Nvidia.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bo2nNxnHXgFe"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.dates as mdates\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import the dataset from Nvidia.\n",
        "Stock_Nvidia_df = pd.read_csv('Stock_Nvidia.csv')\n",
        "\n",
        "# Create Scaler Object\n",
        "scaler = StandardScaler()\n",
        "\n",
        "numerical_features = ['Open', 'High', 'Low', 'Close', 'Volume']\n",
        "\n",
        "# Fit the scaler to the numerical features.\n",
        "scaler.fit(Stock_Nvidia_df[numerical_features])\n",
        "\n",
        "# Transform the data.\n",
        "Stock_Nvidia_df[numerical_features] = scaler.transform(Stock_Nvidia_df[numerical_features])\n",
        "\n",
        "# Create the tensor of input features\n",
        "X = torch.tensor(Stock_Nvidia_df[numerical_features].values, dtype=torch.float32)\n",
        "Y = torch.tensor(Stock_Nvidia_df['Close'].values, dtype=torch.float32)\n"
      ],
      "metadata": {
        "id": "MaP07HGTY1ED"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model of the dataset.\n",
        "x_train, x_test, y_train, y_test = train_test_split(X,Y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "XOHzFCamisCS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# set a random seed.\n",
        "torch.manual_seed(42)\n",
        "\n",
        "# Define the model using nn.Sequential.\n",
        "model = nn.Sequential(\n",
        "    nn.Linear(5, 10),\n",
        "    nn.ReLU(),\n",
        "    nn.Linear(10, 1),\n",
        "    nn.Sigmoid()\n",
        ")\n",
        "\n",
        "# MSE Loss Function + optimizer.\n",
        "loss_fn = nn.MSELoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "\n",
        "num_epochs = 1000\n",
        "\n",
        "epoch_format = 'Epoch[{0}'+ str(num_epochs)+'], MSE Loss:{1}'\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "  predictions = model(x_train)\n",
        "  MSE = loss_fn(predictions, y_train)\n",
        "  MSE.backward()\n",
        "  optimizer.step()\n",
        "  optimizer.zero_grad()\n",
        "\n",
        "  if (epoch + 1) % 100 == 0:\n",
        "    print(epoch_format.format(epoch + 1, MSE.item()))\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_eIPSowKi9b6",
        "outputId": "7ab80670-60a7-49bc-b584-7711e57abde9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch[1001000], MSE Loss:1.2851313352584839\n",
            "Epoch[2001000], MSE Loss:1.153443455696106\n",
            "Epoch[3001000], MSE Loss:1.1113479137420654\n",
            "Epoch[4001000], MSE Loss:1.0959606170654297\n",
            "Epoch[5001000], MSE Loss:1.0884671211242676\n",
            "Epoch[6001000], MSE Loss:1.0841143131256104\n",
            "Epoch[7001000], MSE Loss:1.081347942352295\n",
            "Epoch[8001000], MSE Loss:1.079489827156067\n",
            "Epoch[9001000], MSE Loss:1.0781779289245605\n",
            "Epoch[10001000], MSE Loss:1.0772162675857544\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Formula for Torch Save.\n",
        "torch.save(model, 'model.pth')\n",
        "\n"
      ],
      "metadata": {
        "id": "fKTrPXKF9y6t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the model.\n",
        "load_model = torch.load('model.pth')\n",
        "load_model.eval()\n",
        "with torch.no_grad():\n",
        "  predictions = load_model(x_test)\n",
        "  MSE = loss_fn(predictions, y_test)\n",
        "  print(MSE)\n",
        "\n",
        "  **** Note: I use tensor(pytorch) the format load model will not use the format again because it is a privacy concern from Github. I am using google colab than jupyter notebook for my work. ****\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RxxhzaZw-Aao",
        "outputId": "4fdae176-f305-478c-cc5f-4f4a28c2658c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(0.7142)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-23-1dc574b945c3>:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
            "  load_model =  torch.load('model.pth')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Visualization of the dataset of Nvidia.\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.scatter(Stock_Nvidia_df['Date'], Stock_Nvidia_df['Close'], label='Actual', color='blue')\n",
        "plt.plot(Stock_Nvidia_df['Date'], Stock_Nvidia_df['Close'].rolling(window=100).mean(), label='Moving Average', color='red')\n",
        "plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=10))\n",
        "plt.xlabel('Date')\n",
        "plt.xticks(rotation=45)\n",
        "plt.ylabel('Close Price')\n",
        "plt.title('Nvidia Stock Price')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "wRFmoztA-_V8",
        "outputId": "9d75d6f9-46c5-4e12-dfc0-b1c138573bde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}