deep_iris_and_titanic_and_tensorflow_.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Neural Network data iris and train_and_test2**"
      ],
      "metadata": {
        "id": "cJkMGAi-7yuU"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "atJiHu2LpXez",
        "outputId": "4557a1c1-b2c7-4306-8e27-dd2989e835d6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
              "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
              "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
              "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
              "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
              "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0923d259-c5f5-418a-8221-b395bfd922e1\" class=\"colab-df-container\">\n",
              "    <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>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0923d259-c5f5-418a-8221-b395bfd922e1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0923d259-c5f5-418a-8221-b395bfd922e1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0923d259-c5f5-418a-8221-b395bfd922e1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-4a7017b2-6716-42ae-9875-d6e25d46c1c1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4a7017b2-6716-42ae-9875-d6e25d46c1c1')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4a7017b2-6716-42ae-9875-d6e25d46c1c1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43,\n        \"min\": 1,\n        \"max\": 150,\n        \"num_unique_values\": 150,\n        \"samples\": [\n          74,\n          19,\n          119\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.828066127977863,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4335943113621737,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7644204199522626,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7631607417008411,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Iris-setosa\",\n          \"Iris-versicolor\",\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "#training plot loss\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "data=pd.read_csv('Iris.csv')\n",
        "data.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelBinarizer, StandardScaler\n",
        "import numpy as np\n",
        "\n",
        "#data = data.drop(columns=['Id'])\n",
        "\n",
        "data['Species'] = data['Species'].apply(lambda x: 1 if x == 'Iris-setosa' else 0)\n",
        "\n",
        "X = data.drop(columns=['Species'])\n",
        "y = data['Species']\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "model = Sequential([\n",
        "    Dense(16, input_dim=4, activation='relu'),\n",
        "    Dense(8, activation='relu'),\n",
        "    Dense(3, activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(X_train, y_train, epochs=20, batch_size=32, verbose=1, validation_data=(X_test, y_test))\n",
        "\n",
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "id": "cq3mKhsezkZP",
        "outputId": "9c2a8a41-5559-445d-cbd8-a90e37f19a57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Exception encountered when calling Sequential.call().\n\n\u001b[1mInput 0 of layer \"dense_39\" is incompatible with the layer: expected axis -1 of input shape to have value 4, but received input with shape (None, 5)\u001b[0m\n\nArguments received by Sequential.call():\n  • inputs=tf.Tensor(shape=(None, 5), dtype=float32)\n  • training=True\n  • mask=None",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-29-778a21ee04af>\u001b[0m in \u001b[0;36m<cell line: 30>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'adam'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'binary_crossentropy'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'accuracy'\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[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\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     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Training Loss'\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/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    120\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;31m# `keras.config.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    123\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\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/keras/src/layers/input_spec.py\u001b[0m in \u001b[0;36massert_input_compatibility\u001b[0;34m(input_spec, inputs, layer_name)\u001b[0m\n\u001b[1;32m    225\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    226\u001b[0m                 }:\n\u001b[0;32m--> 227\u001b[0;31m                     raise ValueError(\n\u001b[0m\u001b[1;32m    228\u001b[0m                         \u001b[0;34mf'Input {input_index} of layer \"{layer_name}\" is '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m                         \u001b[0;34mf\"incompatible with the layer: expected axis {axis} \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Exception encountered when calling Sequential.call().\n\n\u001b[1mInput 0 of layer \"dense_39\" is incompatible with the layer: expected axis -1 of input shape to have value 4, but received input with shape (None, 5)\u001b[0m\n\nArguments received by Sequential.call():\n  • inputs=tf.Tensor(shape=(None, 5), dtype=float32)\n  • training=True\n  • mask=None"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "data=pd.read_csv('train_and_test2.csv')\n",
        "data.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "JBzbiGja43uj",
        "outputId": "ac9b4e73-a94e-40b6-b536-9cf096c4664a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Passengerid   Age     Fare  Sex  sibsp  zero  zero.1  zero.2  zero.3  \\\n",
              "0            1  22.0   7.2500    0      1     0       0       0       0   \n",
              "1            2  38.0  71.2833    1      1     0       0       0       0   \n",
              "2            3  26.0   7.9250    1      0     0       0       0       0   \n",
              "3            4  35.0  53.1000    1      1     0       0       0       0   \n",
              "4            5  35.0   8.0500    0      0     0       0       0       0   \n",
              "\n",
              "   zero.4  ...  zero.12  zero.13  zero.14  Pclass  zero.15  zero.16  Embarked  \\\n",
              "0       0  ...        0        0        0       3        0        0       2.0   \n",
              "1       0  ...        0        0        0       1        0        0       0.0   \n",
              "2       0  ...        0        0        0       3        0        0       2.0   \n",
              "3       0  ...        0        0        0       1        0        0       2.0   \n",
              "4       0  ...        0        0        0       3        0        0       2.0   \n",
              "\n",
              "   zero.17  zero.18  2urvived  \n",
              "0        0        0         0  \n",
              "1        0        0         1  \n",
              "2        0        0         1  \n",
              "3        0        0         1  \n",
              "4        0        0         0  \n",
              "\n",
              "[5 rows x 28 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d9c64a10-6cd2-4249-86c5-e134acd0e584\" class=\"colab-df-container\">\n",
              "    <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>Passengerid</th>\n",
              "      <th>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Sex</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>zero</th>\n",
              "      <th>zero.1</th>\n",
              "      <th>zero.2</th>\n",
              "      <th>zero.3</th>\n",
              "      <th>zero.4</th>\n",
              "      <th>...</th>\n",
              "      <th>zero.12</th>\n",
              "      <th>zero.13</th>\n",
              "      <th>zero.14</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>zero.15</th>\n",
              "      <th>zero.16</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>zero.17</th>\n",
              "      <th>zero.18</th>\n",
              "      <th>2urvived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>22.0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>38.0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>26.0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>35.0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>35.0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 28 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d9c64a10-6cd2-4249-86c5-e134acd0e584')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d9c64a10-6cd2-4249-86c5-e134acd0e584 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d9c64a10-6cd2-4249-86c5-e134acd0e584');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-7f8f705f-6347-4a11-8ccd-491184615166\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7f8f705f-6347-4a11-8ccd-491184615166')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7f8f705f-6347-4a11-8ccd-491184615166 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Input\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "data = data.drop(columns=['Passengerid', 'zero', 'zero.1', 'zero.2', 'zero.3', 'zero.4', 'zero.12', 'zero.13', 'zero.14', 'zero.15', 'zero.16', 'zero.17', 'zero.18'])\n",
        "\n",
        "data = pd.get_dummies(data, columns=['Sex', 'Embarked'], drop_first=True)\n",
        "\n",
        "X = data.drop(columns=['2urvived'])\n",
        "y = data['2urvived']\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
        "\n",
        "model = Sequential([\n",
        "    Input(shape=(X_train.shape[1],)),\n",
        "    Dense(16, activation='relu'),\n",
        "    Dense(8, activation='relu'),\n",
        "    Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(X_train, y_train, epochs=50, batch_size=32, verbose=1, validation_data=(X_test, y_test))\n",
        "\n",
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "CiZoU81G9ztt",
        "outputId": "3a06f910-44bd-4fb0-ac32-63d9304a6ea5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - accuracy: 0.3256 - loss: 0.7562 - val_accuracy: 0.5076 - val_loss: 0.6956\n",
            "Epoch 2/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5907 - loss: 0.6948 - val_accuracy: 0.7176 - val_loss: 0.6713\n",
            "Epoch 3/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7232 - loss: 0.6661 - val_accuracy: 0.7405 - val_loss: 0.6523\n",
            "Epoch 4/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7560 - loss: 0.6382 - val_accuracy: 0.7214 - val_loss: 0.6288\n",
            "Epoch 5/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7557 - loss: 0.6081 - val_accuracy: 0.7252 - val_loss: 0.6032\n",
            "Epoch 6/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7417 - loss: 0.5870 - val_accuracy: 0.7328 - val_loss: 0.5713\n",
            "Epoch 7/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7630 - loss: 0.5339 - val_accuracy: 0.7290 - val_loss: 0.5419\n",
            "Epoch 8/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7561 - loss: 0.5176 - val_accuracy: 0.7405 - val_loss: 0.5197\n",
            "Epoch 9/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7527 - loss: 0.5075 - val_accuracy: 0.7366 - val_loss: 0.5030\n",
            "Epoch 10/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7966 - loss: 0.4592 - val_accuracy: 0.7481 - val_loss: 0.4938\n",
            "Epoch 11/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7795 - loss: 0.4591 - val_accuracy: 0.7557 - val_loss: 0.4870\n",
            "Epoch 12/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7993 - loss: 0.4451 - val_accuracy: 0.7672 - val_loss: 0.4844\n",
            "Epoch 13/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8043 - loss: 0.4325 - val_accuracy: 0.7748 - val_loss: 0.4806\n",
            "Epoch 14/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8027 - loss: 0.4305 - val_accuracy: 0.7786 - val_loss: 0.4793\n",
            "Epoch 15/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8047 - loss: 0.4231 - val_accuracy: 0.7786 - val_loss: 0.4769\n",
            "Epoch 16/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8109 - loss: 0.4415 - val_accuracy: 0.7863 - val_loss: 0.4756\n",
            "Epoch 17/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7993 - loss: 0.4487 - val_accuracy: 0.7901 - val_loss: 0.4740\n",
            "Epoch 18/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8220 - loss: 0.4168 - val_accuracy: 0.7901 - val_loss: 0.4735\n",
            "Epoch 19/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8086 - loss: 0.4252 - val_accuracy: 0.7863 - val_loss: 0.4715\n",
            "Epoch 20/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8205 - loss: 0.4252 - val_accuracy: 0.7939 - val_loss: 0.4718\n",
            "Epoch 21/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7965 - loss: 0.4593 - val_accuracy: 0.7939 - val_loss: 0.4706\n",
            "Epoch 22/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8040 - loss: 0.4273 - val_accuracy: 0.7939 - val_loss: 0.4715\n",
            "Epoch 23/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8047 - loss: 0.4261 - val_accuracy: 0.7977 - val_loss: 0.4699\n",
            "Epoch 24/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8103 - loss: 0.4225 - val_accuracy: 0.7939 - val_loss: 0.4693\n",
            "Epoch 25/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7838 - loss: 0.4477 - val_accuracy: 0.7977 - val_loss: 0.4681\n",
            "Epoch 26/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7837 - loss: 0.4649 - val_accuracy: 0.7977 - val_loss: 0.4681\n",
            "Epoch 27/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8055 - loss: 0.4331 - val_accuracy: 0.7977 - val_loss: 0.4683\n",
            "Epoch 28/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8112 - loss: 0.4263 - val_accuracy: 0.8015 - val_loss: 0.4670\n",
            "Epoch 29/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8045 - loss: 0.4185 - val_accuracy: 0.8015 - val_loss: 0.4672\n",
            "Epoch 30/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7929 - loss: 0.4447 - val_accuracy: 0.8015 - val_loss: 0.4658\n",
            "Epoch 31/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8120 - loss: 0.4159 - val_accuracy: 0.8015 - val_loss: 0.4663\n",
            "Epoch 32/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7957 - loss: 0.4313 - val_accuracy: 0.8015 - val_loss: 0.4660\n",
            "Epoch 33/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8058 - loss: 0.4308 - val_accuracy: 0.8015 - val_loss: 0.4653\n",
            "Epoch 34/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8056 - loss: 0.4264 - val_accuracy: 0.8015 - val_loss: 0.4649\n",
            "Epoch 35/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8096 - loss: 0.4207 - val_accuracy: 0.8053 - val_loss: 0.4650\n",
            "Epoch 36/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7992 - loss: 0.4271 - val_accuracy: 0.8015 - val_loss: 0.4657\n",
            "Epoch 37/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8135 - loss: 0.4225 - val_accuracy: 0.8053 - val_loss: 0.4649\n",
            "Epoch 38/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8027 - loss: 0.4282 - val_accuracy: 0.8015 - val_loss: 0.4653\n",
            "Epoch 39/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8112 - loss: 0.4290 - val_accuracy: 0.8015 - val_loss: 0.4652\n",
            "Epoch 40/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8039 - loss: 0.4149 - val_accuracy: 0.7939 - val_loss: 0.4664\n",
            "Epoch 41/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7779 - loss: 0.4500 - val_accuracy: 0.8015 - val_loss: 0.4647\n",
            "Epoch 42/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8134 - loss: 0.4255 - val_accuracy: 0.8015 - val_loss: 0.4656\n",
            "Epoch 43/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8096 - loss: 0.4105 - val_accuracy: 0.7977 - val_loss: 0.4657\n",
            "Epoch 44/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7795 - loss: 0.4531 - val_accuracy: 0.8015 - val_loss: 0.4656\n",
            "Epoch 45/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8141 - loss: 0.4098 - val_accuracy: 0.8053 - val_loss: 0.4651\n",
            "Epoch 46/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8050 - loss: 0.4407 - val_accuracy: 0.8015 - val_loss: 0.4663\n",
            "Epoch 47/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8068 - loss: 0.4271 - val_accuracy: 0.8015 - val_loss: 0.4656\n",
            "Epoch 48/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7975 - loss: 0.4479 - val_accuracy: 0.7939 - val_loss: 0.4662\n",
            "Epoch 49/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8124 - loss: 0.4129 - val_accuracy: 0.7901 - val_loss: 0.4668\n",
            "Epoch 50/50\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7952 - loss: 0.4429 - val_accuracy: 0.7977 - val_loss: 0.4660\n"
          ]
        },
        {
          "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": [
        "!mkdir -p ~/.kaggle\n",
        "!mv kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kcnjPvE1NRH6",
        "outputId": "b9cfb45d-e5f4-4e23-de15-1aae23954e42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mv: cannot stat 'kaggle.json': No such file or directory\n",
            "chmod: cannot access '/root/.kaggle/kaggle.json': No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d shree1992/housedata"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R-dP7rfpOLme",
        "outputId": "2dbe3af1-b1c7-4df5-dc64-afb131db1fd4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset URL: https://www.kaggle.com/datasets/shree1992/housedata\n",
            "License(s): unknown\n",
            "Downloading housedata.zip to /content\n",
            "  0% 0.00/432k [00:00<?, ?B/s]\n",
            "100% 432k/432k [00:00<00:00, 70.6MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/housedata.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i0Kbk8pQO4td",
        "outputId": "c840353e-13be-4e4a-bf53-9e00b1f26c9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/housedata.zip\n",
            "  inflating: data.csv                \n",
            "  inflating: data.dat                \n",
            "  inflating: output.csv              \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv('data.csv')\n",
        "\n",
        "data.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "hO0QS07bO6v-",
        "outputId": "7b248312-e529-4aa3-f5f9-de87d609b209"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  date      price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
              "0  2014-05-02 00:00:00   313000.0       3.0       1.50         1340      7912   \n",
              "1  2014-05-02 00:00:00  2384000.0       5.0       2.50         3650      9050   \n",
              "2  2014-05-02 00:00:00   342000.0       3.0       2.00         1930     11947   \n",
              "3  2014-05-02 00:00:00   420000.0       3.0       2.25         2000      8030   \n",
              "4  2014-05-02 00:00:00   550000.0       4.0       2.50         1940     10500   \n",
              "\n",
              "   floors  waterfront  view  condition  sqft_above  sqft_basement  yr_built  \\\n",
              "0     1.5           0     0          3        1340              0      1955   \n",
              "1     2.0           0     4          5        3370            280      1921   \n",
              "2     1.0           0     0          4        1930              0      1966   \n",
              "3     1.0           0     0          4        1000           1000      1963   \n",
              "4     1.0           0     0          4        1140            800      1976   \n",
              "\n",
              "   yr_renovated                    street       city  statezip country  \n",
              "0          2005      18810 Densmore Ave N  Shoreline  WA 98133     USA  \n",
              "1             0           709 W Blaine St    Seattle  WA 98119     USA  \n",
              "2             0  26206-26214 143rd Ave SE       Kent  WA 98042     USA  \n",
              "3             0           857 170th Pl NE   Bellevue  WA 98008     USA  \n",
              "4          1992         9105 170th Ave NE    Redmond  WA 98052     USA  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8facf4e7-0f11-4570-b683-74305d35fd8c\" class=\"colab-df-container\">\n",
              "    <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>date</th>\n",
              "      <th>price</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>bathrooms</th>\n",
              "      <th>sqft_living</th>\n",
              "      <th>sqft_lot</th>\n",
              "      <th>floors</th>\n",
              "      <th>waterfront</th>\n",
              "      <th>view</th>\n",
              "      <th>condition</th>\n",
              "      <th>sqft_above</th>\n",
              "      <th>sqft_basement</th>\n",
              "      <th>yr_built</th>\n",
              "      <th>yr_renovated</th>\n",
              "      <th>street</th>\n",
              "      <th>city</th>\n",
              "      <th>statezip</th>\n",
              "      <th>country</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014-05-02 00:00:00</td>\n",
              "      <td>313000.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.50</td>\n",
              "      <td>1340</td>\n",
              "      <td>7912</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1340</td>\n",
              "      <td>0</td>\n",
              "      <td>1955</td>\n",
              "      <td>2005</td>\n",
              "      <td>18810 Densmore Ave N</td>\n",
              "      <td>Shoreline</td>\n",
              "      <td>WA 98133</td>\n",
              "      <td>USA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014-05-02 00:00:00</td>\n",
              "      <td>2384000.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.50</td>\n",
              "      <td>3650</td>\n",
              "      <td>9050</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>3370</td>\n",
              "      <td>280</td>\n",
              "      <td>1921</td>\n",
              "      <td>0</td>\n",
              "      <td>709 W Blaine St</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>WA 98119</td>\n",
              "      <td>USA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-05-02 00:00:00</td>\n",
              "      <td>342000.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>1930</td>\n",
              "      <td>11947</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1930</td>\n",
              "      <td>0</td>\n",
              "      <td>1966</td>\n",
              "      <td>0</td>\n",
              "      <td>26206-26214 143rd Ave SE</td>\n",
              "      <td>Kent</td>\n",
              "      <td>WA 98042</td>\n",
              "      <td>USA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-05-02 00:00:00</td>\n",
              "      <td>420000.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.25</td>\n",
              "      <td>2000</td>\n",
              "      <td>8030</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1963</td>\n",
              "      <td>0</td>\n",
              "      <td>857 170th Pl NE</td>\n",
              "      <td>Bellevue</td>\n",
              "      <td>WA 98008</td>\n",
              "      <td>USA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-05-02 00:00:00</td>\n",
              "      <td>550000.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.50</td>\n",
              "      <td>1940</td>\n",
              "      <td>10500</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1140</td>\n",
              "      <td>800</td>\n",
              "      <td>1976</td>\n",
              "      <td>1992</td>\n",
              "      <td>9105 170th Ave NE</td>\n",
              "      <td>Redmond</td>\n",
              "      <td>WA 98052</td>\n",
              "      <td>USA</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8facf4e7-0f11-4570-b683-74305d35fd8c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8facf4e7-0f11-4570-b683-74305d35fd8c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8facf4e7-0f11-4570-b683-74305d35fd8c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b934326b-12c8-46db-8162-9412a5bb6a79\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b934326b-12c8-46db-8162-9412a5bb6a79')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b934326b-12c8-46db-8162-9412a5bb6a79 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 4600,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 70,\n        \"samples\": [\n          \"2014-05-24 00:00:00\",\n          \"2014-05-02 00:00:00\",\n          \"2014-06-20 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 563834.7025471414,\n        \"min\": 0.0,\n        \"max\": 26590000.0,\n        \"num_unique_values\": 1741,\n        \"samples\": [\n          689900.0,\n          170500.0,\n          466800.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9088481155258186,\n        \"min\": 0.0,\n        \"max\": 9.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          8.0,\n          5.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7837810746502805,\n        \"min\": 0.0,\n        \"max\": 8.0,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          3.25,\n          5.75,\n          1.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sqft_living\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 963,\n        \"min\": 370,\n        \"max\": 13540,\n        \"num_unique_values\": 566,\n        \"samples\": [\n          1785,\n          2701,\n          2840\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sqft_lot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35884,\n        \"min\": 638,\n        \"max\": 1074218,\n        \"num_unique_values\": 3113,\n        \"samples\": [\n          5540,\n          9292,\n          17334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"floors\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5382883772969885,\n        \"min\": 1.0,\n        \"max\": 3.5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1.5,\n          2.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"waterfront\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"view\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"condition\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sqft_above\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 862,\n        \"min\": 370,\n        \"max\": 9410,\n        \"num_unique_values\": 511,\n        \"samples\": [\n          2280,\n          2230\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sqft_basement\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 464,\n        \"min\": 0,\n        \"max\": 4820,\n        \"num_unique_values\": 207,\n        \"samples\": [\n          460,\n          310\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yr_built\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29,\n        \"min\": 1900,\n        \"max\": 2014,\n        \"num_unique_values\": 115,\n        \"samples\": [\n          1926,\n          1976\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yr_renovated\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 979,\n        \"min\": 0,\n        \"max\": 2014,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          2005,\n          1988\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"street\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4525,\n        \"samples\": [\n          \"743 N 80th St\",\n          \"157 Pike St NE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 44,\n        \"samples\": [\n          \"Medina\",\n          \"Inglewood-Finn Hill\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"statezip\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 77,\n        \"samples\": [\n          \"WA 98052\",\n          \"WA 98075\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"USA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Input\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "data = data.drop(columns=['date', 'street', 'city', 'statezip', 'country'])\n",
        "\n",
        "threshold = 500000\n",
        "data['price'] = (data['price'] > threshold).astype(int)\n",
        "\n",
        "X = data.drop(columns=['price'])\n",
        "y = data['price']\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
        "\n",
        "model = Sequential([\n",
        "    Input(shape=(X_train.shape[1],)),\n",
        "    Dense(16, activation='relu'),\n",
        "    Dense(8, activation='relu'),\n",
        "    Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(X_train, y_train, epochs=50, batch_size=32, verbose=1, validation_data=(X_test, y_test))\n",
        "\n",
        "plt.figure(figsize=(12, 5))\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "test_loss, test_accuracy = model.evaluate(X_test, y_test, verbose=0)\n",
        "print(f'Test Accuracy: {test_accuracy:.4f}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "CKRQXI5mQnau",
        "outputId": "ba0f2a69-237b-4ec9-f8cb-ffdf18328367"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.6088 - loss: 0.6370 - val_accuracy: 0.7054 - val_loss: 0.5657\n",
            "Epoch 2/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7397 - loss: 0.5248 - val_accuracy: 0.7380 - val_loss: 0.5350\n",
            "Epoch 3/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.7608 - loss: 0.5098 - val_accuracy: 0.7489 - val_loss: 0.5163\n",
            "Epoch 4/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7823 - loss: 0.4836 - val_accuracy: 0.7652 - val_loss: 0.5055\n",
            "Epoch 5/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7917 - loss: 0.4736 - val_accuracy: 0.7674 - val_loss: 0.5023\n",
            "Epoch 6/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7718 - loss: 0.4807 - val_accuracy: 0.7717 - val_loss: 0.4981\n",
            "Epoch 7/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7853 - loss: 0.4770 - val_accuracy: 0.7696 - val_loss: 0.4978\n",
            "Epoch 8/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7782 - loss: 0.4641 - val_accuracy: 0.7750 - val_loss: 0.4964\n",
            "Epoch 9/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7894 - loss: 0.4509 - val_accuracy: 0.7750 - val_loss: 0.4949\n",
            "Epoch 10/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.7853 - loss: 0.4608 - val_accuracy: 0.7696 - val_loss: 0.4951\n",
            "Epoch 11/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.7882 - loss: 0.4609 - val_accuracy: 0.7685 - val_loss: 0.4943\n",
            "Epoch 12/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.7932 - loss: 0.4637 - val_accuracy: 0.7652 - val_loss: 0.4954\n",
            "Epoch 13/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.7779 - loss: 0.4647 - val_accuracy: 0.7674 - val_loss: 0.4952\n",
            "Epoch 14/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7960 - loss: 0.4513 - val_accuracy: 0.7717 - val_loss: 0.4945\n",
            "Epoch 15/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7984 - loss: 0.4512 - val_accuracy: 0.7641 - val_loss: 0.4948\n",
            "Epoch 16/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7934 - loss: 0.4620 - val_accuracy: 0.7696 - val_loss: 0.4945\n",
            "Epoch 17/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7956 - loss: 0.4483 - val_accuracy: 0.7717 - val_loss: 0.4938\n",
            "Epoch 18/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7962 - loss: 0.4561 - val_accuracy: 0.7707 - val_loss: 0.4935\n",
            "Epoch 19/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7893 - loss: 0.4562 - val_accuracy: 0.7674 - val_loss: 0.4946\n",
            "Epoch 20/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7870 - loss: 0.4594 - val_accuracy: 0.7685 - val_loss: 0.4954\n",
            "Epoch 21/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7758 - loss: 0.4715 - val_accuracy: 0.7685 - val_loss: 0.4951\n",
            "Epoch 22/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.7939 - loss: 0.4562 - val_accuracy: 0.7652 - val_loss: 0.4952\n",
            "Epoch 23/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.7926 - loss: 0.4554 - val_accuracy: 0.7696 - val_loss: 0.4957\n",
            "Epoch 24/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7894 - loss: 0.4530 - val_accuracy: 0.7707 - val_loss: 0.4965\n",
            "Epoch 25/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7948 - loss: 0.4457 - val_accuracy: 0.7707 - val_loss: 0.4960\n",
            "Epoch 26/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7881 - loss: 0.4546 - val_accuracy: 0.7696 - val_loss: 0.4947\n",
            "Epoch 27/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.7957 - loss: 0.4556 - val_accuracy: 0.7707 - val_loss: 0.4955\n",
            "Epoch 28/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7915 - loss: 0.4520 - val_accuracy: 0.7674 - val_loss: 0.4956\n",
            "Epoch 29/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7845 - loss: 0.4567 - val_accuracy: 0.7685 - val_loss: 0.4978\n",
            "Epoch 30/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7909 - loss: 0.4549 - val_accuracy: 0.7707 - val_loss: 0.4967\n",
            "Epoch 31/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8023 - loss: 0.4360 - val_accuracy: 0.7685 - val_loss: 0.4964\n",
            "Epoch 32/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.7924 - loss: 0.4482 - val_accuracy: 0.7663 - val_loss: 0.4951\n",
            "Epoch 33/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7896 - loss: 0.4455 - val_accuracy: 0.7707 - val_loss: 0.4959\n",
            "Epoch 34/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7958 - loss: 0.4479 - val_accuracy: 0.7717 - val_loss: 0.4976\n",
            "Epoch 35/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7909 - loss: 0.4558 - val_accuracy: 0.7696 - val_loss: 0.4994\n",
            "Epoch 36/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.7938 - loss: 0.4503 - val_accuracy: 0.7696 - val_loss: 0.4970\n",
            "Epoch 37/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7790 - loss: 0.4679 - val_accuracy: 0.7728 - val_loss: 0.4976\n",
            "Epoch 38/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8016 - loss: 0.4427 - val_accuracy: 0.7685 - val_loss: 0.4972\n",
            "Epoch 39/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7923 - loss: 0.4477 - val_accuracy: 0.7674 - val_loss: 0.4989\n",
            "Epoch 40/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.7949 - loss: 0.4403 - val_accuracy: 0.7696 - val_loss: 0.4964\n",
            "Epoch 41/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8022 - loss: 0.4362 - val_accuracy: 0.7728 - val_loss: 0.4981\n",
            "Epoch 42/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7914 - loss: 0.4427 - val_accuracy: 0.7674 - val_loss: 0.4978\n",
            "Epoch 43/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7880 - loss: 0.4592 - val_accuracy: 0.7696 - val_loss: 0.4987\n",
            "Epoch 44/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8102 - loss: 0.4346 - val_accuracy: 0.7707 - val_loss: 0.4990\n",
            "Epoch 45/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8105 - loss: 0.4323 - val_accuracy: 0.7728 - val_loss: 0.4985\n",
            "Epoch 46/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8060 - loss: 0.4321 - val_accuracy: 0.7728 - val_loss: 0.4999\n",
            "Epoch 47/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8029 - loss: 0.4421 - val_accuracy: 0.7707 - val_loss: 0.4992\n",
            "Epoch 48/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8020 - loss: 0.4375 - val_accuracy: 0.7717 - val_loss: 0.4995\n",
            "Epoch 49/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.7968 - loss: 0.4441 - val_accuracy: 0.7707 - val_loss: 0.5017\n",
            "Epoch 50/50\n",
            "\u001b[1m115/115\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8131 - loss: 0.4296 - val_accuracy: 0.7739 - val_loss: 0.4986\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy: 0.7739\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **deep data tensorflow Neural Network**"
      ],
      "metadata": {
        "id": "_iaEo0VI8PpO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.datasets import fashion_mnist\n",
        "\n",
        "(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()\n",
        "\n",
        "print(\"Training data shape:\", x_train.shape)\n",
        "print(\"Training labels shape:\", y_train.shape)\n",
        "print(\"Test data shape:\", x_test.shape)\n",
        "print(\"Test labels shape:\", y_test.shape)\n",
        "\n",
        "plt.imshow(x_train[0], cmap='gray')\n",
        "plt.title(f\"Label: {y_train[0]}\")\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize=(10, 10))\n",
        "for i in range(9):\n",
        "    plt.subplot(3, 3, i+1)\n",
        "    plt.imshow(x_train[i], cmap='gray')\n",
        "    plt.title(f\"Label: {y_train[i]}\")\n",
        "    plt.axis('off')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "t2AQ9fVL6Wpy",
        "outputId": "bc2134c5-69a3-4d69-c118-5c64a5e2a6c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training data shape: (60000, 28, 28)\n",
            "Training labels shape: (60000,)\n",
            "Test data shape: (10000, 28, 28)\n",
            "Test labels shape: (10000,)\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 1000x1000 with 9 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.datasets import fashion_mnist\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "x_train = x_train / 255.0\n",
        "x_test = x_test / 255.0\n",
        "\n",
        "y_train = to_categorical(y_train, 10)\n",
        "y_test = to_categorical(y_test, 10)\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Flatten(input_shape=(28, 28)))\n",
        "\n",
        "model.add(Dense(128, activation='relu'))\n",
        "\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(x_train, y_train, epochs=10, batch_size=32, validation_data=(x_test, y_test))\n",
        "\n",
        "test_loss, test_accuracy = model.evaluate(x_test, y_test)\n",
        "print(f\"Test accuracy: {test_accuracy * 100:.2f}%\")\n",
        "\n",
        "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.title('Model Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 909
        },
        "id": "gzP9Tsos6iBg",
        "outputId": "3ba3a5ca-5a6a-4b42-ee4c-316be0282fd9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(**kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 4ms/step - accuracy: 0.7771 - loss: 0.6342 - val_accuracy: 0.8501 - val_loss: 0.4263\n",
            "Epoch 2/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.8648 - loss: 0.3816 - val_accuracy: 0.8618 - val_loss: 0.3856\n",
            "Epoch 3/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 7ms/step - accuracy: 0.8760 - loss: 0.3437 - val_accuracy: 0.8608 - val_loss: 0.3922\n",
            "Epoch 4/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 3ms/step - accuracy: 0.8836 - loss: 0.3173 - val_accuracy: 0.8724 - val_loss: 0.3573\n",
            "Epoch 5/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.8925 - loss: 0.2956 - val_accuracy: 0.8661 - val_loss: 0.3621\n",
            "Epoch 6/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 6ms/step - accuracy: 0.8958 - loss: 0.2835 - val_accuracy: 0.8722 - val_loss: 0.3515\n",
            "Epoch 7/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.8996 - loss: 0.2673 - val_accuracy: 0.8763 - val_loss: 0.3416\n",
            "Epoch 8/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9038 - loss: 0.2598 - val_accuracy: 0.8809 - val_loss: 0.3445\n",
            "Epoch 9/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 3ms/step - accuracy: 0.9082 - loss: 0.2479 - val_accuracy: 0.8808 - val_loss: 0.3304\n",
            "Epoch 10/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9108 - loss: 0.2372 - val_accuracy: 0.8846 - val_loss: 0.3314\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8846 - loss: 0.3320\n",
            "Test accuracy: 88.46%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}