Gold_Price_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e1BOngv5KqrF"
   },
   "source": [
    "**Importing the libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "eoQl3BgGK0OB"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QQtT8lM6LXbc"
   },
   "source": [
    "**Data Collection and Processing**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "a9dd-AYzLbRA"
   },
   "outputs": [],
   "source": [
    "gold_data = pd.read_csv('gld_price_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 35,
     "status": "ok",
     "timestamp": 1677246365094,
     "user": {
      "displayName": "Furkan Taşkın",
      "userId": "09462941121768919259"
     },
     "user_tz": -180
    },
    "id": "9teVi1C3Qd3h",
    "outputId": "a0bfc946-8fbd-4707-c2cb-b7c62ab84267"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-7555f131-3d76-47f4-ba5b-2f4605a097fe\">\n",
       "    <div 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>Date</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>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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7555f131-3d76-47f4-ba5b-2f4605a097fe')\"\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 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\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",
       "    [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-7555f131-3d76-47f4-ba5b-2f4605a097fe 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-7555f131-3d76-47f4-ba5b-2f4605a097fe');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "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"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print first 5 rows in the df\n",
    "gold_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 31,
     "status": "ok",
     "timestamp": 1677246365095,
     "user": {
      "displayName": "Furkan Taşkın",
      "userId": "09462941121768919259"
     },
     "user_tz": -180
    },
    "id": "ITb1DIqqR45v",
    "outputId": "1e503a19-af77-48e8-a520-482cb13dd953"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-389a7450-f1f0-4b98-acae-f07436f8cb75\">\n",
       "    <div 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>Date</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>2285</th>\n",
       "      <td>5/8/2018</td>\n",
       "      <td>2671.919922</td>\n",
       "      <td>124.589996</td>\n",
       "      <td>14.0600</td>\n",
       "      <td>15.5100</td>\n",
       "      <td>1.186789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2286</th>\n",
       "      <td>5/9/2018</td>\n",
       "      <td>2697.790039</td>\n",
       "      <td>124.330002</td>\n",
       "      <td>14.3700</td>\n",
       "      <td>15.5300</td>\n",
       "      <td>1.184722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-389a7450-f1f0-4b98-acae-f07436f8cb75')\"\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 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\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",
       "    [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-389a7450-f1f0-4b98-acae-f07436f8cb75 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-389a7450-f1f0-4b98-acae-f07436f8cb75');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "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"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print last 5 rows in the dataframe\n",
    "gold_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 30,
     "status": "ok",
     "timestamp": 1677246365096,
     "user": {
      "displayName": "Furkan Taşkın",
      "userId": "09462941121768919259"
     },
     "user_tz": -180
    },
    "id": "qiqEzvgOSima",
    "outputId": "5a89e8a0-c0b4-4254-f803-13106e96b50a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2290, 6)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of rows and columns\n",
    "gold_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 22,
     "status": "ok",
     "timestamp": 1677246365096,
     "user": {
      "displayName": "Furkan Taşkın",
      "userId": "09462941121768919259"
     },
     "user_tz": -180
    },
    "id": "2ja0lb48VfVD",
    "outputId": "e6448e0d-1177-4edb-ba27-9016143e5bfb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "source": [
    "#getting some basic information about the data\n",
    "gold_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 20,
     "status": "ok",
     "timestamp": 1677246365097,
     "user": {
      "displayName": "Furkan Taşkın",
      "userId": "09462941121768919259"
     },
     "user_tz": -180
    },
    "id": "0wy5T7qUWkew",
    "outputId": "b4b99127-42dd-460d-a31f-502dbd9781b1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date       0\n",
       "SPX        0\n",
       "GLD        0\n",
       "USO        0\n",
       "SLV        0\n",
       "EUR/USD    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the number of missing values\n",
    "gold_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2UMQGg45pvOQ"
   },
   "outputs": [],
   "source": [
    "#correlation between the various features\n",
    "correlation= gold_data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 558
    },
    "executionInfo": {
     "elapsed": 826,
     "status": "ok",
     "timestamp": 1677246365907,
     "user": {
      "displayName": "Furkan Taşkın",
      "userId": "09462941121768919259"
     },
     "user_tz": -180
    },
    "id": "ccgIyZxop6GJ",
    "outputId": "e2d42c54-3cc5-4e93-bfb5-860cad286fe5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#constructing a heatmap to understand the correlation\n",
    "plt.figure(figsize = (16,9))\n",
    "sns.heatmap(correlation, cbar=True, square=True,\n",
    "           fmt='.2f',annot=True, annot_kws={'size':8},\n",
    "            cmap='Oranges')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 22,
     "status": "ok",
     "timestamp": 1677246365910,
     "user": {
      "displayName": "Furkan Taşkın",
      "userId": "09462941121768919259"
     },
     "user_tz": -180
    },
    "id": "dZ-t2yCXkf8w",
    "outputId": "5211f586-ca1b-42f8-aae7-4b4111550c38"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "source": [
    "#Correlation values of Gold\n",
    "print(correlation['GLD'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 193
    },
    "executionInfo": {
     "elapsed": 11,
     "status": "error",
     "timestamp": 1693224725570,
     "user": {
      "displayName": "Furkan Taşkın",
      "userId": "09462941121768919259"
     },
     "user_tz": -180
    },
    "id": "pJlazNLmlMva",
    "outputId": "052eac0a-04ee-4b0d-c461-02504c2fca22"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-f2ce1efe5b37>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#Distribution of the GLD Price\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgold_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'GLD'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;34m'orange'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'sns' is not defined"
     ]
    }
   ],
   "source": [
    "#Distribution of the GLD Price\n",
    "sns.distplot(gold_data['GLD'], color ='orange')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JAFt0S9im_Eb"
   },
   "outputs": [],
   "source": [
    "#Splitting the Features and Dependent Variable\n",
    "X=gold_data.drop(['Date','GLD'],axis=1)\n",
    "y=gold_data['GLD']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 20,
     "status": "ok",
     "timestamp": 1677246366773,
     "user": {
      "displayName": "Furkan Taşkın",
      "userId": "09462941121768919259"
     },
     "user_tz": -180
    },
    "id": "vykMGr-tn2-O",
    "outputId": "85104f5b-4bdc-43dd-840b-fe78e82df4b8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "source": [
    "#Checking the features\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1677246366774,
     "user": {
      "displayName": "Furkan Taşkın",
      "userId": "09462941121768919259"
     },
     "user_tz": -180
    },
    "id": "x57kHhgIoDtJ",
    "outputId": "ddb4e4d6-249a-41dd-d139-10b56bf0faf4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "source": [
    "#Checking the Dependent Variable\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5JwOx4UZoRDC"
   },
   "source": [
    "**Splitting the dataset into the Training set and Test set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "K6oUNZJToVjK"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
    "test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1677246394065,
     "user": {
      "displayName": "Furkan Taşkın",
      "userId": "09462941121768919259"
     },
     "user_tz": -180
    },
    "id": "Z_-t9LfFrKCp",
    "outputId": "915dba7b-976f-4c33-e8cb-44ef25f6c40a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              SPX        USO        SLV   EUR/USD\n",
      "1255  1692.770020  36.880001  20.980000  1.351698\n",
      "2100  2425.530029   9.270000  14.990000  1.147394\n",
      "711   1309.660034  42.049999  36.119999  1.410596\n",
      "1328  1838.699951  33.689999  19.510000  1.361804\n",
      "53    1298.420044  82.290001  18.250999  1.563893\n",
      "...           ...        ...        ...       ...\n",
      "2085  2429.389893   9.520000  16.040001  1.119620\n",
      "1362  1877.170044  36.299999  20.010000  1.387867\n",
      "828   1209.880005  33.310001  30.230000  1.376500\n",
      "567   1089.469971  34.740002  17.530001  1.284406\n",
      "1693  1952.290039  14.930000  13.990000  1.129089\n",
      "\n",
      "[458 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 372,
     "status": "ok",
     "timestamp": 1677246401078,
     "user": {
      "displayName": "Furkan Taşkın",
      "userId": "09462941121768919259"
     },
     "user_tz": -180
    },
    "id": "Iyy1G5terM2A",
    "outputId": "e648be3b-bcd5-454d-9476-8cbd1e0ea029"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              SPX        USO        SLV   EUR/USD\n",
      "486   1165.810059  39.560001  16.600000  1.356705\n",
      "31    1349.989990  75.930000  16.952000  1.467610\n",
      "1754  2041.890015  10.740000  13.080000  1.087453\n",
      "1490  1862.760010  31.100000  16.670000  1.280442\n",
      "1526  2026.140015  23.280001  16.370001  1.239495\n",
      "...           ...        ...        ...       ...\n",
      "1033  1461.050049  34.119999  33.570000  1.305824\n",
      "1731  2023.040039  13.060000  13.590000  1.081502\n",
      "763   1283.500000  36.009998  34.369999  1.427002\n",
      "835   1218.280029  35.200001  32.330002  1.386001\n",
      "1653  2077.419922  19.100000  14.900000  1.103363\n",
      "\n",
      "[1832 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GLYwnA2IolOT"
   },
   "source": [
    "**Training the Random Forest Regression model on the whole dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 534,
     "status": "ok",
     "timestamp": 1677246367873,
     "user": {
      "displayName": "Furkan Taşkın",
      "userId": "09462941121768919259"
     },
     "user_tz": -180
    },
    "id": "42mg93Nm34CP",
    "outputId": "bcc94d2c-95d5-49cb-9cfa-cc2e69a3a971"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(random_state=0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "regressor = RandomForestRegressor(n_estimators = 100, random_state = 0)\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jFBjOZecor3F"
   },
   "source": [
    "**Predicting the Test set results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cQrgB6wRqi3_"
   },
   "outputs": [],
   "source": [
    "y_pred = regressor.predict(X_test)\n",
    "np.set_printoptions(precision=2)\n",
    "y_pred=np.array(y_pred)\n",
    "y_test=np.array(y_test)\n",
    "print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zppXep7eqppz"
   },
   "source": [
    "**Evaluating The Model & R Squared**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1677246368521,
     "user": {
      "displayName": "Furkan Taşkın",
      "userId": "09462941121768919259"
     },
     "user_tz": -180
    },
    "id": "MiRBTPmOqtpv",
    "outputId": "f386cee2-190b-4e4a-8416-d4c783651dbb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R Squared error :  0.9899648553789232\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_error = r2_score(y_test, y_pred)\n",
    "print(\"R Squared error : \", r2_error)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HIwJUJOdxkd-"
   },
   "source": [
    "**Applying k-Fold Cross Validation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5771,
     "status": "ok",
     "timestamp": 1677246374288,
     "user": {
      "displayName": "Furkan Taşkın",
      "userId": "09462941121768919259"
     },
     "user_tz": -180
    },
    "id": "4S0NyK4Txm6F",
    "outputId": "c7ce4abb-0ad6-4eab-a43c-5e6cc3584ed5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 98.74 %\n",
      "Standard Deviation: 0.44 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "accuracies = cross_val_score(estimator = regressor , X = X_train, y = y_train, cv = 10)\n",
    "print(\"Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
    "print(\"Standard Deviation: {:.2f} %\".format(accuracies.std()*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sQdNsIyRt0bd"
   },
   "source": [
    "**Compare the Actual Values and Predicted Values in a Plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1KKQzTrNt9hl"
   },
   "outputs": [],
   "source": [
    "#Converting the y_test to list\n",
    "y_test=list(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1677246374291,
     "user": {
      "displayName": "Furkan Taşkın",
      "userId": "09462941121768919259"
     },
     "user_tz": -180
    },
    "id": "zPwDdTi_uVSU",
    "outputId": "a2769347-0bcc-4615-d463-54374cb5aa3e"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Actual Values in a Plot\n",
    "plt.plot(y_test, color='red', label='Actual Value')\n",
    "plt.title('Actual Price')\n",
    "plt.xlabel('Number of Values')\n",
    "plt.ylabel('Gold Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "executionInfo": {
     "elapsed": 661,
     "status": "ok",
     "timestamp": 1677246374941,
     "user": {
      "displayName": "Furkan Taşkın",
      "userId": "09462941121768919259"
     },
     "user_tz": -180
    },
    "id": "pA87SmPuv2Wd",
    "outputId": "b2626d5d-7746-4504-eff2-fd44d2d02253"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Predicted Values in a Plot\n",
    "plt.plot(y_pred, color='blue' , label='Predicted Value')\n",
    "plt.title('Predicted Price')\n",
    "plt.xlabel('Number of Values')\n",
    "plt.ylabel('GOLD Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "executionInfo": {
     "elapsed": 35,
     "status": "ok",
     "timestamp": 1677246374943,
     "user": {
      "displayName": "Furkan Taşkın",
      "userId": "09462941121768919259"
     },
     "user_tz": -180
    },
    "id": "WlMnUTiWwQk7",
    "outputId": "0ef9d613-d868-4efc-d1e3-2a9004d9959e"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Actual Values vs Predicted Values in a Plot\n",
    "plt.plot(y_test, color='red', label='Actual Value')\n",
    "plt.plot(y_pred, color='blue' , label='Predicted Value')\n",
    "plt.title('Actual Price vs Predicted Price')\n",
    "plt.xlabel('Number of Values')\n",
    "plt.ylabel('GOLD Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyN2ixNkM0xGOp7G2Wtdt1bi",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}