LR_model.ipynb

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      "source": [
        "# ***import*** ***libraries***"
      ],
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      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "nuyEz9lCyE1j"
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      "source": [
        "from sklearn.metrics import  mean_squared_error\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import yfinance as yf"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ***Import*** ***Data***"
      ],
      "metadata": {
        "id": "mGYHJiYIbeHw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#read Data\n",
        "\n",
        "df = yf.download(\"BTC-USD\", start=\"2010-01-01\", end=\"2024-01-01\")\n",
        "df.head()"
      ],
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      "execution_count": 50,
      "outputs": [
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          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
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          "data": {
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2014-09-17  465.864014  468.174011  452.421997  457.334015  457.334015   \n",
              "2014-09-18  456.859985  456.859985  413.104004  424.440002  424.440002   \n",
              "2014-09-19  424.102997  427.834991  384.532013  394.795990  394.795990   \n",
              "2014-09-20  394.673004  423.295990  389.882996  408.903992  408.903992   \n",
              "2014-09-21  408.084991  412.425995  393.181000  398.821014  398.821014   \n",
              "\n",
              "              Volume  \n",
              "Date                  \n",
              "2014-09-17  21056800  \n",
              "2014-09-18  34483200  \n",
              "2014-09-19  37919700  \n",
              "2014-09-20  36863600  \n",
              "2014-09-21  26580100  "
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              "      <th>2014-09-17</th>\n",
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              "      <td>408.084991</td>\n",
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            }
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ***create new columns***"
      ],
      "metadata": {
        "id": "mxSJC99bbnWB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Tolerance\"]  = df[\"High\"] - df[\"Low\"]\n",
        "df[\"Benefit\"]  = df[\"Open\"] - df[\"Close\"]\n",
        "df[\"Tomorrow\"] =  df[\"Close\"].shift(-1)\n",
        "df.head()\n",
        "df.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "fuXlzPZI7hwd"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ***split Dataset***"
      ],
      "metadata": {
        "id": "RdVn7ov4bwlv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#parameters\n",
        "\n",
        "X = df[[\"Open\",\t\"High\"\t,\"Low\"\t,\"Volume\", \"Tolerance\",\t\"Benefit\"]]\n",
        "\n",
        "y = df[\"Tomorrow\"]\n",
        "\n",
        "\n",
        "df.dropna(inplace=True)\n",
        "\n",
        "#split Data\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(X,y, train_size=0.2, shuffle=False )\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "KITlWt_a7SCf"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ***import Linearregression model***"
      ],
      "metadata": {
        "id": "WgXYPngscBuc"
      }
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      "cell_type": "code",
      "source": [
        "#model\n",
        "\n",
        "model = LinearRegression()\n",
        "model.fit(x_train,y_train)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "K8DC2cQU84wr",
        "outputId": "4795b6e2-fc13-43ae-d3f9-1ccace9bdfd4"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-7 {color: black;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ***predict section***"
      ],
      "metadata": {
        "id": "jDSTfRHecLbc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#predict\n",
        "\n",
        "all_pred =  model.predict(X)\n",
        "train_pred = model.predict(x_train)\n",
        "test_pred = model.predict(x_test)"
      ],
      "metadata": {
        "id": "GwhWqFxy9Zos"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ***calculate RMSE***"
      ],
      "metadata": {
        "id": "RY60K4Nac5U8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Rmse\n",
        "\n",
        "rmse_all = np.sqrt(mean_squared_error(y, all_pred))\n",
        "rmse_train = np.sqrt(mean_squared_error(y_train, train_pred))\n",
        "rmse_test = np.sqrt(mean_squared_error(y_test, test_pred))\n"
      ],
      "metadata": {
        "id": "DTQ9mq7G93Hh"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'rmse all: {rmse_all}')\n",
        "print(f'rmse train: {rmse_train}')\n",
        "print(f'rmse test: {rmse_test}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iRLKUlBd-UoN",
        "outputId": "4e67e808-3a64-47c5-bc77-6bd16a8c9108"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rmse all: 1133.5892344077495\n",
            "rmse train: 12.528473891207867\n",
            "rmse test: 1267.282422643045\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# visualization real Data vs  model Predict\n",
        "\n"
      ],
      "metadata": {
        "id": "zaK8O3IgdQ_i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#visualization\n",
        "\n",
        "plt.figure(figsize=(14, 7))\n",
        "plt.plot(df.index, y, label=\"Real Close\", color='blue', linewidth=1)\n",
        "plt.plot(df.index, all_pred, label=\"Predicted Close\", color='red', linewidth=1)\n",
        "plt.title('Bitcoin Closing Prices: Real vs Predicted')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Price (USD)')\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "TwakV6H9Tzpt",
        "outputId": "32ec4200-2171-4062-d619-4a5a025dd161"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ***model testing***"
      ],
      "metadata": {
        "id": "7mxopYIwdq_0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#test model\n",
        "\n",
        "\n",
        "model.predict([[61414.81,\t62148.37,\t59836.53, 31753030589, 2311.840000000004,\t734.7200000000012]])\n",
        "#real close : 58116.98\n",
        "#predict close : 61142.70\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kDuFCkkcUUiH",
        "outputId": "363359dc-464b-4f59-e31e-b4fb316f2fec"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:465: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([61142.70679667])"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict([[58033.88,\t59359.43,\t57178.41, 26175260526,2181.019999999997 ,291.3799999999974]])\n",
        "# real close: 57344.91\n",
        "# predict close : 57837.267"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vX2VbNOkXU5l",
        "outputId": "2d017133-f5bd-4a1d-875c-5e99ed6098fe"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:465: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([57837.26733668])"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**powered by Miracle⚡️**"
      ],
      "metadata": {
        "id": "fX96sah_cfPw"
      }
    }
  ]
}