stock_price_predictor.ipynb

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      "source": [
        "!pip install yfinance"
      ],
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting yfinance\n",
            "  Downloading yfinance-0.1.90-py2.py3-none-any.whl (29 kB)\n",
            "Requirement already satisfied: appdirs>=1.4.4 in /usr/local/lib/python3.8/dist-packages (from yfinance) (1.4.4)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.8/dist-packages (from yfinance) (0.0.11)\n",
            "Requirement already satisfied: numpy>=1.16.5 in /usr/local/lib/python3.8/dist-packages (from yfinance) (1.21.6)\n",
            "Requirement already satisfied: pandas>=1.3.0 in /usr/local/lib/python3.8/dist-packages (from yfinance) (1.3.5)\n",
            "Requirement already satisfied: lxml>=4.9.1 in /usr/local/lib/python3.8/dist-packages (from yfinance) (4.9.2)\n",
            "Collecting requests>=2.26\n",
            "  Downloading requests-2.28.1-py3-none-any.whl (62 kB)\n",
            "\u001b[K     |████████████████████████████████| 62 kB 1.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=1.3.0->yfinance) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=1.3.0->yfinance) (2022.6)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas>=1.3.0->yfinance) (1.15.0)\n",
            "Requirement already satisfied: charset-normalizer<3,>=2 in /usr/local/lib/python3.8/dist-packages (from requests>=2.26->yfinance) (2.1.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests>=2.26->yfinance) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests>=2.26->yfinance) (2022.12.7)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests>=2.26->yfinance) (1.24.3)\n",
            "Installing collected packages: requests, yfinance\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
            "Successfully installed requests-2.28.1 yfinance-0.1.90\n"
          ]
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    {
      "cell_type": "code",
      "source": [
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import yfinance as yf      #helps us to download market data from finance\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "l1fwfYrezylE"
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      "execution_count": 2,
      "outputs": []
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      "source": [
        "stocks=input(\"enter the code of the stock: \")\n",
        "data=yf.download(stocks, \"2008-01-01\", \"2021-01-18\")\n",
        "data.head()"
      ],
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          "height": 272
        },
        "id": "yszzy8400NT0",
        "outputId": "e9efab8f-e3c7-4066-a4fc-8143097785cb"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "enter the code of the stock: GC=F\n",
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
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              "                  Open        High         Low       Close   Adj Close  Volume\n",
              "Date                                                                          \n",
              "2008-01-02  848.700012  857.799988  846.400024  857.000000  857.000000     130\n",
              "2008-01-03  863.000000  865.500000  856.299988  866.400024  866.400024     181\n",
              "2008-01-04  861.400024  865.500000  860.200012  863.099976  863.099976      28\n",
              "2008-01-07  860.900024  860.900024  857.400024  859.599976  859.599976       4\n",
              "2008-01-08  861.599976  879.400024  861.599976  878.000000  878.000000      21"
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 3281 entries, 2008-01-02 to 2021-01-15\n",
            "Data columns (total 6 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Open       3281 non-null   float64\n",
            " 1   High       3281 non-null   float64\n",
            " 2   Low        3281 non-null   float64\n",
            " 3   Close      3281 non-null   float64\n",
            " 4   Adj Close  3281 non-null   float64\n",
            " 5   Volume     3281 non-null   int64  \n",
            "dtypes: float64(5), int64(1)\n",
            "memory usage: 179.4 KB\n"
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              "              Open         High          Low        Close    Adj Close  \\\n",
              "count  3281.000000  3281.000000  3281.000000  3281.000000  3281.000000   \n",
              "mean   1316.287291  1323.427614  1308.463700  1316.037885  1316.037885   \n",
              "std     262.732678   264.022456   261.294638   262.754237   262.754237   \n",
              "min     705.000000   715.500000   681.000000   704.900024   704.900024   \n",
              "25%    1183.400024  1189.400024  1176.199951  1183.400024  1183.400024   \n",
              "50%    1282.500000  1287.099976  1277.800049  1282.099976  1282.099976   \n",
              "75%    1486.199951  1491.599976  1477.300049  1484.000000  1484.000000   \n",
              "max    2045.500000  2063.000000  2040.000000  2051.500000  2051.500000   \n",
              "\n",
              "              Volume  \n",
              "count    3281.000000  \n",
              "mean     5606.854008  \n",
              "std     29568.392331  \n",
              "min         0.000000  \n",
              "25%        41.000000  \n",
              "50%       137.000000  \n",
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              "      <th>mean</th>\n",
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              "      <td>262.754237</td>\n",
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              "      <th>min</th>\n",
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              "      <td>0.000000</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>1176.199951</td>\n",
              "      <td>1183.400024</td>\n",
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              "      <th>50%</th>\n",
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              "      <td>1287.099976</td>\n",
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              "      <td>1282.099976</td>\n",
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              "      <td>1491.599976</td>\n",
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              "      <td>1484.000000</td>\n",
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              "      <td>2051.500000</td>\n",
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              "\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-a63cabf6-2b50-4896-af47-c120fd3763c5 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-a63cabf6-2b50-4896-af47-c120fd3763c5');\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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.Close.plot(figsize=(10,7), color='r')  # close is the target variable\n",
        "plt.ylabel(\"{} Prices\".format(stocks))\n",
        "plt.title(\"{} Price series\". format(stocks))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "cT2juHfu0wqg",
        "outputId": "47c83b58-a1c1-48c7-e02b-0c1d7f96b936"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(data[\"Close\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "zvDIOj3K28JY",
        "outputId": "670e77b1-b7f3-4a66-8384-b5a3c25ef14f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8d6d1fe580>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(data[\"Open\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "NZ1Doxr_3SoV",
        "outputId": "593f356a-f0a9-45ca-dfdc-821a0ea88aa9"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8d6d0fe160>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(data[\"High\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "GSC1885r3dct",
        "outputId": "0ef0bb35-0f46-4738-cbe5-e9ea9c964ddc"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8d6d0ba130>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=data.drop(\"Close\", axis=1)\n",
        "y=data[\"Close\"]\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test=train_test_split(x,y,test_size=0.2, random_state=0)  #random state is used to reproduce the problem the same every time it is run\n",
        "print(x_train.shape)\n",
        "print(x_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XycF0Ii75ME2",
        "outputId": "26a5ee34-1492-44f5-8fdb-5bc57d6eb416"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2624, 5)\n",
            "(657, 5)\n",
            "(2624,)\n",
            "(657,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lr= LinearRegression()\n",
        "lr.fit(x_train, y_train)\n",
        "pred1=lr.predict(x_test)"
      ],
      "metadata": {
        "id": "7RFSiRT96IOp"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lI3E1ayN6W-K",
        "outputId": "583dc7bb-5182-45dc-f2fe-3092995e34be"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1728.69995117, 1093.30004883, 1259.69995117, 1107.19995117,\n",
              "       1386.80004883,  901.70001221, 1250.59997559, 1290.09997559,\n",
              "       1209.5       , 1898.59997559, 1155.19995117, 1921.59997559,\n",
              "       1579.30004883, 1218.09997559,  955.79998779, 1721.09997559,\n",
              "       1373.09997559, 1318.69995117,  934.59997559, 1326.5       ,\n",
              "       1312.69995117, 1324.30004883, 1156.30004883, 1060.09997559,\n",
              "       1214.19995117, 1645.5       , 1197.69995117, 1869.90002441,\n",
              "       1648.69995117, 1284.19995117, 1392.59997559, 1277.69995117,\n",
              "        865.40002441, 1613.40002441, 1352.30004883, 1350.19995117,\n",
              "       1320.59997559, 1062.40002441, 1234.90002441, 1166.90002441,\n",
              "       1366.59997559, 1187.90002441, 1290.40002441, 1490.30004883,\n",
              "       1265.59997559, 1293.5       , 1226.59997559, 1183.59997559,\n",
              "       1826.69995117, 1273.69995117, 1380.        , 1352.40002441,\n",
              "       1330.5       , 1656.19995117, 1642.09997559, 1310.69995117,\n",
              "       1104.40002441, 1239.69995117, 1279.59997559,  899.79998779,\n",
              "       1309.69995117, 1296.        , 1117.80004883, 1331.5       ,\n",
              "       1273.40002441, 1077.19995117, 1253.80004883, 1231.40002441,\n",
              "       1167.59997559, 1244.30004883, 1057.30004883, 1232.80004883,\n",
              "       1328.19995117, 1183.        ,  880.59997559, 1342.09997559,\n",
              "       1662.59997559, 1516.69995117, 1182.69995117, 1233.59997559,\n",
              "       1070.5       , 1548.40002441,  904.90002441, 1716.59997559,\n",
              "       1221.19995117, 1664.19995117, 1282.        , 1377.59997559,\n",
              "       1615.        , 1324.19995117, 1306.09997559,  901.29998779,\n",
              "       1508.        , 1331.59997559, 1338.09997559, 1276.19995117,\n",
              "       1512.30004883, 1298.90002441, 1274.09997559, 1273.        ,\n",
              "       1731.80004883, 1225.        , 1308.5       , 1267.40002441,\n",
              "       1247.30004883, 1736.90002441, 1205.09997559, 1288.19995117,\n",
              "       1293.30004883, 1468.80004883,  735.90002441, 1615.5       ,\n",
              "       1323.59997559, 1769.09997559, 1258.09997559, 1142.69995117,\n",
              "       1275.59997559, 1575.19995117, 1455.5       , 1186.09997559,\n",
              "       1206.30004883, 1089.5       , 1227.80004883, 1874.69995117,\n",
              "       1244.09997559, 1418.5       , 1756.69995117,  839.29998779,\n",
              "        927.        , 1403.09997559, 1780.5       ,  855.40002441,\n",
              "       1601.30004883, 1046.40002441, 1309.19995117, 1577.09997559,\n",
              "       1307.80004883, 1725.09997559, 1967.59997559, 1271.90002441,\n",
              "       1232.5       , 1249.19995117, 1110.80004883, 1206.69995117,\n",
              "       1196.59997559, 1732.        , 1242.30004883, 1308.40002441,\n",
              "       1612.90002441, 1256.69995117, 1053.40002441, 1273.69995117,\n",
              "       1088.40002441, 1290.09997559, 1737.80004883, 1272.40002441,\n",
              "       1219.30004883, 1325.90002441, 1203.40002441, 1730.30004883,\n",
              "       1482.30004883, 1612.69995117, 1475.59997559, 1195.5       ,\n",
              "       1721.80004883, 1152.30004883, 1245.90002441, 1312.        ,\n",
              "        732.        , 1189.5       , 1285.69995117, 1265.09997559,\n",
              "       1211.90002441, 1178.19995117, 1764.19995117, 1219.80004883,\n",
              "        954.        , 1446.30004883, 1723.        ,  907.40002441,\n",
              "       1187.19995117, 1919.5       , 1341.30004883, 1123.59997559,\n",
              "       1374.69995117, 1152.19995117, 1798.69995117, 1313.69995117,\n",
              "       1206.90002441,  900.09997559, 1336.40002441, 1900.80004883,\n",
              "       1372.19995117, 1306.30004883, 1305.80004883,  862.70001221,\n",
              "       1337.09997559, 1528.59997559, 1498.40002441, 1392.59997559,\n",
              "       1336.69995117, 1130.09997559,  928.79998779, 1385.5       ,\n",
              "       1224.5       , 1194.69995117, 1888.59997559, 1111.40002441,\n",
              "       1208.09997559, 1104.19995117, 1294.69995117, 1286.80004883,\n",
              "       1646.        , 1717.5       , 1056.19995117, 1221.        ,\n",
              "       1686.59997559, 1187.        , 1336.69995117, 1102.69995117,\n",
              "       1369.5       , 1234.40002441,  895.59997559,  937.70001221,\n",
              "       1241.90002441, 1825.69995117, 1120.40002441, 1298.09997559,\n",
              "       1209.59997559, 1198.30004883, 1452.90002441, 1595.59997559,\n",
              "        913.59997559, 1668.90002441, 1842.40002441, 1290.19995117,\n",
              "       1132.5       , 1269.90002441, 1413.90002441, 1133.09997559,\n",
              "       1492.19995117, 1181.09997559, 1244.5       , 1313.90002441,\n",
              "       1323.5       , 1572.        , 1183.69995117, 1333.80004883,\n",
              "       1638.59997559, 1340.69995117, 1340.30004883, 1287.        ,\n",
              "       1669.40002441, 1244.        , 1271.40002441, 1656.40002441,\n",
              "       1781.30004883,  763.79998779, 1338.        , 1235.40002441,\n",
              "       1153.30004883, 1172.90002441, 1153.09997559, 1499.69995117,\n",
              "        808.20001221,  883.09997559, 1294.69995117,  926.20001221,\n",
              "       1564.5       , 1333.69995117, 1426.09997559, 1352.90002441,\n",
              "        895.59997559, 1194.09997559, 1263.69995117,  892.90002441,\n",
              "       1249.19995117, 1723.19995117, 1305.69995117, 1303.80004883,\n",
              "       1771.09997559, 1613.80004883,  923.        , 1206.19995117,\n",
              "        928.70001221, 1281.        , 1127.90002441,  941.79998779,\n",
              "       1376.69995117, 1608.90002441, 1226.19995117, 1320.80004883,\n",
              "       1331.5       , 1472.59997559, 1274.90002441, 1198.5       ,\n",
              "       1289.09997559, 1097.90002441,  802.70001221, 1249.30004883,\n",
              "       1556.80004883, 1463.09997559, 1727.90002441, 1254.        ,\n",
              "       1954.19995117, 1953.5       ,  964.5       , 1225.30004883,\n",
              "       1594.19995117, 1293.40002441, 1507.30004883, 1208.09997559,\n",
              "       1089.90002441, 1271.90002441, 1226.5       , 1286.90002441,\n",
              "       1272.        ,  716.79998779, 1402.80004883, 1290.59997559,\n",
              "        942.09997559, 1242.19995117, 1728.69995117, 1781.69995117,\n",
              "        890.90002441, 1634.19995117, 1276.19995117, 1774.80004883,\n",
              "        910.5       ,  866.40002441, 1294.30004883, 1513.80004883,\n",
              "       1326.40002441,  878.79998779, 1256.59997559, 1199.09997559,\n",
              "       1710.90002441, 1229.90002441, 1159.69995117, 1342.09997559,\n",
              "        958.29998779, 1564.30004883, 1161.59997559, 1211.        ,\n",
              "       1116.09997559, 1218.59997559, 1077.5       , 1195.30004883,\n",
              "       1287.69995117, 1264.69995117, 1194.30004883, 1245.5       ,\n",
              "       1279.09997559, 1283.        , 1265.40002441, 1215.30004883,\n",
              "       1162.80004883, 1849.59997559, 1306.30004883, 1293.59997559,\n",
              "       1223.30004883, 1897.30004883,  913.59997559, 1730.59997559,\n",
              "       1268.5       ,  927.09997559, 1479.80004883,  819.40002441,\n",
              "       1131.80004883, 1177.90002441,  956.59997559,  992.29998779,\n",
              "       1233.80004883, 1295.09997559, 1168.40002441, 1344.80004883,\n",
              "       1423.40002441, 1217.40002441, 1312.90002441, 1306.30004883,\n",
              "       1311.69995117, 1203.90002441, 1683.40002441, 1514.5       ,\n",
              "       1113.30004883,  965.70001221, 1227.80004883, 1195.5       ,\n",
              "       1267.        , 1385.69995117, 1131.59997559, 1283.09997559,\n",
              "       1551.69995117, 1710.5       , 1492.30004883, 1289.30004883,\n",
              "       1540.09997559, 1639.19995117, 1324.5       , 1317.40002441,\n",
              "       1611.90002441, 1276.80004883, 1290.19995117, 1290.80004883,\n",
              "       1281.        , 1295.59997559, 1574.59997559, 1652.40002441,\n",
              "       1282.        , 1186.90002441, 1852.30004883, 1537.80004883,\n",
              "        957.        ,  885.90002441,  953.20001221, 1320.09997559,\n",
              "       1138.59997559, 1086.69995117, 1772.5       , 1658.59997559,\n",
              "       1285.5       , 1285.80004883,  963.09997559, 1695.80004883,\n",
              "       1088.19995117,  883.59997559,  787.59997559, 1192.59997559,\n",
              "       1526.30004883, 1506.09997559, 1195.        ,  878.        ,\n",
              "       1228.5       , 1255.09997559, 1964.59997559,  745.59997559,\n",
              "       1566.        , 1274.80004883, 1321.69995117, 1195.        ,\n",
              "       1334.69995117, 1294.19995117, 1288.59997559,  903.90002441,\n",
              "       1291.69995117, 1616.59997559, 1107.80004883, 1386.69995117,\n",
              "       1257.59997559, 1054.19995117, 1770.        , 1872.59997559,\n",
              "       1265.5       , 1392.        , 1316.69995117, 1136.30004883,\n",
              "       1174.59997559, 1264.5       , 1200.59997559, 1679.5       ,\n",
              "        997.5       , 1669.30004883, 1379.09997559, 1612.90002441,\n",
              "       1794.09997559, 1356.5       , 1309.90002441, 1757.09997559,\n",
              "       1908.5       , 1200.19995117, 1310.80004883,  922.5       ,\n",
              "       1197.90002441, 1200.        , 1157.59997559, 1333.40002441,\n",
              "       1985.        , 1271.90002441,  880.70001221,  921.59997559,\n",
              "       1572.09997559, 1112.59997559, 1335.40002441, 1135.30004883,\n",
              "       1514.90002441, 1238.69995117, 1713.30004883, 1637.5       ,\n",
              "       1211.30004883, 1225.        , 1791.19995117, 1297.09997559,\n",
              "        995.79998779, 1316.19995117, 1268.        , 1291.        ,\n",
              "       1229.30004883, 1197.5       , 1090.19995117, 1253.80004883,\n",
              "        847.09997559,  925.40002441, 1221.        , 1268.30004883,\n",
              "       1453.40002441, 1283.40002441, 1858.30004883,  905.5       ,\n",
              "       1650.30004883, 1445.59997559, 1467.40002441,  846.59997559,\n",
              "       1282.5       , 1767.69995117, 1633.69995117, 1414.90002441,\n",
              "       1246.80004883, 1281.5       , 1326.09997559, 1342.5       ,\n",
              "       1420.59997559, 1669.59997559, 1223.80004883,  871.5       ,\n",
              "       1303.09997559, 1141.5       , 1132.90002441, 1571.09997559,\n",
              "       1718.90002441, 1192.80004883, 1657.90002441, 1186.80004883,\n",
              "       1222.30004883, 1434.5       , 1195.40002441, 1340.19995117,\n",
              "       1293.69995117, 1233.40002441, 1105.09997559, 1192.59997559,\n",
              "       1474.        , 1075.80004883, 1171.09997559, 1171.5       ,\n",
              "       1191.5       , 1371.19995117, 1608.80004883, 1153.5       ,\n",
              "       1142.30004883, 1790.30004883, 1327.19995117, 1103.5       ,\n",
              "       1174.        , 1118.        , 1279.40002441, 1269.30004883,\n",
              "       1660.        , 1524.5       , 1932.59997559, 1934.40002441,\n",
              "        901.        , 1250.80004883, 1244.80004883, 1272.        ,\n",
              "       1713.        ,  923.59997559, 1313.30004883,  906.5       ,\n",
              "       1944.69995117, 1672.40002441, 1558.30004883, 1245.19995117,\n",
              "       1453.59997559, 1560.59997559, 1714.69995117, 1202.40002441,\n",
              "        820.29998779, 1285.69995117, 1659.09997559, 1307.5       ,\n",
              "       1509.90002441, 1659.        , 1240.        , 1747.        ,\n",
              "       1176.19995117, 1263.90002441, 1415.69995117, 1670.80004883,\n",
              "       1248.19995117, 1213.80004883, 1187.19995117, 1324.19995117,\n",
              "       1134.80004883, 1159.40002441,  818.40002441, 1776.40002441,\n",
              "       1268.40002441, 1564.59997559, 1284.90002441, 1252.40002441,\n",
              "       1077.59997559,  750.5       , 1338.        , 1352.19995117,\n",
              "       1519.5       , 1716.5       , 1229.30004883, 1811.40002441,\n",
              "        957.29998779, 1273.30004883, 1763.        , 1055.40002441,\n",
              "       1415.30004883, 1719.59997559, 1223.69995117, 1076.69995117,\n",
              "        836.40002441, 1156.40002441,  841.70001221, 1247.80004883,\n",
              "       1842.90002441, 1690.40002441, 1324.        , 1339.        ,\n",
              "       1228.90002441, 1229.80004883, 1590.09997559, 1557.80004883,\n",
              "       1850.30004883, 1541.19995117, 1263.19995117, 1343.30004883,\n",
              "        944.70001221, 1700.30004883, 1497.30004883, 1250.30004883,\n",
              "       1313.5       , 1178.        , 1615.        , 1312.        ,\n",
              "       1501.        , 1532.        ,  878.59997559, 1703.        ,\n",
              "       1207.90002441, 1631.        ,  892.79998779, 1671.40002441,\n",
              "       1332.80004883, 1091.5       , 1382.80004883,  882.20001221,\n",
              "       1411.40002441, 1271.80004883, 1385.59997559, 1253.90002441,\n",
              "       1175.69995117])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "\n",
        "def calculate_metrics(y_test, y_pred):\n",
        "  '''\n",
        "  y_test: ground truth\n",
        "  y_pred: model predict\n",
        "  '''\n",
        "  mse=mean_squared_error(y_test, y_pred)\n",
        "  rmse=np.sqrt(mse)\n",
        "  r2_scors=r2_score(y_test, y_pred)\n",
        "  print(\"MSE:\", mse)\n",
        "  print(\"RMSE:\", rmse)\n",
        "  print(\"R2_SCORE\", r2_scors)\n",
        "\n",
        "  "
      ],
      "metadata": {
        "id": "X2zm-exT6mey"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "calculate_metrics(y_test, pred1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QvB0jCxS7zMp",
        "outputId": "414ec3b4-d9bd-4412-e8eb-4765f01ddfb3"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE: 2.0557920865428854e-23\n",
            "RMSE: 4.53408434696895e-12\n",
            "R2_SCORE 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import Lasso, Ridge\n",
        "la=Lasso().fit(x_train, y_train)\n",
        "ri=Ridge().fit(x_train, y_train)\n",
        "la_p=la.predict(x_test)\n",
        "ri_p=ri.predict(x_test)"
      ],
      "metadata": {
        "id": "wASmHHhW75Hd"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "calculate_metrics(y_test, la_p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eCApYMVm8R1V",
        "outputId": "a111b890-e27c-4806-b6a4-ba5e28a985cc"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE: 10.94214147347952\n",
            "RMSE: 3.307890789231035\n",
            "R2_SCORE 0.9998360348053783\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "calculate_metrics(y_test, ri_p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qLXgTC7S8Z7h",
        "outputId": "d164b15e-05f0-4703-f57f-d97f9413e448"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE: 5.7173188703658376e-09\n",
            "RMSE: 7.56129543819433e-05\n",
            "R2_SCORE 0.9999999999999143\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVR\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "svr=SVR()\n",
        "parm_grid={'C': [0.1, 1, 10, 100, 1000], 'gamma': [1,0.1,0.01, 0.001, 0.0001], 'kernel': ['rbf']}\n",
        "grid= GridSearchCV(SVR(), parm_grid, refit=True, verbose=3)\n",
        "grid.fit(x_train, y_train)\n"
      ],
      "metadata": {
        "id": "e1z4hXNY9MRb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8662480e-cf7c-4389-cc22-73f685644c08"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 25 candidates, totalling 125 fits\n",
            "[CV 1/5] END .......C=0.1, gamma=1, kernel=rbf;, score=-0.019 total time=   0.3s\n",
            "[CV 2/5] END .......C=0.1, gamma=1, kernel=rbf;, score=-0.028 total time=   0.3s\n",
            "[CV 3/5] END .......C=0.1, gamma=1, kernel=rbf;, score=-0.003 total time=   0.3s\n",
            "[CV 4/5] END .......C=0.1, gamma=1, kernel=rbf;, score=-0.009 total time=   0.3s\n",
            "[CV 5/5] END .......C=0.1, gamma=1, kernel=rbf;, score=-0.033 total time=   0.3s\n",
            "[CV 1/5] END .....C=0.1, gamma=0.1, kernel=rbf;, score=-0.019 total time=   0.3s\n",
            "[CV 2/5] END .....C=0.1, gamma=0.1, kernel=rbf;, score=-0.028 total time=   0.3s\n",
            "[CV 3/5] END .....C=0.1, gamma=0.1, kernel=rbf;, score=-0.003 total time=   0.3s\n",
            "[CV 4/5] END .....C=0.1, gamma=0.1, kernel=rbf;, score=-0.009 total time=   0.3s\n",
            "[CV 5/5] END .....C=0.1, gamma=0.1, kernel=rbf;, score=-0.033 total time=   0.3s\n",
            "[CV 1/5] END ....C=0.1, gamma=0.01, kernel=rbf;, score=-0.019 total time=   0.3s\n",
            "[CV 2/5] END ....C=0.1, gamma=0.01, kernel=rbf;, score=-0.027 total time=   0.3s\n",
            "[CV 3/5] END ....C=0.1, gamma=0.01, kernel=rbf;, score=-0.003 total time=   0.3s\n",
            "[CV 4/5] END ....C=0.1, gamma=0.01, kernel=rbf;, score=-0.009 total time=   0.3s\n",
            "[CV 5/5] END ....C=0.1, gamma=0.01, kernel=rbf;, score=-0.033 total time=   0.3s\n",
            "[CV 1/5] END ...C=0.1, gamma=0.001, kernel=rbf;, score=-0.017 total time=   0.4s\n",
            "[CV 2/5] END ...C=0.1, gamma=0.001, kernel=rbf;, score=-0.025 total time=   0.4s\n",
            "[CV 3/5] END ...C=0.1, gamma=0.001, kernel=rbf;, score=-0.000 total time=   0.4s\n",
            "[CV 4/5] END ...C=0.1, gamma=0.001, kernel=rbf;, score=-0.006 total time=   0.4s\n",
            "[CV 5/5] END ...C=0.1, gamma=0.001, kernel=rbf;, score=-0.031 total time=   0.4s\n",
            "[CV 1/5] END ..C=0.1, gamma=0.0001, kernel=rbf;, score=-0.003 total time=   0.4s\n",
            "[CV 2/5] END ..C=0.1, gamma=0.0001, kernel=rbf;, score=-0.009 total time=   0.4s\n",
            "[CV 3/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.015 total time=   0.4s\n",
            "[CV 4/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.011 total time=   1.2s\n",
            "[CV 5/5] END ..C=0.1, gamma=0.0001, kernel=rbf;, score=-0.016 total time=   0.7s\n",
            "[CV 1/5] END .........C=1, gamma=1, kernel=rbf;, score=-0.019 total time=   0.3s\n",
            "[CV 2/5] END .........C=1, gamma=1, kernel=rbf;, score=-0.027 total time=   0.3s\n",
            "[CV 3/5] END .........C=1, gamma=1, kernel=rbf;, score=-0.003 total time=   0.3s\n",
            "[CV 4/5] END .........C=1, gamma=1, kernel=rbf;, score=-0.009 total time=   0.3s\n",
            "[CV 5/5] END .........C=1, gamma=1, kernel=rbf;, score=-0.033 total time=   0.3s\n",
            "[CV 1/5] END .......C=1, gamma=0.1, kernel=rbf;, score=-0.019 total time=   0.3s\n",
            "[CV 2/5] END .......C=1, gamma=0.1, kernel=rbf;, score=-0.027 total time=   0.3s\n",
            "[CV 3/5] END .......C=1, gamma=0.1, kernel=rbf;, score=-0.003 total time=   0.3s\n",
            "[CV 4/5] END .......C=1, gamma=0.1, kernel=rbf;, score=-0.009 total time=   0.3s\n",
            "[CV 5/5] END .......C=1, gamma=0.1, kernel=rbf;, score=-0.033 total time=   0.3s\n",
            "[CV 1/5] END ......C=1, gamma=0.01, kernel=rbf;, score=-0.018 total time=   0.3s\n",
            "[CV 2/5] END ......C=1, gamma=0.01, kernel=rbf;, score=-0.027 total time=   0.4s\n",
            "[CV 3/5] END ......C=1, gamma=0.01, kernel=rbf;, score=-0.001 total time=   0.3s\n",
            "[CV 4/5] END ......C=1, gamma=0.01, kernel=rbf;, score=-0.007 total time=   0.3s\n",
            "[CV 5/5] END ......C=1, gamma=0.01, kernel=rbf;, score=-0.032 total time=   0.4s\n",
            "[CV 1/5] END .....C=1, gamma=0.001, kernel=rbf;, score=-0.000 total time=   0.4s\n",
            "[CV 2/5] END .....C=1, gamma=0.001, kernel=rbf;, score=-0.007 total time=   0.4s\n",
            "[CV 3/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.019 total time=   0.4s\n",
            "[CV 4/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.015 total time=   0.4s\n",
            "[CV 5/5] END .....C=1, gamma=0.001, kernel=rbf;, score=-0.012 total time=   0.4s\n",
            "[CV 1/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.119 total time=   0.4s\n",
            "[CV 2/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.127 total time=   0.4s\n",
            "[CV 3/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.138 total time=   0.4s\n",
            "[CV 4/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.145 total time=   0.4s\n",
            "[CV 5/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.112 total time=   0.4s\n",
            "[CV 1/5] END ........C=10, gamma=1, kernel=rbf;, score=-0.020 total time=   0.3s\n",
            "[CV 2/5] END ........C=10, gamma=1, kernel=rbf;, score=-0.028 total time=   0.3s\n",
            "[CV 3/5] END ........C=10, gamma=1, kernel=rbf;, score=-0.003 total time=   0.3s\n",
            "[CV 4/5] END ........C=10, gamma=1, kernel=rbf;, score=-0.009 total time=   0.3s\n",
            "[CV 5/5] END ........C=10, gamma=1, kernel=rbf;, score=-0.032 total time=   0.3s\n",
            "[CV 1/5] END ......C=10, gamma=0.1, kernel=rbf;, score=-0.020 total time=   0.3s\n",
            "[CV 2/5] END ......C=10, gamma=0.1, kernel=rbf;, score=-0.028 total time=   0.3s\n",
            "[CV 3/5] END ......C=10, gamma=0.1, kernel=rbf;, score=-0.002 total time=   0.3s\n",
            "[CV 4/5] END ......C=10, gamma=0.1, kernel=rbf;, score=-0.009 total time=   0.3s\n",
            "[CV 5/5] END ......C=10, gamma=0.1, kernel=rbf;, score=-0.033 total time=   0.3s\n",
            "[CV 1/5] END .....C=10, gamma=0.01, kernel=rbf;, score=-0.006 total time=   0.4s\n",
            "[CV 2/5] END .....C=10, gamma=0.01, kernel=rbf;, score=-0.015 total time=   0.3s\n",
            "[CV 3/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.013 total time=   0.3s\n",
            "[CV 4/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.005 total time=   0.3s\n",
            "[CV 5/5] END .....C=10, gamma=0.01, kernel=rbf;, score=-0.020 total time=   0.3s\n",
            "[CV 1/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.144 total time=   0.4s\n",
            "[CV 2/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.146 total time=   0.4s\n",
            "[CV 3/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.159 total time=   0.4s\n",
            "[CV 4/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.164 total time=   0.4s\n",
            "[CV 5/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.130 total time=   0.4s\n",
            "[CV 1/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.553 total time=   0.4s\n",
            "[CV 2/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.626 total time=   0.4s\n",
            "[CV 3/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.597 total time=   0.5s\n",
            "[CV 4/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.597 total time=   0.4s\n",
            "[CV 5/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.566 total time=   0.4s\n",
            "[CV 1/5] END .......C=100, gamma=1, kernel=rbf;, score=-0.016 total time=   0.4s\n",
            "[CV 2/5] END .......C=100, gamma=1, kernel=rbf;, score=-0.025 total time=   0.4s\n",
            "[CV 3/5] END .......C=100, gamma=1, kernel=rbf;, score=-0.001 total time=   0.4s\n",
            "[CV 4/5] END .......C=100, gamma=1, kernel=rbf;, score=-0.006 total time=   0.4s\n",
            "[CV 5/5] END .......C=100, gamma=1, kernel=rbf;, score=-0.027 total time=   0.4s\n",
            "[CV 1/5] END .....C=100, gamma=0.1, kernel=rbf;, score=-0.010 total time=   0.4s\n",
            "[CV 2/5] END .....C=100, gamma=0.1, kernel=rbf;, score=-0.022 total time=   0.4s\n",
            "[CV 3/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.004 total time=   0.4s\n",
            "[CV 4/5] END .....C=100, gamma=0.1, kernel=rbf;, score=-0.004 total time=   0.4s\n",
            "[CV 5/5] END .....C=100, gamma=0.1, kernel=rbf;, score=-0.025 total time=   0.4s\n",
            "[CV 1/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.097 total time=   0.5s\n",
            "[CV 2/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.080 total time=   0.5s\n",
            "[CV 3/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.104 total time=   0.5s\n",
            "[CV 4/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.086 total time=   0.5s\n",
            "[CV 5/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.072 total time=   0.5s\n",
            "[CV 1/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.496 total time=   1.5s\n",
            "[CV 2/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.560 total time=   1.5s\n",
            "[CV 3/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.540 total time=   1.5s\n",
            "[CV 4/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.537 total time=   1.4s\n",
            "[CV 5/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.504 total time=   1.4s\n",
            "[CV 1/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.805 total time=   2.6s\n",
            "[CV 2/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.882 total time=   2.5s\n",
            "[CV 3/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.871 total time=   2.4s\n",
            "[CV 4/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.830 total time=   3.3s\n",
            "[CV 5/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.845 total time=   3.2s\n",
            "[CV 1/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.000 total time=   0.5s\n",
            "[CV 2/5] END ......C=1000, gamma=1, kernel=rbf;, score=-0.002 total time=   0.6s\n",
            "[CV 3/5] END ......C=1000, gamma=1, kernel=rbf;, score=-0.005 total time=   0.5s\n",
            "[CV 4/5] END ......C=1000, gamma=1, kernel=rbf;, score=-0.002 total time=   0.5s\n",
            "[CV 5/5] END ......C=1000, gamma=1, kernel=rbf;, score=-0.002 total time=   0.6s\n",
            "[CV 1/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.017 total time=   0.5s\n",
            "[CV 2/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.003 total time=   0.6s\n",
            "[CV 3/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.011 total time=   0.6s\n",
            "[CV 4/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.001 total time=   0.6s\n",
            "[CV 5/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.002 total time=   0.5s\n",
            "[CV 1/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.206 total time=   0.8s\n",
            "[CV 2/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.198 total time=   0.7s\n",
            "[CV 3/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.199 total time=   0.8s\n",
            "[CV 4/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.170 total time=   0.7s\n",
            "[CV 5/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.174 total time=   0.7s\n",
            "[CV 1/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.618 total time=   7.0s\n",
            "[CV 2/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.713 total time=   7.0s\n",
            "[CV 3/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.688 total time=   6.7s\n",
            "[CV 4/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.656 total time=   8.3s\n",
            "[CV 5/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.686 total time=   7.7s\n",
            "[CV 1/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.850 total time=  28.1s\n",
            "[CV 2/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.921 total time=  23.8s\n",
            "[CV 3/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.909 total time=  31.3s\n",
            "[CV 4/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.879 total time=  27.8s\n",
            "[CV 5/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.884 total time=  24.9s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(estimator=SVR(),\n",
              "             param_grid={'C': [0.1, 1, 10, 100, 1000],\n",
              "                         'gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
              "                         'kernel': ['rbf']},\n",
              "             verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svr=SVR(C=10, gamma=0.01, kernel='rbf')\n",
        "svr.fit(x_train, y_train)\n",
        "svr_pred=svr.predict(x_test)"
      ],
      "metadata": {
        "id": "oHl2xYStRGDR"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "\n",
        "\n",
        "joblib.dump(ri, 'model.pkl')\n",
        "ridge_from_joblib=model=joblib.load(\"model.pkl\")"
      ],
      "metadata": {
        "id": "rY6BlFYFSFQK"
      },
      "execution_count": 22,
      "outputs": []
    }
  ]
}