Automated_EDA_Using_Pandas_Profiling.ipynb

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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Harshika-123/Machine_Learning/blob/master/Automated_EDA_Using_Pandas_Profiling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install ydata-profiling # install the correct package with the correct name\n",
        "\n",
        "import seaborn as sns # import necessary libraries\n",
        "from ydata_profiling import ProfileReport # import the correct class from the correct module\n",
        "\n",
        "df = sns.load_dataset('tips') # load the dataset\n",
        "\n",
        "profile = ProfileReport(df, explorative=True, dark_mode=True) # generate the profile report\n",
        "profile.to_file('output.html') # save the report to an HTML file"
      ],
      "metadata": {
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        "id": "Bv3XJNBlJpu9",
        "outputId": "5dd4c803-209a-4963-b3fb-23990f7a558c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: ydata-profiling in /usr/local/lib/python3.10/dist-packages (4.10.0)\n",
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            "Requirement already satisfied: pandas!=1.4.0,<3,>1.1 in /usr/local/lib/python3.10/dist-packages (from ydata-profiling) (2.1.4)\n",
            "Requirement already satisfied: matplotlib<3.10,>=3.5 in /usr/local/lib/python3.10/dist-packages (from ydata-profiling) (3.7.1)\n",
            "Requirement already satisfied: pydantic>=2 in /usr/local/lib/python3.10/dist-packages (from ydata-profiling) (2.9.2)\n",
            "Requirement already satisfied: PyYAML<6.1,>=5.0.0 in /usr/local/lib/python3.10/dist-packages (from ydata-profiling) (6.0.2)\n",
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            "Requirement already satisfied: numpy<2.2,>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from ydata-profiling) (1.26.4)\n",
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            "Requirement already satisfied: attrs>=19.3.0 in /usr/local/lib/python3.10/dist-packages (from visions<0.7.7,>=0.7.5->visions[type_image_path]<0.7.7,>=0.7.5->ydata-profiling) (24.2.0)\n",
            "Requirement already satisfied: networkx>=2.4 in /usr/local/lib/python3.10/dist-packages (from visions<0.7.7,>=0.7.5->visions[type_image_path]<0.7.7,>=0.7.5->ydata-profiling) (3.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from patsy>=0.5.6->statsmodels<1,>=0.13.2->ydata-profiling) (1.16.0)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a2b6e96e51b9406899cca247e11b2c77"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a6465407bc374a1ba369abba36be9684"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8471395858634dbab4473d061107a490"
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    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nb5-9GdaKBnO"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}