notebook00.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Anomaly Detection\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detecting anomalies just by seeing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the necessary packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Shpw the plots in your Jupyter Notebook\n",
    "%matplotlib inline\n",
    "# Use a predefined style set\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first create a dummy dataset for ourselves. The dataset will contain just two columns - \n",
    "- Name of the employees of an organization\n",
    "- Salaries of those employees (in USD) within a range of 1000 to 2500 (Monthly)\n",
    "\n",
    "For generating the names (and make them look like the real ones) we will use a Python library called `Faker` (read the documentation [here](https://faker.readthedocs.io/en/latest/index.html)). For generating salaries, we will use the good old `numpy`. After generating these, we will merge them in a `pandas` DataFrame. We are going to generate records for 100 employees. Let's begin. \n",
    "\n",
    "**Note**: Synthesizing dummy datasets for experimental purposes is indeed an essential skill."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from faker import Faker\n",
    "# To ensure the results are reproducible\n",
    "Faker.seed(42)\n",
    "fake = Faker()\n",
    "\n",
    "names_list = []\n",
    "\n",
    "fake = Faker()\n",
    "for _ in range(100):\n",
    "  names_list.append(fake.name())\n",
    "\n",
    "# Verify if 100 names were generated\n",
    "len(names_list)\n",
    "#print(f'{names_list}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To ensure the results are reproducible\n",
    "np.random.seed(7)\n",
    "\n",
    "salaries = []\n",
    "for _ in range(100):\n",
    "    salary = np.random.randint(1000, 2500)\n",
    "    salaries.append(salary)\n",
    "\n",
    "    # Verify if 100 salariy values were generated\n",
    "len(salaries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Person</th>\n",
       "      <th>Salary (in USD)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Allison Hill</td>\n",
       "      <td>1175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Noah Rhodes</td>\n",
       "      <td>2220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angie Henderson</td>\n",
       "      <td>1537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Daniel Wagner</td>\n",
       "      <td>1502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cristian Santos</td>\n",
       "      <td>1211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Person  Salary (in USD)\n",
       "0     Allison Hill             1175\n",
       "1      Noah Rhodes             2220\n",
       "2  Angie Henderson             1537\n",
       "3    Daniel Wagner             1502\n",
       "4  Cristian Santos             1211"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create pandas DataFrame\n",
    "salary_df = pd.DataFrame({'Person': names_list,'Salary (in USD)': salaries})\n",
    "\n",
    "# Print a subsection of the DataFrame\n",
    "salary_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now manually change the salary entries of two individuals. In reality, this can actually happen for a number of reasons such as the data recording software may have got corrupted at the time of recording the respective data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_df.at[16, 'Salary (in USD)'] = 23\n",
    "salary_df.at[65, 'Salary (in USD)'] = 17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Person             Holly Wood\n",
      "Salary (in USD)            23\n",
      "Name: 16, dtype: object\n",
      "Person             Amber Kidd\n",
      "Salary (in USD)            17\n",
      "Name: 65, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Verify if the salaries were changed\n",
    "print(salary_df.loc[16])\n",
    "print(salary_df.loc[65])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplots are great! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned in the earlier sections, the generation of anomalies within data directly depends on the generation of the data points itself. To simulate this, our approach is good enough to proceed. Let's now some basic statistics (like minimum value, maximum value, 1st quartile values etc.) in the form of a boxplot. \n",
    "\n",
    "Boxplot, because we get the following information all in just one place that too visually\n",
    "\n",
    "![](https://pro.arcgis.com/en/pro-app/latest/help/analysis/geoprocessing/charts/GUID-0E2C3730-C535-40CD-8152-80D794A996A7-web.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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++k0cgd/m97//vaqrq7Vw4UI1NjbqxRdf1EsvvaT58+dLkurr65WTk6P09HTNnj1bR48e1cqVKxUZGakxY8a0dsoAAKCDaXXADB48WIMHDz7/QR0OuVyuFveVlZVp9+7dWrZsmfr16ydJuueee7Rs2TLdddddio6O1vbt2+Xz+TRnzhw5HA716dNHJSUl2rx5MwEDAACuzDUw+/fv16xZszR//ny98sor+u677+x9hYWFioyMtONFktLT02VZloqKiuwx1113XcDKTUZGho4fP67a2torMWUAAGCQVq/AXEhmZqZuuOEGxcbGqqKiQm+88YZ+9atfaenSperSpYs8Ho969OgRcJ+QkBB169ZNHo9HkuTxeBQbGxswpnlFx+PxqFu3bmd9X6/XK6/Xa9+2LEsRERH21wAu0V+ePpbVfp5LzfNoL/OR1C5/TkBH1uYBc9NNN9lfJyUlqW/fvpo3b54KCgqUnp7e1t/OtmnTJm3cuNG+nZKSotzcXMXExFyx7wl0Bg11NfpGUu/eMQqNjw/2dALExcUFewq29vxzAjqiNg+YH7rqqqvUvXt3VVRUKD09XS6XSydOnAgY09jYqNraWnuVxeVy2asxzZpvn+vamokTJ2rChAn27ea/gNxut3w+X5s8FqAz8le5JUlVVW5ZkT2DPJszLMtSXFycKioq5Pf7gz0dSe3z5wSYyOFwXNTiwxUPmG+//Va1tbWKioqSJKWlpamurk7FxcVKTU2VJO3bt09+v1/9+/e3x7zxxhvy+Xz2dTB79uxRQkJCi6ePJMnpdMrpdLa4r738ggNM1Pz08fu/d6Od8Pv97eb53Z5/TkBH1OqLeE+dOqWSkhKVlJRIkiorK1VSUqKqqiqdOnVK//Zv/6bCwkJVVlZq7969evbZZxUXF6eMjAxJUmJiojIzM/XSSy+pqKhIBw4c0Jo1azR8+HBFR0dLkkaMGCGHw6FVq1aptLRUO3bs0JYtWwJWWAAAQOfV6hWYw4cPa/Hixfbt/Px8SVJWVpb9ni2ffPKJ6urqFB0drUGDBmnKlCkBqyP/9E//pLy8PC1ZssR+I7t77rnH3t+1a1ctXLhQeXl5euyxx9S9e3dNmjSJl1ADAABJkuVvL+uvV4jb7Q54dRKA1vEfOaymnAfVZeFvZPXtd+E7/Agsy1J8fLzKy8vbzymkdvhzAkzkdDov6hoYPgsJAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYJwr/llIAAzXcFqS5D96OMgT+Su/r0F1B3aryREqOUKDPR1Jkr+8NNhTADoVAgbAefkrys78m79C7eM9b8/4v2BP4FzCI4I9A6BTIGAAnJeVeeOZf+MSpdCwIM/mLypK1bT6eXWZtUCK6xPs2fxVeISsqxKCPQugUyBgAJyX1b2HrP/302BPI5D1l3/i+0hJfO4Q0BlxES8AADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwjqO1d9i/f7/eeustff3116qurtbDDz+sYcOG2fv9fr82bNigP/zhD6qrq9OAAQM0a9YsxcfH22Nqa2u1Zs0affHFF7IsSzfccINmzpyp8PBwe8yRI0eUl5enw4cPq0ePHho3bpxuu+22y3y4AACgI2h1wJw+fVrJyckaNWqUfv3rX5+1/80339SWLVs0d+5cxcbGav369Vq6dKmef/55hYaGSpJ+//vfq7q6WgsXLlRjY6NefPFFvfTSS5o/f74kqb6+Xjk5OUpPT9fs2bN19OhRrVy5UpGRkRozZsxlPmQAweA/fVqqKGubg1WUnjlmeankb4PjxSXKCgtrgwMB+LG0OmAGDx6swYMHt7jP7/frnXfe0e23366f/OQnkqQHHnhAs2fP1meffaabbrpJZWVl2r17t5YtW6Z+/fpJku655x4tW7ZMd911l6Kjo7V9+3b5fD7NmTNHDodDffr0UUlJiTZv3kzAAKaqKFNTzoNtesim1c+3yXG6LPyN1LdfmxwLwI+j1QFzPpWVlfJ4PBo0aJC9rWvXrurfv78KCwt10003qbCwUJGRkXa8SFJ6erosy1JRUZGGDRumwsJCXXfddXI4/jq9jIwMvfnmm6qtrVW3bt3O+t5er1der9e+bVmWIiIi7K8BBFl8okKe/E3bHMvrVU/fadU4wiSn8/KPF5fI7wnAMG0aMB6PR5LUs2fPgO09e/a093k8HvXo0SNgf0hIiLp16xYwJjY2NmCMy+Wy97UUMJs2bdLGjRvt2ykpKcrNzVVMTMxlPCIAbSo5pU0PF9mmRwNgkjYNmGCaOHGiJkyYYN9u/mvK7XbL5/MFa1oArgDLshQXF6eKigr5/W1xEQyA9sLhcFzU4kObBkzzKklNTY2ioqLs7TU1NUpOTrbHnDhxIuB+jY2Nqq2tte/vcrns1Zhmzbebx/yQ0+mU8xxLyfyCAzomv9/P8xvopNr0fWBiY2Plcrm0d+9ee1t9fb2KioqUlpYmSUpLS1NdXZ2Ki4vtMfv27ZPf71f//v3tMV999VXAysmePXuUkJDQ4ukjAADQubQ6YE6dOqWSkhKVlJRIOnPhbklJiaqqqmRZlv7+7/9e//Vf/6XPP/9cR48e1YoVKxQVFWW/KikxMVGZmZl66aWXVFRUpAMHDmjNmjUaPny4oqOjJUkjRoyQw+HQqlWrVFpaqh07dmjLli0Bp4gAAEDnZflbuf5aUFCgxYsXn7U9KytLc+fOtd/I7oMPPlB9fb0GDBign//850pISLDH1tbWKi8vL+CN7O65555zvpFd9+7dNW7cOP3DP/xDqx+g2+0OeHUSAPNZlqX4+HiVl5dzCgnoYJxO50VdA9PqgDENAQN0PAQM0HFdbMDwWUgAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOI62PuCGDRu0cePGgG0JCQn67W9/K0lqaGhQfn6+duzYIa/Xq4yMDM2aNUsul8seX1VVpVdeeUUFBQUKDw9XVlaWpk2bppCQkLaeLgAAMFCbB4wk9enTR08++aR9u0uXvy70rF27Vrt27dKCBQvUtWtX5eXlafny5Xr66aclSU1NTVq2bJlcLpdycnJUXV2tFStWKCQkRNOmTbsS0wUAAIa5IqeQunTpIpfLZf/Xo0cPSVJ9fb0+/PBD3X333Ro4cKBSU1M1Z84cHTx4UIWFhZKkL7/8UmVlZZo3b56Sk5M1ePBgTZkyRVu3bpXP57sS0wUAAIa5IiswFRUVuu++++R0OpWWlqZp06apd+/eKi4uVmNjo9LT0+2xV199tXr37q3CwkKlpaWpsLBQSUlJAaeUMjMztXr1apWWliolJaXF7+n1euX1eu3blmUpIiLC/hpAx9H8nOa5DXRebR4w11xzjebMmaOEhARVV1dr48aNWrRokZYvXy6PxyOHw6HIyMiA+/Ts2VMej0eS5PF4AuKleX/zvnPZtGlTwLU3KSkpys3NVUxMTJs8LgDtT1xcXLCnACBI2jxgBg8ebH/dt29fO2g+/fRThYaGtvW3s02cOFETJkywbzf/ZeZ2uzn1BHQwlmUpLi5OFRUV8vv9wZ4OgDbkcDguavHhipxC+r7IyEglJCSooqJCgwYNks/nU11dXcAqTE1Njb3q4nK5VFRUFHCMmpoae9+5OJ1OOZ3OFvfxCw7omPx+P89voJO64u8Dc+rUKVVUVMjlcik1NVUhISHau3evvf/48eOqqqpSWlqaJCktLU1Hjx61o0WS9uzZo4iICCUmJl7p6QIAAAO0+QpMfn6+hg4dqt69e6u6ulobNmxQly5dNGLECHXt2lWjRo1Sfn6+unXrpq5du2rNmjVKS0uzAyYjI0OJiYlasWKFpk+fLo/Ho3Xr1mns2LHnXGEBAACdi+Vv4/XX3/72t/rqq6/03XffqUePHhowYICmTp1qX2zX/EZ2f/rTn+Tz+Vp8Izu3263Vq1eroKBAYWFhysrK0vTp0y/pjezcbnfAq5MAmM+yLMXHx6u8vJxTSEAH43Q6L+oamDYPmPaGgAE6HgIG6LguNmD4LCQAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBxHsCcAAK3R0NCgtWvXqqqqSr1799bdd9+t0NDQYE8LwI/M8vv9/mBP4kpyu93yer3BngaANpCTk6OXX35ZjY2N9raQkBDde++9WrhwYRBnBqCtOJ1OxcTEXHAcp5AAGCEnJ0crV65UVFSUnnvuOZWXl+u5555TVFSUVq5cqZycnGBPEcCPiBUYAO1eQ0OD+vfvr6ioKH3xxRdyOp2Kj49XeXm5vF6vhgwZourqahUVFXE6CTBch1iBeffddzV37lxNnz5djz/+uIqKioI9JQBBsHbtWjU2NurRRx+VwxF46Z7D4dAjjzyixsZGrV27NkgzBPBja7cBs2PHDuXn5+uOO+5Qbm6u+vbtq6VLl6qmpibYUwPwIzty5Igk6ZZbbmlx/5gxYwLGAej42m3AbN68WaNHj9bIkSOVmJio2bNnKzQ0VB999FGwpwbgR9a3b19J0vvvv9/i/g8++CBgHICOr11eA+Pz+XTnnXdqwYIFGjZsmL19xYoVqq+v16OPPnrWfbxeb8C1LpZlKSIiQm63Wz6f70eZN4Aro6GhQf369VNUVJR27dolp9OpuLg4VVRUyOv16vrrr1d1dbUOHz7MNTCA4RwOx0VdA9Mu3wfmxIkTampqksvlCtjucrl0/PjxFu+zadMmbdy40b6dkpKi3Nzci/ohAGj/FixYoOeee04/+clPtGTJEk2YMEGbN2/WokWLVFVVpUceeYQVGKATaZcBcykmTpyoCRMm2Lcty5IkVmCADuLBBx9UbW2tXn75Zd1333329pCQEP3iF7/Qgw8+qPLy8iDOEEBbMHoFpkePHurSpYs8Hk/Ado/Hc9aqTDOn0ymn09nivnZ4lgzAJVi4cKEeffTRFt+Jl+c50Lm0y4BxOBxKTU3Vvn377GtgmpqatG/fPo0bNy7IswMQTKGhobr33nvt94EhXIDOqV0GjCRNmDBBL7zwglJTU9W/f3+98847On36tLKzs4M9NQAAEGTtNmCGDx+uEydOaMOGDfJ4PEpOTtbjjz9+zlNIAACg82iXL6NuS3yUANDxWJbFKSSgg+oQHyUAAADQEgIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABin3b4PTFtxODr8QwQ6LZ7fQMdzsc/rDv8+MAAAoOPhFBIA45w8eVK//OUvdfLkyWBPBUCQEDAAjOP3+/X111/zLrxAJ0bAAAAA4xAwAADAOAQMAOM4nU7dcccdcjqdwZ4KgCDhVUgAAMA4rMAAAADjEDAAAMA4BAwAADAOAQPgon388cf62c9+FtQ5/Mu//Iu2b99u3548ebJ27twZxBldmt27d+uRRx5RU1NTsKcCGIkPEgE6iRMnTmj9+vXatWuXampqFBkZqeTkZE2aNEkDBgwI9vQuyueffy6Px6Phw4fb215++WVFRkZe1nEnT56shx9+WMOGDQvY/sILL6iurk6PPvqoJKmyslJvvPGG9u/fr9raWnXv3l2pqamaPn26rr76avtYzcLCwhQVFaVrr71Wf/d3f6fU1FR7X2ZmptavX6/t27fr5ptvvqz5A50RAQN0EsuXL5fP59PcuXN11VVXqaamRnv37lVtbe2POg+fz3fJH8K4ZcsWjRw5Ul26/HXx2OVytdHMzs/n8+npp59WQkKCHnroIUVFRenbb7/V7t27VVdXFzB2zpw5yszMVENDg8rLy/XBBx/o8ccf1y9+8QtlZWXZ47KysrRlyxYCBrgEBAzQCdTV1emrr77SU089pb/5m7+RJMXExKh///4B4zZv3qyPPvpIlZWV6tatm4YMGaI777xT4eHhLR63oqJC+fn5OnTokE6dOqXExET94z/+owYNGmSPmTt3rkaOHKmKigp99tlnGjZsmKqqqpSYmKif//zn9rgTJ07ovvvu0+OPP6709PSzvteJEye0b9++s05hfX/1pLKyUg888IAeeughvfvuuzp06JDi4+M1e/ZspaWlXeqPT5JUVlamb775RosWLVJMTIykMz/DllavunbtaodVbGysMjIytGLFCq1Zs0ZDhgxRt27dJElDhw7VmjVrVFFRobi4uMuaH9DZcA0M0AmEh4crPDxcO3fulNfrPec4y7I0c+ZMLV++XHPnztW+ffv02muvnXP8qVOnNHjwYD355JN69tlnlZGRodzcXFVVVQWM+5//+R/17dtXubm5mjRpkkaPHq3t27cHzOWPf/yjoqOjNXDgwBa/14EDBxQaGmqfqjmfdevW6dZbb9Wzzz6r+Ph4/e53v1NjY+MF73c+PXr0kGVZ+vOf/3xJ161MmDBBJ0+e1J49e+xtvXv3Vs+ePXXgwIHLmhvQGREwQCcQEhKiOXPm6JNPPtHPfvYzPfnkk3r99dd15MiRgHHjx4/XwIEDFRsbq4EDB2rq1Kn69NNPz3nc5ORk3XLLLUpKSlJ8fLymTp2quLg4ff755wHjBg4cqFtvvVVxcXGKi4uzrzX57LPP7DGffPKJsrOzZVlWi9/L7XbL5XIFnD46l1tvvVXXX3+9EhISNHnyZLndblVUVFzwfucTHR2tmTNnasOGDZo5c6YWL16sjRs36ptvvrmo+yckJNiP44fH/eE2ABfGKSSgk7jxxht1/fXX68CBAyosLNTu3bv11ltv6f7771d2drYkac+ePfrv//5vHTt2TCdPnlRjY6O8Xq9Onz6tsLCws4556tQpbdiwQf/7v/+r6upqNTY2qqGh4awVmH79+gXcDg0N1c0336yPPvpIw4cPV3FxsY4ePWpfLNuShoaGi/7ogKSkJPvr5lM5NTU1F7V6cz7jxo1TVlaWCgoKdOjQIf35z3/Wpk2b9Mtf/jLgtNn5/DDQQkNDdfr06cuaF9AZETBAJxIaGqpBgwZp0KBBuuOOO7Rq1Spt2LBB2dnZqqysVG5urm655RZNnTpV3bp104EDB7Rq1Sr5fL4WAyY/P1979+7VXXfdpbi4OIWGhtoXC39fS/cdPXq0HnnkEX377bf6+OOPNXDgQPvakpZ07979rItlz+X7Fwk3B8P5PjUlIiJC9fX1Z22vq6tT165dzxo7dOhQDR06VFOnTtXSpUv1n//5nxcMmLKyMklnron5vtraWvXo0eP8DwjAWTiFBHRiiYmJ9l//xcXFampq0owZM5SWlqaEhARVV1ef9/4HDx5UVlaWhg0bpqSkJLlcros+HZKUlKR+/frpD3/4g7Zv366RI0eed3xKSoo8Hs8VedVUQkKCiouLA7Y1NTXpyJEj9qmflliWpYSEhItaQXnnnXcUERERcIFyQ0ODKioqlJKScumTBzopVmCATuC7777T888/r5EjR6pv376KiIjQ4cOH9eabb2ro0KGSpLi4ODU2Nurdd9/VkCFDdPDgQb3//vvnPW58fLx27txpH2P9+vXnXen4oVGjRmnNmjUKCws76z1YfiglJUXdu3fXwYMHNWTIkIv+Hhdj/PjxWrVqla6++moNGjRIp0+f1pYtW1RXV6dRo0ZJkkpKSrRhwwbdfPPNSkxMlMPh0P79+/XRRx/ptttuCzhefX29PB6PvF6vysvL9f777+uzzz7TAw88EPCeNYcOHZLT6bzsV0gBnREBA3QC4eHhuuaaa/T222/rm2++UWNjo3r16qXRo0fr9ttvl3TmgtwZM2bozTff1Ouvv67rrrtO06ZN04oVK8553BkzZmjlypVauHChunfvrttuu00nT5686HmNGDFCa9eu1U033aTQ0NDzju3SpYuys7O1bdu2Ng+YESNGSDrzMvLXX39doaGhSk1N1eLFi+1raKKjoxUTE6P/+I//kNvtlmVZiomJ0eTJkzV+/PiA47344ouSJKfTqejoaA0YMEC/+tWvAt7ITpL+9Kc/acSIES2eYgNwfpa/NX8uAUAbqqys1Lx587Rs2bKz/ufeEo/HowULFig3N/e818uY4MSJE/rnf/5nPfPMM2ddFwPgwrgGBsCPzufzyePxaN26dUpLS7uoeJHOvKLo/vvvP+tVTiZyu92aNWsW8QJcIlZgAPzoCgoKtHjxYsXHx+uhhx4KeNkzAFwMAgYAABiHU0gAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOP8fPDrsqFbWQ7UAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "salary_df['Salary (in USD)'].plot(kind='box')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the tiny circle point in the bottom. You instantly get a feeling of something wrong in there as _it deviates hugely from the rest of the data_. Now, you decide to look at the data from another visual perspective i.e. in terms of _histograms_. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How about histograms? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = salary_df['Salary (in USD)'].plot(kind='hist')\n",
    "ax.set_xlabel('Salary (in USD)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above histogram plot also, we can see there's one particular bin that is just not right _as it deviates hugely from the rest of the data_ (phrase repeated intentionally to put emphasis on the deviation part). We can also infer that there are only two employees for which the salaries seem to be distorted (look at the y-axis). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So what might be an immediate way to confirm that the dataset contains anomalies? Let's take a look at the minimum and maximum values of the column `Salary (in USD)`.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum salary 17\n",
      "Maximum salary 2498\n"
     ]
    }
   ],
   "source": [
    "print('Minimum salary ' + str(salary_df['Salary (in USD)'].min()))\n",
    "print('Maximum salary ' + str(salary_df['Salary (in USD)'].max()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the **minimum value**. From the accounts department of this hypothetical organization, you got to know that the minimum salary of an employee there is $1000. But you found out something different. Hence, its worth enough to conclude that this is indeed an anomaly. Let's now try to look at the data from a different perspective other than just simply plotting it. \n",
    "\n",
    "**Note**: Although our dataset contains only one feature (i.e. `Salary (in USD)`) that contains anomalies but in reality, there can be a lot features which will have anomalies in them. Even there also, these little visualizations will help you a lot. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering based approach for anomaly detection\n",
    "\n",
    "We have seen how clustering and anomaly detection are closely related but they serve different purposes. But clustering can be used for anomaly detection. In this approach, we start by grouping the similar kind of objects. Mathematically, this similarity is measured by distance measurement functions like Euclidean distance, Manhattan distance and so on. Euclidean distance is a very popular choice when choosing in between several distance measurement functions. Let's take a look at what Euclidean distance is all about. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### An extremely short note on Euclidean distance\n",
    "\n",
    "If there are n points on a two-dimensional space(refer the following figure) and their coordinates are denoted by(x_i, y_i), then the Euclidean distance between any two points((x1, y1) and(x2, y2)) on this space is given by:\n",
    "\n",
    "$$\n",
    "\\sqrt{\\left(x_{1}-x_{2}\\right)^{2}+\\left(y_{1}-y_{2}\\right)^{2}}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to use [K-Means clustering](https://en.wikipedia.org/wiki/K-means_clustering) which will help us cluster the data points (salary values in our case). The implementation that we are going to be using for `KMeans` uses Euclidean distance internally. Let's get started. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the salary values to a numpy array\n",
    "salary_raw = salary_df['Salary (in USD)'].values\n",
    "\n",
    "# For compatibility with the SciPy implementation\n",
    "#salary_raw = pok.reshape(-1, 1)\n",
    "salary_raw = salary_raw.astype('float64')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now import the `kmeans` module from `scipy.cluster.vq`. SciPy stands for Scientific Python and provides a variety of convenient utilities for performing scientific experiments. Follow its documentation [here](https://docs.scipy.org/doc/). We will then apply `KMeans` to `salary_raw`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.cluster.vq import vq, kmeans\n",
    "\n",
    "# Supply the data and the number of clusters to kmeans()\n",
    "codebook, distortion = kmeans(salary_raw, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above chunk of code, we fed the salary data points the `kmeans()`. We also specified the number of clusters to which we want to group the data points. `cookbook` is the centroids generated by `kmeans()` and `distortion` is the averagedEuclidean distance between the data points fed and the centroids generated by `kmeans()`. \n",
    "\n",
    "Let's assign the groups of the data points by calling th `vq()` method. It takes - \n",
    "- The data points\n",
    "- The centroid as generated by the clustering algorithm (`kmeans()` in our case)\n",
    "\n",
    "It then returns the groups of the data points and the distance between the observation and its nearest group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "groups, cdist = vq(salary_raw, codebook)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now plot the groups we have got. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(salary_raw, np.arange(0,100), c=groups)\n",
    "plt.xlabel('Salaries in (USD)')\n",
    "plt.ylabel('Indices')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can you point to the anomalies? I bet you can! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above method for anomaly detection is purely [unsupervised](https://machinelearningmastery.com/supervised-and-unsupervised-machine-learning-algorithms/) in nature. \n",
    "If we had the class-labels of the data points, we could have easily converted this to a [supervised learning](https://machinelearningmastery.com/supervised-and-unsupervised-machine-learning-algorithms/) problem, specifically a _classification_ problem. "
   ]
  }
 ],
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