Gold_Price_Prediction_with_Python.ipynb

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    "colab": {
      "name": "Gold Price Prediction with Python",
      "provenance": [],
      "authorship_tag": "ABX9TyOen8Z6VoT1R5Cnk9lw41yx",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
  },
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      ]
    },
    {
      "cell_type": "code",
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              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "K90a9oHsLXgl",
        "outputId": "5ee5f14b-5cf2-4b1f-986a-be5b6805178f"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-55990842-8816-4cf5-95d9-8db90e1b32c5\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-55990842-8816-4cf5-95d9-8db90e1b32c5\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving gold_price.csv to gold_price.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "id": "KnQ3ytdLLaKg",
        "outputId": "0a9c967d-de61-4cad-9b03-9d06b9c56bff"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "%matplotlib inline\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "from sklearn.linear_model import LinearRegression\n",
        "df = pd.read_csv(\"gold_price.csv\", parse_dates=True, index_col='Date')\n",
        "df.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>USD (AM)</th>\n",
              "      <th>USD (PM)</th>\n",
              "      <th>GBP (AM)</th>\n",
              "      <th>GBP (PM)</th>\n",
              "      <th>EURO (AM)</th>\n",
              "      <th>EURO (PM)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2001-01-02</th>\n",
              "      <td>272.80</td>\n",
              "      <td>271.10</td>\n",
              "      <td>183.026</td>\n",
              "      <td>181.617</td>\n",
              "      <td>288.677</td>\n",
              "      <td>287.334</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001-01-03</th>\n",
              "      <td>269.00</td>\n",
              "      <td>267.15</td>\n",
              "      <td>178.916</td>\n",
              "      <td>177.390</td>\n",
              "      <td>281.823</td>\n",
              "      <td>281.655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001-01-04</th>\n",
              "      <td>268.75</td>\n",
              "      <td>267.10</td>\n",
              "      <td>178.869</td>\n",
              "      <td>178.352</td>\n",
              "      <td>282.538</td>\n",
              "      <td>282.049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001-01-05</th>\n",
              "      <td>268.00</td>\n",
              "      <td>267.40</td>\n",
              "      <td>178.488</td>\n",
              "      <td>178.148</td>\n",
              "      <td>280.775</td>\n",
              "      <td>280.882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001-01-08</th>\n",
              "      <td>268.60</td>\n",
              "      <td>268.30</td>\n",
              "      <td>178.769</td>\n",
              "      <td>178.664</td>\n",
              "      <td>282.410</td>\n",
              "      <td>282.481</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            USD (AM)  USD (PM)  GBP (AM)  GBP (PM)  EURO (AM)  EURO (PM)\n",
              "Date                                                                    \n",
              "2001-01-02    272.80    271.10   183.026   181.617    288.677    287.334\n",
              "2001-01-03    269.00    267.15   178.916   177.390    281.823    281.655\n",
              "2001-01-04    268.75    267.10   178.869   178.352    282.538    282.049\n",
              "2001-01-05    268.00    267.40   178.488   178.148    280.775    280.882\n",
              "2001-01-08    268.60    268.30   178.769   178.664    282.410    282.481"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "x74-GmWWLxk1",
        "outputId": "1bcc661e-25c2-4627-8139-680dd94c578d"
      },
      "source": [
        "df.head(100)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>USD (AM)</th>\n",
              "      <th>USD (PM)</th>\n",
              "      <th>GBP (AM)</th>\n",
              "      <th>GBP (PM)</th>\n",
              "      <th>EURO (AM)</th>\n",
              "      <th>EURO (PM)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2001-01-02</th>\n",
              "      <td>272.80</td>\n",
              "      <td>271.10</td>\n",
              "      <td>183.026</td>\n",
              "      <td>181.617</td>\n",
              "      <td>288.677</td>\n",
              "      <td>287.334</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001-01-03</th>\n",
              "      <td>269.00</td>\n",
              "      <td>267.15</td>\n",
              "      <td>178.916</td>\n",
              "      <td>177.390</td>\n",
              "      <td>281.823</td>\n",
              "      <td>281.655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001-01-04</th>\n",
              "      <td>268.75</td>\n",
              "      <td>267.10</td>\n",
              "      <td>178.869</td>\n",
              "      <td>178.352</td>\n",
              "      <td>282.538</td>\n",
              "      <td>282.049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001-01-05</th>\n",
              "      <td>268.00</td>\n",
              "      <td>267.40</td>\n",
              "      <td>178.488</td>\n",
              "      <td>178.148</td>\n",
              "      <td>280.775</td>\n",
              "      <td>280.882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001-01-08</th>\n",
              "      <td>268.60</td>\n",
              "      <td>268.30</td>\n",
              "      <td>178.769</td>\n",
              "      <td>178.664</td>\n",
              "      <td>282.410</td>\n",
              "      <td>282.481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001-05-18</th>\n",
              "      <td>272.10</td>\n",
              "      <td>273.00</td>\n",
              "      <td>190.373</td>\n",
              "      <td>190.976</td>\n",
              "      <td>309.486</td>\n",
              "      <td>311.288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001-05-21</th>\n",
              "      <td>288.35</td>\n",
              "      <td>291.25</td>\n",
              "      <td>200.717</td>\n",
              "      <td>202.173</td>\n",
              "      <td>328.904</td>\n",
              "      <td>332.250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001-05-22</th>\n",
              "      <td>282.75</td>\n",
              "      <td>284.15</td>\n",
              "      <td>196.956</td>\n",
              "      <td>198.845</td>\n",
              "      <td>324.329</td>\n",
              "      <td>327.362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001-05-23</th>\n",
              "      <td>283.85</td>\n",
              "      <td>283.15</td>\n",
              "      <td>199.683</td>\n",
              "      <td>198.897</td>\n",
              "      <td>330.712</td>\n",
              "      <td>329.627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001-05-24</th>\n",
              "      <td>283.45</td>\n",
              "      <td>286.05</td>\n",
              "      <td>200.177</td>\n",
              "      <td>202.442</td>\n",
              "      <td>329.977</td>\n",
              "      <td>332.616</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            USD (AM)  USD (PM)  GBP (AM)  GBP (PM)  EURO (AM)  EURO (PM)\n",
              "Date                                                                    \n",
              "2001-01-02    272.80    271.10   183.026   181.617    288.677    287.334\n",
              "2001-01-03    269.00    267.15   178.916   177.390    281.823    281.655\n",
              "2001-01-04    268.75    267.10   178.869   178.352    282.538    282.049\n",
              "2001-01-05    268.00    267.40   178.488   178.148    280.775    280.882\n",
              "2001-01-08    268.60    268.30   178.769   178.664    282.410    282.481\n",
              "...              ...       ...       ...       ...        ...        ...\n",
              "2001-05-18    272.10    273.00   190.373   190.976    309.486    311.288\n",
              "2001-05-21    288.35    291.25   200.717   202.173    328.904    332.250\n",
              "2001-05-22    282.75    284.15   196.956   198.845    324.329    327.362\n",
              "2001-05-23    283.85    283.15   199.683   198.897    330.712    329.627\n",
              "2001-05-24    283.45    286.05   200.177   202.442    329.977    332.616\n",
              "\n",
              "[100 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wj1lhhfyL0HL"
      },
      "source": [
        "df['Return'] = df['USD (PM)'].pct_change() * 100\n",
        "df['Lagged_Return'] = df.Return.shift()\n",
        "df = df.dropna()\n",
        "train = df['2001':'2018']\n",
        "test = df['2019']\n",
        "# Create train and test sets for dependent and independent variables\n",
        "X_train = train[\"Lagged_Return\"].to_frame()\n",
        "y_train = train[\"Return\"]\n",
        "X_test = test[\"Lagged_Return\"].to_frame()\n",
        "y_test = test[\"Return\"]"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h4uNZhbpMI8B"
      },
      "source": [
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n",
        "predictions = model.predict(X_test)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "F96bBEdSL3Z_",
        "outputId": "08a2a427-835d-45fa-f531-3f6e258f8ebd"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "out_of_sample_results = y_test.to_frame()\n",
        "# Add a column of \"out-of-sample\" predictions to that dataframe:  \n",
        "out_of_sample_results[\"Out-of-Sample Predictions\"] = model.predict(X_test)\n",
        "out_of_sample_results.plot(subplots=True, title='Gold prices, USD')\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}