scott_final_report  copy 2.ipynb

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   "source": [
    "\n",
    "# Final Report\n",
    "## Anomoly Detection Project\n",
    "* Corey Hermesch\n",
    "* Scott Barnett"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bc23e11-8e2e-4a48-a6bb-78996e908d56",
   "metadata": {},
   "source": [
    "## Goals\n",
    "* Find the answers to the provided questions\n",
    "* Deliver a final report to the data science team \n",
    "* Deliver a slide of findings to the Data Science team"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "771ff34b-4b39-46ec-9e72-893a4ed8b369",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "938f8785-6d6f-4df7-b121-6578d6cd045e",
   "metadata": {},
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   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pydataset import data\n",
    "import scipy.stats as stats\n",
    "import seaborn as sns\n",
    "import env\n",
    "import os\n",
    "import scott_functions\n",
    "import sklearn\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import mutual_info_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix, plot_confusion_matrix\n",
    "import wrangle\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2a7890d-065c-48df-9653-7e35f206e9a3",
   "metadata": {},
   "source": [
    "## Acquire\n",
    "\n",
    "* Data acquired from \n",
    "    * \n",
    "    * Files were concated \n",
    "* It contained *****SHAPE****** rows and  columns\n",
    "* The data was aquired on 14 JUNE 2023\n",
    "* Each row represents \n",
    "* Each column represents a feature of the \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c910c06f-78c6-43eb-8448-086c4d8e05da",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Prepare\n",
    "\n",
    "* Initial preperation\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0e59c79-77be-4df4-8068-f920443a89f1",
   "metadata": {},
   "source": [
    "### Data Dictionary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32facd0e-082e-4cff-a186-c2534977de78",
   "metadata": {},
   "source": [
    "| Feature | Datatype | Key | Definition |\n",
    "|---|---|---|---|\n",
    "| Date | datetime64 | YYYY-MM-DD | Date of activity; Index |\n",
    "| endpoint | object | unique | The highest value reached that day |\n",
    "| user_id | int64 | unique # | Unique ID # assigned to user |\n",
    "| cohort_id | int64 | unique # | Unique ID # assigned to cohort |\n",
    "| source_ip | object | IP ##.###.##.## | Unique IP address assigned to location of user |\n",
    "| name | object | unique | Name assigned to cohort |\n",
    "| start_date | object | YYYY-MM-DD | Date cohort started |\n",
    "| end_date | object | YYYY-MM-DD | Date cohort graduated |\n",
    "| program_id | int64 | 1,2,3,4 | Designation given to type of program |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b4d21e0-248c-459f-848c-ba82b6dc612b",
   "metadata": {},
   "source": [
    "### Bring in Data for Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "82b55ba1-fa0b-4898-8161-35c3b922e48e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#wrangling the data in\n",
    "df=wrangle.wrangle_curriculum_logs()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d20ea549-d03b-4671-aaf0-2fac30ea4d0e",
   "metadata": {},
   "source": [
    "### Sneak Peak at the Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e52be9b6-cd07-43ed-a13b-48c4f8b9a659",
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>endpoint</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
       "      <th>source_ip</th>\n",
       "      <th>name</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-26</th>\n",
       "      <td>/</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>Hampton</td>\n",
       "      <td>2015-09-22</td>\n",
       "      <td>2016-02-06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26</th>\n",
       "      <td>javascript-i/introduction/working-with-data-types-operators-and-variables</td>\n",
       "      <td>39</td>\n",
       "      <td>22</td>\n",
       "      <td>97.105.19.61</td>\n",
       "      <td>Teddy</td>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>2018-05-17</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26</th>\n",
       "      <td>javascript-i/javascript-with-html</td>\n",
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       "      <td>Teddy</td>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>2018-05-17</td>\n",
       "      <td>2</td>\n",
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       "                                                                             endpoint  \\\n",
       "date                                                                                    \n",
       "2018-01-26                                                                          /   \n",
       "2018-01-26  javascript-i/introduction/working-with-data-types-operators-and-variables   \n",
       "2018-01-26                                          javascript-i/javascript-with-html   \n",
       "\n",
       "            user_id  cohort_id     source_ip     name start_date   end_date  \\\n",
       "date                                                                          \n",
       "2018-01-26        1          8  97.105.19.61  Hampton 2015-09-22 2016-02-06   \n",
       "2018-01-26       39         22  97.105.19.61    Teddy 2018-01-08 2018-05-17   \n",
       "2018-01-26       39         22  97.105.19.61    Teddy 2018-01-08 2018-05-17   \n",
       "\n",
       "            program_id  \n",
       "date                    \n",
       "2018-01-26           1  \n",
       "2018-01-26           2  \n",
       "2018-01-26           2  "
      ]
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     "execution_count": 3,
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   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc2c096e-d449-4973-82a6-b9631eaff7a4",
   "metadata": {},
   "source": [
    "### Summary of Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "753af9a8-b82b-47e2-9076-326184f44731",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>cohort_id</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>900223.000000</td>\n",
       "      <td>900223.000000</td>\n",
       "      <td>900223.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>458.825707</td>\n",
       "      <td>45.651349</td>\n",
       "      <td>1.963440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>249.296767</td>\n",
       "      <td>33.800035</td>\n",
       "      <td>0.618478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>269.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>475.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>660.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>981.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>4.000000</td>\n",
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       "             user_id      cohort_id     program_id\n",
       "count  900223.000000  900223.000000  900223.000000\n",
       "mean      458.825707      45.651349       1.963440\n",
       "std       249.296767      33.800035       0.618478\n",
       "min         1.000000       0.000000       0.000000\n",
       "25%       269.000000      26.000000       2.000000\n",
       "50%       475.000000      33.000000       2.000000\n",
       "75%       660.000000      57.000000       2.000000\n",
       "max       981.000000     139.000000       4.000000"
      ]
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   "source": [
    "df.describe()"
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 900223 entries, 2018-01-26 to 2021-04-21\n",
      "Data columns (total 8 columns):\n",
      " #   Column      Non-Null Count   Dtype         \n",
      "---  ------      --------------   -----         \n",
      " 0   endpoint    900222 non-null  object        \n",
      " 1   user_id     900223 non-null  int64         \n",
      " 2   cohort_id   900223 non-null  int64         \n",
      " 3   source_ip   900223 non-null  object        \n",
      " 4   name        900223 non-null  object        \n",
      " 5   start_date  900223 non-null  datetime64[ns]\n",
      " 6   end_date    900223 non-null  datetime64[ns]\n",
      " 7   program_id  900223 non-null  int64         \n",
      "dtypes: datetime64[ns](2), int64(3), object(3)\n",
      "memory usage: 61.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d2f6eeb-6226-4091-a869-56950471443d",
   "metadata": {},
   "source": [
    "## Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff2352d5-1b7d-403a-9fef-a4b7981220f7",
   "metadata": {},
   "source": [
    "* All features independently evaluated against wine quality\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4410da8-55bc-4b3f-bad7-04b1188e60e3",
   "metadata": {},
   "source": [
    "### 1. Which lesson appears to attract the most traffic consistently across cohorts (per program)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d96f2084-f1df-49cd-940e-b39912484a55",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get visualizaions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adf54ddb-4e56-4261-ae34-edf219e68783",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Charting Observations: \n",
    "* The "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af9f6edf-f09c-4a95-8199-5d0649f54a35",
   "metadata": {},
   "source": [
    "#### Result\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32f2fd68-bee8-443b-bd6a-3e086245332f",
   "metadata": {},
   "source": [
    "### 2. Is there a cohort that referred to a lesson significantly more than other cohorts seemed to gloss over?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4499b706-c3fb-42bd-81a0-2574b1c0ffc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get visualizaions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e34ea7b4-665e-4122-a668-291e48a928ca",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Charting Observations: \n",
    "* The "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8addbd1-4bd7-4e8d-a31f-ef85d85ff665",
   "metadata": {},
   "source": [
    "#### Result\n",
    "Based on the results "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64726d7d-8fff-4083-8411-b0f6f7f5ce43",
   "metadata": {},
   "source": [
    "### 3. Are there students who, when active, hardly access the curriculum? If so, what information do you have about these students?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b3834665-74fb-41fc-aa4e-b02921d651d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get visualizaions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea54d254-8b85-41b4-9335-fcdc076cb996",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Charting Observations: \n",
    "* The "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9390f3f9-df5e-4f7c-a040-5ba798c69f74",
   "metadata": {},
   "source": [
    "#### Result\n",
    "Based on the results of the statistics and the observation of the chart, the number of bedrooms the home has is statisically relevant and will have some positive impact on the model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfa04963-0b3f-4ba3-8e3f-84c0fc4717f9",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 4. Is there any suspicious activity, such as users/machines/etc accessing the curriculum who shouldn’t be? Does it appear that any web-scraping is happening? Are there any suspicious IP addresses?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7e76c1fa-4395-469e-a6fe-f538f1902fad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get visualizaions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38644e18-2e7d-4daf-941a-0d3e74185812",
   "metadata": {},
   "source": [
    "#### Chart Observations: \n",
    "* "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b57943d2-3edd-46de-8a43-b85c9ffb0c0d",
   "metadata": {},
   "source": [
    "#### Result\n",
    "* Based on the results of the statistics and the observation of the chart, the lot size is statisically relevant and will have some positive impact on the model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12cd6986-bffb-4f57-96bd-3897a855b504",
   "metadata": {},
   "source": [
    "### 5. At some point in 2019, the ability for students and alumni to access both curriculums (web dev to ds, ds to web dev) should have been shut off. Do you see any evidence of that happening? Did it happen before?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03933b4a-a65c-4840-a295-c13beac00edc",
   "metadata": {},
   "source": [
    "### 6. What topics are grads continuing to reference after graduation and into their jobs (for each program)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3f3853a5-6fc2-483e-9738-75b7ffd6a962",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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raZKRDh066MMPP9TKlSv18ssvZxlTqVKlctwf/44/LYGZUfy5pV69elq6dKmMMdq/f78iIiI0fvx4eXh4ZHpe3bp1U2Rk5HUdJy3JtHnzZu3YsUOtWrWy6mrXrq3AwEBt2rRJmzdvVrly5axrn3Zd0taYy86VK1fSPd0xt36HrFq1SpKsEV7XE1vaqLh33nlHFy5csJKyjzzyyE3FBAAo2hgpBQDATQgPD1d8fHy6ZELak+XCw8Ptyjds2GCNPpCuLgT86aefqlq1aipfvnyuxHTPPfcoPj5ey5YtsytfsGCBgoOD1bRp01w5Tk7VqFFD5cqV05IlS+ymB504cULbt2/P8D3Lly+3G7EUFxen1atX684775STk5O8vLzUtGlTLV++3G7ETGpqqhYtWqTy5csrNDT0pmO/dtRXTvXo0UMBAQGaN2+e5s+fr9DQUN1xxx1Wvc1mk7Ozs90IkoSEBC1cuDDbfVeuXFmStH//frvytIRCmho1aigkJET79u1LN+ItbUsbuZWRu+++W/Xq1dOkSZMyXRx/7dq1unTp0nX1R2bxr169Ottzz0xO+slms6l+/fqaPn26SpYsqZ9++inTtgEBAemuVXbq1Kmj0qVLa+PGjdq2bZuV2Ek7dsuWLbVmzRrt3LnT7ql7HTp0kLOzs3777bdM++lan3zyid3rtKc9/vuY12vdunX66KOP1KJFC+tevd7YHnnkEV2+fFlLlixRRESEmjdvrpo1a95wTACAoo+RUgAA3ISHH35Y7733nvr376/jx4+rXr16+u677zRx4kR17txZbdu2tWsfGBiou+66S6NHj5aXl5fef/99HT58WEuXLs32WL/88ou1Js7Zs2d16dIl66l9tWvXtp7k1alTJ7Vr105PPfWUYmNjVb16dS1ZskRr1qzRokWLcjyVZvfu3fLz80tXXrt2bfn6+uZoH5JUokQJTZgwQY899pjuuecePf7447pw4YLGjh2b6fQ9JycntWvXTi+88IJSU1M1ZcoUxcbGaty4cVabSZMmqV27dmrTpo2GDx8uV1dXvf/++zp48KCWLFmS47WzsuLj46NKlSrpyy+/VHh4uPz9/RUYGGglVjLj5uamvn376t1335UxRpMnT7ar79Kli6ZNm6Y+ffroiSee0Pnz5/Xmm2/ajRzKTFBQkNq2batJkyapVKlSqlSpkjZs2KDly5enazt79mx16tRJHTp00IABA3TLLbfon3/+0aFDh/TTTz/p888/z/Q4Tk5OWrFihdq3b6/mzZvrqaeeUps2beTl5aUTJ07oiy++0OrVqxUdHS0p5/3RuXNn+fv7a+DAgRo/frycnZ0VERGhkydPZnvumUkb/ffhhx/Kx8dH7u7uqlKlinbs2KH3339fPXr0UNWqVWWM0fLly3XhwgW1a9fuho+XEZvNptatW+uLL76QMcZupJR0dQrf0KFDZYyxS0pVrlxZ48eP16hRo/T777+rY8eOKlWqlP766y/98MMP8vLysrvvXV1d9dZbbyk+Pl633nqrtm/frtdee02dOnWyS3xmJjU1VTt37pR0dbTaH3/8oW+++UafffaZatWqpc8+++yGY6tZs6aaN2+uSZMm6eTJk/rwww9v+HoCAIqJ/FlfHQCAwimjp1edP3/ePPnkk6ZcuXLG2dnZVKpUyYwcOTLdU8skmWeeeca8//77plq1asbFxcXUrFnTfPLJJzk6dlZPxbv26XBxcXHm2WefNUFBQcbV1dWEhYVZT8O7meNIMuvWrTPG/N/T3t544410+8gopo8++siEhIQYV1dXExoaaubNm2f69++f4dP3pkyZYsaNG2fKly9vXF1dTcOGDc3atWvTHWfbtm3mrrvuMl5eXsbDw8M0a9Ys3ZO+Mnv6XkZPIcvoyXDr1683DRs2NG5ubkaS6d+/fzZX8Kp9+/YZScbJycmcPn06Xf28efNMjRo1jJubm6lataqZNGmSmTt3bron0F379D1jjDlz5oy57777jL+/v/Hz8zMPPfSQ9aS5fz99Ly2OXr16mTJlyhgXFxcTFBRk7rrrLuvJcNm5cOGCmTBhgmnUqJHx9vY2Li4upmLFiuahhx4y33//vV3bnPSHMVef0NiiRQvj5eVlbrnlFjNmzBjz0UcfZfj0vS5duqR7f0bXZMaMGaZKlSrGycnJug6HDx82vXv3NtWqVTMeHh7Gz8/P3HbbbSYiIiJH53693n//fSPJlC5dOl3d3r17rZ+hI0eOpKtfuXKladOmjfH19TVubm6mUqVK5r777jPr16+32qTdt/v37zetW7c2Hh4ext/f3zz11FMmPj4+2/jSnt6Xtnl4eJiKFSuabt26mXnz5pnExMQM35eT2NJ8+OGH1r5jYmKyjQkAULzZjLnmMRsAACBP2Gw2PfPMM5o5c2Z+h1JgHT9+XFWqVNEbb7yh4cOH53c4AAAAyEOsKQUAAAAAAACHIykFAAAAAAAAh2P6HgAAAAAAAByOkVIAAAAAAABwOJJSAAAAAAAAcDiSUgAAAAAAAHA45/wOoLBITU3V6dOn5ePjI5vNlt/hAAAAAAAAFEjGGMXFxSk4OFglSmQ+HoqkVA6dPn1aFSpUyO8wAAAAAAAACoWTJ0+qfPnymdaTlMohHx8fSVcvqK+vbz5HAwAAAAAAUDDFxsaqQoUKVi4lMySlcihtyp6vry9JKQAAAAAAgGxkt/wRSaliJOZSkqLikxR7OVm+Hi4K9HKVn6drfocFAAAAAACKIZJSxcTpCwkasWy/th2JsspahgRq8r1hCi7pkY+RAQAAAACA4ijzJdBRZMRcSkqXkJKkrUei9PKy/Yq5lJRPkQEAAAAAgOKKpFQxEBWflC4hlWbrkShFxZOUAgAAAAAAjsX0vWIg9nJylvVx2dSjYGBNMAAAAABAUUJSqhjwdXfJst4nm3rkP9YEAwAAAAAUNUzfKwYCvV3VMiQww7qWIYEK9Ga0TUHGmmAAAAAAgKKIpFQx4Ofpqsn3hqVLTLUMCdSUe8OYAlbAsSYYAAAAAKAoYvpeMRFc0kPv9m6oqPgkxV1Olo+7iwK9WZOoMGBNMAAAAABAUURSqhjx8yQJVRixJhgAAAAAoChi+h5QwLEmGAAAAACgKCIpBRRwrAkGAAAAACiKmL4HFAKsCQYAAAAAKGpISgGFBGuCAQAAAACKEqbvAQAAAAAAwOFISgEAAAAAAMDhSEoBAAAAAADA4UhKAQAAAAAAwOFISgEAAAAAAMDhSEoBAAAAAADA4UhKAQAAAAAAwOFISgEAAAAAAMDhSEoBAAAAAADA4UhKAQAAAAAAwOFISgEAAAAAAMDhSEoBAAAAAADA4UhKAQAAAAAAwOFISgEAAAAAAMDhSEoBAAAAAADA4UhKAQAAAAAAwOFISgEAAAAAAMDhSEoBAAAAAADA4UhKAQAAAAAAwOFISgEAAAAAAMDhSEoBAAAAAADA4UhKAQAAAAAAwOFISgEAAAAAAMDhSEoBAAAAAADA4UhKAQAAAAAAwOHyNSk1duxY2Ww2uy0oKMiqN8Zo7NixCg4OloeHh1q3bq2ff/7Zbh+JiYkaMmSIAgMD5eXlpe7du+vUqVN2baKjo9WvXz/5+fnJz89P/fr104ULFxxxigAAAAAAAMhAvo+UqlOnjs6cOWNtBw4csOqmTp2qadOmaebMmYqMjFRQUJDatWunuLg4q83QoUO1YsUKLV26VN99953i4+PVtWtXpaSkWG369OmjvXv3as2aNVqzZo327t2rfv36OfQ8AQAAAAAA8H+c8z0AZ2e70VFpjDGaMWOGRo0apZ49e0qSFixYoLJly2rx4sUaNGiQYmJiNHfuXC1cuFBt27aVJC1atEgVKlTQ+vXr1aFDBx06dEhr1qzRzp071bRpU0nSnDlz1Lx5c/3666+qUaOG404WAAAAAAAAkgrASKkjR44oODhYVapU0YMPPqjff/9dknTs2DGdPXtW7du3t9q6ubmpVatW2r59uyRp9+7dSk5OtmsTHBysunXrWm127NghPz8/KyElSc2aNZOfn5/VJiOJiYmKjY212wAAAAAAAJA78jUp1bRpU3388cdau3at5syZo7Nnz6pFixY6f/68zp49K0kqW7as3XvKli1r1Z09e1aurq4qVapUlm3KlCmT7thlypSx2mRk0qRJ1hpUfn5+qlChwk2dKwAAAAAAAP5Pvk7f69Spk/XvevXqqXnz5qpWrZoWLFigZs2aSZJsNpvde4wx6cqudW2bjNpnt5+RI0fqhRdesF7HxsaSmAJwQ2IuJSkqPkmxl5Pl6+GiQC9X+Xm65ndYAAAAAJCv8n1NqX/z8vJSvXr1dOTIEfXo0UPS1ZFO5cqVs9qcO3fOGj0VFBSkpKQkRUdH242WOnfunFq0aGG1+euvv9Id6++//043Cuvf3Nzc5ObmlhunBaAYO30hQSOW7de2I1FWWcuQQE2+N0zBJT3yMTIAAAAAyF/5vqbUvyUmJurQoUMqV66cqlSpoqCgIK1bt86qT0pK0pYtW6yEU+PGjeXi4mLX5syZMzp48KDVpnnz5oqJidEPP/xgtdm1a5diYmKsNgCQF2IuJaVLSEnS1iNRennZfsVcSsqnyAAAAAAg/+XrSKnhw4erW7duqlixos6dO6fXXntNsbGx6t+/v2w2m4YOHaqJEycqJCREISEhmjhxojw9PdWnTx9Jkp+fnwYOHKhhw4YpICBA/v7+Gj58uOrVq2c9ja9WrVrq2LGjHn/8cc2ePVuS9MQTT6hr1648eQ9AnoqKT0qXkEqz9UiUouKTmMYHAAAAoNjK16TUqVOn1Lt3b0VFRal06dJq1qyZdu7cqUqVKkmSXnrpJSUkJOjpp59WdHS0mjZtqm+//VY+Pj7WPqZPny5nZ2f16tVLCQkJCg8PV0REhJycnKw2n3zyiZ599lnrKX3du3fXzJkzHXuyAIqd2MvJWdbHZVMPAAAAAEWZzRhj8juIwiA2NlZ+fn6KiYmRr69vfocDoBD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\n",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scott_functions.question6(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79cb3115-2e4b-4b4b-9cec-05910031d8d3",
   "metadata": {},
   "source": [
    "### 7. Which lessons are least accessed?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ce11bd7-c13e-47dd-ba56-395885e85b6f",
   "metadata": {},
   "source": [
    "### 8. Anything else I should be aware of?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5d02794-a06d-4949-bfdb-9e35522423fc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "593736ce-4878-4340-b518-c74002fff7df",
   "metadata": {},
   "source": [
    "## Exploration Summary\n",
    "* All t"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5400eb4b-252e-42b1-b6a6-13ae4cdd699c",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a214b0b1-aa98-4447-b24f-1c9ab98919c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting chart to provide the context of the target variable showing distribution of the values and baseline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e9ef6dd-7a7e-40e8-90e3-52fbb6246213",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "### Summary\n",
    "* \n",
    "* \n",
    "* \n",
    "* \n",
    "### Recommendations\n",
    "* \n",
    "* \n",
    "\n",
    "### Next Steps\n",
    "* If provided more time to work on the project "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "411ae7a4-00b8-4623-a1e1-ddc644197a30",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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