GoldPricePrediction.ipynb

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      "source": [
        "*importing libraries*"
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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": "code",
      "source": [
        "gold_data = pd.read_csv('/content/gld_price_data.csv')"
      ],
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        "id": "5PLWyvcy9rpj"
      },
      "execution_count": 5,
      "outputs": []
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      "source": [
        "gold_data.head()"
      ],
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              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>SPX</th>\n",
              "      <th>GLD</th>\n",
              "      <th>USO</th>\n",
              "      <th>SLV</th>\n",
              "      <th>EUR/USD</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1/2/2008</td>\n",
              "      <td>1447.160034</td>\n",
              "      <td>84.860001</td>\n",
              "      <td>78.470001</td>\n",
              "      <td>15.180</td>\n",
              "      <td>1.471692</td>\n",
              "    </tr>\n",
              "    <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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              "    <tr>\n",
              "      <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",
              "    </tr>\n",
              "    <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",
              "    </tr>\n",
              "    <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",
              "      <td>1.557099</td>\n",
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            "text/plain": [
              "       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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        "# printing the last five rows of dataframe\n",
        "gold_data.tail()"
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              "      <th>2285</th>\n",
              "      <td>5/8/2018</td>\n",
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              "      <td>124.589996</td>\n",
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              "      <td>14.3700</td>\n",
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              "      <th>2287</th>\n",
              "      <td>5/10/2018</td>\n",
              "      <td>2723.070068</td>\n",
              "      <td>125.180000</td>\n",
              "      <td>14.4100</td>\n",
              "      <td>15.7400</td>\n",
              "      <td>1.191753</td>\n",
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              "    <tr>\n",
              "      <th>2288</th>\n",
              "      <td>5/14/2018</td>\n",
              "      <td>2730.129883</td>\n",
              "      <td>124.489998</td>\n",
              "      <td>14.3800</td>\n",
              "      <td>15.5600</td>\n",
              "      <td>1.193118</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",
              "      <td>15.4542</td>\n",
              "      <td>1.182033</td>\n",
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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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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "# number of rows and colums\n",
        "gold_data.shape"
      ],
      "metadata": {
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        },
        "id": "_NhOvWRP-SJs",
        "outputId": "d1cad36c-ed84-49c8-d55b-1852523ec500"
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      "execution_count": 8,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "(2290, 6)"
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          "execution_count": 8
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# gettiing the info of data\n",
        "gold_data.info()"
      ],
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        "id": "86wAMWvi-aK9",
        "outputId": "e01627f8-e297-465a-bb87-0d0c0b057e22"
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      "execution_count": 9,
      "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 the number of missing values\n",
        "gold_data.isnull().sum()"
      ],
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      "execution_count": 10,
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          "data": {
            "text/plain": [
              "Date       0\n",
              "SPX        0\n",
              "GLD        0\n",
              "USO        0\n",
              "SLV        0\n",
              "EUR/USD    0\n",
              "dtype: int64"
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      "cell_type": "code",
      "source": [
        "# stats of the data\n",
        "gold_data.describe()"
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              "  <div id=\"df-6a9756c1-470a-47ea-94dc-7e8ac5e34a97\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "\n",
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              "<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>1.303296</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>22.882499</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",
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              "          document.querySelector('#df-6a9756c1-470a-47ea-94dc-7e8ac5e34a97 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-6a9756c1-470a-47ea-94dc-7e8ac5e34a97');\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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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "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     1.303296\n",
              "75%    2073.010070   132.840004    37.827501    22.882499     1.369971\n",
              "max    2872.870117   184.589996   117.480003    47.259998     1.598798"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "correlation <br/>\n",
        "1.postive <br/>\n",
        "2.negative"
      ],
      "metadata": {
        "id": "V3Ac6Zja--Ei"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# finding co-relation\n",
        "correlation = gold_data.corr()"
      ],
      "metadata": {
        "id": "vvj_Z7Va-7L2"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# making heat map to understand correlation\n",
        "plt.figure(figsize=(8,8))\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": 286
        },
        "id": "QUoCsUoD_MmZ",
        "outputId": "533c1e41-c468-46bf-89fb-68f5ef75446e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f614fa88e10>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(correlation['GLD'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y-yXy5wnAGuf",
        "outputId": "dbb4956c-2610-4800-9166-aef8604337a0"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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": "code",
      "source": [
        "# checking the distribution of the gld price\n",
        "sns.distplot(gold_data['GLD'],color='green')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "xYxwmd8hAMVL",
        "outputId": "b8f4fdc6-28cd-4ff1-a02e-4d089bf16247"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6146ce3990>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "splitting the features and target\n"
      ],
      "metadata": {
        "id": "v-wzktcpAqxN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = gold_data.drop(['Date','GLD'],axis=1)\n",
        "Y = gold_data['GLD']"
      ],
      "metadata": {
        "id": "qcpYPmVDAndH"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)\n",
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-7U89K7wA8z8",
        "outputId": "60cdd6d4-959a-4835-ffd2-b3f76e49ed71"
      },
      "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": "code",
      "source": [
        "# spiltting into traing and test\n",
        "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.2,random_state=2)"
      ],
      "metadata": {
        "id": "djtw1CJSBDOF"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# training the model using random forest regressor\n",
        "regressor = RandomForestRegressor(n_estimators=100)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "n0Mh_V3sBfUi"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# training the model\n",
        "regressor.fit(X_train,Y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ldcv4eU1EJvR",
        "outputId": "dc120f42-5b23-48dc-a156-572e682a72e4"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor()"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model evaluation based on test data"
      ],
      "metadata": {
        "id": "otEqCCltETHn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_data_prediction = regressor.predict(X_test)"
      ],
      "metadata": {
        "id": "WNLN9mqAERZT"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(test_data_prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zDB90t_9Ek2y",
        "outputId": "0b779cee-a34e-4b39-f6fe-305f5da354cf"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[168.36019904  81.87719999 115.96029974 127.58380102 120.7838012\n",
            " 154.72969753 150.18699898 126.02310089 117.51449878 126.02270106\n",
            " 116.631501   172.00840084 141.88009888 168.25529905 115.26310041\n",
            " 117.67840067 138.61850313 170.0574009  159.29370277 156.16969851\n",
            " 154.98810019 125.18840012 176.35599968 157.49250308 125.23780056\n",
            "  93.67969974  77.36590028 121.12990014 119.09449918 167.52110014\n",
            "  88.19370064 125.38980032  91.27030017 117.68859988 121.07739908\n",
            " 136.19420094 115.56880116 114.69610095 148.3253993  107.48840061\n",
            " 104.34490221  87.13439816 126.57620025 118.26850023 151.94559863\n",
            " 119.70289992 108.38430013 108.04979837  93.21080059 127.1099978\n",
            "  74.6533003  113.66539853 121.43160034 111.47969919 118.92339883\n",
            " 120.69039928 159.81270041 167.87280119 146.8936969   85.71119872\n",
            "  94.24910019  86.78319907  90.45150014 118.894501   126.32450077\n",
            " 127.47559985 169.95019967 122.2659994  117.24009906  98.74320025\n",
            " 167.67630055 143.06339836 131.80660247 121.11010208 121.27129975\n",
            " 119.79330055 114.48240155 118.15060054 107.26430055 127.91260013\n",
            " 113.80320011 107.84690017 116.91080068 119.6386987   88.79470034\n",
            "  88.14689872 146.96970209 127.15420028 113.4511003  110.53739859\n",
            " 108.49199931  77.17629893 169.83270155 114.12579907 121.54509953\n",
            " 127.8813016  154.95339841  91.60229929 136.36600056 159.30440377\n",
            " 125.78060054 125.22490045 130.75110256 114.81480129 119.76539992\n",
            "  92.04689977 110.21919883 166.64529948 157.38089934 114.3649997\n",
            " 106.5063011   79.84079957 113.26370059 125.81300042 107.42789872\n",
            " 119.30000106 155.58090349 159.29359824 120.66439964 134.61280264\n",
            " 101.46199979 117.39629811 119.22250027 112.80390074 102.7587987\n",
            " 160.07529751  99.16850015 148.0352994  125.57800114 169.70249898\n",
            " 125.99079871 127.42329706 127.53020155 113.55309952 112.83680064\n",
            " 123.50149916 102.06959886  89.50799969 124.53979956 101.5249992\n",
            " 107.09029914 113.63590045 117.78610064  99.33739958 121.63440063\n",
            " 162.77830021  87.31399882 106.85789958 117.20410058 127.57590123\n",
            " 124.23700047  80.89699936 120.41190053 158.17419864  87.95630021\n",
            " 110.24969972 118.84629921 172.11849891 103.04829903 105.84220074\n",
            " 122.82550054 158.30989821  87.57809842  93.13410014 112.47740031\n",
            " 177.17199986 114.29359979 119.28310035  94.76490109 125.44250038\n",
            " 165.88690094 114.77150087 116.64500141  88.23439868 149.29360219\n",
            " 120.25999925  89.3510997  112.2899999  117.59030058 118.86810111\n",
            "  88.10499948  94.05979969 116.73350011 118.52070223 120.23570031\n",
            " 126.8994975  121.74519954 149.88180048 165.94640043 118.53139962\n",
            " 120.46770123 150.89280058 118.44979936 172.62809915 105.35319917\n",
            " 104.99120108 149.75240223 113.81950046 124.93120116 148.39639988\n",
            " 119.62480132 115.2067004  112.83199997 113.49250216 140.31850132\n",
            " 117.96319748 102.96150024 115.99100117 103.9303018   98.72670026\n",
            " 117.43560046  90.79799997  91.72130063 153.15629865 102.69209953\n",
            " 154.53790093 114.21650156 138.99080123  90.21409854 115.49299966\n",
            " 114.68950023 122.77460077 121.5104006  165.47540126  92.99139939\n",
            " 135.44350127 121.32989929 120.66530077 104.73889991 143.09580312\n",
            " 121.53519921 116.58860037 113.57750069 127.29919729 122.15129968\n",
            " 125.8310992  121.25010031  86.78049912 132.6741014  145.29040204\n",
            "  92.73609929 159.00109976 159.4000031  126.36569862 165.53899919\n",
            " 108.77489954 110.14230111 103.81259843  94.35230104 128.00900306\n",
            " 107.14920027 162.86300024 121.59330059 132.17009982 130.7370021\n",
            " 160.80510032  90.13879855 175.25510182 127.50319998 126.90699839\n",
            "  86.25199892 124.46639947 150.29649732  89.58150019 106.97799969\n",
            " 108.97079994  84.13229905 136.54690074 154.67660292 138.77390368\n",
            "  74.31070011 151.8627011  125.98279937 126.8112997  127.55899877\n",
            " 108.62229926 156.39840053 114.63390133 116.86240149 125.20229933\n",
            " 154.25300153 121.28289977 156.46379829  93.02280061 125.61820175\n",
            " 125.74240054  87.86140058  92.33109923 126.24239964 128.07390344\n",
            " 113.24680011 117.44209706 120.7770002  127.28099806 119.83180125\n",
            " 136.3314006   93.86419922 119.87040052 112.94590113  94.31609933\n",
            " 108.9001999   86.57669908 109.28569917  89.61169995  92.25160009\n",
            " 131.64540374 162.48989939  89.10400045 119.86270055 133.53220186\n",
            " 123.96340018 128.15650146 102.03009853  88.86869881 131.32890104\n",
            " 120.01020052 108.63180005 167.46120103 115.25680049  86.58109874\n",
            " 118.80570072  91.08699964 162.0631005  116.36660025 121.6196001\n",
            " 159.98199719 120.12499936 112.56799943 108.60679902 126.80569968\n",
            "  76.09740026 103.01300001 127.84690282 121.81229967  92.64209978\n",
            " 132.39510029 118.00140082 115.98619979 154.51060285 159.82000109\n",
            " 110.13789952 153.3852978  119.36390072 160.27640062 118.60580081\n",
            " 158.20649984 115.03969965 116.79040043 148.55469868 114.82550065\n",
            " 125.65779869 164.98589902 117.73250006 125.18509913 153.24750382\n",
            " 153.4284025  132.26140027 114.8856001  121.19510175 124.78210095\n",
            "  89.76620044 123.36699963 155.21240132 111.90880076 106.83709959\n",
            " 161.57380041 118.60319934 165.47029997 133.87770111 114.95529956\n",
            " 153.020099   168.71610062 115.03279965 114.16920129 159.53309912\n",
            "  85.44849833 126.97300064 127.93360037 128.84070031 124.27520038\n",
            " 123.95180106  90.50640129 153.47279988  97.08579984 138.37920032\n",
            "  89.15059924 107.81570021 115.04980045 112.76330072 123.65739916\n",
            "  91.42329891 125.37860113 162.54879866 119.98229867 165.00560055\n",
            " 126.98119731 112.25110019 127.44919926  94.88839887  91.0336\n",
            " 103.23169908 120.74929985  82.90489943 126.34720026 160.25850397\n",
            " 117.30220068 118.2343998  120.17269997 122.58529963 119.99580138\n",
            " 121.34480023 118.15850059 107.13899988 148.61000088 126.44939856\n",
            " 115.6802009   73.90819978 127.81740101 154.8050007  122.44040001\n",
            " 125.57340069  88.83620018 103.35559898 124.66490063 120.22290006\n",
            "  73.36350066 151.55120027 121.23000056 104.73060006  86.38659775\n",
            " 114.9668988  172.29499721 120.23160034 160.84389815 113.25889962\n",
            " 121.38570014 118.4881008   95.91809984 118.77069991 125.99410056\n",
            " 118.46519945  95.95770042 154.38800133 121.95710026 147.75690064\n",
            " 158.82450213 113.83200023 122.52219931 149.91309758 127.08380014\n",
            " 165.82070072 135.1623004  119.80899915 167.0683984  108.29299912\n",
            " 121.72649835 138.10010137 106.13889901]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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": "5Op09E09Eo_H",
        "outputId": "8c6762f7-bd93-4743-fe63-cddbaa4cb5c2"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R squared error:  0.9890481714421148\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "comparing the actual values and predicted values in a plot\n"
      ],
      "metadata": {
        "id": "zCrwAGJSFJPx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y_test = list(Y_test)"
      ],
      "metadata": {
        "id": "iw9lwj2kFGX6"
      },
      "execution_count": 32,
      "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.ylabel('GLD price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "UxY_-1DGFSpF",
        "outputId": "e3c29ac9-7b6f-4cae-80d2-fdf72cbbe61c"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}