Taxi_fare.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.ensemble import AdaBoostRegressor,GradientBoostingRegressor,RandomForestRegressor\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "\n",
        "from sklearn.metrics import classification_report,accuracy_score,confusion_matrix\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "le=LinearRegression()\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
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      "source": [
        "df=pd.read_csv(\"/content/TaxiFare.csv\")"
      ],
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        "df.sample(5)"
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              "      unique_id  amount      date_time_of_pickup  longitude_of_pickup  \\\n",
              "16836   30:00.0    5.30  2010-07-17 21:30:00 UTC           -73.979917   \n",
              "18530   30:00.0   57.33  2014-05-05 14:30:00 UTC           -73.949235   \n",
              "30871   05:00.0    9.30  2010-02-21 01:05:00 UTC           -73.976932   \n",
              "15185   03:00.0    5.50  2013-08-11 11:03:00 UTC           -74.001388   \n",
              "2134    22:55.0   10.50  2013-03-23 01:22:55 UTC           -73.960985   \n",
              "\n",
              "       latitude_of_pickup  longitude_of_dropoff  latitude_of_dropoff  \\\n",
              "16836           40.685000            -73.985908            40.689852   \n",
              "18530           40.741662            -73.974805            40.753860   \n",
              "30871           40.681545            -73.998003            40.718255   \n",
              "15185           40.741182            -73.993420            40.752230   \n",
              "2134            40.760832            -73.984143            40.780242   \n",
              "\n",
              "       no_of_passenger  \n",
              "16836                2  \n",
              "18530                1  \n",
              "30871                3  \n",
              "15185                1  \n",
              "2134                 1  "
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              "  unique_id  amount      date_time_of_pickup  longitude_of_pickup  \\\n",
              "0   26:21.0     4.5  2009-06-15 17:26:21 UTC           -73.844311   \n",
              "1   52:16.0    16.9  2010-01-05 16:52:16 UTC           -74.016048   \n",
              "2   35:00.0     5.7  2011-08-18 00:35:00 UTC           -73.982738   \n",
              "3   30:42.0     7.7  2012-04-21 04:30:42 UTC           -73.987130   \n",
              "4   51:00.0     5.3  2010-03-09 07:51:00 UTC           -73.968095   \n",
              "\n",
              "   latitude_of_pickup  longitude_of_dropoff  latitude_of_dropoff  \\\n",
              "0           40.721319            -73.841610            40.712278   \n",
              "1           40.711303            -73.979268            40.782004   \n",
              "2           40.761270            -73.991242            40.750562   \n",
              "3           40.733143            -73.991567            40.758092   \n",
              "4           40.768008            -73.956655            40.783762   \n",
              "\n",
              "   no_of_passenger  \n",
              "0                1  \n",
              "1                1  \n",
              "2                2  \n",
              "3                1  \n",
              "4                1  "
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              "      unique_id  amount      date_time_of_pickup  longitude_of_pickup  \\\n",
              "49995   25:15.0    15.0  2013-06-12 23:25:15 UTC           -73.999973   \n",
              "49996   19:18.0     7.5  2015-06-22 17:19:18 UTC           -73.984756   \n",
              "49997   53:00.0     6.9  2011-01-30 04:53:00 UTC           -74.002698   \n",
              "49998   09:00.0     4.5  2012-11-06 07:09:00 UTC           -73.946062   \n",
              "49999   13:14.0    10.9  2010-01-13 08:13:14 UTC           -73.932603   \n",
              "\n",
              "       latitude_of_pickup  longitude_of_dropoff  latitude_of_dropoff  \\\n",
              "49995           40.748531            -74.016899            40.705993   \n",
              "49996           40.768211            -73.987366            40.760597   \n",
              "49997           40.739428            -73.998108            40.759483   \n",
              "49998           40.777567            -73.953450            40.779687   \n",
              "49999           40.763805            -73.932603            40.763805   \n",
              "\n",
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              "49995                1  \n",
              "49996                1  \n",
              "49997                1  \n",
              "49998                2  \n",
              "49999                1  "
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            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AOCXRtKV0gfa",
        "outputId": "a67eed2f-a986-4895-809f-cc78d0942d47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.hist(figsize=(10,20))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "UCclsutG0nth",
        "outputId": "4fc06cc3-3232-458a-e316-27358ca70b0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'amount'}>,\n",
              "        <Axes: title={'center': 'longitude_of_pickup'}>],\n",
              "       [<Axes: title={'center': 'latitude_of_pickup'}>,\n",
              "        <Axes: title={'center': 'longitude_of_dropoff'}>],\n",
              "       [<Axes: title={'center': 'latitude_of_dropoff'}>,\n",
              "        <Axes: title={'center': 'no_of_passenger'}>]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x2000 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KPyJFZbA0zRN",
        "outputId": "befe44f0-bb0c-4161-94fe-1859e90f9329"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['unique_id', 'amount', 'date_time_of_pickup', 'longitude_of_pickup',\n",
              "       'latitude_of_pickup', 'longitude_of_dropoff', 'latitude_of_dropoff',\n",
              "       'no_of_passenger'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe(include='all').T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "id": "nKyJcx9l08fJ",
        "outputId": "284b6d79-f211-4f60-bb55-e37ccef3f5c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        count unique                      top freq       mean  \\\n",
              "unique_id               50000   3597                  26:00.0  457        NaN   \n",
              "amount                50000.0    NaN                      NaN  NaN  11.364171   \n",
              "date_time_of_pickup     50000  49555  2011-09-03 01:30:00 UTC    3        NaN   \n",
              "longitude_of_pickup   50000.0    NaN                      NaN  NaN -72.509756   \n",
              "latitude_of_pickup    50000.0    NaN                      NaN  NaN  39.933759   \n",
              "longitude_of_dropoff  50000.0    NaN                      NaN  NaN -72.504616   \n",
              "latitude_of_dropoff   50000.0    NaN                      NaN  NaN  39.926251   \n",
              "no_of_passenger       50000.0    NaN                      NaN  NaN    1.66784   \n",
              "\n",
              "                           std        min        25%        50%        75%  \\\n",
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              "\n",
              "                             max  \n",
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              "amount                     200.0  \n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZCSVFA3-1Cix",
        "outputId": "3573a8b4-1a07-4ef4-e8ac-b224b4a64db6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "unique_id                object\n",
              "amount                  float64\n",
              "date_time_of_pickup      object\n",
              "longitude_of_pickup     float64\n",
              "latitude_of_pickup      float64\n",
              "longitude_of_dropoff    float64\n",
              "latitude_of_dropoff     float64\n",
              "no_of_passenger           int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "81NIErfE3Eqq",
        "outputId": "ee196dd1-c1ae-4d34-bb67-7159e169f91a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 50000 entries, 0 to 49999\n",
            "Data columns (total 8 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   unique_id             50000 non-null  object \n",
            " 1   amount                50000 non-null  float64\n",
            " 2   date_time_of_pickup   50000 non-null  object \n",
            " 3   longitude_of_pickup   50000 non-null  float64\n",
            " 4   latitude_of_pickup    50000 non-null  float64\n",
            " 5   longitude_of_dropoff  50000 non-null  float64\n",
            " 6   latitude_of_dropoff   50000 non-null  float64\n",
            " 7   no_of_passenger       50000 non-null  int64  \n",
            "dtypes: float64(5), int64(1), object(2)\n",
            "memory usage: 3.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
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      "metadata": {
        "colab": {
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        },
        "id": "-JCJoygj3QR2",
        "outputId": "5d6d1f98-604b-42d6-eba1-53e216e4af2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "unique_id               0\n",
              "amount                  0\n",
              "date_time_of_pickup     0\n",
              "longitude_of_pickup     0\n",
              "latitude_of_pickup      0\n",
              "longitude_of_dropoff    0\n",
              "latitude_of_dropoff     0\n",
              "no_of_passenger         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(y=df['amount'])\n",
        "plt.title(\"Amount charged\",color=\"Yellow\")\n",
        "plt.ylabel(\"AMOUNT\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        },
        "id": "QHXE_Z8n3vnR",
        "outputId": "2325a4ed-cdf6-4034-8b7e-9a07ecba07ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=\"amount\",hue=\"no_of_passenger\",data=df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "RUA1FAVlOD2T",
        "outputId": "410673f7-594e-4b1b-e149-b9e2604bfc71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='amount', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Vz5i8lDPron",
        "outputId": "883b1b5c-0c5f-4e3f-dce9-54de12633ddd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "unique_id                object\n",
              "amount                  float64\n",
              "longitude_of_pickup     float64\n",
              "latitude_of_pickup      float64\n",
              "longitude_of_dropoff    float64\n",
              "latitude_of_dropoff     float64\n",
              "no_of_passenger           int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_mat=df.corr()\n",
        "sns.heatmap(correlation_mat,annot=True,linewidth=.5,cmap=\"YlGnBu\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "id": "6H5dHgx3P1s8",
        "outputId": "e9b08521-8f5c-4884-f314-b1f7c3e2d1f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There is a strong relation between longitude of pickup and longitude of dropoff, latitude of pickup and laitude of droppoff"
      ],
      "metadata": {
        "id": "uaT48YADRD3b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "sgmun1qZR-6h",
        "outputId": "d373471b-b1ed-4bf8-f957-9186a4e60aad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             amount  longitude_of_pickup  latitude_of_pickup  \\\n",
              "count  50000.000000         50000.000000        50000.000000   \n",
              "mean      11.364171           -72.509756           39.933759   \n",
              "std        9.685557            10.393860            6.224857   \n",
              "min       -5.000000           -75.423848          -74.006893   \n",
              "25%        6.000000           -73.992062           40.734880   \n",
              "50%        8.500000           -73.981840           40.752678   \n",
              "75%       12.500000           -73.967148           40.767360   \n",
              "max      200.000000            40.783472          401.083332   \n",
              "\n",
              "       longitude_of_dropoff  latitude_of_dropoff  no_of_passenger  \n",
              "count          50000.000000         50000.000000     50000.000000  \n",
              "mean             -72.504616            39.926251         1.667840  \n",
              "std               10.407570             6.014737         1.289195  \n",
              "min              -84.654241           -74.006377         0.000000  \n",
              "25%              -73.991152            40.734372         1.000000  \n",
              "50%              -73.980082            40.753372         1.000000  \n",
              "75%              -73.963584            40.768167         2.000000  \n",
              "max               40.851027            43.415190         6.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b147d571-51cb-4e18-93d1-86155009a101\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>amount</th>\n",
              "      <th>longitude_of_pickup</th>\n",
              "      <th>latitude_of_pickup</th>\n",
              "      <th>longitude_of_dropoff</th>\n",
              "      <th>latitude_of_dropoff</th>\n",
              "      <th>no_of_passenger</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>50000.000000</td>\n",
              "      <td>50000.000000</td>\n",
              "      <td>50000.000000</td>\n",
              "      <td>50000.000000</td>\n",
              "      <td>50000.000000</td>\n",
              "      <td>50000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>11.364171</td>\n",
              "      <td>-72.509756</td>\n",
              "      <td>39.933759</td>\n",
              "      <td>-72.504616</td>\n",
              "      <td>39.926251</td>\n",
              "      <td>1.667840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.685557</td>\n",
              "      <td>10.393860</td>\n",
              "      <td>6.224857</td>\n",
              "      <td>10.407570</td>\n",
              "      <td>6.014737</td>\n",
              "      <td>1.289195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-5.000000</td>\n",
              "      <td>-75.423848</td>\n",
              "      <td>-74.006893</td>\n",
              "      <td>-84.654241</td>\n",
              "      <td>-74.006377</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>-73.992062</td>\n",
              "      <td>40.734880</td>\n",
              "      <td>-73.991152</td>\n",
              "      <td>40.734372</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8.500000</td>\n",
              "      <td>-73.981840</td>\n",
              "      <td>40.752678</td>\n",
              "      <td>-73.980082</td>\n",
              "      <td>40.753372</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>12.500000</td>\n",
              "      <td>-73.967148</td>\n",
              "      <td>40.767360</td>\n",
              "      <td>-73.963584</td>\n",
              "      <td>40.768167</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>40.783472</td>\n",
              "      <td>401.083332</td>\n",
              "      <td>40.851027</td>\n",
              "      <td>43.415190</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b147d571-51cb-4e18-93d1-86155009a101')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b147d571-51cb-4e18-93d1-86155009a101 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-b147d571-51cb-4e18-93d1-86155009a101');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "sns.heatmap(df.isnull(),yticklabels=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "id": "Ni5m7U-FRj9t",
        "outputId": "80fd9558-5a56-493a-db9d-a45cf70e1e82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BBF5rQ-tSoeu",
        "outputId": "990ed1e5-979e-48f4-8132-074bc3aeb672"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 50000 entries, 0 to 49999\n",
            "Data columns (total 7 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   unique_id             50000 non-null  object \n",
            " 1   amount                50000 non-null  float64\n",
            " 2   longitude_of_pickup   50000 non-null  float64\n",
            " 3   latitude_of_pickup    50000 non-null  float64\n",
            " 4   longitude_of_dropoff  50000 non-null  float64\n",
            " 5   latitude_of_dropoff   50000 non-null  float64\n",
            " 6   no_of_passenger       50000 non-null  int64  \n",
            "dtypes: float64(5), int64(1), object(1)\n",
            "memory usage: 2.7+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MpUN5bk1S_pJ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}