Gold_Price_Prediction.ipynb

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      "source": [
        "Importing Necessary Libraries"
      ],
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      }
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        "id": "rB7CNXk8HmJB"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn import metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Collection And Processing"
      ],
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        "id": "s0brespbIwHq"
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    {
      "cell_type": "code",
      "source": [
        "#loading csv daat to a Pandas Dataframe\n",
        "goldData = pd.read_csv('/content/gld_price_data.csv')"
      ],
      "metadata": {
        "id": "gwfaLcUbItew"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Print 5 rows in the Daatframe\n",
        "goldData.head()"
      ],
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      "execution_count": 5,
      "outputs": [
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              "       Date          SPX        GLD        USO     SLV   EUR/USD\n",
              "0  1/2/2008  1447.160034  84.860001  78.470001  15.180  1.471692\n",
              "1  1/3/2008  1447.160034  85.570000  78.370003  15.285  1.474491\n",
              "2  1/4/2008  1411.630005  85.129997  77.309998  15.167  1.475492\n",
              "3  1/7/2008  1416.180054  84.769997  75.500000  15.053  1.468299\n",
              "4  1/8/2008  1390.189941  86.779999  76.059998  15.590  1.557099"
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              "      <td>1/2/2008</td>\n",
              "      <td>1447.160034</td>\n",
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              "      <td>15.180</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1/3/2008</td>\n",
              "      <td>1447.160034</td>\n",
              "      <td>85.570000</td>\n",
              "      <td>78.370003</td>\n",
              "      <td>15.285</td>\n",
              "      <td>1.474491</td>\n",
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              "      <th>2</th>\n",
              "      <td>1/4/2008</td>\n",
              "      <td>1411.630005</td>\n",
              "      <td>85.129997</td>\n",
              "      <td>77.309998</td>\n",
              "      <td>15.167</td>\n",
              "      <td>1.475492</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1/7/2008</td>\n",
              "      <td>1416.180054</td>\n",
              "      <td>84.769997</td>\n",
              "      <td>75.500000</td>\n",
              "      <td>15.053</td>\n",
              "      <td>1.468299</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1/8/2008</td>\n",
              "      <td>1390.189941</td>\n",
              "      <td>86.779999</td>\n",
              "      <td>76.059998</td>\n",
              "      <td>15.590</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "goldData",
              "summary": "{\n  \"name\": \"goldData\",\n  \"rows\": 2290,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 2290,\n        \"samples\": [\n          \"11/24/2017\",\n          \"10/24/2013\",\n          \"10/21/2013\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SPX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 519.1115402032954,\n        \"min\": 676.530029,\n        \"max\": 2872.870117,\n        \"num_unique_values\": 2277,\n        \"samples\": [\n          1339.219971,\n          1137.140015,\n          1325.76001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GLD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23.28334574711753,\n        \"min\": 70.0,\n        \"max\": 184.589996,\n        \"num_unique_values\": 1930,\n        \"samples\": [\n          118.279999,\n          120.099998,\n          120.32\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"USO\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.523517029775018,\n        \"min\": 7.96,\n        \"max\": 117.480003,\n        \"num_unique_values\": 1514,\n        \"samples\": [\n          82.290001,\n          53.84,\n          13.22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SLV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.092566374075398,\n        \"min\": 8.85,\n        \"max\": 47.259998,\n        \"num_unique_values\": 1331,\n        \"samples\": [\n          22.889999,\n          14.92,\n          13.37\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EUR/USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13154721612113973,\n        \"min\": 1.039047,\n        \"max\": 1.598798,\n        \"num_unique_values\": 2066,\n        \"samples\": [\n          1.094355,\n          1.185986,\n          1.248003\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "#Print last 5 rows of the dataFrame\n",
        "goldData.tail()"
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            "text/plain": [
              "           Date          SPX         GLD      USO      SLV   EUR/USD\n",
              "2285   5/8/2018  2671.919922  124.589996  14.0600  15.5100  1.186789\n",
              "2286   5/9/2018  2697.790039  124.330002  14.3700  15.5300  1.184722\n",
              "2287  5/10/2018  2723.070068  125.180000  14.4100  15.7400  1.191753\n",
              "2288  5/14/2018  2730.129883  124.489998  14.3800  15.5600  1.193118\n",
              "2289  5/16/2018  2725.780029  122.543800  14.4058  15.4542  1.182033"
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              "      <td>5/10/2018</td>\n",
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              "      <th>2288</th>\n",
              "      <td>5/14/2018</td>\n",
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              "      <th>2289</th>\n",
              "      <td>5/16/2018</td>\n",
              "      <td>2725.780029</td>\n",
              "      <td>122.543800</td>\n",
              "      <td>14.4058</td>\n",
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              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .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",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ca4c46a0-4c27-4358-91eb-bf28cbe2bb6f 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-ca4c46a0-4c27-4358-91eb-bf28cbe2bb6f');\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-28b1137a-d35f-429a-93c8-64e01be6c33a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-28b1137a-d35f-429a-93c8-64e01be6c33a')\"\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",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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-28b1137a-d35f-429a-93c8-64e01be6c33a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Number of Rows and Columns\n",
        "goldData.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VUWNl6eNJn0C",
        "outputId": "80137dcb-1b1a-47c5-a554-c6fadc9023a4"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2290, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Getting Information about Data\n",
        "goldData.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lf7RWnlnJyql",
        "outputId": "48de4436-8ffb-4537-af74-6b7a9f3a032b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2290 entries, 0 to 2289\n",
            "Data columns (total 6 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   Date     2290 non-null   object \n",
            " 1   SPX      2290 non-null   float64\n",
            " 2   GLD      2290 non-null   float64\n",
            " 3   USO      2290 non-null   float64\n",
            " 4   SLV      2290 non-null   float64\n",
            " 5   EUR/USD  2290 non-null   float64\n",
            "dtypes: float64(5), object(1)\n",
            "memory usage: 107.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking number of missing values\n",
        "goldData.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QCs_J3xsJ84j",
        "outputId": "eb34773d-27f3-4f13-f6d6-844ebd461b8a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date       0\n",
              "SPX        0\n",
              "GLD        0\n",
              "USO        0\n",
              "SLV        0\n",
              "EUR/USD    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Statistical Meaures of Data\n",
        "goldData.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "ORGkP2I3KIZH",
        "outputId": "f92c27b8-f437-4147-fa6d-143ef3a664c2"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               SPX          GLD          USO          SLV      EUR/USD\n",
              "count  2290.000000  2290.000000  2290.000000  2290.000000  2290.000000\n",
              "mean   1654.315776   122.732875    31.842221    20.084997     1.283653\n",
              "std     519.111540    23.283346    19.523517     7.092566     0.131547\n",
              "min     676.530029    70.000000     7.960000     8.850000     1.039047\n",
              "25%    1239.874969   109.725000    14.380000    15.570000     1.171313\n",
              "50%    1551.434998   120.580002    33.869999    17.268500     1.303297\n",
              "75%    2073.010070   132.840004    37.827501    22.882500     1.369971\n",
              "max    2872.870117   184.589996   117.480003    47.259998     1.598798"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-908b4be9-8449-49dd-853a-11af36736db7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SPX</th>\n",
              "      <th>GLD</th>\n",
              "      <th>USO</th>\n",
              "      <th>SLV</th>\n",
              "      <th>EUR/USD</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2290.000000</td>\n",
              "      <td>2290.000000</td>\n",
              "      <td>2290.000000</td>\n",
              "      <td>2290.000000</td>\n",
              "      <td>2290.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1654.315776</td>\n",
              "      <td>122.732875</td>\n",
              "      <td>31.842221</td>\n",
              "      <td>20.084997</td>\n",
              "      <td>1.283653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>519.111540</td>\n",
              "      <td>23.283346</td>\n",
              "      <td>19.523517</td>\n",
              "      <td>7.092566</td>\n",
              "      <td>0.131547</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>676.530029</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>7.960000</td>\n",
              "      <td>8.850000</td>\n",
              "      <td>1.039047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1239.874969</td>\n",
              "      <td>109.725000</td>\n",
              "      <td>14.380000</td>\n",
              "      <td>15.570000</td>\n",
              "      <td>1.171313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1551.434998</td>\n",
              "      <td>120.580002</td>\n",
              "      <td>33.869999</td>\n",
              "      <td>17.268500</td>\n",
              "      <td>1.303297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2073.010070</td>\n",
              "      <td>132.840004</td>\n",
              "      <td>37.827501</td>\n",
              "      <td>22.882500</td>\n",
              "      <td>1.369971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2872.870117</td>\n",
              "      <td>184.589996</td>\n",
              "      <td>117.480003</td>\n",
              "      <td>47.259998</td>\n",
              "      <td>1.598798</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-908b4be9-8449-49dd-853a-11af36736db7')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .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",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-908b4be9-8449-49dd-853a-11af36736db7 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-908b4be9-8449-49dd-853a-11af36736db7');\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-c27fea8d-406e-47a8-abb0-bfa63217de3e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c27fea8d-406e-47a8-abb0-bfa63217de3e')\"\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",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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-c27fea8d-406e-47a8-abb0-bfa63217de3e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"goldData\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"SPX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 799.5899257140711,\n        \"min\": 519.1115402032954,\n        \"max\": 2872.870117,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1654.3157764318778,\n          1551.4349975,\n          2290.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GLD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 772.4941009585959,\n        \"min\": 23.28334574711753,\n        \"max\": 2290.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          122.73287507292576,\n          120.580002,\n          2290.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"USO\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 797.0923304678021,\n        \"min\": 7.96,\n        \"max\": 2290.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          31.842220856768563,\n          33.869999,\n          2290.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SLV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 802.7114276824649,\n        \"min\": 7.092566374075398,\n        \"max\": 2290.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          20.084996585152837,\n          17.2685,\n          2290.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EUR/USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 809.2384922213776,\n        \"min\": 0.13154721612113973,\n        \"max\": 2290.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.2836530519650655,\n          1.3032965,\n          2290.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Correlation:\n",
        "1. Positive Correlation\n",
        "2. Negative Correlation"
      ],
      "metadata": {
        "id": "IZeYVGioKc92"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "correlation = goldData.corr()"
      ],
      "metadata": {
        "id": "zc9ee8q1KSTC"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert 'Date' column to datetime because upper column shows error\n",
        "if 'Date' in goldData.columns:\n",
        "    goldData['Date'] = pd.to_datetime(goldData['Date'], errors='coerce')\n",
        "\n",
        "# Exclude non-numeric columns from correlation calculation\n",
        "numeric_goldData = goldData.select_dtypes(include=[float, int])\n",
        "\n",
        "# Handle missing values if any were introduced during conversion\n",
        "numeric_goldData = numeric_goldData.fillna(numeric_goldData.mean())\n",
        "\n",
        "# Calculate correlation\n",
        "correlation = numeric_goldData.corr()\n",
        "print(correlation)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DYiW6MbLLgkk",
        "outputId": "e3b271fe-15be-4429-e032-48a03623459d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              SPX       GLD       USO       SLV   EUR/USD\n",
            "SPX      1.000000  0.049345 -0.591573 -0.274055 -0.672017\n",
            "GLD      0.049345  1.000000 -0.186360  0.866632 -0.024375\n",
            "USO     -0.591573 -0.186360  1.000000  0.167547  0.829317\n",
            "SLV     -0.274055  0.866632  0.167547  1.000000  0.321631\n",
            "EUR/USD -0.672017 -0.024375  0.829317  0.321631  1.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Constructing the heatmap to understand the correlation\n",
        "plt.figure(figsize = (6,6))\n",
        "sns.heatmap(correlation, cbar=True, square=True, fmt='.1f',annot=True, annot_kws={'size':8}, cmap='Blues')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "3fcfvyNnKuVn",
        "outputId": "75d5fb4c-20df-42f5-96aa-c365ea9110cc"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(correlation['GLD'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-bep-vstLu3d",
        "outputId": "fc19075d-bdd9-457e-9b55-fb27efb0e753"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Date       0.209118\n",
            "SPX        0.049345\n",
            "GLD        1.000000\n",
            "USO       -0.186360\n",
            "SLV        0.866632\n",
            "EUR/USD   -0.024375\n",
            "Name: GLD, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, displot and histplot can be used distplot has been depricated and shows warnings\n",
        "\n"
      ],
      "metadata": {
        "id": "6lnmr5kkMutH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Distribution of the gold Price\n",
        "sns.displot(goldData['GLD'],color='black')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "M4bN83waMEX2",
        "outputId": "88565e86-33b5-4227-bcd6-fbe789bf1bbc"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7d8d74d37df0>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Splitting the Features and Target"
      ],
      "metadata": {
        "id": "g-BgnepVMzZK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = goldData.drop(['Date','GLD'],axis=1) #REMOVING DATE and GLD COLUMN\n",
        "Y = goldData['GLD']\n",
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mIGnVCioMxhD",
        "outputId": "a15bfb78-eb42-4dd8-dc16-6f1e96ac67c0"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              SPX        USO      SLV   EUR/USD\n",
            "0     1447.160034  78.470001  15.1800  1.471692\n",
            "1     1447.160034  78.370003  15.2850  1.474491\n",
            "2     1411.630005  77.309998  15.1670  1.475492\n",
            "3     1416.180054  75.500000  15.0530  1.468299\n",
            "4     1390.189941  76.059998  15.5900  1.557099\n",
            "...           ...        ...      ...       ...\n",
            "2285  2671.919922  14.060000  15.5100  1.186789\n",
            "2286  2697.790039  14.370000  15.5300  1.184722\n",
            "2287  2723.070068  14.410000  15.7400  1.191753\n",
            "2288  2730.129883  14.380000  15.5600  1.193118\n",
            "2289  2725.780029  14.405800  15.4542  1.182033\n",
            "\n",
            "[2290 rows x 4 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uih5DmtQNGXm",
        "outputId": "a23424e9-d608-445a-cadf-c93d25c29a35"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0        84.860001\n",
            "1        85.570000\n",
            "2        85.129997\n",
            "3        84.769997\n",
            "4        86.779999\n",
            "           ...    \n",
            "2285    124.589996\n",
            "2286    124.330002\n",
            "2287    125.180000\n",
            "2288    124.489998\n",
            "2289    122.543800\n",
            "Name: GLD, Length: 2290, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Splitting Data\n",
        "1. Training Data\n",
        "2. Test Data"
      ],
      "metadata": {
        "id": "e9pmAg4RNUrN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state=2)\n",
        "#80 20 Ratio"
      ],
      "metadata": {
        "id": "FYEqUil8NQOm"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Training: Random Forest Regressor"
      ],
      "metadata": {
        "id": "k38GPaWoNeGk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "regressor = RandomForestRegressor(n_estimators=100)"
      ],
      "metadata": {
        "id": "haZEFQG7NiZ2"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training our model\n",
        "regressor.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "id": "jS6kWNUdNpZL",
        "outputId": "66e015ff-d3c1-4b9e-a8ef-7cdb3a1bb0de"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Evaluation\n"
      ],
      "metadata": {
        "id": "aX41OeEJN2qs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Prediction on Test Data\n",
        "test_data_prediction = regressor.predict(X_test)"
      ],
      "metadata": {
        "id": "MLLQTqwLN128"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(test_data_prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aKl-7wwgOT0K",
        "outputId": "6c26730a-5e78-4f8c-9aa8-f9386ba9c00a"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[168.44459945  82.25739945 116.2110004  127.64870068 120.62510114\n",
            " 154.83019774 150.58929906 126.21900027 117.53189856 125.9213007\n",
            " 116.76920096 171.46510084 142.20749823 167.7176992  115.29160001\n",
            " 117.56530036 138.03150314 170.10130027 159.16320307 157.72029882\n",
            " 155.23670034 125.26220011 175.19439998 157.12050356 125.1691004\n",
            "  93.95299956  78.23639998 120.85000015 119.16919975 167.50019927\n",
            "  88.09510102 125.43570006  91.27670104 117.76710036 121.05849952\n",
            " 136.06780081 115.36880149 115.50570048 148.1608995  107.19380069\n",
            " 104.57790241  87.11669807 126.62260046 118.09459967 154.12769919\n",
            " 119.68660031 108.31719978 108.24859853  93.09280029 127.18289747\n",
            "  74.82250044 113.67439925 121.04039986 111.22299937 118.94039919\n",
            " 120.73009955 159.36720054 169.74020172 147.29149688  85.90849862\n",
            "  94.04210039  86.89299891  90.45940019 118.97070078 126.44870074\n",
            " 127.64589985 167.90230008 122.26569962 117.48929916  98.71110049\n",
            " 168.10900181 143.30079792 131.7116025  121.26980207 120.69459954\n",
            " 119.75230064 114.21650185 118.48230082 107.18650094 127.79350102\n",
            " 114.05489955 107.38639951 116.79210053 119.51959843  89.21550022\n",
            "  88.40869883 147.22890197 127.28040012 113.42090049 109.89589819\n",
            " 107.95589913  77.33009918 169.50550212 114.05969924 121.61849923\n",
            " 127.52270193 154.97249815  91.65929904 135.7742013  158.40240317\n",
            " 124.91460069 125.36370057 130.68410098 114.73120116 119.8492998\n",
            "  92.03669982 110.24619908 165.62529949 157.21629802 114.16089933\n",
            " 106.65590093  79.26929963 113.37350034 125.85910092 107.44379913\n",
            " 119.47270119 156.08580368 158.61679954 119.85250023 135.15500363\n",
            " 101.31369983 117.35569811 119.17840023 112.99900076 102.79729913\n",
            " 159.59009828  98.98510026 148.7210996  125.53620094 169.49789908\n",
            " 125.45429884 127.29689751 127.34160198 113.82329891 113.19010082\n",
            " 123.37239935 102.23069898  89.16080012 124.55199968 101.54849962\n",
            " 107.24909871 113.39780034 117.26180084  99.55289986 121.65170071\n",
            " 163.42519843  87.42059889 106.74180003 117.12490089 127.56130136\n",
            " 124.00840095  80.79119887 120.70100088 157.79859802  88.02029958\n",
            " 110.46549947 118.89319904 172.33519887 103.07819895 105.71900026\n",
            " 122.37380013 158.35979764  87.87389818  93.36440087 112.59990051\n",
            " 176.67440035 114.55879947 119.20000022  94.72950143 125.72010061\n",
            " 165.88180095 114.63910073 116.63180153  88.40859875 149.02040067\n",
            " 120.26219931  89.42399977 112.33550012 117.22140016 118.97460107\n",
            "  88.17349937  94.21000061 116.9308006  118.51270199 120.19560048\n",
            " 126.76229829 121.80930031 151.72289992 164.9571014  118.6681994\n",
            " 120.33420147 149.92160087 118.39269917 172.36019932 105.34039915\n",
            " 104.90590125 149.34370131 113.79680056 124.90080118 147.28219958\n",
            " 119.61340117 115.40710075 112.71690014 113.48570157 142.30700217\n",
            " 118.08639742 102.97959998 115.912101   103.74780171  98.95870042\n",
            " 117.66380027  90.58270025  91.43700021 153.34119875 102.74769983\n",
            " 154.97300105 114.39410123 138.7134014   90.17609824 115.51049951\n",
            " 114.15009952 123.07869994 121.7441005  165.36730128  92.88829951\n",
            " 135.55040118 121.46039918 120.68910081 104.5702001  141.71450339\n",
            " 121.24729945 116.71750041 113.47120107 126.99189724 122.62469946\n",
            " 125.73919959 121.33740022  86.85989908 132.2097005  144.61830215\n",
            "  92.69519965 159.5269001  158.76720249 126.56249909 164.87479934\n",
            " 108.70759954 110.13360113 103.70589841  94.32030046 127.54090276\n",
            " 107.12130014 162.14870034 121.63440069 132.02379989 130.74570102\n",
            " 160.11760055  90.16039855 174.14550221 127.23910059 126.79029828\n",
            "  86.38339904 124.61749962 150.39409722  89.65210023 106.8521999\n",
            " 109.0838995   84.19829897 136.6514004  154.94210186 139.44060353\n",
            "  73.64910041 151.82260059 126.20920024 126.76440002 127.53679907\n",
            " 108.64749923 156.09899926 114.27890166 117.02130127 125.38909973\n",
            " 154.1021012  121.1764998  156.36169895  92.9272004  125.47130156\n",
            " 125.94410036  87.82590082  92.14619921 126.04509934 127.95490364\n",
            " 113.02830005 117.57189739 120.94909994 127.05599807 119.57490104\n",
            " 136.23270084  93.76979919 119.57410046 113.39200081  94.24249949\n",
            " 108.72359974  87.04149917 109.06599972  89.64450006  92.3782003\n",
            " 131.71400238 162.37670041  89.1681001  119.75380084 133.62980209\n",
            " 123.68119992 128.22630171 101.92829832  88.87389914 131.81270043\n",
            " 119.70390018 108.56689986 166.03920124 115.26860014  86.63579935\n",
            " 118.9641006   91.11769954 161.99870007 116.48000043 121.54509991\n",
            " 160.27289833 120.17919914 112.59979924 108.45789873 126.71850007\n",
            "  75.90630036 103.03199977 127.45750253 121.76059916  92.5683003\n",
            " 131.90640134 118.32110114 116.12999971 154.24960274 159.60240084\n",
            " 110.03199981 155.57779767 119.31080107 159.97120135 118.70449995\n",
            " 157.41219875 115.01689965 116.67120036 149.33289906 114.58090091\n",
            " 125.49069875 165.32859961 117.68070037 124.90999902 153.23190387\n",
            " 153.54940242 132.17389942 114.73470057 121.32000198 124.55650078\n",
            "  89.83790042 123.13110027 154.60710173 111.60530021 106.82489993\n",
            " 162.09470143 118.58709948 165.85340027 134.16820103 114.6369995\n",
            " 153.07639917 168.65340013 114.96960016 114.28590151 159.00659901\n",
            "  85.17049906 127.12160107 127.92890094 128.89009971 124.29600068\n",
            " 123.83900052  90.49220079 153.19420028  97.00909977 137.00950045\n",
            "  88.90419933 107.18619961 115.0756004  112.91950087 124.07839927\n",
            "  91.35829872 125.43410137 162.30509841 120.11389893 165.08900085\n",
            " 126.75219825 112.30640009 127.62229921  95.25839924  90.98790009\n",
            " 103.59569912 120.72399982  83.28699939 126.35369999 159.98530469\n",
            " 117.34000051 118.26460003 120.28690011 122.8516995  120.07110124\n",
            " 121.41749985 118.20750082 107.15809951 148.32290049 125.98099822\n",
            " 115.74410069  73.90919981 127.82040068 154.92950113 122.64479978\n",
            " 125.61500065  88.88600008 103.52879856 124.10310063 120.2057002\n",
            "  73.58680066 151.51150056 120.99140028 104.77469986  86.29069783\n",
            " 115.15899937 172.10539969 119.60690038 159.74239749 113.19809956\n",
            " 121.19120049 118.54250117  96.07159982 119.07900003 125.90840051\n",
            " 118.55569954  95.81660087 154.19090128 121.99140003 148.10439926\n",
            " 159.28580205 113.63560025 122.64769929 149.39429812 127.11630034\n",
            " 165.81680085 134.72620073 119.98709905 167.44769862 108.43039919\n",
            " 121.53139888 140.17360154 106.29929922]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# R squared error (Metrics gives us this error)\n",
        "error_score = metrics.r2_score(Y_test, test_data_prediction)\n",
        "print(\"R squared error : \", error_score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3lr29matOUqd",
        "outputId": "1d32f6fd-2e36-4a99-81b3-d5a4d5479ca5"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R squared error :  0.9883587559550432\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Comparing Actual Values and Predicted Values in a Plot"
      ],
      "metadata": {
        "id": "HM140374OhxW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y_test = list(Y_test)"
      ],
      "metadata": {
        "id": "G7cPkMfVOnyX"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(Y_test, color='blue', label = 'Actual Value')\n",
        "plt.plot(test_data_prediction, color='green', label='Predicted Value')\n",
        "plt.title('Actual Price vs Predicted Price')\n",
        "plt.xlabel('Number of values')\n",
        "plt.ylabel('GLD Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "PyscLNFCOogw",
        "outputId": "965e59c9-87fe-4c10-b7e0-abcd3376ae15"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EiYOeQ4hOqgb"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}