{
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   "source": [
    "# Lab -- Publicly Accessible Datasets | Submission Template"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k245HSnZv0nX"
   },
   "source": [
    "Template for deliverable for https://classes.daveeargle.com/security-analytics-assignments/labs/lab-publicly-accessible-datasets.html\n",
    "\n",
    "Deliverable:\n",
    "1. Make a copy of this file, and fill it in.\n",
    "4. Submit to canvas *either* of the following:\n",
    "    * A link to a publicly accessible copy of your completed .ipynb notebook\n",
    "    * A file upload of your completed .ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset\n",
    "\n",
    "Use the dataset from [this OpenML phish_url dataset](https://www.openml.org/d/4534) for all tasks in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hv0QKSZOwyb3"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OpenML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 456
    },
    "id": "arAai3wUvrKi",
    "outputId": "70ed6a90-79c2-4923-9ddf-4f121d49c0b8"
   },
   "outputs": [],
   "source": [
    "def do_openml():\n",
    "  # Use url hacking to get a direct download url from the OpenML page for the dataset.\n",
    "  \n",
    "  # For this lab, do _not_ use sklearn's fetch_openml(). \n",
    "  # The generalization described in the lab doc does _not work_ consistently.\n",
    "  # But I'm leaving it as-is because the purpose of this is to teach you url hacking.\n",
    "  df = pd.read_csv()\n",
    "  return df\n",
    "do_openml()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Github"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 456
    },
    "id": "3N8Lqhrgv9UO",
    "outputId": "2fd013ce-3b1d-4cc4-cb33-e1f38bb805f7"
   },
   "outputs": [],
   "source": [
    "def do_github():\n",
    "  # Upload the dataset to a github repository, and use a direct download link for it below\n",
    "  \n",
    "  df = pd.read_csv()\n",
    "  return df\n",
    "do_github()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cloud Storage (Google Drive or Dropbox)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 456
    },
    "id": "B-wR4F9Mv_Km",
    "outputId": "ab7b3ca8-e948-47d0-a990-f108167a4e1b"
   },
   "outputs": [],
   "source": [
    "def do_cloud_storage():\n",
    "  # Upload the phish_url dataset to one of the following cloud storage providers:\n",
    "  # * Google Drive\n",
    "  # * Dropbox\n",
    "  #\n",
    "  # Use url hacking to get a direct download link for it, and use it below.\n",
    "  df = pd.read_csv()\n",
    "  return df\n",
    "do_cloud_storage()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GCP Cloud Storage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 456
    },
    "id": "JxOnFT0rwFfn",
    "outputId": "f700e39b-5347-4197-ec69-98651cb0b48e"
   },
   "outputs": [],
   "source": [
    "def do_gcp():\n",
    "  # Create a public GCP bucket, and upload the dataset to it. \n",
    "  # Use a direct download link to load the file below.\n",
    "  df = pd.read_csv()\n",
    "  return df\n",
    "do_gcp()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AWS S3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jWqlxOCR4wEy"
   },
   "outputs": [],
   "source": [
    "def do_aws():\n",
    "  # Create a public aws s3 bucket, and upload the dataset to it. \n",
    "  # Use a direct download link to load the file below.\n",
    "  df = pd.read_csv()\n",
    "  return df\n",
    "do_aws()"
   ]
  }
 ],
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