Probation.ipynb

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          "output_type": "error",
          "traceback": [
            "\u001b[1;31mRunning cells with 'Python 3.9.6 64-bit' requires ipykernel package.\n",
            "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
            "\u001b[1;31mCommand: 'c:/Users/harsh/AppData/Local/Programs/Python/Python39/python.exe -m pip install ipykernel -U --user --force-reinstall'"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import datetime as dt\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x9OASkNotJw9"
      },
      "outputs": [],
      "source": [
        "data=pd.read_csv('D:\\Hacktoberfest\\ML-Project\\coin_Dogecoin.csv')"
      ]
    },
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              "      <th>SNo</th>\n",
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              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>DOGE</td>\n",
              "      <td>2013-12-16 23:59:59</td>\n",
              "      <td>0.000866</td>\n",
              "      <td>0.000150</td>\n",
              "      <td>0.000299</td>\n",
              "      <td>0.000205</td>\n",
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              "      <td>1.509085e+06</td>\n",
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              "      <td>Dogecoin</td>\n",
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              "      <td>Dogecoin</td>\n",
              "      <td>DOGE</td>\n",
              "      <td>2013-12-19 23:59:59</td>\n",
              "      <td>0.001520</td>\n",
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              "      <td>0.0</td>\n",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Dogecoin</td>\n",
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              "   SNo      Name Symbol  ...     Close  Volume     Marketcap\n",
              "0    1  Dogecoin   DOGE  ...  0.000205     0.0  1.509085e+06\n",
              "1    2  Dogecoin   DOGE  ...  0.000269     0.0  2.169688e+06\n",
              "2    3  Dogecoin   DOGE  ...  0.000362     0.0  3.188943e+06\n",
              "3    4  Dogecoin   DOGE  ...  0.001162     0.0  1.115034e+07\n",
              "4    5  Dogecoin   DOGE  ...  0.000704     0.0  7.284337e+06\n",
              "\n",
              "[5 rows x 10 columns]"
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      "source": [
        "data.head()"
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "data.tail()"
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              "      <td>Dogecoin</td>\n",
              "      <td>DOGE</td>\n",
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              "      <td>Dogecoin</td>\n",
              "      <td>DOGE</td>\n",
              "      <td>2021-07-02 23:59:59</td>\n",
              "      <td>0.247997</td>\n",
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              "      <td>2757</td>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>DOGE</td>\n",
              "      <td>2021-07-03 23:59:59</td>\n",
              "      <td>0.250214</td>\n",
              "      <td>0.242454</td>\n",
              "      <td>0.245106</td>\n",
              "      <td>0.246411</td>\n",
              "      <td>9.170158e+08</td>\n",
              "      <td>3.210491e+10</td>\n",
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              "      <th>2757</th>\n",
              "      <td>2758</td>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>DOGE</td>\n",
              "      <td>2021-07-04 23:59:59</td>\n",
              "      <td>0.252567</td>\n",
              "      <td>0.243425</td>\n",
              "      <td>0.246425</td>\n",
              "      <td>0.246483</td>\n",
              "      <td>9.735115e+08</td>\n",
              "      <td>3.211767e+10</td>\n",
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              "      <th>2758</th>\n",
              "      <td>2759</td>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>DOGE</td>\n",
              "      <td>2021-07-05 23:59:59</td>\n",
              "      <td>0.246419</td>\n",
              "      <td>0.227838</td>\n",
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              "      <td>Dogecoin</td>\n",
              "      <td>DOGE</td>\n",
              "      <td>2021-07-06 23:59:59</td>\n",
              "      <td>0.241910</td>\n",
              "      <td>0.229842</td>\n",
              "      <td>0.231216</td>\n",
              "      <td>0.234422</td>\n",
              "      <td>1.265920e+09</td>\n",
              "      <td>3.055252e+10</td>\n",
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              "       SNo      Name Symbol  ...     Close        Volume     Marketcap\n",
              "0        1  Dogecoin   DOGE  ...  0.000205  0.000000e+00  1.509085e+06\n",
              "1        2  Dogecoin   DOGE  ...  0.000269  0.000000e+00  2.169688e+06\n",
              "2        3  Dogecoin   DOGE  ...  0.000362  0.000000e+00  3.188943e+06\n",
              "3        4  Dogecoin   DOGE  ...  0.001162  0.000000e+00  1.115034e+07\n",
              "4        5  Dogecoin   DOGE  ...  0.000704  0.000000e+00  7.284337e+06\n",
              "...    ...       ...    ...  ...       ...           ...           ...\n",
              "2755  2756  Dogecoin   DOGE  ...  0.245264  1.321471e+09  3.194925e+10\n",
              "2756  2757  Dogecoin   DOGE  ...  0.246411  9.170158e+08  3.210491e+10\n",
              "2757  2758  Dogecoin   DOGE  ...  0.246483  9.735115e+08  3.211767e+10\n",
              "2758  2759  Dogecoin   DOGE  ...  0.231614  1.267949e+09  3.018344e+10\n",
              "2759  2760  Dogecoin   DOGE  ...  0.234422  1.265920e+09  3.055252e+10\n",
              "\n",
              "[2760 rows x 10 columns]"
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              "      <td>DOGE</td>\n",
              "      <td>2021-07-03 23:59:59</td>\n",
              "      <td>0.250214</td>\n",
              "      <td>0.242454</td>\n",
              "      <td>0.245106</td>\n",
              "      <td>0.246411</td>\n",
              "      <td>9.170158e+08</td>\n",
              "      <td>3.210491e+10</td>\n",
              "      <td>2021-07-03</td>\n",
              "      <td>23:59:59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2757</th>\n",
              "      <td>2758</td>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>DOGE</td>\n",
              "      <td>2021-07-04 23:59:59</td>\n",
              "      <td>0.252567</td>\n",
              "      <td>0.243425</td>\n",
              "      <td>0.246425</td>\n",
              "      <td>0.246483</td>\n",
              "      <td>9.735115e+08</td>\n",
              "      <td>3.211767e+10</td>\n",
              "      <td>2021-07-04</td>\n",
              "      <td>23:59:59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2758</th>\n",
              "      <td>2759</td>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>DOGE</td>\n",
              "      <td>2021-07-05 23:59:59</td>\n",
              "      <td>0.246419</td>\n",
              "      <td>0.227838</td>\n",
              "      <td>0.246419</td>\n",
              "      <td>0.231614</td>\n",
              "      <td>1.267949e+09</td>\n",
              "      <td>3.018344e+10</td>\n",
              "      <td>2021-07-05</td>\n",
              "      <td>23:59:59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2759</th>\n",
              "      <td>2760</td>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>DOGE</td>\n",
              "      <td>2021-07-06 23:59:59</td>\n",
              "      <td>0.241910</td>\n",
              "      <td>0.229842</td>\n",
              "      <td>0.231216</td>\n",
              "      <td>0.234422</td>\n",
              "      <td>1.265920e+09</td>\n",
              "      <td>3.055252e+10</td>\n",
              "      <td>2021-07-06</td>\n",
              "      <td>23:59:59</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2760 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       SNo      Name Symbol  ...     Marketcap       Dates      Time\n",
              "0        1  Dogecoin   DOGE  ...  1.509085e+06  2013-12-16  23:59:59\n",
              "1        2  Dogecoin   DOGE  ...  2.169688e+06  2013-12-17  23:59:59\n",
              "2        3  Dogecoin   DOGE  ...  3.188943e+06  2013-12-18  23:59:59\n",
              "3        4  Dogecoin   DOGE  ...  1.115034e+07  2013-12-19  23:59:59\n",
              "4        5  Dogecoin   DOGE  ...  7.284337e+06  2013-12-20  23:59:59\n",
              "...    ...       ...    ...  ...           ...         ...       ...\n",
              "2755  2756  Dogecoin   DOGE  ...  3.194925e+10  2021-07-02  23:59:59\n",
              "2756  2757  Dogecoin   DOGE  ...  3.210491e+10  2021-07-03  23:59:59\n",
              "2757  2758  Dogecoin   DOGE  ...  3.211767e+10  2021-07-04  23:59:59\n",
              "2758  2759  Dogecoin   DOGE  ...  3.018344e+10  2021-07-05  23:59:59\n",
              "2759  2760  Dogecoin   DOGE  ...  3.055252e+10  2021-07-06  23:59:59\n",
              "\n",
              "[2760 rows x 12 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "\n",
        "data['Dates'] = pd.to_datetime(data['Date']).dt.date\n",
        "data['Time'] = pd.to_datetime(data['Date']).dt.time\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "ZuqGS5rkVenw",
        "outputId": "bb576d6c-b68a-4357-fce3-a67400db677f"
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        {
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              "      <td>3.188943e+06</td>\n",
              "      <td>2013-12-18</td>\n",
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              "      <td>2013-12-20 23:59:59</td>\n",
              "      <td>0.001143</td>\n",
              "      <td>0.000662</td>\n",
              "      <td>0.001143</td>\n",
              "      <td>0.000704</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>7.284337e+06</td>\n",
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              "      <td>3.210491e+10</td>\n",
              "      <td>2021-07-03</td>\n",
              "      <td>23:59:59</td>\n",
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              "      <th>2757</th>\n",
              "      <td>2758</td>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>DOGE</td>\n",
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              "      <td>2021-07-05</td>\n",
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              "      <td>3.055252e+10</td>\n",
              "      <td>2021-07-06</td>\n",
              "      <td>23:59:59</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>2760 rows × 12 columns</p>\n",
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            "text/plain": [
              "       sno      name symbol  ...     marketcap       dates      time\n",
              "0        1  Dogecoin   DOGE  ...  1.509085e+06  2013-12-16  23:59:59\n",
              "1        2  Dogecoin   DOGE  ...  2.169688e+06  2013-12-17  23:59:59\n",
              "2        3  Dogecoin   DOGE  ...  3.188943e+06  2013-12-18  23:59:59\n",
              "3        4  Dogecoin   DOGE  ...  1.115034e+07  2013-12-19  23:59:59\n",
              "4        5  Dogecoin   DOGE  ...  7.284337e+06  2013-12-20  23:59:59\n",
              "...    ...       ...    ...  ...           ...         ...       ...\n",
              "2755  2756  Dogecoin   DOGE  ...  3.194925e+10  2021-07-02  23:59:59\n",
              "2756  2757  Dogecoin   DOGE  ...  3.210491e+10  2021-07-03  23:59:59\n",
              "2757  2758  Dogecoin   DOGE  ...  3.211767e+10  2021-07-04  23:59:59\n",
              "2758  2759  Dogecoin   DOGE  ...  3.018344e+10  2021-07-05  23:59:59\n",
              "2759  2760  Dogecoin   DOGE  ...  3.055252e+10  2021-07-06  23:59:59\n",
              "\n",
              "[2760 rows x 12 columns]"
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          "execution_count": 6,
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        "data"
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        {
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              "      <td>1.000000</td>\n",
              "      <td>0.989208</td>\n",
              "      <td>0.665606</td>\n",
              "      <td>0.989258</td>\n",
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              "      <td>0.989208</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.704855</td>\n",
              "      <td>0.999991</td>\n",
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              "    <tr>\n",
              "      <th>volume</th>\n",
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              "      <td>0.726273</td>\n",
              "      <td>0.640496</td>\n",
              "      <td>0.665606</td>\n",
              "      <td>0.704855</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.703837</td>\n",
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              "    <tr>\n",
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              "      <td>0.989258</td>\n",
              "      <td>0.999991</td>\n",
              "      <td>0.703837</td>\n",
              "      <td>1.000000</td>\n",
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              "                sno      high       low  ...     close    volume  marketcap\n",
              "sno        1.000000  0.331338  0.339448  ...  0.335719  0.245079   0.335493\n",
              "high       0.331338  1.000000  0.983996  ...  0.993554  0.726273   0.993496\n",
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              "open       0.334542  0.992309  0.991880  ...  0.989208  0.665606   0.989258\n",
              "close      0.335719  0.993554  0.993411  ...  1.000000  0.704855   0.999991\n",
              "volume     0.245079  0.726273  0.640496  ...  0.704855  1.000000   0.703837\n",
              "marketcap  0.335493  0.993496  0.993519  ...  0.999991  0.703837   1.000000\n",
              "\n",
              "[7 rows x 7 columns]"
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          },
          "execution_count": 7,
          "metadata": {},
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      ],
      "source": [
        "data.corr()"
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    },
    {
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2760 entries, 0 to 2759\n",
            "Data columns (total 12 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   sno        2760 non-null   int64  \n",
            " 1   name       2760 non-null   object \n",
            " 2   symbol     2760 non-null   object \n",
            " 3   date       2760 non-null   object \n",
            " 4   high       2760 non-null   float64\n",
            " 5   low        2760 non-null   float64\n",
            " 6   open       2760 non-null   float64\n",
            " 7   close      2760 non-null   float64\n",
            " 8   volume     2760 non-null   float64\n",
            " 9   marketcap  2760 non-null   float64\n",
            " 10  dates      2760 non-null   object \n",
            " 11  time       2760 non-null   object \n",
            "dtypes: float64(6), int64(1), object(5)\n",
            "memory usage: 258.9+ KB\n"
          ]
        }
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      "source": [
        "data.info()"
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    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        {
          "data": {
            "text/plain": [
              "sno          0\n",
              "name         0\n",
              "symbol       0\n",
              "date         0\n",
              "high         0\n",
              "low          0\n",
              "open         0\n",
              "close        0\n",
              "volume       0\n",
              "marketcap    0\n",
              "dates        0\n",
              "time         0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R1EFrMNZdwuR"
      },
      "outputs": [],
      "source": [
        "data=data.drop(['sno','symbol','date','time'],axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "Pfrd-x09fAdT",
        "outputId": "b90e4c17-31bc-44be-e72b-533795ac80a6"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>open</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>marketcap</th>\n",
              "      <th>dates</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>0.000866</td>\n",
              "      <td>0.000150</td>\n",
              "      <td>0.000299</td>\n",
              "      <td>0.000205</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.509085e+06</td>\n",
              "      <td>2013-12-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>0.000289</td>\n",
              "      <td>0.000116</td>\n",
              "      <td>0.000207</td>\n",
              "      <td>0.000269</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.169688e+06</td>\n",
              "      <td>2013-12-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>0.000362</td>\n",
              "      <td>0.000205</td>\n",
              "      <td>0.000267</td>\n",
              "      <td>0.000362</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.188943e+06</td>\n",
              "      <td>2013-12-18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>0.001520</td>\n",
              "      <td>0.000328</td>\n",
              "      <td>0.000395</td>\n",
              "      <td>0.001162</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.115034e+07</td>\n",
              "      <td>2013-12-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>0.001143</td>\n",
              "      <td>0.000662</td>\n",
              "      <td>0.001143</td>\n",
              "      <td>0.000704</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>7.284337e+06</td>\n",
              "      <td>2013-12-20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2755</th>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>0.247997</td>\n",
              "      <td>0.238848</td>\n",
              "      <td>0.243982</td>\n",
              "      <td>0.245264</td>\n",
              "      <td>1.321471e+09</td>\n",
              "      <td>3.194925e+10</td>\n",
              "      <td>2021-07-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2756</th>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>0.250214</td>\n",
              "      <td>0.242454</td>\n",
              "      <td>0.245106</td>\n",
              "      <td>0.246411</td>\n",
              "      <td>9.170158e+08</td>\n",
              "      <td>3.210491e+10</td>\n",
              "      <td>2021-07-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2757</th>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>0.252567</td>\n",
              "      <td>0.243425</td>\n",
              "      <td>0.246425</td>\n",
              "      <td>0.246483</td>\n",
              "      <td>9.735115e+08</td>\n",
              "      <td>3.211767e+10</td>\n",
              "      <td>2021-07-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2758</th>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>0.246419</td>\n",
              "      <td>0.227838</td>\n",
              "      <td>0.246419</td>\n",
              "      <td>0.231614</td>\n",
              "      <td>1.267949e+09</td>\n",
              "      <td>3.018344e+10</td>\n",
              "      <td>2021-07-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2759</th>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>0.241910</td>\n",
              "      <td>0.229842</td>\n",
              "      <td>0.231216</td>\n",
              "      <td>0.234422</td>\n",
              "      <td>1.265920e+09</td>\n",
              "      <td>3.055252e+10</td>\n",
              "      <td>2021-07-06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2760 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          name      high       low  ...        volume     marketcap       dates\n",
              "0     Dogecoin  0.000866  0.000150  ...  0.000000e+00  1.509085e+06  2013-12-16\n",
              "1     Dogecoin  0.000289  0.000116  ...  0.000000e+00  2.169688e+06  2013-12-17\n",
              "2     Dogecoin  0.000362  0.000205  ...  0.000000e+00  3.188943e+06  2013-12-18\n",
              "3     Dogecoin  0.001520  0.000328  ...  0.000000e+00  1.115034e+07  2013-12-19\n",
              "4     Dogecoin  0.001143  0.000662  ...  0.000000e+00  7.284337e+06  2013-12-20\n",
              "...        ...       ...       ...  ...           ...           ...         ...\n",
              "2755  Dogecoin  0.247997  0.238848  ...  1.321471e+09  3.194925e+10  2021-07-02\n",
              "2756  Dogecoin  0.250214  0.242454  ...  9.170158e+08  3.210491e+10  2021-07-03\n",
              "2757  Dogecoin  0.252567  0.243425  ...  9.735115e+08  3.211767e+10  2021-07-04\n",
              "2758  Dogecoin  0.246419  0.227838  ...  1.267949e+09  3.018344e+10  2021-07-05\n",
              "2759  Dogecoin  0.241910  0.229842  ...  1.265920e+09  3.055252e+10  2021-07-06\n",
              "\n",
              "[2760 rows x 8 columns]"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "BhDm3i5neomF",
        "outputId": "b0e5582c-5c3a-4fd2-99c8-663455282833"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f345c974410>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f345c0d7f10>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f345c09c550>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f345c051a50>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f345c014090>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f345bfc9690>]],\n",
              "      dtype=object)"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1080x1080 with 6 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "data.hist(figsize=(15,15))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "1i0K9zAA_5Z5",
        "outputId": "41b58acf-c99e-44d0-fa5c-de55359d07df"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>open</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>marketcap</th>\n",
              "      <th>dates</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>0.000866</td>\n",
              "      <td>0.000150</td>\n",
              "      <td>0.000299</td>\n",
              "      <td>0.000205</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.509085e+06</td>\n",
              "      <td>2013-12-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>0.000289</td>\n",
              "      <td>0.000116</td>\n",
              "      <td>0.000207</td>\n",
              "      <td>0.000269</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.169688e+06</td>\n",
              "      <td>2013-12-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>0.000362</td>\n",
              "      <td>0.000205</td>\n",
              "      <td>0.000267</td>\n",
              "      <td>0.000362</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.188943e+06</td>\n",
              "      <td>2013-12-18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>0.001520</td>\n",
              "      <td>0.000328</td>\n",
              "      <td>0.000395</td>\n",
              "      <td>0.001162</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.115034e+07</td>\n",
              "      <td>2013-12-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>0.001143</td>\n",
              "      <td>0.000662</td>\n",
              "      <td>0.001143</td>\n",
              "      <td>0.000704</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>7.284337e+06</td>\n",
              "      <td>2013-12-20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2755</th>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>0.247997</td>\n",
              "      <td>0.238848</td>\n",
              "      <td>0.243982</td>\n",
              "      <td>0.245264</td>\n",
              "      <td>1.321471e+09</td>\n",
              "      <td>3.194925e+10</td>\n",
              "      <td>2021-07-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2756</th>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>0.250214</td>\n",
              "      <td>0.242454</td>\n",
              "      <td>0.245106</td>\n",
              "      <td>0.246411</td>\n",
              "      <td>9.170158e+08</td>\n",
              "      <td>3.210491e+10</td>\n",
              "      <td>2021-07-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2757</th>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>0.252567</td>\n",
              "      <td>0.243425</td>\n",
              "      <td>0.246425</td>\n",
              "      <td>0.246483</td>\n",
              "      <td>9.735115e+08</td>\n",
              "      <td>3.211767e+10</td>\n",
              "      <td>2021-07-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2758</th>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>0.246419</td>\n",
              "      <td>0.227838</td>\n",
              "      <td>0.246419</td>\n",
              "      <td>0.231614</td>\n",
              "      <td>1.267949e+09</td>\n",
              "      <td>3.018344e+10</td>\n",
              "      <td>2021-07-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2759</th>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>0.241910</td>\n",
              "      <td>0.229842</td>\n",
              "      <td>0.231216</td>\n",
              "      <td>0.234422</td>\n",
              "      <td>1.265920e+09</td>\n",
              "      <td>3.055252e+10</td>\n",
              "      <td>2021-07-06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2760 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          name      high       low  ...        volume     marketcap       dates\n",
              "0     Dogecoin  0.000866  0.000150  ...  0.000000e+00  1.509085e+06  2013-12-16\n",
              "1     Dogecoin  0.000289  0.000116  ...  0.000000e+00  2.169688e+06  2013-12-17\n",
              "2     Dogecoin  0.000362  0.000205  ...  0.000000e+00  3.188943e+06  2013-12-18\n",
              "3     Dogecoin  0.001520  0.000328  ...  0.000000e+00  1.115034e+07  2013-12-19\n",
              "4     Dogecoin  0.001143  0.000662  ...  0.000000e+00  7.284337e+06  2013-12-20\n",
              "...        ...       ...       ...  ...           ...           ...         ...\n",
              "2755  Dogecoin  0.247997  0.238848  ...  1.321471e+09  3.194925e+10  2021-07-02\n",
              "2756  Dogecoin  0.250214  0.242454  ...  9.170158e+08  3.210491e+10  2021-07-03\n",
              "2757  Dogecoin  0.252567  0.243425  ...  9.735115e+08  3.211767e+10  2021-07-04\n",
              "2758  Dogecoin  0.246419  0.227838  ...  1.267949e+09  3.018344e+10  2021-07-05\n",
              "2759  Dogecoin  0.241910  0.229842  ...  1.265920e+09  3.055252e+10  2021-07-06\n",
              "\n",
              "[2760 rows x 8 columns]"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data = data.drop_duplicates(subset=['high','low'])\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "1ew9VWd9q6OX",
        "outputId": "d455671a-7ade-4aae-8429-357787ef05fa"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f345bdeec10>"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "data.boxplot(column=\"high\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "abE6wIlIrPU7"
      },
      "outputs": [],
      "source": [
        "data=data[data[\"high\"]>0.4]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 855
        },
        "id": "PS9IrNrITHYD",
        "outputId": "989e2215-bd19-41a8-ef26-07c6771bffb0"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2678</th>\n",
              "      <td>0.437700</td>\n",
              "      <td>0.180488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2681</th>\n",
              "      <td>0.431751</td>\n",
              "      <td>0.309080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2682</th>\n",
              "      <td>0.422347</td>\n",
              "      <td>0.271618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2694</th>\n",
              "      <td>0.400029</td>\n",
              "      <td>0.364874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2695</th>\n",
              "      <td>0.445567</td>\n",
              "      <td>0.373631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2696</th>\n",
              "      <td>0.605998</td>\n",
              "      <td>0.400800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2697</th>\n",
              "      <td>0.695262</td>\n",
              "      <td>0.523822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2698</th>\n",
              "      <td>0.663616</td>\n",
              "      <td>0.545102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2699</th>\n",
              "      <td>0.731972</td>\n",
              "      <td>0.520174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2700</th>\n",
              "      <td>0.737567</td>\n",
              "      <td>0.608168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2701</th>\n",
              "      <td>0.697625</td>\n",
              "      <td>0.425706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2702</th>\n",
              "      <td>0.569687</td>\n",
              "      <td>0.421291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2703</th>\n",
              "      <td>0.546651</td>\n",
              "      <td>0.445034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2704</th>\n",
              "      <td>0.519975</td>\n",
              "      <td>0.385376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2705</th>\n",
              "      <td>0.519461</td>\n",
              "      <td>0.357175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2706</th>\n",
              "      <td>0.589690</td>\n",
              "      <td>0.464622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2707</th>\n",
              "      <td>0.562676</td>\n",
              "      <td>0.491299</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2708</th>\n",
              "      <td>0.548145</td>\n",
              "      <td>0.451023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2709</th>\n",
              "      <td>0.515221</td>\n",
              "      <td>0.458551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2710</th>\n",
              "      <td>0.515599</td>\n",
              "      <td>0.470384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2711</th>\n",
              "      <td>0.684735</td>\n",
              "      <td>0.218267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2712</th>\n",
              "      <td>0.433192</td>\n",
              "      <td>0.296253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2713</th>\n",
              "      <td>0.411975</td>\n",
              "      <td>0.314766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2725</th>\n",
              "      <td>0.444590</td>\n",
              "      <td>0.352588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2726</th>\n",
              "      <td>0.440763</td>\n",
              "      <td>0.378819</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2727</th>\n",
              "      <td>0.400982</td>\n",
              "      <td>0.351442</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          high       low\n",
              "2678  0.437700  0.180488\n",
              "2681  0.431751  0.309080\n",
              "2682  0.422347  0.271618\n",
              "2694  0.400029  0.364874\n",
              "2695  0.445567  0.373631\n",
              "2696  0.605998  0.400800\n",
              "2697  0.695262  0.523822\n",
              "2698  0.663616  0.545102\n",
              "2699  0.731972  0.520174\n",
              "2700  0.737567  0.608168\n",
              "2701  0.697625  0.425706\n",
              "2702  0.569687  0.421291\n",
              "2703  0.546651  0.445034\n",
              "2704  0.519975  0.385376\n",
              "2705  0.519461  0.357175\n",
              "2706  0.589690  0.464622\n",
              "2707  0.562676  0.491299\n",
              "2708  0.548145  0.451023\n",
              "2709  0.515221  0.458551\n",
              "2710  0.515599  0.470384\n",
              "2711  0.684735  0.218267\n",
              "2712  0.433192  0.296253\n",
              "2713  0.411975  0.314766\n",
              "2725  0.444590  0.352588\n",
              "2726  0.440763  0.378819\n",
              "2727  0.400982  0.351442"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x=pd.DataFrame(data,columns=['high','low'])\n",
        "x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vrNd9LNhW-86"
      },
      "outputs": [],
      "source": [
        "y=pd.DataFrame(data,columns=['marketcap'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "49DdKua5qYbE"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XuRcM-z6XPdI",
        "outputId": "0a9896b5-e917-455e-9270-47903667d092"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "         marketcap\n",
            "2700  8.242978e+10\n",
            "2713  4.660975e+10\n",
            "2704  4.993290e+10\n",
            "2702  5.829061e+10\n",
            "2696  7.008193e+10\n",
            "2710  6.172964e+10\n",
            "2725  5.497691e+10\n",
            "2712  5.189730e+10\n"
          ]
        }
      ],
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=100)\n",
        "print(y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2o3_raMPZW7r"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JD-N4UxwaCOz",
        "outputId": "9c659c14-b39a-48e2-9df7-b157543acdd4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "regressor=LinearRegression()\n",
        "regressor.fit(x_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WZey8o2OdMQO",
        "outputId": "5a4e797d-1d5e-4454-8b8b-396d0c9e3122"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[8.98e+10 8.24e+10]\n",
            " [4.82e+10 4.66e+10]\n",
            " [5.97e+10 4.99e+10]\n",
            " [6.53e+10 5.83e+10]\n",
            " [6.53e+10 7.01e+10]\n",
            " [6.68e+10 6.17e+10]\n",
            " [5.31e+10 5.50e+10]\n",
            " [4.77e+10 5.19e+10]]\n"
          ]
        }
      ],
      "source": [
        "y_pred=regressor.predict(x_test)\n",
        "np.set_printoptions(precision=2)\n",
        "print(np.concatenate((y_pred.reshape(len(y_pred),1),y_test),1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_3VjJtz0rbK2",
        "outputId": "88e98d4f-5db7-40f4-b4e7-8584ad415a3c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.791020635037468\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import r2_score\n",
        "print(r2_score(y_pred,y_test))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Untitled1.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.9.6 64-bit",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.9.6"
    },
    "vscode": {
      "interpreter": {
        "hash": "e7420c16fed80ca44bc7227413fb390036ba73eba4a8216c7e659bd6f2f9b1f1"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}