{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Orrm23/DeepSeek-Coder/blob/main/14_Stock_prediction_using_SVM_REGRESSION.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NdHIE4CSDCp3"
      },
      "source": [
        "# **Day-14 | Stock Prediction using SUPPORT VECTOR REGRESSION**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1NTrKL3eIgZ8"
      },
      "source": [
        "### *Importing Libraries*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ae6Pxuc-CNeu"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6oo4HsbHInXM"
      },
      "source": [
        "### *Load Dataset from Local directory*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w0WCVounIsJ5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "72f5c938-c267-41f9-ee2a-6d6563b5664f"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-0176166e-506d-47a4-b7b8-ce8cc8eee744\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-0176166e-506d-47a4-b7b8-ce8cc8eee744\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving data.csv to data.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NHijCKx7I0k8"
      },
      "source": [
        "### *Load Dataset*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zxBak91bI2yh"
      },
      "source": [
        "dataset = pd.read_csv('data.csv')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5XKSRUPWI5Q-"
      },
      "source": [
        "### *Summarize Dataset*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "63BR2xiKI7oZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7bcfc903-7216-4aa7-c079-b13837f2fb72"
      },
      "source": [
        "print(dataset.shape)\n",
        "print(dataset.head(5))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(94, 2)\n",
            "            x           y\n",
            "0  168.181818  160.840244\n",
            "1  187.878788  159.413657\n",
            "2  207.575758  157.136809\n",
            "3  227.272727  159.357847\n",
            "4  246.969697  157.542862\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zv8cn1g8Jix-"
      },
      "source": [
        "### *Segregate Dataset into Input X & Output Y*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iR3g4pDjJoj9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0374808-f07f-487b-d913-fa21394a2d3d"
      },
      "source": [
        "X = dataset.iloc[:, :-1].values\n",
        "X"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 168.18181818],\n",
              "       [ 187.87878788],\n",
              "       [ 207.57575758],\n",
              "       [ 227.27272727],\n",
              "       [ 246.96969697],\n",
              "       [ 266.66666667],\n",
              "       [ 286.36363636],\n",
              "       [ 306.06060606],\n",
              "       [ 325.75757576],\n",
              "       [ 345.45454545],\n",
              "       [ 365.15151515],\n",
              "       [ 384.84848485],\n",
              "       [ 404.54545455],\n",
              "       [ 424.24242424],\n",
              "       [ 443.93939394],\n",
              "       [ 463.63636364],\n",
              "       [ 483.33333333],\n",
              "       [ 503.03030303],\n",
              "       [ 522.72727273],\n",
              "       [ 542.42424242],\n",
              "       [ 562.12121212],\n",
              "       [ 581.81818182],\n",
              "       [ 601.51515152],\n",
              "       [ 621.21212121],\n",
              "       [ 640.90909091],\n",
              "       [ 660.60606061],\n",
              "       [ 680.3030303 ],\n",
              "       [ 700.        ],\n",
              "       [ 719.6969697 ],\n",
              "       [ 739.39393939],\n",
              "       [ 759.09090909],\n",
              "       [ 778.78787879],\n",
              "       [ 798.48484848],\n",
              "       [ 818.18181818],\n",
              "       [ 837.87878788],\n",
              "       [ 857.57575758],\n",
              "       [ 877.27272727],\n",
              "       [ 896.96969697],\n",
              "       [ 916.66666667],\n",
              "       [ 936.36363636],\n",
              "       [ 956.06060606],\n",
              "       [ 975.75757576],\n",
              "       [ 995.45454545],\n",
              "       [1015.15151515],\n",
              "       [1034.84848485],\n",
              "       [1054.54545455],\n",
              "       [1074.24242424],\n",
              "       [1093.93939394],\n",
              "       [1113.63636364],\n",
              "       [1133.33333333],\n",
              "       [1153.03030303],\n",
              "       [1172.72727273],\n",
              "       [1192.42424242],\n",
              "       [1212.12121212],\n",
              "       [1231.81818182],\n",
              "       [1251.51515152],\n",
              "       [1271.21212121],\n",
              "       [1290.90909091],\n",
              "       [1310.60606061],\n",
              "       [1330.3030303 ],\n",
              "       [1350.        ],\n",
              "       [1369.6969697 ],\n",
              "       [1389.39393939],\n",
              "       [1409.09090909],\n",
              "       [1428.78787879],\n",
              "       [1448.48484848],\n",
              "       [1468.18181818],\n",
              "       [1487.87878788],\n",
              "       [1507.57575758],\n",
              "       [1527.27272727],\n",
              "       [1546.96969697],\n",
              "       [1566.66666667],\n",
              "       [1586.36363636],\n",
              "       [1606.06060606],\n",
              "       [1625.75757576],\n",
              "       [1645.45454545],\n",
              "       [1665.15151515],\n",
              "       [1684.84848485],\n",
              "       [1704.54545455],\n",
              "       [1724.24242424],\n",
              "       [1743.93939394],\n",
              "       [1763.63636364],\n",
              "       [1783.33333333],\n",
              "       [1803.03030303],\n",
              "       [1822.72727273],\n",
              "       [1842.42424242],\n",
              "       [1862.12121212],\n",
              "       [1881.81818182],\n",
              "       [1901.51515152],\n",
              "       [1921.21212121],\n",
              "       [1940.90909091],\n",
              "       [1960.60606061],\n",
              "       [1980.3030303 ],\n",
              "       [2000.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LyJ8ghMFKcMe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1ffa4118-7ce7-47fe-c462-1b63f2830057"
      },
      "source": [
        "Y = dataset.iloc[:, -1].values\n",
        "Y"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([160.84024381, 159.41365734, 157.1368088 , 159.35784736,\n",
              "       157.54286158, 157.73520716, 159.34756091, 155.23404557,\n",
              "       155.80774009, 158.3299704 , 157.62585291, 160.47697951,\n",
              "       158.22940639, 157.41781684, 163.37069148, 160.18481104,\n",
              "       160.96838974, 158.18080666, 160.13850728, 161.6460876 ,\n",
              "       159.31922497, 162.56957785, 160.81387414, 161.62873371,\n",
              "       161.20567768, 166.31061698, 162.77603585, 160.88457814,\n",
              "       164.84205952, 160.95225209, 164.00863628, 159.86853854,\n",
              "       161.32847639, 164.57554065, 165.85572104, 164.91849414,\n",
              "       164.54143071, 164.36748958, 162.20962269, 163.92394795,\n",
              "       164.63932852, 167.87182021, 166.64178203, 162.62543484,\n",
              "       166.99665279, 165.77528998, 165.38858024, 168.16274652,\n",
              "       169.19836268, 169.19589357, 165.85186798, 167.10884798,\n",
              "       168.58676929, 170.07230238, 167.35983334, 168.14383356,\n",
              "       166.49945126, 166.51667766, 170.73111225, 172.01551036,\n",
              "       169.35597976, 171.70403549, 170.61721144, 168.80066958,\n",
              "       171.01067   , 173.56092162, 170.6101661 , 174.00807519,\n",
              "       165.83626737, 172.91653228, 171.64379111, 171.06865197,\n",
              "       172.04715792, 168.08546823, 171.81823198, 173.1687706 ,\n",
              "       175.60730324, 171.81194441, 171.42846734, 172.23891016,\n",
              "       175.27019817, 174.29386586, 172.77381293, 175.0568379 ,\n",
              "       174.42142783, 176.36153241, 173.21710593, 174.16285752,\n",
              "       174.23093521, 172.28509132, 176.00133146, 176.12817115,\n",
              "       175.81325722, 175.53082573])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z5kccmBcw1PQ"
      },
      "source": [
        "### *Splitting Dataset for Testing our Model*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pc4kGEuDw36Q"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=0.20,random_state=0)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XP-L6ZRyL30I"
      },
      "source": [
        "### *Training Dataset using Support Vector Regression*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c4x1C89ZVjr9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "7713001d-363a-449f-8ccb-b224e7435310"
      },
      "source": [
        "from sklearn.svm import SVR\n",
        "model = SVR()\n",
        "model.fit(x_train,y_train)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVR</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVR()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4YOj1wVjerek"
      },
      "source": [
        "### *Prediction for all test data for validation*\n",
        "### *SSres is the sum of squares of the residual errors.*\n",
        "### *SStot is the total sum of the errors.*\n",
        "# ![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mt_Z7EDqWhdB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "869e495f-65a3-43e1-c195-e201aad9217f"
      },
      "source": [
        "ypred = model.predict(x_test)\n",
        "\n",
        "from sklearn.metrics import r2_score,mean_squared_error\n",
        "mse = mean_squared_error(y_test,ypred)\n",
        "rmse=np.sqrt(mse)\n",
        "print(\"Root Mean Square Error:\",rmse)\n",
        "r2score = r2_score(y_test,ypred)\n",
        "print(\"R2Score\",r2score*100)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Root Mean Square Error: 2.3594718844452056\n",
            "R2Score 86.64242653738367\n"
          ]
        }
      ]
    }
  ]
}