dcconKy8vtXkqvEf0Z8+1vf1uDBw9WWVmZbrnlFi1dulStra02r67n8vl8uuGGG3TuuedqxYoVevPNN9WnTx/deeeddi+tR8ixewGwx4UXXqgLL7zwoNesXLlS1157rb75zW/q+uuv76KV9S6lpaXyer0JH/N6vXI4HCotLbVnUb3I0qVLddddd2nu3Lm66KKL7F5Oj7ds2TJ99NFHevnll+1eSq8SbZWJf7Vn6NChMgxDtbW1OuSQQ+xaWo+2bNkyVVdX64477pDL5VKfPn1066236sILL5TX61Xfvn3tXmK3RgUW7aqqqtJ1112nWbNmEV4zaOLEidq2bVtCL1pFRYXKy8tVWFho48p6vg8//FCzZs3SI488QnjtIgsWLFBtba3OPPNMTZ48WZdccokkafLkyVq4cKHNq+u5ysrKVFRUlLB5qKamRm63m77vDAoGgwqFQgmvsvl8PhtX1LM4DF6/RDu++c1vauLEibrjjjvsXkqP99WvflVjxozRnDlztGPHDl133XX65je/qSuuuMLupfVYgUBAF1xwga6++mpddtlldi+n19i7d6+am5tj72/fvl2XXXaZ/vOf/6ikpET5+fk2rq5nmzdvnpYsWaI//OEPKioq0k033aTDDjtM8+bNs3tpPVZdXZ3OPfdczZgxQzfccINaWlp09913a9++ffrTn/5k9/K6PQIsDrBt2zadccYZcrvdB0wdePLJJ3XCCSfYtLKeafv27Zo7d66WL1+uoqIizZgxQzfffDMTHzJoxYoVuuKKK+TxeA743CuvvKKhQ4fasKrep7q6WtOmTdPatWvtXkqP5/P5NG/ePC1cuFB+v19f/OIXNXfuXF7pybBVq1bpgQce0Jo1a+TxeHTiiSdq9uzZGjx4sN1L6/YIsAAAAOhW6IEFAABAt0KABQAAQLdCgAUAAEC3QoAFAABAt0KABQAAQLdCgAUAAEC3QoAFAABAt0KABQAAQLdCgAUAAEC3QoAFAABAt0KABQAAQLdCgAUAAEC38v8Bea3+XLJcKaAAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df_taxi['passenger_count'].plot(kind='kde')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zsfD228qJL2x",
        "outputId": "fe72daef-a067-40d6-e5e5-ab3f1aa27da1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4.599394993810832\n"
          ]
        }
      ],
      "source": [
        "skew_fare=df_taxi['fare_amount'].skew()\n",
        "print(skew_fare)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kb9np54YJpT0",
        "outputId": "e251ca97-29fd-4e03-c247-aa1c159af236"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4.072171721417425\n"
          ]
        }
      ],
      "source": [
        "skew_trip_distance=df_taxi['trip_distance'].skew()\n",
        "print(skew_trip_distance)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FkYET9n1LJ_O"
      },
      "outputs": [],
      "source": [
        "df_taxi['fare_amount_real'] = np.log1p(df_taxi['fare_amount'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xCLY_JTeLREZ",
        "outputId": "87a45998-573a-47ec-a561-c4215c6dd10b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8413181207377096\n"
          ]
        }
      ],
      "source": [
        "print(df_taxi['fare_amount_real'].skew())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m62w8KvlLoaJ"
      },
      "outputs": [],
      "source": [
        "df_taxi['passenger_count_real'] = np.log1p(df_taxi['passenger_count'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UrZC3erRM1IU",
        "outputId": "770d1ee8-734f-4298-a648-aed651df79e0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.7587736035664898\n"
          ]
        }
      ],
      "source": [
        "print(df_taxi['passenger_count_real'].skew())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_ACkCYluM5I6"
      },
      "outputs": [],
      "source": [
        "df_taxi = df_taxi.drop(columns=['pickup_latitude', 'pickup_longitude', 'dropoff_latitude', 'dropoff_longitude'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7WlOatJWNvLO"
      },
      "outputs": [],
      "source": [
        "df_taxi = df_taxi.drop(columns=['passenger_count', 'fare_amount'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Fzf2sNvLN3Z7",
        "outputId": "943ab16e-5137-4a23-f7f5-cae910b8c8d6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         trip_distance  haversine_distance  month  day  hour  \\\n",
              "2604045           1.75            2.727861      1   28    12   \n",
              "1261799           1.80            2.683295      1   11     9   \n",
              "571940            0.74            0.786191      1   31     9   \n",
              "323249            4.75            5.358867      1   10    11   \n",
              "313634           20.93           23.108070      2    3    18   \n",
              "\n",
              "         fare_amount_real  passenger_count_real  \n",
              "2604045          2.140066              0.693147  \n",
              "1261799          2.251292              0.693147  \n",
              "571940           1.704748              0.693147  \n",
              "323249           2.995732              0.693147  \n",
              "313634           3.970292              0.693147  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-302e3ca6-32a8-4697-b1a0-afef8da106ca\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>trip_distance</th>\n",
              "      <th>haversine_distance</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>hour</th>\n",
              "      <th>fare_amount_real</th>\n",
              "      <th>passenger_count_real</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2604045</th>\n",
              "      <td>1.75</td>\n",
              "      <td>2.727861</td>\n",
              "      <td>1</td>\n",
              "      <td>28</td>\n",
              "      <td>12</td>\n",
              "      <td>2.140066</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1261799</th>\n",
              "      <td>1.80</td>\n",
              "      <td>2.683295</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>9</td>\n",
              "      <td>2.251292</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>571940</th>\n",
              "      <td>0.74</td>\n",
              "      <td>0.786191</td>\n",
              "      <td>1</td>\n",
              "      <td>31</td>\n",
              "      <td>9</td>\n",
              "      <td>1.704748</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>323249</th>\n",
              "      <td>4.75</td>\n",
              "      <td>5.358867</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>2.995732</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>313634</th>\n",
              "      <td>20.93</td>\n",
              "      <td>23.108070</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>18</td>\n",
              "      <td>3.970292</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-302e3ca6-32a8-4697-b1a0-afef8da106ca')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "      --bg-color: #E8F0FE;\n",
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              "\n",
              "  @keyframes spin {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_taxi",
              "summary": "{\n  \"name\": \"df_taxi\",\n  \"rows\": 80000,\n  \"fields\": [\n    {\n      \"column\": \"trip_distance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.5478838223689975,\n        \"min\": 0.0,\n        \"max\": 148.0,\n        \"num_unique_values\": 1986,\n        \"samples\": [\n          1.09,\n          0.63,\n          17.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"haversine_distance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 537.2832418780947,\n        \"min\": 0.0,\n        \"max\": 8689.251252368545,\n        \"num_unique_values\": 78386,\n        \"samples\": [\n          2.157357471000299,\n          1.0995631333225302,\n          2.530865214085287\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          14,\n          13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hour\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          21,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare_amount_real\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5616322965486339,\n        \"min\": 0.0,\n        \"max\": 5.916202062607435,\n        \"num_unique_values\": 269,\n        \"samples\": [\n          2.3978952727983707,\n          3.6428355156125294\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"passenger_count_real\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3557134549294181,\n        \"min\": 0.0,\n        \"max\": 1.9459101490553132,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.6931471805599453,\n          1.6094379124341003\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 210
        }
      ],
      "source": [
        "df_taxi.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5qCefdd9N42k"
      },
      "outputs": [],
      "source": [
        "\n",
        "def remove_outliers_all_columns(df,col):\n",
        "    numeric_columns = df[col]\n",
        "    if col in numeric_columns:\n",
        "        Q1 = df[col].quantile(0.25).compute()\n",
        "        Q3 = df[col].quantile(0.75).compute()\n",
        "        IQR = Q3 - Q1\n",
        "\n",
        "        # Define the acceptable range\n",
        "        lower_bound = Q1 - 1.5 * IQR\n",
        "        upper_bound = Q3 + 1.5 * IQR\n",
        "\n",
        "        # Remove the outliers\n",
        "        df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)]\n",
        "\n",
        "    return df\n",
        "\n",
        "# Example usage:\n",
        "df_taxi= remove_outliers_all_columns(df_taxi, 'trip_distance')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zRzKPAaYOf5h",
        "outputId": "2b2a807c-7740-4160-910c-9d653edc4a2a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(80000, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 212
        }
      ],
      "source": [
        "df_taxi.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PNP5OyxHOlVd"
      },
      "outputs": [],
      "source": [
        "from scipy import stats\n",
        "\n",
        "# The second returned argument is the lambda value, we only need the transformed data\n",
        "df_taxi['trip_distance'] = np.log1p(df_taxi['trip_distance'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6UES0tX1OyW1",
        "outputId": "b97013cb-7793-478c-b995-e0dcb8263922"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0975923376551637"
            ]
          },
          "metadata": {},
          "execution_count": 214
        }
      ],
      "source": [
        "df_taxi['trip_distance'].skew()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_taxi['haversine_distance'] = np.log1p(df_taxi['haversine_distance'])"
      ],
      "metadata": {
        "id": "G9vaqbqaOJLH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ys7xkXjSPD2U",
        "outputId": "7c674576-2644-4865-d30a-e14d9f56b771"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         trip_distance  haversine_distance  month  day  hour  \\\n",
              "2604045       1.011601            1.315835      1   28    12   \n",
              "1261799       1.029619            1.303808      1   11     9   \n",
              "571940        0.553885            0.580085      1   31     9   \n",
              "323249        1.749200            1.849850      1   10    11   \n",
              "313634        3.087856            3.182547      2    3    18   \n",
              "\n",
              "         fare_amount_real  passenger_count_real  \n",
              "2604045          2.140066              0.693147  \n",
              "1261799          2.251292              0.693147  \n",
              "571940           1.704748              0.693147  \n",
              "323249           2.995732              0.693147  \n",
              "313634           3.970292              0.693147  "
            ],
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              "    <div>\n",
              "<style scoped>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>trip_distance</th>\n",
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              "      <td>1.011601</td>\n",
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              "      <th>313634</th>\n",
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              "      <td>3.182547</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>18</td>\n",
              "      <td>3.970292</td>\n",
              "      <td>0.693147</td>\n",
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              "  </tbody>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      fill: #FFFFFF;\n",
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              "        document.querySelector('#df-727320ed-1777-4842-b408-c8b3c833034e 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-727320ed-1777-4842-b408-c8b3c833034e');\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",
              "\n",
              "\n",
              "<div id=\"df-ae73df7e-2c24-4b30-abb9-557ced96548c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ae73df7e-2c24-4b30-abb9-557ced96548c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ae73df7e-2c24-4b30-abb9-557ced96548c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_taxi",
              "summary": "{\n  \"name\": \"df_taxi\",\n  \"rows\": 80000,\n  \"fields\": [\n    {\n      \"column\": \"trip_distance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6068582392158545,\n        \"min\": 0.0,\n        \"max\": 5.003946305945459,\n        \"num_unique_values\": 1986,\n        \"samples\": [\n          0.7371640659767197,\n          0.48858001481867097,\n          2.89314568477889\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"haversine_distance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7957115240970452,\n        \"min\": 0.0,\n        \"max\": 9.069957130657183,\n        \"num_unique_values\": 78386,\n        \"samples\": [\n          1.1497354343979596,\n          0.7417292913365949,\n          1.261542944052646\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          14,\n          13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hour\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          21,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare_amount_real\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5616322965486339,\n        \"min\": 0.0,\n        \"max\": 5.916202062607435,\n        \"num_unique_values\": 269,\n        \"samples\": [\n          2.3978952727983707,\n          3.6428355156125294\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"passenger_count_real\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3557134549294181,\n        \"min\": 0.0,\n        \"max\": 1.9459101490553132,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.6931471805599453,\n          1.6094379124341003\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 216
        }
      ],
      "source": [
        "df_taxi.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_taxi['day'] = np.log1p(df_taxi['day'])"
      ],
      "metadata": {
        "id": "muQ2XeL5EhOA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yXhMCmcmPPV-",
        "outputId": "b66f453a-8587-488b-f17c-07a9fe1e18b4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "trip_distance            0\n",
              "haversine_distance       0\n",
              "month                    0\n",
              "day                      0\n",
              "hour                     0\n",
              "fare_amount_real        23\n",
              "passenger_count_real     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 218
        }
      ],
      "source": [
        "df_taxi.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Eiws-goJQHls"
      },
      "outputs": [],
      "source": [
        "df_taxi = df_taxi.dropna(subset=['fare_amount_real'],axis=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lN-A6G6jQP3s",
        "outputId": "8b1aa5fa-f25d-4191-fef8-9efe44a57f44"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Info] This is the GPU trainer!!\n",
            "[LightGBM] [Info] Total Bins 0\n",
            "[LightGBM] [Info] Number of data points in the train set: 2, number of used features: 0\n",
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Warning] Using sparse features with CUDA is currently not supported.\n",
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Info] This is the GPU trainer!!\n",
            "[LightGBM] [Info] Total Bins 0\n",
            "[LightGBM] [Info] Number of data points in the train set: 2, number of used features: 0\n",
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Warning] Using sparse features with CUDA is currently not supported.\n",
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Info] This is the GPU trainer!!\n",
            "[LightGBM] [Info] Total Bins 0\n",
            "[LightGBM] [Info] Number of data points in the train set: 2, number of used features: 0\n",
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Warning] Using sparse features with CUDA is currently not supported.\n",
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Info] This is the GPU trainer!!\n",
            "[LightGBM] [Info] Total Bins 0\n",
            "[LightGBM] [Info] Number of data points in the train set: 2, number of used features: 0\n",
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Warning] Using sparse features with CUDA is currently not supported.\n",
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Info] This is the GPU trainer!!\n",
            "[LightGBM] [Info] Total Bins 0\n",
            "[LightGBM] [Info] Number of data points in the train set: 2, number of used features: 0\n",
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Warning] Using sparse features with CUDA is currently not supported.\n",
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Info] This is the GPU trainer!!\n",
            "[LightGBM] [Info] Total Bins 0\n",
            "[LightGBM] [Info] Number of data points in the train set: 2, number of used features: 0\n",
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Warning] Using sparse features with CUDA is currently not supported.\n",
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Info] This is the GPU trainer!!\n",
            "[LightGBM] [Info] Total Bins 0\n",
            "[LightGBM] [Info] Number of data points in the train set: 2, number of used features: 0\n",
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Warning] Using sparse features with CUDA is currently not supported.\n",
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Info] This is the GPU trainer!!\n",
            "[LightGBM] [Info] Total Bins 0\n",
            "[LightGBM] [Info] Number of data points in the train set: 2, number of used features: 0\n",
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Warning] Using sparse features with CUDA is currently not supported.\n",
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Info] This is the GPU trainer!!\n",
            "[LightGBM] [Info] Total Bins 0\n",
            "[LightGBM] [Info] Number of data points in the train set: 2, number of used features: 0\n",
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Warning] Using sparse features with CUDA is currently not supported.\n",
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Info] This is the GPU trainer!!\n",
            "[LightGBM] [Info] Total Bins 0\n",
            "[LightGBM] [Info] Number of data points in the train set: 2, number of used features: 0\n",
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Warning] Using sparse features with CUDA is currently not supported.\n",
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Info] This is the GPU trainer!!\n",
            "[LightGBM] [Info] Total Bins 0\n",
            "[LightGBM] [Info] Number of data points in the train set: 2, number of used features: 0\n",
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Warning] Using sparse features with CUDA is currently not supported.\n",
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Info] This is the GPU trainer!!\n",
            "[LightGBM] [Info] Total Bins 0\n",
            "[LightGBM] [Info] Number of data points in the train set: 2, number of used features: 0\n",
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Warning] Using sparse features with CUDA is currently not supported.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7d1af74f4fa0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_10489_row8_col1, #T_10489_row15_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_10489\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_10489_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_10489_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_10489_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_10489_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_10489_row0_col1\" class=\"data row0 col1\" >123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_10489_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_10489_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
              "      <td id=\"T_10489_row1_col1\" class=\"data row1 col1\" >fare_amount_real</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_10489_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_10489_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
              "      <td id=\"T_10489_row2_col1\" class=\"data row2 col1\" >Regression</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_10489_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_10489_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
              "      <td id=\"T_10489_row3_col1\" class=\"data row3 col1\" >(81459, 7)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_10489_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_10489_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_10489_row4_col1\" class=\"data row4 col1\" >(81459, 7)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_10489_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_10489_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
              "      <td id=\"T_10489_row5_col1\" class=\"data row5 col1\" >(57021, 7)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_10489_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_10489_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
              "      <td id=\"T_10489_row6_col1\" class=\"data row6 col1\" >(24438, 7)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_10489_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_10489_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
              "      <td id=\"T_10489_row7_col1\" class=\"data row7 col1\" >6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_10489_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_10489_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
              "      <td id=\"T_10489_row8_col1\" class=\"data row8 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_10489_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_10489_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
              "      <td id=\"T_10489_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_10489_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_10489_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_10489_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_10489_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_10489_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_10489_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_10489_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_10489_row12_col0\" class=\"data row12 col0\" >Fold Generator</td>\n",
              "      <td id=\"T_10489_row12_col1\" class=\"data row12 col1\" >KFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_10489_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_10489_row13_col0\" class=\"data row13 col0\" >Fold Number</td>\n",
              "      <td id=\"T_10489_row13_col1\" class=\"data row13 col1\" >10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_10489_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_10489_row14_col0\" class=\"data row14 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_10489_row14_col1\" class=\"data row14 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_10489_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_10489_row15_col0\" class=\"data row15 col0\" >Use GPU</td>\n",
              "      <td id=\"T_10489_row15_col1\" class=\"data row15 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_10489_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_10489_row16_col0\" class=\"data row16 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_10489_row16_col1\" class=\"data row16 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_10489_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_10489_row17_col0\" class=\"data row17 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_10489_row17_col1\" class=\"data row17 col1\" >reg-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_10489_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "      <td id=\"T_10489_row18_col0\" class=\"data row18 col0\" >USI</td>\n",
              "      <td id=\"T_10489_row18_col1\" class=\"data row18 col1\" >f20b</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Info] This is the GPU trainer!!\n",
            "[LightGBM] [Info] Total Bins 0\n",
            "[LightGBM] [Info] Number of data points in the train set: 2, number of used features: 0\n",
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Warning] Using sparse features with CUDA is currently not supported.\n",
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Info] This is the GPU trainer!!\n",
            "[LightGBM] [Info] Total Bins 0\n",
            "[LightGBM] [Info] Number of data points in the train set: 2, number of used features: 0\n",
            "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
            "[LightGBM] [Warning] Using sparse features with CUDA is currently not supported.\n"
          ]
        }
      ],
      "source": [
        "from pycaret.regression import *\n",
        "s = setup(df_taxi, target = 'fare_amount_real', index=False,session_id = 123,use_gpu=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645,
          "referenced_widgets": [
            "c9f9df65397547d4af3b806102fc6205",
            "b636db4f816b4c42be9cc407e29dc677",
            "1252c62126fc4449bf50f9ef8a68a2d8",
            "f45e947843894d42bdd9b6502ef8ce68",
            "6f8f38abca404491ab9817869406587e",
            "e2166c238547464d97fa9394c727d828",
            "3d1fdf54057d48f68695d5295ce6041c",
            "625c7c1d15264d8d99f3bb9b6dc2d5f2",
            "88f39dac86604f0c8dec7b8c43d5e9dc",
            "f3b3e5faa4684d4088af57ca1a9ccb2f",
            "dbf4700424b94507b09ec04b8bb9bfde"
          ]
        },
        "id": "0tvS2TP9QbIs",
        "outputId": "37123f54-5dbd-48c6-b852-18b2e39224ee"
      },
      "outputs": [
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              "</style>\n",
              "<table id=\"T_84411\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_84411_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_84411_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
              "      <th id=\"T_84411_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
              "      <th id=\"T_84411_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
              "      <th id=\"T_84411_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
              "      <th id=\"T_84411_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
              "      <th id=\"T_84411_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
              "      <th id=\"T_84411_level0_col7\" class=\"col_heading level0 col7\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_84411_level0_row0\" class=\"row_heading level0 row0\" >gbr</th>\n",
              "      <td id=\"T_84411_row0_col0\" class=\"data row0 col0\" >Gradient Boosting Regressor</td>\n",
              "      <td id=\"T_84411_row0_col1\" class=\"data row0 col1\" >0.1269</td>\n",
              "      <td id=\"T_84411_row0_col2\" class=\"data row0 col2\" >0.0489</td>\n",
              "      <td id=\"T_84411_row0_col3\" class=\"data row0 col3\" >0.2209</td>\n",
              "      <td id=\"T_84411_row0_col4\" class=\"data row0 col4\" >0.8464</td>\n",
              "      <td id=\"T_84411_row0_col5\" class=\"data row0 col5\" >0.0674</td>\n",
              "      <td id=\"T_84411_row0_col6\" class=\"data row0 col6\" >0.0784</td>\n",
              "      <td id=\"T_84411_row0_col7\" class=\"data row0 col7\" >5.7920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84411_level0_row1\" class=\"row_heading level0 row1\" >lightgbm</th>\n",
              "      <td id=\"T_84411_row1_col0\" class=\"data row1 col0\" >Light Gradient Boosting Machine</td>\n",
              "      <td id=\"T_84411_row1_col1\" class=\"data row1 col1\" >0.1253</td>\n",
              "      <td id=\"T_84411_row1_col2\" class=\"data row1 col2\" >0.0490</td>\n",
              "      <td id=\"T_84411_row1_col3\" class=\"data row1 col3\" >0.2211</td>\n",
              "      <td id=\"T_84411_row1_col4\" class=\"data row1 col4\" >0.8461</td>\n",
              "      <td id=\"T_84411_row1_col5\" class=\"data row1 col5\" >0.0675</td>\n",
              "      <td id=\"T_84411_row1_col6\" class=\"data row1 col6\" >0.0771</td>\n",
              "      <td id=\"T_84411_row1_col7\" class=\"data row1 col7\" >1.3060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84411_level0_row2\" class=\"row_heading level0 row2\" >xgboost</th>\n",
              "      <td id=\"T_84411_row2_col0\" class=\"data row2 col0\" >Extreme Gradient Boosting</td>\n",
              "      <td id=\"T_84411_row2_col1\" class=\"data row2 col1\" >0.1289</td>\n",
              "      <td id=\"T_84411_row2_col2\" class=\"data row2 col2\" >0.0523</td>\n",
              "      <td id=\"T_84411_row2_col3\" class=\"data row2 col3\" >0.2285</td>\n",
              "      <td id=\"T_84411_row2_col4\" class=\"data row2 col4\" >0.8357</td>\n",
              "      <td id=\"T_84411_row2_col5\" class=\"data row2 col5\" >0.0695</td>\n",
              "      <td id=\"T_84411_row2_col6\" class=\"data row2 col6\" >0.0769</td>\n",
              "      <td id=\"T_84411_row2_col7\" class=\"data row2 col7\" >0.3240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84411_level0_row3\" class=\"row_heading level0 row3\" >ridge</th>\n",
              "      <td id=\"T_84411_row3_col0\" class=\"data row3 col0\" >Ridge Regression</td>\n",
              "      <td id=\"T_84411_row3_col1\" class=\"data row3 col1\" >0.1305</td>\n",
              "      <td id=\"T_84411_row3_col2\" class=\"data row3 col2\" >0.0537</td>\n",
              "      <td id=\"T_84411_row3_col3\" class=\"data row3 col3\" >0.2315</td>\n",
              "      <td id=\"T_84411_row3_col4\" class=\"data row3 col4\" >0.8313</td>\n",
              "      <td id=\"T_84411_row3_col5\" class=\"data row3 col5\" >0.0700</td>\n",
              "      <td id=\"T_84411_row3_col6\" class=\"data row3 col6\" >0.0762</td>\n",
              "      <td id=\"T_84411_row3_col7\" class=\"data row3 col7\" >0.0340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84411_level0_row4\" class=\"row_heading level0 row4\" >lar</th>\n",
              "      <td id=\"T_84411_row4_col0\" class=\"data row4 col0\" >Least Angle Regression</td>\n",
              "      <td id=\"T_84411_row4_col1\" class=\"data row4 col1\" >0.1304</td>\n",
              "      <td id=\"T_84411_row4_col2\" class=\"data row4 col2\" >0.0537</td>\n",
              "      <td id=\"T_84411_row4_col3\" class=\"data row4 col3\" >0.2315</td>\n",
              "      <td id=\"T_84411_row4_col4\" class=\"data row4 col4\" >0.8313</td>\n",
              "      <td id=\"T_84411_row4_col5\" class=\"data row4 col5\" >0.0700</td>\n",
              "      <td id=\"T_84411_row4_col6\" class=\"data row4 col6\" >0.0762</td>\n",
              "      <td id=\"T_84411_row4_col7\" class=\"data row4 col7\" >0.0340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84411_level0_row5\" class=\"row_heading level0 row5\" >br</th>\n",
              "      <td id=\"T_84411_row5_col0\" class=\"data row5 col0\" >Bayesian Ridge</td>\n",
              "      <td id=\"T_84411_row5_col1\" class=\"data row5 col1\" >0.1305</td>\n",
              "      <td id=\"T_84411_row5_col2\" class=\"data row5 col2\" >0.0537</td>\n",
              "      <td id=\"T_84411_row5_col3\" class=\"data row5 col3\" >0.2315</td>\n",
              "      <td id=\"T_84411_row5_col4\" class=\"data row5 col4\" >0.8313</td>\n",
              "      <td id=\"T_84411_row5_col5\" class=\"data row5 col5\" >0.0700</td>\n",
              "      <td id=\"T_84411_row5_col6\" class=\"data row5 col6\" >0.0762</td>\n",
              "      <td id=\"T_84411_row5_col7\" class=\"data row5 col7\" >0.0620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84411_level0_row6\" class=\"row_heading level0 row6\" >lr</th>\n",
              "      <td id=\"T_84411_row6_col0\" class=\"data row6 col0\" >Linear Regression</td>\n",
              "      <td id=\"T_84411_row6_col1\" class=\"data row6 col1\" >0.1304</td>\n",
              "      <td id=\"T_84411_row6_col2\" class=\"data row6 col2\" >0.0537</td>\n",
              "      <td id=\"T_84411_row6_col3\" class=\"data row6 col3\" >0.2315</td>\n",
              "      <td id=\"T_84411_row6_col4\" class=\"data row6 col4\" >0.8313</td>\n",
              "      <td id=\"T_84411_row6_col5\" class=\"data row6 col5\" >0.0700</td>\n",
              "      <td id=\"T_84411_row6_col6\" class=\"data row6 col6\" >0.0762</td>\n",
              "      <td id=\"T_84411_row6_col7\" class=\"data row6 col7\" >0.0600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84411_level0_row7\" class=\"row_heading level0 row7\" >omp</th>\n",
              "      <td id=\"T_84411_row7_col0\" class=\"data row7 col0\" >Orthogonal Matching Pursuit</td>\n",
              "      <td id=\"T_84411_row7_col1\" class=\"data row7 col1\" >0.1302</td>\n",
              "      <td id=\"T_84411_row7_col2\" class=\"data row7 col2\" >0.0540</td>\n",
              "      <td id=\"T_84411_row7_col3\" class=\"data row7 col3\" >0.2322</td>\n",
              "      <td id=\"T_84411_row7_col4\" class=\"data row7 col4\" >0.8303</td>\n",
              "      <td id=\"T_84411_row7_col5\" class=\"data row7 col5\" >0.0702</td>\n",
              "      <td id=\"T_84411_row7_col6\" class=\"data row7 col6\" >0.0761</td>\n",
              "      <td id=\"T_84411_row7_col7\" class=\"data row7 col7\" >0.0340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84411_level0_row8\" class=\"row_heading level0 row8\" >huber</th>\n",
              "      <td id=\"T_84411_row8_col0\" class=\"data row8 col0\" >Huber Regressor</td>\n",
              "      <td id=\"T_84411_row8_col1\" class=\"data row8 col1\" >0.1264</td>\n",
              "      <td id=\"T_84411_row8_col2\" class=\"data row8 col2\" >0.0551</td>\n",
              "      <td id=\"T_84411_row8_col3\" class=\"data row8 col3\" >0.2344</td>\n",
              "      <td id=\"T_84411_row8_col4\" class=\"data row8 col4\" >0.8270</td>\n",
              "      <td id=\"T_84411_row8_col5\" class=\"data row8 col5\" >0.0706</td>\n",
              "      <td id=\"T_84411_row8_col6\" class=\"data row8 col6\" >0.0727</td>\n",
              "      <td id=\"T_84411_row8_col7\" class=\"data row8 col7\" >1.1260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84411_level0_row9\" class=\"row_heading level0 row9\" >rf</th>\n",
              "      <td id=\"T_84411_row9_col0\" class=\"data row9 col0\" >Random Forest Regressor</td>\n",
              "      <td id=\"T_84411_row9_col1\" class=\"data row9 col1\" >0.1382</td>\n",
              "      <td id=\"T_84411_row9_col2\" class=\"data row9 col2\" >0.0564</td>\n",
              "      <td id=\"T_84411_row9_col3\" class=\"data row9 col3\" >0.2373</td>\n",
              "      <td id=\"T_84411_row9_col4\" class=\"data row9 col4\" >0.8226</td>\n",
              "      <td id=\"T_84411_row9_col5\" class=\"data row9 col5\" >0.0721</td>\n",
              "      <td id=\"T_84411_row9_col6\" class=\"data row9 col6\" >0.0823</td>\n",
              "      <td id=\"T_84411_row9_col7\" class=\"data row9 col7\" >19.5200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84411_level0_row10\" class=\"row_heading level0 row10\" >knn</th>\n",
              "      <td id=\"T_84411_row10_col0\" class=\"data row10 col0\" >K Neighbors Regressor</td>\n",
              "      <td id=\"T_84411_row10_col1\" class=\"data row10 col1\" >0.1520</td>\n",
              "      <td id=\"T_84411_row10_col2\" class=\"data row10 col2\" >0.0620</td>\n",
              "      <td id=\"T_84411_row10_col3\" class=\"data row10 col3\" >0.2488</td>\n",
              "      <td id=\"T_84411_row10_col4\" class=\"data row10 col4\" >0.8052</td>\n",
              "      <td id=\"T_84411_row10_col5\" class=\"data row10 col5\" >0.0752</td>\n",
              "      <td id=\"T_84411_row10_col6\" class=\"data row10 col6\" >0.0867</td>\n",
              "      <td id=\"T_84411_row10_col7\" class=\"data row10 col7\" >0.2610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84411_level0_row11\" class=\"row_heading level0 row11\" >et</th>\n",
              "      <td id=\"T_84411_row11_col0\" class=\"data row11 col0\" >Extra Trees Regressor</td>\n",
              "      <td id=\"T_84411_row11_col1\" class=\"data row11 col1\" >0.1435</td>\n",
              "      <td id=\"T_84411_row11_col2\" class=\"data row11 col2\" >0.0643</td>\n",
              "      <td id=\"T_84411_row11_col3\" class=\"data row11 col3\" >0.2534</td>\n",
              "      <td id=\"T_84411_row11_col4\" class=\"data row11 col4\" >0.7979</td>\n",
              "      <td id=\"T_84411_row11_col5\" class=\"data row11 col5\" >0.0765</td>\n",
              "      <td id=\"T_84411_row11_col6\" class=\"data row11 col6\" >0.0830</td>\n",
              "      <td id=\"T_84411_row11_col7\" class=\"data row11 col7\" >11.2620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84411_level0_row12\" class=\"row_heading level0 row12\" >par</th>\n",
              "      <td id=\"T_84411_row12_col0\" class=\"data row12 col0\" >Passive Aggressive Regressor</td>\n",
              "      <td id=\"T_84411_row12_col1\" class=\"data row12 col1\" >0.2113</td>\n",
              "      <td id=\"T_84411_row12_col2\" class=\"data row12 col2\" >0.0920</td>\n",
              "      <td id=\"T_84411_row12_col3\" class=\"data row12 col3\" >0.3003</td>\n",
              "      <td id=\"T_84411_row12_col4\" class=\"data row12 col4\" >0.7106</td>\n",
              "      <td id=\"T_84411_row12_col5\" class=\"data row12 col5\" >0.0902</td>\n",
              "      <td id=\"T_84411_row12_col6\" class=\"data row12 col6\" >0.1047</td>\n",
              "      <td id=\"T_84411_row12_col7\" class=\"data row12 col7\" >0.0860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84411_level0_row13\" class=\"row_heading level0 row13\" >dt</th>\n",
              "      <td id=\"T_84411_row13_col0\" class=\"data row13 col0\" >Decision Tree Regressor</td>\n",
              "      <td id=\"T_84411_row13_col1\" class=\"data row13 col1\" >0.1869</td>\n",
              "      <td id=\"T_84411_row13_col2\" class=\"data row13 col2\" >0.1029</td>\n",
              "      <td id=\"T_84411_row13_col3\" class=\"data row13 col3\" >0.3206</td>\n",
              "      <td id=\"T_84411_row13_col4\" class=\"data row13 col4\" >0.6763</td>\n",
              "      <td id=\"T_84411_row13_col5\" class=\"data row13 col5\" >0.0984</td>\n",
              "      <td id=\"T_84411_row13_col6\" class=\"data row13 col6\" >0.0996</td>\n",
              "      <td id=\"T_84411_row13_col7\" class=\"data row13 col7\" >0.3900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84411_level0_row14\" class=\"row_heading level0 row14\" >ada</th>\n",
              "      <td id=\"T_84411_row14_col0\" class=\"data row14 col0\" >AdaBoost Regressor</td>\n",
              "      <td id=\"T_84411_row14_col1\" class=\"data row14 col1\" >0.2250</td>\n",
              "      <td id=\"T_84411_row14_col2\" class=\"data row14 col2\" >0.1062</td>\n",
              "      <td id=\"T_84411_row14_col3\" class=\"data row14 col3\" >0.3228</td>\n",
              "      <td id=\"T_84411_row14_col4\" class=\"data row14 col4\" >0.6663</td>\n",
              "      <td id=\"T_84411_row14_col5\" class=\"data row14 col5\" >0.0958</td>\n",
              "      <td id=\"T_84411_row14_col6\" class=\"data row14 col6\" >0.1229</td>\n",
              "      <td id=\"T_84411_row14_col7\" class=\"data row14 col7\" >1.1900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84411_level0_row15\" class=\"row_heading level0 row15\" >lasso</th>\n",
              "      <td id=\"T_84411_row15_col0\" class=\"data row15 col0\" >Lasso Regression</td>\n",
              "      <td id=\"T_84411_row15_col1\" class=\"data row15 col1\" >0.4388</td>\n",
              "      <td id=\"T_84411_row15_col2\" class=\"data row15 col2\" >0.3180</td>\n",
              "      <td id=\"T_84411_row15_col3\" class=\"data row15 col3\" >0.5639</td>\n",
              "      <td id=\"T_84411_row15_col4\" class=\"data row15 col4\" >-0.0003</td>\n",
              "      <td id=\"T_84411_row15_col5\" class=\"data row15 col5\" >0.1613</td>\n",
              "      <td id=\"T_84411_row15_col6\" class=\"data row15 col6\" >0.2096</td>\n",
              "      <td id=\"T_84411_row15_col7\" class=\"data row15 col7\" >0.0610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84411_level0_row16\" class=\"row_heading level0 row16\" >en</th>\n",
              "      <td id=\"T_84411_row16_col0\" class=\"data row16 col0\" >Elastic Net</td>\n",
              "      <td id=\"T_84411_row16_col1\" class=\"data row16 col1\" >0.4388</td>\n",
              "      <td id=\"T_84411_row16_col2\" class=\"data row16 col2\" >0.3180</td>\n",
              "      <td id=\"T_84411_row16_col3\" class=\"data row16 col3\" >0.5639</td>\n",
              "      <td id=\"T_84411_row16_col4\" class=\"data row16 col4\" >-0.0003</td>\n",
              "      <td id=\"T_84411_row16_col5\" class=\"data row16 col5\" >0.1613</td>\n",
              "      <td id=\"T_84411_row16_col6\" class=\"data row16 col6\" >0.2096</td>\n",
              "      <td id=\"T_84411_row16_col7\" class=\"data row16 col7\" >0.0550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84411_level0_row17\" class=\"row_heading level0 row17\" >llar</th>\n",
              "      <td id=\"T_84411_row17_col0\" class=\"data row17 col0\" >Lasso Least Angle Regression</td>\n",
              "      <td id=\"T_84411_row17_col1\" class=\"data row17 col1\" >0.4388</td>\n",
              "      <td id=\"T_84411_row17_col2\" class=\"data row17 col2\" >0.3180</td>\n",
              "      <td id=\"T_84411_row17_col3\" class=\"data row17 col3\" >0.5639</td>\n",
              "      <td id=\"T_84411_row17_col4\" class=\"data row17 col4\" >-0.0003</td>\n",
              "      <td id=\"T_84411_row17_col5\" class=\"data row17 col5\" >0.1613</td>\n",
              "      <td id=\"T_84411_row17_col6\" class=\"data row17 col6\" >0.2096</td>\n",
              "      <td id=\"T_84411_row17_col7\" class=\"data row17 col7\" >0.0360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84411_level0_row18\" class=\"row_heading level0 row18\" >dummy</th>\n",
              "      <td id=\"T_84411_row18_col0\" class=\"data row18 col0\" >Dummy Regressor</td>\n",
              "      <td id=\"T_84411_row18_col1\" class=\"data row18 col1\" >0.4388</td>\n",
              "      <td id=\"T_84411_row18_col2\" class=\"data row18 col2\" >0.3180</td>\n",
              "      <td id=\"T_84411_row18_col3\" class=\"data row18 col3\" >0.5639</td>\n",
              "      <td id=\"T_84411_row18_col4\" class=\"data row18 col4\" >-0.0003</td>\n",
              "      <td id=\"T_84411_row18_col5\" class=\"data row18 col5\" >0.1613</td>\n",
              "      <td id=\"T_84411_row18_col6\" class=\"data row18 col6\" >0.2096</td>\n",
              "      <td id=\"T_84411_row18_col7\" class=\"data row18 col7\" >0.0220</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/81 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c9f9df65397547d4af3b806102fc6205"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ],
      "source": [
        "best = compare_models()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "idj-kulNQm2B",
        "outputId": "fe1afe1f-89f4-4d2a-d054-55cefc8317e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GradientBoostingRegressor(random_state=123)\n"
          ]
        }
      ],
      "source": [
        "print(best)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_model=save_model(best, 'fare_prediction_model')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0i572o_wXYWf",
        "outputId": "037d08cc-381a-48da-f412-394b7752b0c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Transformation Pipeline and Model Successfully Saved\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.download('fare_prediction_model.pkl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "FcsdFhUFXik2",
        "outputId": "652f6c04-fe97-4ea2-be71-3d94b484b38d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_851a812b-15ae-4b52-9a5c-a34fe9b3ed1f\", \"fare_prediction_model.pkl\", 191455)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = predict_model(best, data=df_taxi)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "GrrZgbgKYydH",
        "outputId": "f21a326b-bb2c-4994-841a-cee5f31e3c64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7d1c2e053fa0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_6ce2d\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_6ce2d_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_6ce2d_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
              "      <th id=\"T_6ce2d_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
              "      <th id=\"T_6ce2d_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
              "      <th id=\"T_6ce2d_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
              "      <th id=\"T_6ce2d_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
              "      <th id=\"T_6ce2d_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_6ce2d_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_6ce2d_row0_col0\" class=\"data row0 col0\" >Gradient Boosting Regressor</td>\n",
              "      <td id=\"T_6ce2d_row0_col1\" class=\"data row0 col1\" >0.1259</td>\n",
              "      <td id=\"T_6ce2d_row0_col2\" class=\"data row0 col2\" >0.0466</td>\n",
              "      <td id=\"T_6ce2d_row0_col3\" class=\"data row0 col3\" >0.2159</td>\n",
              "      <td id=\"T_6ce2d_row0_col4\" class=\"data row0 col4\" >0.8523</td>\n",
              "      <td id=\"T_6ce2d_row0_col5\" class=\"data row0 col5\" >0.0657</td>\n",
              "      <td id=\"T_6ce2d_row0_col6\" class=\"data row0 col6\" >0.0733</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions.head()"
      ],
      "metadata": {
        "id": "e76HArqCgU0X",
        "outputId": "dfdf9d3a-dc91-40a3-c79f-46896818c882",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   trip_distance  haversine_distance  month       day  hour  \\\n",
              "0       1.283708            1.605143      1  2.079442    12   \n",
              "1       1.091923            1.152579      1  2.079442    12   \n",
              "2       0.405465            0.647966      1  0.693147     0   \n",
              "3       0.974560            1.154006      2  1.386294    13   \n",
              "4       1.504077            1.530147      1  2.302585    19   \n",
              "\n",
              "   passenger_count_real  fare_amount_real  prediction_label  \n",
              "0              1.791759          2.397895          2.559140  \n",
              "1              0.693147          2.251292          2.387510  \n",
              "2              0.693147          1.609438          1.723909  \n",
              "3              1.791759          2.140066          2.267314  \n",
              "4              0.693147          2.674149          2.732529  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b7c44bb5-3a8e-48ee-a676-4c98aa9f9137\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>trip_distance</th>\n",
              "      <th>haversine_distance</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>hour</th>\n",
              "      <th>passenger_count_real</th>\n",
              "      <th>fare_amount_real</th>\n",
              "      <th>prediction_label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.283708</td>\n",
              "      <td>1.605143</td>\n",
              "      <td>1</td>\n",
              "      <td>2.079442</td>\n",
              "      <td>12</td>\n",
              "      <td>1.791759</td>\n",
              "      <td>2.397895</td>\n",
              "      <td>2.559140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.091923</td>\n",
              "      <td>1.152579</td>\n",
              "      <td>1</td>\n",
              "      <td>2.079442</td>\n",
              "      <td>12</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>2.251292</td>\n",
              "      <td>2.387510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.405465</td>\n",
              "      <td>0.647966</td>\n",
              "      <td>1</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>0</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>1.609438</td>\n",
              "      <td>1.723909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.974560</td>\n",
              "      <td>1.154006</td>\n",
              "      <td>2</td>\n",
              "      <td>1.386294</td>\n",
              "      <td>13</td>\n",
              "      <td>1.791759</td>\n",
              "      <td>2.140066</td>\n",
              "      <td>2.267314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.504077</td>\n",
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