project 68  Gold_price_prediction_.ipynb

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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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              "      <th>Date</th>\n",
              "      <th>SPX</th>\n",
              "      <th>GLD</th>\n",
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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",
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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",
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              "      <td>1/7/2008</td>\n",
              "      <td>1416.180054</td>\n",
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              "      <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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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\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}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('goldprice.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The above data columns means as follows:\n",
        "\n",
        "“GLD” which stands for GOLD using other features such as “SPX” which I think is S&P 500 stock, “USO” which is United States Oil Fund, “SLV” which is also a stock, and “EUR/USD” which are currencies"
      ],
      "metadata": {
        "id": "ZbNXsJGsZqli"
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      "source": [
        "df.shape"
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        "id": "Ts4gZoT3ZfPY",
        "outputId": "e0c95b02-e382-461f-9bd9-20ee64a6be3b"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2290, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataset dimensions say that it has 2290 rows and 6 columns"
      ],
      "metadata": {
        "id": "wyntjJjZZ5M4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
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        "id": "5gz88oxPZ_RV",
        "outputId": "bcafb608-0599-49b9-c49b-040c38d11f5b"
      },
      "execution_count": 11,
      "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": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The above results interprets the dataset does not contain any null values."
      ],
      "metadata": {
        "id": "b8dD2BFwaEBX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  dropping date columns as it is irrelevant\n",
        "\n",
        "df = df.drop(columns=['Date'])\n",
        "print(df.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7GZnGxWuaMr7",
        "outputId": "a275b58e-438a-4b5e-c93e-57d48959b574"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['SPX', 'GLD', 'USO', 'SLV', 'EUR/USD'], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To find correlation between all the columns of the dataset we use a heatmap below"
      ],
      "metadata": {
        "id": "O-Bl-pgtasS-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "\n",
        "px.imshow(df.corr(),\n",
        "          text_auto=True,\n",
        "          color_continuous_scale='Viridis',\n",
        "          title='Correlation Matrix',\n",
        "          width=800,\n",
        "          height=600,\n",
        "          aspect='auto')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "9A5iMEjRax3D",
        "outputId": "be8bde7e-d56c-45dc-e071-227455e5a072"
      },
      "execution_count": 15,
      "outputs": [
        {
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It is observed that the SVL column has a huge correlation with GLD column with the highest of 86% followed by EUR/USD with USO i.e 82%"
      ],
      "metadata": {
        "id": "GbUeT3jEcdxx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(columns=['GLD'])\n",
        "y = df['GLD']"
      ],
      "metadata": {
        "id": "FBOjmeUfcy7r"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=345)"
      ],
      "metadata": {
        "id": "mERKahmuc53S"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape, y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TZa3TnTKc-ME",
        "outputId": "edfbc1c3-ce20-4fd6-98a4-5b62cfe33c5f"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1832, 4), (1832,))"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TqsiBK29c-Pf",
        "outputId": "605267f6-fb07-49bb-818d-c9229ab7806b"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((458, 4), (458,))"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. RANDOM FOREST REGRESSOR"
      ],
      "metadata": {
        "id": "-fXeelGmdHqM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "rfr = RandomForestRegressor()\n",
        "\n",
        "rfr.fit(X_train, y_train)\n",
        "\n",
        "rfr_prediction = rfr.predict(X_test)"
      ],
      "metadata": {
        "id": "MSHfI0YidDz1"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score"
      ],
      "metadata": {
        "id": "1pNQAy-LdQH9"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mean_absolute_error(y_test, rfr_prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q78g7JDCdSnu",
        "outputId": "073332a1-0d42-4f79-dd19-f8162d9b1164"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.3091553520087287"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_squared_error(y_test, rfr_prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xzzIzXYsdXFq",
        "outputId": "bb0ccb10-2036-4dee-db84-e5c2b92701a2"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.685533263716892"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r2_score(y_test, rfr_prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ll3X2WySdaif",
        "outputId": "79c33917-3c6d-47dc-dec9-7980a023ebbe"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9872199999547485"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It is observed that in random forest regressor the r2 score is 0.98 which is good. The ideal range should be between 0.5 to 1.0 for a model to perform well."
      ],
      "metadata": {
        "id": "ytgLV9JjdgB-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. DECISION TREE REGRESSOR"
      ],
      "metadata": {
        "id": "VDcSNUlHd2pr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        "dtr = DecisionTreeRegressor()\n",
        "dtr.fit(X_train, y_train)\n",
        "dtr_prediction = dtr.predict(X_test)"
      ],
      "metadata": {
        "id": "rQZAnDkPdrZt"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mean_absolute_error(y_test, dtr_prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DW5CGGg9d_gk",
        "outputId": "b19bd8c6-024f-4e05-85b4-fa7c48fed9e9"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.4669212467248907"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_squared_error(y_test, dtr_prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nsvghr4zeB_e",
        "outputId": "39c2a9d2-7a14-4bb3-effa-fe5a15be45ea"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.825263030586024"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r2_score(y_test, dtr_prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l9ecR3VheB3G",
        "outputId": "d50822fb-cf79-4c5e-b973-0237a1066aaa"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.981218123218837"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, in decision tree regressor the r2 score is 0.98 which is also good as similar to random forest gegressor  "
      ],
      "metadata": {
        "id": "6iZXnqFxeH8n"
      }
    }
  ]
}