NY_Stock_Exchange.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1D8DHXpC5F2_LVTQfuSwplPcoYaBqxdAG",
      "authorship_tag": "ABX9TyMNN9zXobpobpmpdlXtKOMb",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/httperror505/Neural-Network/blob/colab/NY_Stock_Exchange.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 983
        },
        "id": "xnGbe4ZfxB-_",
        "outputId": "d74619ee-f639-4d7f-c97a-666d44e7fae6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "Mean Squared Error: 5.959938608007035e-28\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Import libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Mount Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Load the dataset from Google Drive\n",
        "file_path = \"/content/drive/MyDrive/Datasets/prices.csv\"\n",
        "df = pd.read_csv(file_path)\n",
        "\n",
        "# Display the first few rows of the dataset\n",
        "df.head()\n",
        "\n",
        "# Assuming your dataset has columns named 'feature1', 'feature2', ..., 'target'\n",
        "# Replace these column names with your actual column names\n",
        "\n",
        "# Select features and target variable\n",
        "X = df[['open', 'low', 'high', 'close']]\n",
        "y = df['close']\n",
        "\n",
        "# Split the dataset into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Create and train the linear regression model\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "print(f'Mean Squared Error: {mse}')\n",
        "\n",
        "# Plot predictions vs actual values\n",
        "plt.scatter(y_test, y_pred)\n",
        "plt.xlabel(\"Actual Values\")\n",
        "plt.ylabel(\"Predicted Values\")\n",
        "plt.title(\"Linear Regression: Actual vs Predicted\")\n",
        "plt.show()\n",
        "\n",
        "# Optional: Plot a graph of the coefficients\n",
        "coefficients = pd.DataFrame({'Feature': X.columns, 'Coefficient': model.coef_})\n",
        "coefficients.plot(kind='bar', x='Feature', y='Coefficient', title='Linear Regression Coefficients')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Mount Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Load the dataset from Google Drive\n",
        "file_path = \"/content/drive/MyDrive/Datasets/prices-split-adjusted.csv\"\n",
        "df = pd.read_csv(file_path)\n",
        "\n",
        "# Display the first few rows of the dataset\n",
        "df.head()\n",
        "\n",
        "# Assuming your dataset has columns named 'feature1', 'feature2', ..., 'target'\n",
        "# Replace these column names with your actual column names\n",
        "\n",
        "# Select features and target variable\n",
        "X = df[['open', 'low', 'high', 'volume']]\n",
        "y = df['close']\n",
        "\n",
        "# Split the dataset into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Create and train the linear regression model\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "print(f'Mean Squared Error: {mse}')\n",
        "\n",
        "# Plot predictions vs actual values\n",
        "plt.scatter(y_test, y_pred)\n",
        "plt.xlabel(\"Actual Values\")\n",
        "plt.ylabel(\"Predicted Values\")\n",
        "plt.title(\"Linear Regression: Actual vs Predicted\")\n",
        "plt.show()\n",
        "\n",
        "# Optional: Plot a graph of the coefficients\n",
        "coefficients = pd.DataFrame({'Feature': X.columns, 'Coefficient': model.coef_})\n",
        "coefficients.plot(kind='bar', x='Feature', y='Coefficient', title='Linear Regression Coefficients')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 999
        },
        "id": "zEC-4NuGWioe",
        "outputId": "35861449-24c3-4a26-a5fe-6d169b9c70ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "Mean Squared Error: 0.34834011860463443\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Mount Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Load the dataset from Google Drive\n",
        "file_path = \"/content/drive/MyDrive/Datasets/prices-split-adjusted.csv\"\n",
        "df = pd.read_csv(file_path)\n",
        "\n",
        "# Display the first few rows of the dataset\n",
        "df.head()\n",
        "\n",
        "# Assuming your dataset has columns named 'feature1', 'feature2', ..., 'target'\n",
        "# Replace these column names with your actual column names\n",
        "\n",
        "# Select features and target variable\n",
        "X = df[['open', 'low', 'high', 'volume']]\n",
        "y = df['close']\n",
        "\n",
        "# Split the dataset into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Create and train the linear regression model\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "print(f'Mean Squared Error: {mse}')\n",
        "\n",
        "# Plot predictions vs actual values\n",
        "plt.scatter(y_test, y_pred)\n",
        "plt.xlabel(\"Actual Values\")\n",
        "plt.ylabel(\"Predicted Values\")\n",
        "plt.title(\"Linear Regression: Actual vs Predicted\")\n",
        "plt.show()\n",
        "\n",
        "# Optional: Plot a graph of the coefficients\n",
        "coefficients = pd.DataFrame({'Feature': X.columns, 'Coefficient': model.coef_})\n",
        "coefficients.plot(kind='bar', x='Feature', y='Coefficient', title='Linear Regression Coefficients')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "BmxkgPrFXcaJ",
        "outputId": "e0f383f7-4a2e-43ac-823c-2a7cb1fde409"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-8-5aa8da0c893d>\u001b[0m in \u001b[0;36m<cell line: 28>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;31m# Use TF-IDF to convert text data into numerical features\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0mvectorizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTfidfVectorizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvectorizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'combined_text'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;31m# Select target variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/feature_extraction/text.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, raw_documents, y)\u001b[0m\n\u001b[1;32m   2131\u001b[0m             \u001b[0msublinear_tf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msublinear_tf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2132\u001b[0m         )\n\u001b[0;32m-> 2133\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_documents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2134\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tfidf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2135\u001b[0m         \u001b[0;31m# X is already a transformed view of raw_documents so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/feature_extraction/text.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, raw_documents, y)\u001b[0m\n\u001b[1;32m   1386\u001b[0m                     \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1387\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1388\u001b[0;31m         \u001b[0mvocabulary\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_count_vocab\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_documents\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfixed_vocabulary_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1389\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1390\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbinary\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/feature_extraction/text.py\u001b[0m in \u001b[0;36m_count_vocab\u001b[0;34m(self, raw_documents, fixed_vocab)\u001b[0m\n\u001b[1;32m   1273\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mdoc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mraw_documents\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1274\u001b[0m             \u001b[0mfeature_counter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1275\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mfeature\u001b[0m \u001b[0;32min\u001b[0m \u001b[0manalyze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdoc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1276\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1277\u001b[0m                     \u001b[0mfeature_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvocabulary\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfeature\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/feature_extraction/text.py\u001b[0m in \u001b[0;36m_analyze\u001b[0;34m(doc, analyzer, tokenizer, ngrams, preprocessor, decoder, stop_words)\u001b[0m\n\u001b[1;32m    104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdecoder\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 106\u001b[0;31m         \u001b[0mdoc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdecoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdoc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    107\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0manalyzer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m         \u001b[0mdoc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0manalyzer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdoc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/feature_extraction/text.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, doc)\u001b[0m\n\u001b[1;32m    237\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdoc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 239\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m    240\u001b[0m                 \u001b[0;34m\"np.nan is an invalid document, expected byte or unicode string.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    241\u001b[0m             )\n",
            "\u001b[0;31mValueError\u001b[0m: np.nan is an invalid document, expected byte or unicode string."
          ]
        }
      ]
    }
  ]
}