Stock_Price_Prediction.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/hussain0048/Projects-/blob/master/Stock_Price_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# **Table of Content**"
      ],
      "metadata": {
        "id": "ERVfMiNK8e4X"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "1.   Import Nessary Library\n",
        "2.   Read Data\n",
        "3.   Preprocessing\n",
        "4.   Data Transformation\n",
        "5.   Handling missing Value\n",
        "6.   Standardizing data\n",
        "7.   Data spliting\n",
        "8.   Build model\n",
        "9.   References\n",
        "\n"
      ],
      "metadata": {
        "id": "23Ni1PX3Mn4R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **1- Import Nessary library**"
      ],
      "metadata": {
        "id": "w2R1VnTF8vCg"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "GBykoGJh8HTV"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **2- Read Data**"
      ],
      "metadata": {
        "id": "oVX-mik98_Tp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install wget"
      ],
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        "colab": {
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        },
        "id": "O-NAA8f1_xm3",
        "outputId": "e10e5797-9bf1-4be1-acab-430d3db2c85c"
      },
      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Collecting wget\n",
            "  Downloading wget-3.2.zip (10 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: wget\n",
            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9656 sha256=ba3212b63af0fc8695d7bcefd446af37755490d7ba2b31ad211c4b39fa56bcec\n",
            "  Stored in directory: /root/.cache/pip/wheels/8b/f1/7f/5c94f0a7a505ca1c81cd1d9208ae2064675d97582078e6c769\n",
            "Successfully built wget\n",
            "Installing collected packages: wget\n",
            "Successfully installed wget-3.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p2Xi0R4K9MRD",
        "outputId": "8d9ac339-d546-4158-f5c0-1234b826af90"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!rm markets.csv\n",
        "!wget https://raw.githubusercontent.com/hussain0048/Projects-/master/stock_data.csv -O stock_data.csv\n",
        "\n",
        "df = pd.read_csv(\"stock_data.csv\", sep=\",\", header=0)"
      ],
      "metadata": {
        "id": "XgGWKd99_Y5w",
        "outputId": "58f8c7c5-4a5d-4aef-f628-87e608699b57",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rm: cannot remove 'markets.csv': No such file or directory\n",
            "--2024-04-20 03:25:53--  https://raw.githubusercontent.com/hussain0048/Projects-/master/stock_data.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1185839 (1.1M) [text/plain]\n",
            "Saving to: ‘stock_data.csv’\n",
            "\n",
            "stock_data.csv      100%[===================>]   1.13M  --.-KB/s    in 0.05s   \n",
            "\n",
            "2024-04-20 03:25:54 (21.7 MB/s) - ‘stock_data.csv’ saved [1185839/1185839]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **3- Preprocessing**"
      ],
      "metadata": {
        "id": "fq1zzNnNAQwe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cols = [1,2,3,4,5,7]"
      ],
      "metadata": {
        "id": "jIppq9whAc7_"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[df.columns[cols]]"
      ],
      "metadata": {
        "id": "fO6E4xU0Ag_u"
      },
      "execution_count": 5,
      "outputs": []
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      "execution_count": null,
      "outputs": [
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              "            Open       High        Low      Close    Volume Stock\n",
              "0        0.42388    0.42902    0.41874    0.42388  23220030  AAPL\n",
              "1        0.42388    0.42516    0.41366    0.42134  18022532  AAPL\n",
              "2        0.42516    0.43668    0.42516    0.42902  42498199  AAPL\n",
              "3        0.42902    0.43157    0.41618    0.41618  37125801  AAPL\n",
              "4        0.43927    0.44052    0.43927    0.43927  57822062  AAPL\n",
              "...          ...        ...        ...        ...       ...   ...\n",
              "19581  178.56000  180.45000  178.31000  180.17000  13275578    FB\n",
              "19582  180.50000  180.74800  179.40300  180.25000  12903836    FB\n",
              "19583  179.79000  180.35000  179.11000  179.56000  10467606    FB\n",
              "19584  178.31000  179.40000  177.09000  179.30000  12602188    FB\n",
              "19585  178.35000  179.10000  177.96000  178.46000  11060355    FB\n",
              "\n",
              "[19586 rows x 6 columns]"
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              "      <td>12602188</td>\n",
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              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c0b3f911-0f17-430f-9ec7-892398a4a809 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_e3428d1a-0b21-4e18-a0a5-5309c7af0a2e\">\n",
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              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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",
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              "      [theme=dark] .colab-df-generate:hover {\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#id_e3428d1a-0b21-4e18-a0a5-5309c7af0a2e button.colab-df-generate');\n",
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              "        google.colab.notebook.generateWithVariable('df');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 19586,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 59.492049291899725,\n        \"min\": 0.0672,\n        \"max\": 386.69,\n        \"num_unique_values\": 10503,\n        \"samples\": [\n          0.44309,\n          21.109,\n          307.435\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 60.28779740150971,\n        \"min\": 0.0672,\n        \"max\": 389.61,\n        \"num_unique_values\": 10494,\n        \"samples\": [\n          20.134,\n          35.123000000000005,\n          5.9035\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 58.62254806865709,\n        \"min\": 0.0672,\n        \"max\": 379.345,\n        \"num_unique_values\": 10534,\n        \"samples\": [\n          91.37,\n          22.106,\n          5.8036\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 59.480058471394834,\n        \"min\": 0.0672,\n        \"max\": 385.0,\n        \"num_unique_values\": 10717,\n        \"samples\": [\n          99.37,\n          8.9474,\n          1.68\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 79823661,\n        \"min\": 0,\n        \"max\": 2069769775,\n        \"num_unique_values\": 19062,\n        \"samples\": [\n          18514274,\n          45652779,\n          19572515\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stock\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"TSLA\",\n          \"FB\",\n          \"AAPL\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=df.drop(columns=['Close'])"
      ],
      "metadata": {
        "id": "za2FNmuSArDu"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "3heEMbiPAvL2",
        "outputId": "5c19bf5a-852c-4492-b300-5c2a1c83efdf"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Open       High        Low    Volume Stock\n",
              "0        0.42388    0.42902    0.41874  23220030  AAPL\n",
              "1        0.42388    0.42516    0.41366  18022532  AAPL\n",
              "2        0.42516    0.43668    0.42516  42498199  AAPL\n",
              "3        0.42902    0.43157    0.41618  37125801  AAPL\n",
              "4        0.43927    0.44052    0.43927  57822062  AAPL\n",
              "...          ...        ...        ...       ...   ...\n",
              "19581  178.56000  180.45000  178.31000  13275578    FB\n",
              "19582  180.50000  180.74800  179.40300  12903836    FB\n",
              "19583  179.79000  180.35000  179.11000  10467606    FB\n",
              "19584  178.31000  179.40000  177.09000  12602188    FB\n",
              "19585  178.35000  179.10000  177.96000  11060355    FB\n",
              "\n",
              "[19586 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a64458be-2f99-4a3c-8c06-2d98357632f8\" class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Stock</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.42388</td>\n",
              "      <td>0.42902</td>\n",
              "      <td>0.41874</td>\n",
              "      <td>23220030</td>\n",
              "      <td>AAPL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.42388</td>\n",
              "      <td>0.42516</td>\n",
              "      <td>0.41366</td>\n",
              "      <td>18022532</td>\n",
              "      <td>AAPL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.42516</td>\n",
              "      <td>0.43668</td>\n",
              "      <td>0.42516</td>\n",
              "      <td>42498199</td>\n",
              "      <td>AAPL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.42902</td>\n",
              "      <td>0.43157</td>\n",
              "      <td>0.41618</td>\n",
              "      <td>37125801</td>\n",
              "      <td>AAPL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.43927</td>\n",
              "      <td>0.44052</td>\n",
              "      <td>0.43927</td>\n",
              "      <td>57822062</td>\n",
              "      <td>AAPL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19581</th>\n",
              "      <td>178.56000</td>\n",
              "      <td>180.45000</td>\n",
              "      <td>178.31000</td>\n",
              "      <td>13275578</td>\n",
              "      <td>FB</td>\n",
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              "    <tr>\n",
              "      <th>19582</th>\n",
              "      <td>180.50000</td>\n",
              "      <td>180.74800</td>\n",
              "      <td>179.40300</td>\n",
              "      <td>12903836</td>\n",
              "      <td>FB</td>\n",
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              "    <tr>\n",
              "      <th>19583</th>\n",
              "      <td>179.79000</td>\n",
              "      <td>180.35000</td>\n",
              "      <td>179.11000</td>\n",
              "      <td>10467606</td>\n",
              "      <td>FB</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19584</th>\n",
              "      <td>178.31000</td>\n",
              "      <td>179.40000</td>\n",
              "      <td>177.09000</td>\n",
              "      <td>12602188</td>\n",
              "      <td>FB</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19585</th>\n",
              "      <td>178.35000</td>\n",
              "      <td>179.10000</td>\n",
              "      <td>177.96000</td>\n",
              "      <td>11060355</td>\n",
              "      <td>FB</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19586 rows × 5 columns</p>\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cols = [3]\n",
        "Y = df[df.columns[cols]]"
      ],
      "metadata": {
        "id": "SCNmfe76A4Jn"
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      "execution_count": 8,
      "outputs": []
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    {
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        "Y"
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      "execution_count": 9,
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              "      border-color: transparent;\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "Y",
              "summary": "{\n  \"name\": \"Y\",\n  \"rows\": 19586,\n  \"fields\": [\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 59.480058471394834,\n        \"min\": 0.0672,\n        \"max\": 385.0,\n        \"num_unique_values\": 10717,\n        \"samples\": [\n          99.37,\n          8.9474,\n          1.68\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "X.select_dtypes(include=['object']).columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xAP6m20jCUGf",
        "outputId": "8764638b-6974-4d52-ee43-fce7607d53b3"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Stock'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "I-eSnQW3Cc0H",
        "outputId": "67034102-6667-468c-bae6-e5f7cf08200b"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Open       High        Low    Volume Stock\n",
              "0        0.42388    0.42902    0.41874  23220030  AAPL\n",
              "1        0.42388    0.42516    0.41366  18022532  AAPL\n",
              "2        0.42516    0.43668    0.42516  42498199  AAPL\n",
              "3        0.42902    0.43157    0.41618  37125801  AAPL\n",
              "4        0.43927    0.44052    0.43927  57822062  AAPL\n",
              "...          ...        ...        ...       ...   ...\n",
              "19581  178.56000  180.45000  178.31000  13275578    FB\n",
              "19582  180.50000  180.74800  179.40300  12903836    FB\n",
              "19583  179.79000  180.35000  179.11000  10467606    FB\n",
              "19584  178.31000  179.40000  177.09000  12602188    FB\n",
              "19585  178.35000  179.10000  177.96000  11060355    FB\n",
              "\n",
              "[19586 rows x 5 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Stock</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.42388</td>\n",
              "      <td>0.42902</td>\n",
              "      <td>0.41874</td>\n",
              "      <td>23220030</td>\n",
              "      <td>AAPL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.42388</td>\n",
              "      <td>0.42516</td>\n",
              "      <td>0.41366</td>\n",
              "      <td>18022532</td>\n",
              "      <td>AAPL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.42516</td>\n",
              "      <td>0.43668</td>\n",
              "      <td>0.42516</td>\n",
              "      <td>42498199</td>\n",
              "      <td>AAPL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.42902</td>\n",
              "      <td>0.43157</td>\n",
              "      <td>0.41618</td>\n",
              "      <td>37125801</td>\n",
              "      <td>AAPL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.43927</td>\n",
              "      <td>0.44052</td>\n",
              "      <td>0.43927</td>\n",
              "      <td>57822062</td>\n",
              "      <td>AAPL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19581</th>\n",
              "      <td>178.56000</td>\n",
              "      <td>180.45000</td>\n",
              "      <td>178.31000</td>\n",
              "      <td>13275578</td>\n",
              "      <td>FB</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19582</th>\n",
              "      <td>180.50000</td>\n",
              "      <td>180.74800</td>\n",
              "      <td>179.40300</td>\n",
              "      <td>12903836</td>\n",
              "      <td>FB</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19583</th>\n",
              "      <td>179.79000</td>\n",
              "      <td>180.35000</td>\n",
              "      <td>179.11000</td>\n",
              "      <td>10467606</td>\n",
              "      <td>FB</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19584</th>\n",
              "      <td>178.31000</td>\n",
              "      <td>179.40000</td>\n",
              "      <td>177.09000</td>\n",
              "      <td>12602188</td>\n",
              "      <td>FB</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19585</th>\n",
              "      <td>178.35000</td>\n",
              "      <td>179.10000</td>\n",
              "      <td>177.96000</td>\n",
              "      <td>11060355</td>\n",
              "      <td>FB</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19586 rows × 5 columns</p>\n",
              "</div>\n",
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              "        const element = document.querySelector('#df-a03ab3ca-33fd-4d75-9f22-bb7e1940e0cf');\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",
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              "\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X",
              "summary": "{\n  \"name\": \"X\",\n  \"rows\": 19586,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 59.492049291899725,\n        \"min\": 0.0672,\n        \"max\": 386.69,\n        \"num_unique_values\": 10503,\n        \"samples\": [\n          0.44309,\n          21.109,\n          307.435\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 60.28779740150971,\n        \"min\": 0.0672,\n        \"max\": 389.61,\n        \"num_unique_values\": 10494,\n        \"samples\": [\n          20.134,\n          35.123000000000005,\n          5.9035\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 58.62254806865709,\n        \"min\": 0.0672,\n        \"max\": 379.345,\n        \"num_unique_values\": 10534,\n        \"samples\": [\n          91.37,\n          22.106,\n          5.8036\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 79823661,\n        \"min\": 0,\n        \"max\": 2069769775,\n        \"num_unique_values\": 19062,\n        \"samples\": [\n          18514274,\n          45652779,\n          19572515\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stock\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"TSLA\",\n          \"FB\",\n          \"AAPL\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X['Stock'] = X['Stock'].fillna(X['Stock'].mode()[0])"
      ],
      "metadata": {
        "id": "ccyP5zapCa7_"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **4- Data Transformation**"
      ],
      "metadata": {
        "id": "AuRXMUE5CkOO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert categorical features to continuous features with Label Encoding\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "lencoders = {}\n",
        "for col in X.select_dtypes(include=['object']).columns:\n",
        "    lencoders[col] = LabelEncoder()\n",
        "    X[col] = lencoders[col].fit_transform(X[col])"
      ],
      "metadata": {
        "id": "IlwSKrENCwgW"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "KGzFJ2T3C2s5",
        "outputId": "0909f5f1-a487-4579-9515-1ee88e3be997"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Open       High        Low    Volume  Stock\n",
              "0        0.42388    0.42902    0.41874  23220030      0\n",
              "1        0.42388    0.42516    0.41366  18022532      0\n",
              "2        0.42516    0.43668    0.42516  42498199      0\n",
              "3        0.42902    0.43157    0.41618  37125801      0\n",
              "4        0.43927    0.44052    0.43927  57822062      0\n",
              "...          ...        ...        ...       ...    ...\n",
              "19581  178.56000  180.45000  178.31000  13275578      1\n",
              "19582  180.50000  180.74800  179.40300  12903836      1\n",
              "19583  179.79000  180.35000  179.11000  10467606      1\n",
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              "variable_name": "X",
              "summary": "{\n  \"name\": \"X\",\n  \"rows\": 19586,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 59.492049291899725,\n        \"min\": 0.0672,\n        \"max\": 386.69,\n        \"num_unique_values\": 10503,\n        \"samples\": [\n          0.44309,\n          21.109,\n          307.435\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 60.28779740150971,\n        \"min\": 0.0672,\n        \"max\": 389.61,\n        \"num_unique_values\": 10494,\n        \"samples\": [\n          20.134,\n          35.123000000000005,\n          5.9035\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 58.62254806865709,\n        \"min\": 0.0672,\n        \"max\": 379.345,\n        \"num_unique_values\": 10534,\n        \"samples\": [\n          91.37,\n          22.106,\n          5.8036\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 79823661,\n        \"min\": 0,\n        \"max\": 2069769775,\n        \"num_unique_values\": 19062,\n        \"samples\": [\n          18514274,\n          45652779,\n          19572515\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stock\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5- **Handling Missing Values**"
      ],
      "metadata": {
        "id": "hqIOvCHFLZGI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This code performs multiple imputation by chained equations (MICE) to handle missing values in a dataset."
      ],
      "metadata": {
        "id": "lhFelH7CKjcg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings# This imports the Python module for handling warnings.\n",
        "warnings.filterwarnings(\"ignore\")# This line ignores any warnings that might be raised during the execution of the code.\n",
        "# Multiple Imputation by Chained Equations\n",
        "# This line enables the experimental version of the iterative imputer in the scikit-learn library.\n",
        "# Iterative imputation is a technique for imputing missing values by modeling each feature with missing values as a function of other features.\n",
        "from sklearn.experimental import enable_iterative_imputer\n",
        "from sklearn.impute import IterativeImputer\n",
        "MiceImputed = X.copy(deep=True)\n",
        "mice_imputer = IterativeImputer()\n",
        "MiceImputed.iloc[:, :] = mice_imputer.fit_transform(X)"
      ],
      "metadata": {
        "id": "Uw8qxUD-Jyzx"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "MiceImputed"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "66QAQXhRKGXh",
        "outputId": "980280b7-df9e-4509-d2d7-64866317b17b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Open       High        Low    Volume  Stock\n",
              "0        0.42388    0.42902    0.41874  23220030      0\n",
              "1        0.42388    0.42516    0.41366  18022532      0\n",
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              "3        0.42902    0.43157    0.41618  37125801      0\n",
              "4        0.43927    0.44052    0.43927  57822062      0\n",
              "...          ...        ...        ...       ...    ...\n",
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              "19584  178.31000  179.40000  177.09000  12602188      1\n",
              "19585  178.35000  179.10000  177.96000  11060355      1\n",
              "\n",
              "[19586 rows x 5 columns]"
            ],
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Stock</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.42388</td>\n",
              "      <td>0.42902</td>\n",
              "      <td>0.41874</td>\n",
              "      <td>23220030</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.42388</td>\n",
              "      <td>0.42516</td>\n",
              "      <td>0.41366</td>\n",
              "      <td>18022532</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.42516</td>\n",
              "      <td>0.43668</td>\n",
              "      <td>0.42516</td>\n",
              "      <td>42498199</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.42902</td>\n",
              "      <td>0.43157</td>\n",
              "      <td>0.41618</td>\n",
              "      <td>37125801</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.43927</td>\n",
              "      <td>0.44052</td>\n",
              "      <td>0.43927</td>\n",
              "      <td>57822062</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19581</th>\n",
              "      <td>178.56000</td>\n",
              "      <td>180.45000</td>\n",
              "      <td>178.31000</td>\n",
              "      <td>13275578</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19582</th>\n",
              "      <td>180.50000</td>\n",
              "      <td>180.74800</td>\n",
              "      <td>179.40300</td>\n",
              "      <td>12903836</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19583</th>\n",
              "      <td>179.79000</td>\n",
              "      <td>180.35000</td>\n",
              "      <td>179.11000</td>\n",
              "      <td>10467606</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19584</th>\n",
              "      <td>178.31000</td>\n",
              "      <td>179.40000</td>\n",
              "      <td>177.09000</td>\n",
              "      <td>12602188</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19585</th>\n",
              "      <td>178.35000</td>\n",
              "      <td>179.10000</td>\n",
              "      <td>177.96000</td>\n",
              "      <td>11060355</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19586 rows × 5 columns</p>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5c9c27b9-b113-4739-8c84-bdb102d1fe0f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_5e090433-2551-459c-b34c-7311c518005c\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('MiceImputed')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('MiceImputed');\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "MiceImputed",
              "summary": "{\n  \"name\": \"MiceImputed\",\n  \"rows\": 19586,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 59.492049291899725,\n        \"min\": 0.0672,\n        \"max\": 386.69,\n        \"num_unique_values\": 10503,\n        \"samples\": [\n          0.44309,\n          21.109,\n          307.435\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 60.28779740150971,\n        \"min\": 0.0672,\n        \"max\": 389.61,\n        \"num_unique_values\": 10494,\n        \"samples\": [\n          20.134,\n          35.123000000000005,\n          5.9035\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 58.62254806865709,\n        \"min\": 0.0672,\n        \"max\": 379.345,\n        \"num_unique_values\": 10534,\n        \"samples\": [\n          91.37,\n          22.106,\n          5.8036\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 79823661,\n        \"min\": 0,\n        \"max\": 2069769775,\n        \"num_unique_values\": 19062,\n        \"samples\": [\n          18514274,\n          45652779,\n          19572515\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stock\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=MiceImputed"
      ],
      "metadata": {
        "id": "lDAirrgdLViD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **5-Standardizing data**"
      ],
      "metadata": {
        "id": "BttKBQ_VLjFQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "r_scaler = preprocessing.MinMaxScaler()\n",
        "r_scaler.fit(MiceImputed)\n",
        "modified_data = pd.DataFrame(r_scaler.transform(MiceImputed), index=MiceImputed.index, columns=MiceImputed.columns)"
      ],
      "metadata": {
        "id": "7H97gPIXLuLr"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "modified_data\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Mmf7WOuaLyRT",
        "outputId": "3c8831d9-dca2-452b-b5ab-b88c85880452"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Open      High       Low    Volume     Stock\n",
              "0      0.000923  0.000929  0.000927  0.011219  0.000000\n",
              "1      0.000923  0.000919  0.000913  0.008708  0.000000\n",
              "2      0.000926  0.000948  0.000944  0.020533  0.000000\n",
              "3      0.000936  0.000935  0.000920  0.017937  0.000000\n",
              "4      0.000962  0.000958  0.000981  0.027936  0.000000\n",
              "...         ...       ...       ...       ...       ...\n",
              "19581  0.461672  0.463063  0.469953  0.006414  0.333333\n",
              "19582  0.466689  0.463828  0.472835  0.006234  0.333333\n",
              "19583  0.464853  0.462806  0.472062  0.005057  0.333333\n",
              "19584  0.461025  0.460367  0.466737  0.006089  0.333333\n",
              "19585  0.461129  0.459597  0.469030  0.005344  0.333333\n",
              "\n",
              "[19586 rows x 5 columns]"
            ],
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              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Stock</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000923</td>\n",
              "      <td>0.000929</td>\n",
              "      <td>0.000927</td>\n",
              "      <td>0.011219</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000923</td>\n",
              "      <td>0.000919</td>\n",
              "      <td>0.000913</td>\n",
              "      <td>0.008708</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000926</td>\n",
              "      <td>0.000948</td>\n",
              "      <td>0.000944</td>\n",
              "      <td>0.020533</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000936</td>\n",
              "      <td>0.000935</td>\n",
              "      <td>0.000920</td>\n",
              "      <td>0.017937</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000962</td>\n",
              "      <td>0.000958</td>\n",
              "      <td>0.000981</td>\n",
              "      <td>0.027936</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19581</th>\n",
              "      <td>0.461672</td>\n",
              "      <td>0.463063</td>\n",
              "      <td>0.469953</td>\n",
              "      <td>0.006414</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19582</th>\n",
              "      <td>0.466689</td>\n",
              "      <td>0.463828</td>\n",
              "      <td>0.472835</td>\n",
              "      <td>0.006234</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19583</th>\n",
              "      <td>0.464853</td>\n",
              "      <td>0.462806</td>\n",
              "      <td>0.472062</td>\n",
              "      <td>0.005057</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19584</th>\n",
              "      <td>0.461025</td>\n",
              "      <td>0.460367</td>\n",
              "      <td>0.466737</td>\n",
              "      <td>0.006089</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19585</th>\n",
              "      <td>0.461129</td>\n",
              "      <td>0.459597</td>\n",
              "      <td>0.469030</td>\n",
              "      <td>0.005344</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19586 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-15673b9f-66e2-4d8a-a3c1-0f4d89e1bccc')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-15673b9f-66e2-4d8a-a3c1-0f4d89e1bccc 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-15673b9f-66e2-4d8a-a3c1-0f4d89e1bccc');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ecb9e1ca-df32-458d-9bcf-aa0e4608c5a8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ecb9e1ca-df32-458d-9bcf-aa0e4608c5a8')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ecb9e1ca-df32-458d-9bcf-aa0e4608c5a8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_572e5f09-0095-4813-822b-050ecd692689\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('modified_data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_572e5f09-0095-4813-822b-050ecd692689 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('modified_data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "modified_data",
              "summary": "{\n  \"name\": \"modified_data\",\n  \"rows\": 19586,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15387620515887765,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 10503,\n        \"samples\": [\n          0.0009722396092522221,\n          0.05442462265546678,\n          0.7950069163018839\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15476552872113078,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 10494,\n        \"samples\": [\n          0.05151372326737909,\n          0.08999216517414775,\n          0.014982435819632657\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1545636155574017,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 10534,\n        \"samples\": [\n          0.240728036283695,\n          0.05810727651341576,\n          0.01512453404865774\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03856644460846178,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 19062,\n        \"samples\": [\n          0.008945088590831317,\n          0.02205693577683054,\n          0.0094563729920155\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stock\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.36288175730560757,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.0,\n          0.3333333333333333,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **6-Data Spliiting**"
      ],
      "metadata": {
        "id": "ALSpqa7RL4dA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(modified_data, Y, test_size=0.30, random_state=100)\n"
      ],
      "metadata": {
        "id": "YNNohp82L9vb"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **7- Build Model**"
      ],
      "metadata": {
        "id": "HK1LQzkSMCgJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "learner = LinearRegression() #initializing linear regression model\n",
        "learner.fit(X_train,y_train) #training the linear regression model\n",
        "score=learner.score(X_test,y_test)#testing the linear regression model\n"
      ],
      "metadata": {
        "id": "zZrD-dKxMH_e"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oeKrNzgqMSU-",
        "outputId": "48f9ced9-caae-4c8e-dc4f-f272459ae936"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9999142675478361"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **8- Polynomial model**"
      ],
      "metadata": {
        "id": "g3_OaBeYi1yg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.metrics import r2_score"
      ],
      "metadata": {
        "id": "UoXFaBJQpcdS"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create polynomial features\n",
        "poly_features = PolynomialFeatures(degree=2)  # We choose a polynomial of degree 2\n",
        "X_train_poly = poly_features.fit_transform(X_train)\n"
      ],
      "metadata": {
        "id": "utjn3Hvnpl7b"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit the polynomial regression model\n",
        "poly_reg = LinearRegression()\n",
        "poly_reg.fit(X_train_poly, y_train)"
      ],
      "metadata": {
        "id": "Ga40GZG5pqMa",
        "outputId": "6d3c6acc-1985-42e9-9ef5-869d61d92468",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model on the testing set\n",
        "X_test_poly = poly_features.transform(X_test)\n",
        "y_pred = poly_reg.predict(X_test_poly)"
      ],
      "metadata": {
        "id": "FrZovInWpuWK"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the R^2 score\n",
        "score = r2_score(y_test, y_pred)\n",
        "print(\"R^2 Score:\", score)"
      ],
      "metadata": {
        "id": "r3XNmgIspyEK",
        "outputId": "1693083a-a00d-439a-80dd-104987eb14c3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R^2 Score: 0.9999134124444284\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(X_train, y_train_poly)\n",
        "plt.plot(X_train, poly_reg.predict(X_train_poly))\n",
        "\n"
      ],
      "metadata": {
        "id": "3A_kobs7qECv",
        "outputId": "8c752cb3-cc0d-41fc-a9e8-012fee92d5aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 532
        }
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7df7d373bf40>,\n",
              " <matplotlib.lines.Line2D at 0x7df7d373bfa0>,\n",
              " <matplotlib.lines.Line2D at 0x7df7d373bfd0>,\n",
              " <matplotlib.lines.Line2D at 0x7df7d375c100>,\n",
              " <matplotlib.lines.Line2D at 0x7df7d375c1f0>]"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **References**"
      ],
      "metadata": {
        "id": "IskvLk1hMVPW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "1.   [Stock Price Prediction using Machine Learning](https://thecleverprogrammer.com/2020/11/14/stock-price-prediction-using-machine-learning/)\n",
        "\n"
      ],
      "metadata": {
        "id": "bjSw0XYnMaK-"
      }
    }
  ]
}