Binary_Classification_ML.ipynb

{
 "cells": [
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'wget' is not recognized as an internal or external command,\n",
      "operable program or batch file.\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'diabetes.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-f34dcb51c24f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'wget https://raw.githubusercontent.com/MicrosoftDocs/mslearn-introduction-to-machine-learning/main/Data/ml-basics/diabetes.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mdiabetes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'diabetes.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mdiabetes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m    608\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    609\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 610\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    611\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    612\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    460\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    461\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 462\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    463\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    464\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    817\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    818\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 819\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    820\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    821\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1048\u001b[0m             )\n\u001b[0;32m   1049\u001b[0m         \u001b[1;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1050\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# type: ignore[call-arg]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1051\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1052\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1865\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1866\u001b[0m         \u001b[1;31m# open handles\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1867\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1868\u001b[0m         \u001b[1;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1869\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m\"storage_options\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"encoding\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"memory_map\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"compression\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_open_handles\u001b[1;34m(self, src, kwds)\u001b[0m\n\u001b[0;32m   1360\u001b[0m         \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHanldes\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1361\u001b[0m         \"\"\"\n\u001b[1;32m-> 1362\u001b[1;33m         self.handles = get_handle(\n\u001b[0m\u001b[0;32m   1363\u001b[0m             \u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1364\u001b[0m             \u001b[1;34m\"r\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    640\u001b[0m                 \u001b[0merrors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"replace\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    641\u001b[0m             \u001b[1;31m# Encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 642\u001b[1;33m             handle = open(\n\u001b[0m\u001b[0;32m    643\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    644\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'diabetes.csv'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "!wget https://raw.githubusercontent.com/MicrosoftDocs/mslearn-introduction-to-machine-learning/main/Data/ml-basics/diabetes.csv\n",
    "diabetes = pd.read_csv('diabetes.csv')\n",
    "diabetes.head()"
   ]
  },
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    {
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       "<div class=\"ansiout\">Patient 1 \n",
       "  Features: [0.0, 171.0, 80.0, 34.0, 23.0, 43.50972593, 1.213191354, 21.0] \n",
       "  Label: 0\n",
       "Patient 2 \n",
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       "  Label: 0\n",
       "Patient 3 \n",
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       "  Label: 0\n",
       "Patient 4 \n",
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   "source": [
    "# Separate features and labels\n",
    "features = ['Pregnancies','PlasmaGlucose','DiastolicBloodPressure','TricepsThickness','SerumInsulin','BMI','DiabetesPedigree','Age']\n",
    "label = 'Diabetic'\n",
    "X, y = diabetes[features].values, diabetes[label].values\n",
    "\n",
    "for n in range(0,4):\n",
    "    print(\"Patient\", str(n+1), \"\\n  Features:\",list(X[n]), \"\\n  Label:\", y[n])"
   ]
  },
  {
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   "outputs": [
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       "addedWidgets": {},
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       "data": "<div class=\"ansiout\">[[0.00000000e+00 1.71000000e+02 8.00000000e+01 ... 4.35097259e+01\n  1.21319135e+00 2.10000000e+01]\n [8.00000000e+00 9.20000000e+01 9.30000000e+01 ... 2.12405757e+01\n  1.58364981e-01 2.30000000e+01]\n [7.00000000e+00 1.15000000e+02 4.70000000e+01 ... 4.15115235e+01\n  7.90185680e-02 2.30000000e+01]\n ...\n [0.00000000e+00 9.30000000e+01 8.90000000e+01 ... 1.86906831e+01\n  4.27048955e-01 2.40000000e+01]\n [0.00000000e+00 1.32000000e+02 9.80000000e+01 ... 1.97916451e+01\n  3.02257208e-01 2.30000000e+01]\n [3.00000000e+00 1.14000000e+02 6.50000000e+01 ... 3.62154365e+01\n  1.47362850e-01 3.40000000e+01]]\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "49b8680b-c824-4980-b5c2-6eaeb9b0bc50",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">/databricks/python/lib/python3.8/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify &#39;dtype=object&#39; when creating the ndarray\n",
       "  return array(a, dtype, copy=False, order=order)\n",
       "/databricks/python/lib/python3.8/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify &#39;dtype=object&#39; when creating the ndarray\n",
       "  return array(a, dtype, copy=False, order=order)\n",
       "/databricks/python/lib/python3.8/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify &#39;dtype=object&#39; when creating the ndarray\n",
       "  return array(a, dtype, copy=False, order=order)\n",
       "/databricks/python/lib/python3.8/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify &#39;dtype=object&#39; when creating the ndarray\n",
       "  return array(a, dtype, copy=False, order=order)\n",
       "/databricks/python/lib/python3.8/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify &#39;dtype=object&#39; when creating the ndarray\n",
       "  return array(a, dtype, copy=False, order=order)\n",
       "/databricks/python/lib/python3.8/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify &#39;dtype=object&#39; when creating the ndarray\n",
       "  return array(a, dtype, copy=False, order=order)\n",
       "/databricks/python/lib/python3.8/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify &#39;dtype=object&#39; when creating the ndarray\n",
       "  return array(a, dtype, copy=False, order=order)\n",
       "/databricks/python/lib/python3.8/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify &#39;dtype=object&#39; when creating the ndarray\n",
       "  return array(a, dtype, copy=False, order=order)\n",
       "</div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">/databricks/python/lib/python3.8/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify &#39;dtype=object&#39; when creating the ndarray\n  return array(a, dtype, copy=False, order=order)\n/databricks/python/lib/python3.8/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify &#39;dtype=object&#39; when creating the ndarray\n  return array(a, dtype, copy=False, order=order)\n/databricks/python/lib/python3.8/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify &#39;dtype=object&#39; when creating the ndarray\n  return array(a, dtype, copy=False, order=order)\n/databricks/python/lib/python3.8/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify &#39;dtype=object&#39; when creating the ndarray\n  return array(a, dtype, copy=False, order=order)\n/databricks/python/lib/python3.8/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify &#39;dtype=object&#39; when creating the ndarray\n  return array(a, dtype, copy=False, order=order)\n/databricks/python/lib/python3.8/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify &#39;dtype=object&#39; when creating the ndarray\n  return array(a, dtype, copy=False, order=order)\n/databricks/python/lib/python3.8/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify &#39;dtype=object&#39; when creating the ndarray\n  return array(a, dtype, copy=False, order=order)\n/databricks/python/lib/python3.8/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify &#39;dtype=object&#39; when creating the ndarray\n  return array(a, dtype, copy=False, order=order)\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
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"
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "/plots/4139a62f-fe2c-43b3-ba5c-9c01ee113510.png",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "image"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "/plots/ce09149d-552f-4529-ac67-1eb09f53adf7.png",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "image"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
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"
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     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "/plots/08cecf2e-d155-402a-86ac-5586cd141039.png",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "image"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "/plots/38308a23-5fe6-4f45-8357-6dedf4bbacd6.png",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "image"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "/plots/b93fc98d-eafa-4e7e-bdf4-5e61d89a5eed.png",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "image"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "/plots/04f0158a-c846-49bd-a8ae-992c8db89864.png",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "image"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "/plots/01d1fdd5-fe9e-4a1d-ac4f-224b5e4bd258.png",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "image"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "/plots/ad5fb8be-d8ca-424f-8cf4-6345fdb6fdc3.png",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "image"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "features = ['Pregnancies','PlasmaGlucose','DiastolicBloodPressure','TricepsThickness','SerumInsulin','BMI','DiabetesPedigree','Age']\n",
    "for col in features:\n",
    "    diabetes.boxplot(column=col,by=\"Diabetic\",figsize=(6,6))\n",
    "    plt.title(col)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "bba3ff62-9ea9-47ee-99c9-3bbbb4574b5d",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">X_train: 10500, X_test: 4500\n",
       "</div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">X_train: 10500, X_test: 4500\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.30,random_state=0)\n",
    "\n",
    "print(\"X_train: %d, X_test: %d\"% (X_train.shape[0],X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "d19f60b3-49cf-4884-bc87-faa837e1be78",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">LogisticRegression(C=100.0, solver=&#39;liblinear&#39;)\n",
       "</div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">LogisticRegression(C=100.0, solver=&#39;liblinear&#39;)\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Set regularization rate\n",
    "reg = 0.01\n",
    "\n",
    "model = LogisticRegression(C=1/reg,solver=\"liblinear\").fit(X_train,y_train)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "e28ed521-1a85-4f24-8bd3-1a0208d93449",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">predict: [0 0 0 ... 0 1 0]\n",
       "actual: [0 0 1 ... 1 1 1]\n",
       "</div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">predict: [0 0 0 ... 0 1 0]\nactual: [0 0 1 ... 1 1 1]\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = model.predict(X_test)\n",
    "print(\"predict:\",predictions)\n",
    "print(\"actual:\",y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "80035f66-fcf8-4240-8f6d-f3354d042d97",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">accuracy: 0.7893333333333333\n",
       "</div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">accuracy: 0.7893333333333333\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# evolution of model\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print(\"accuracy:\", accuracy_score(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "cac2253d-f1d4-4daa-ad55-04267a2b86fe",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">              precision    recall  f1-score   support\n",
       "\n",
       "           0       0.81      0.88      0.85      2986\n",
       "           1       0.72      0.60      0.66      1514\n",
       "\n",
       "    accuracy                           0.79      4500\n",
       "   macro avg       0.77      0.74      0.75      4500\n",
       "weighted avg       0.78      0.79      0.78      4500\n",
       "\n",
       "</div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">              precision    recall  f1-score   support\n\n           0       0.81      0.88      0.85      2986\n           1       0.72      0.60      0.66      1514\n\n    accuracy                           0.79      4500\n   macro avg       0.77      0.74      0.75      4500\nweighted avg       0.78      0.79      0.78      4500\n\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn. metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "162f6f82-bd5a-4a13-9f78-2386a5618f86",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">Overall Precision: 0.7242472266244057\n",
       "Overall Recall: 0.6036988110964333\n",
       "</div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">Overall Precision: 0.7242472266244057\nOverall Recall: 0.6036988110964333\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score\n",
    "\n",
    "print(\"Overall Precision:\",precision_score(y_test, predictions))\n",
    "print(\"Overall Recall:\",recall_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "24334b27-abae-45a0-a919-fbd01bec64d5",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">[[2638  348]\n",
       " [ 600  914]]\n",
       "</div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">[[2638  348]\n [ 600  914]]\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Print the confusion matrix\n",
    "cm = confusion_matrix(y_test, predictions)\n",
    "print (cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "b61cc43f-eb67-4ff8-96c1-3027b0850967",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">[[0.81657749 0.18342251]\n",
       " [0.96303915 0.03696085]\n",
       " [0.80873957 0.19126043]\n",
       " ...\n",
       " [0.60693276 0.39306724]\n",
       " [0.1065467  0.8934533 ]\n",
       " [0.63858497 0.36141503]]\n",
       "</div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">[[0.81657749 0.18342251]\n [0.96303915 0.03696085]\n [0.80873957 0.19126043]\n ...\n [0.60693276 0.39306724]\n [0.1065467  0.8934533 ]\n [0.63858497 0.36141503]]\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "/plots/10003be3-1cdb-46b6-81f8-54877810c64f.png",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "image"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_scores = model.predict_proba(X_test)\n",
    "print(y_scores)\n",
    "\n",
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "# calculate ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,0])\n",
    "\n",
    "# plot ROC curve\n",
    "fig = plt.figure(figsize=(6, 6))\n",
    "# Plot the diagonal 50% line\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "# Plot the FPR and TPR achieved by our model\n",
    "plt.plot(fpr, tpr)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "a372bc5e-0525-47fe-9a1e-2f3bd2384334",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">AUC: 0.8568436056949161\n",
       "</div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">AUC: 0.8568436056949161\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "auc = roc_auc_score(y_test,y_scores[:,1])\n",
    "print('AUC: ' + str(auc))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "a2f97744-804b-4b24-9dd8-8b499d3b07b8",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">Pipeline(steps=[(&#39;preprocessor&#39;,\n",
       "                 ColumnTransformer(transformers=[(&#39;num&#39;,\n",
       "                                                  Pipeline(steps=[(&#39;scaler&#39;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [0, 1, 2, 3, 4, 5, 6]),\n",
       "                                                 (&#39;cat&#39;,\n",
       "                                                  Pipeline(steps=[(&#39;onehot&#39;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#39;ignore&#39;))]),\n",
       "                                                  [7])])),\n",
       "                (&#39;logregressor&#39;,\n",
       "                 LogisticRegression(C=100.0, solver=&#39;liblinear&#39;))])\n",
       "</div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">Pipeline(steps=[(&#39;preprocessor&#39;,\n                 ColumnTransformer(transformers=[(&#39;num&#39;,\n                                                  Pipeline(steps=[(&#39;scaler&#39;,\n                                                                   StandardScaler())]),\n                                                  [0, 1, 2, 3, 4, 5, 6]),\n                                                 (&#39;cat&#39;,\n                                                  Pipeline(steps=[(&#39;onehot&#39;,\n                                                                   OneHotEncoder(handle_unknown=&#39;ignore&#39;))]),\n                                                  [7])])),\n                (&#39;logregressor&#39;,\n                 LogisticRegression(C=100.0, solver=&#39;liblinear&#39;))])\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use Pipeline to preprocess data transformations.\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler,OneHotEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import numpy as np\n",
    "\n",
    "# Define preprocessing for numeric columns (normalize them so they're on the same scale)\n",
    "numeric_features = [0,1,2,3,4,5,6]\n",
    "numeric_transformer = Pipeline(steps=[\n",
    "    ('scaler', StandardScaler())])\n",
    "\n",
    "# Define preprocessing for categorical features (encode the Age column)\n",
    "categorical_features = [7]\n",
    "categorical_transformer = Pipeline(steps=[\n",
    "    ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n",
    "\n",
    "# Combine preprocessing steps\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numeric_transformer, numeric_features),\n",
    "        ('cat', categorical_transformer, categorical_features)])\n",
    "\n",
    "# Create preprocessing and training pipeline\n",
    "pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                           ('logregressor', LogisticRegression(C=1/reg, solver=\"liblinear\"))])\n",
    "\n",
    "\n",
    "# fit the pipeline to train a logistic regression model on the training set\n",
    "model = pipeline.fit(X_train, (y_train))\n",
    "print (model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "b7d64c35-cab6-4503-bed6-27ae3e0943e4",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">Confusion Matrix:\n",
       " [[2667  319]\n",
       " [ 406 1108]] \n",
       "\n",
       "Accuracy: 0.8388888888888889\n",
       "Overall Precision: 0.7764540995094604\n",
       "Overall Recall: 0.7318361955085865\n",
       "AUC: 0.9202438327341774\n",
       "</div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">Confusion Matrix:\n [[2667  319]\n [ 406 1108]] \n\nAccuracy: 0.8388888888888889\nOverall Precision: 0.7764540995094604\nOverall Recall: 0.7318361955085865\nAUC: 0.9202438327341774\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "/plots/7478a450-a85c-4eee-a33d-eb234cdfa908.png",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "image"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get predictions from test data\n",
    "predictions = model.predict(X_test)\n",
    "y_scores = model.predict_proba(X_test)\n",
    "\n",
    "# Get evaluation metrics\n",
    "cm = confusion_matrix(y_test, predictions)\n",
    "print ('Confusion Matrix:\\n',cm, '\\n')\n",
    "print('Accuracy:', accuracy_score(y_test, predictions))\n",
    "print(\"Overall Precision:\",precision_score(y_test, predictions))\n",
    "print(\"Overall Recall:\",recall_score(y_test, predictions))\n",
    "auc = roc_auc_score(y_test,y_scores[:,1])\n",
    "print('AUC: ' + str(auc))\n",
    "\n",
    "# calculate ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])\n",
    "\n",
    "# plot ROC curve\n",
    "fig = plt.figure(figsize=(6, 6))\n",
    "# Plot the diagonal 50% line\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "# Plot the FPR and TPR achieved by our model\n",
    "plt.plot(fpr, tpr)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "3dcd43a6-94a7-4cd0-bc81-3d56340ad06c",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">Pipeline(steps=[(&#39;preprocessor&#39;,\n",
       "                 ColumnTransformer(transformers=[(&#39;num&#39;,\n",
       "                                                  Pipeline(steps=[(&#39;scaler&#39;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [0, 1, 2, 3, 4, 5, 6]),\n",
       "                                                 (&#39;cat&#39;,\n",
       "                                                  Pipeline(steps=[(&#39;onehot&#39;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#39;ignore&#39;))]),\n",
       "                                                  [7])])),\n",
       "                (&#39;logregressor&#39;, RandomForestClassifier())])\n",
       "Confusion Matrix:\n",
       " [[2862  124]\n",
       " [ 177 1337]] \n",
       "\n",
       "Accuracy: 0.9331111111111111\n",
       "Overall Precision: 0.9151266255989049\n",
       "Overall Recall: 0.8830911492734478\n",
       "\n",
       "AUC: 0.9829489400557955\n",
       "</div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">Pipeline(steps=[(&#39;preprocessor&#39;,\n                 ColumnTransformer(transformers=[(&#39;num&#39;,\n                                                  Pipeline(steps=[(&#39;scaler&#39;,\n                                                                   StandardScaler())]),\n                                                  [0, 1, 2, 3, 4, 5, 6]),\n                                                 (&#39;cat&#39;,\n                                                  Pipeline(steps=[(&#39;onehot&#39;,\n                                                                   OneHotEncoder(handle_unknown=&#39;ignore&#39;))]),\n                                                  [7])])),\n                (&#39;logregressor&#39;, RandomForestClassifier())])\nConfusion Matrix:\n [[2862  124]\n [ 177 1337]] \n\nAccuracy: 0.9331111111111111\nOverall Precision: 0.9151266255989049\nOverall Recall: 0.8830911492734478\n\nAUC: 0.9829489400557955\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YsCHTpk2jUaNGpKWF93txeLe8BN/EB5g7VqeNilRK0WiUSy+9lEGDBuHu7LnnnqEuAqBC8DtL124mzaBRnXCcNiYSJpFIhIsuuognnngiNKeGJkKFYCszFq+jaYMaVEnXP41IZVJYWEj//v155plnGDx4MHfddZcGkozT3q6YtZvy+WTBarI1FaVIpXPhhRcyatQo7rzzTm677TYVgWJ0sLiY/3y5hPxIVAPMiVRCp59+OocffjjXXHNN0FEqHBWCOHfnxU9/4PC963LInpV3lEGRMNmyZQtTp06lffv2/PGPfww6ToWlrqG4Vbn5/G9FLj1baaRRkcpg8+bNnHrqqXTp0oVFixYFHadCU4sgbt7yDQAc0litAZFUt3HjRnr27ElOTg5PPfUUTZs2DTpShaZCEPff+StJM2ihQiCS0jZs2ECPHj346KOPeO655zjnnHOCjlThqRAQm5f45c9+pOthjTRBvUiKe+GFF/j444958cUXOfvss4OOkxJUCICFKzeybnMBnQ9pFHQUEdlFf/7znzn++ONp1apV0FFShg4WA7OXrgPgUJ0tJJKSVq1aRefOnfnmm28wMxWBHaRCQGx8oaoZaezXoEbQUURkB/3000+0a9eO//73vyxZsiToOCkpqYXAzLqa2Twzm29mv5vtwcz2MbNJZvalmX1tZt2Tmacks5eup0WjWmRoWAmRlLJ06VLatWvHd999x9tvv02nTp2CjpSSkrbnM7N0YCjQDTgE6Gtmh2y12i3Av939SKAP8Fiy8pTk5435TPv+Z/7QtH55f7SI7IKlS5dy8skns3jxYsaNG0d2dnbQkVJWMr8CHwPMd/eF7p4PvAycutU6DhR1zNcBliYxzza9OWMJBRHnzNYaVkIkldStW5fDDjuM9957jxNPPDHoOCktmWcNNQF+LLa8GGiz1Tq3Ae+Z2V+BGkDHJObZpsnfruTA3WvSopEOFIukgoULF7LbbrtRp04d/vOf/wQdp1IIulO8LzDS3fcCugPPm9nvMpnZJWY2zcymrVy5skwDfLt8g84WEkkRc+fO5cQTT+Tcc88NOkqlksxCsAQoPvv7XvHHihsA/BvA3acCWUCDrd/I3Ye7e2t3b92wYcMyC+ju/LQhjyb1qpXZe4pIcsyaNYt27doRiUS46667go5TqSSzEHwOHGhm+5lZJrGDwWO2WucHoAOAmR1MrBCU7Vf+UmzKjxCJOrWyqpTXR4rITvjqq69o3749aWlp5OTkcNhhhwUdqVJJWiFw90LgCmA8MIfY2UGzzWyImfWKr3YNcLGZfQW8BPT3olmly8Hq3HwADSshUoG5O/379ycrK4vJkyfTokWLoCNVOkkdYsLd3wHe2eqxW4vd/wY4IZkZSrMydwsADWtVDSqCiGyHmfHqq6+Snp7OfvvtF3ScSinog8WB+vHnzQDsVVfHCEQqmg8//JBrrrkGd+eAAw5QEUiiUBeC71ZtxAz2rl896CgiUkxOTg5du3Zl7NixrFmzJug4lV6oC0HOtys5aI9aZFVJDzqKiMS9//77dO/enX333ZfJkydTv76u+k+20BaC9VsK+OrHtfRoqakpRSqKd955h549e9K8eXNycnJo1EhDw5eH0BaCSCR2clKtLE3JIFKRHH300UycOJGyvGZIShfaQiAiFcePP8ZGo+nevTsffvihuoPKmQqBiATqhRdeoFmzZowbNw6InS4q5UuFQEQCM3LkSM4991zatm1L27Ztg44TWioEIhKI4cOHc8EFF9CxY0fGjh1LzZo1g44UWqEtBNH4SBZqhoqUv88//5w///nPdO/enTFjxlC9uq7lCVJoC8HqjRpnSCQof/jDH3jhhRd4/fXXycrKCjpO6IW2ECxdGxteonEd/RKKlJcHHniAGTNmANCvXz+qVtU4XxVBaAvB8nWxAecaqRCIJJ27c/vttzNo0CCeeuqpoOPIVkJ7NdWStZtJM9i9lgqBSDK5OzfffDN33303/fv356GHHgo6kmwltIVgwcpc9qlfncyM0DaKRJLO3bn22mu5//77ueSSSxg2bBhpafqbq2hC+z8yf0UuB+yu09VEkqmwsJA5c+ZwxRVX8Pjjj6sIVFChbRGs3JDHMfvpMnaRZIhGo+Tm5lK7dm3eeOMNqlSpolO1K7DQlueNeRFqVtVcxSJlLRKJMGDAANq3b8/mzZvJzMxUEajgQlkI8goj5Eei1KyqeQhEylJhYSHnnXceI0eOpFevXrpGIEWEsmtoY14EgBpVQ7n5IklRUFDAn/70J1599VXuuusubrzxxqAjSYJCuSfcUhArBNU0M5lImRk0aBCvvvoq9913H4MGDQo6juyAUBaC/MIogE4dFSlD1157LUceeSQXXnhh0FFkB4VyT1gQiRWCKumh3HyRMrNp0yYeeOABotEo++67r4pAigrlnjBPLQKRXZabm0uPHj245pprmDp1atBxZBeEsmtoY14hADV1sFhkp6xfv57u3bszdepURo0axQknnBB0JNkFodwTrt8SKwSauF5kx61du5auXbvyxRdf8PLLL9O7d++gI8kuCuWecMOWAgBqZ+mCMpEdNXv2bObMmcPo0aM59dRTg44jZSCUhWBjfuz00eqZOn1UJFH5+flkZmZywgknsGjRIurVqxd0JCkjoTxaWqizhkR2yPLlyzn66KMZMWIEgIpAJRPKFkFhJDZfcUa6xj8R2Z4lS5aQnZ3NkiVL2H///YOOI0kQykJQEFWLQCQRP/zwA9nZ2axYsYJx48bRtm3boCNJEoSzEBTGWwRpahGIlGT9+vWcfPLJrFmzhvfee49jjz026EiSJKEsBJsKCqmSbqSrEIiUqHbt2lx55ZWceOKJtG7dOug4kkShLASrNuTTsGZVjZEusg1z584lNzeX1q1bc/XVVwcdR8pBKAvBytw8GtSqGnQMkQpn1qxZdOjQgfr16zNr1izS03WKdRiE8mjp6tw8GtRUIRApbsaMGbRr146MjAzeeOMNFYEQCWUhWLupgLrVdFWxSJFp06aRnZ1N9erVmTx5MgcddFDQkaQchbJraP3mAmqrEIj84pFHHqFOnTpMmjSJpk2bBh1HylnoCkE06mzIK1QhEAHcHTNj+PDh/PzzzzRu3DjoSBKA0HUN5ceHl6iquQgk5CZOnMjxxx/PqlWrqFq1qopAiIVub1gYjV1MVkXDS0iIjR8/nh49erBhwwYikUjQcSRgCRcCM6uezCDlpSA+O1lGWuhqoAgAY8eOpVevXhx00EFMmjSJPfbYI+hIErDt7g3N7Hgz+waYG18+3MweS3qyJPl1nCG1CCR83n33XU4//XRatWrFxIkTadiwYdCRpAJI5GvxA0AXYDWAu38FnJTMUMn068ijahFI+Bx++OGcddZZTJgwgfr16wcdRyqIhPaG7v7jVg+lbKfi5gJNSiPhM3nyZAoLC9lzzz0ZNWoUderUCTqSVCCJFIIfzex4wM2sipldC8xJcq6k2ZRXVAhCd+ashNSIESNo3749DzzwQNBRpIJKpBD8BbgcaAIsAY4ALktmqGTamB+buL6GWgQSAo8//jgDBgygU6dOXH755UHHkQoqka/FB7n7n4o/YGYnAB8lJ1JybYoXgupV1SKQyu3hhx/mqquuokePHowePZqsrKygI0kFlUiL4JEEH0sJWwpiZw1lVdHBYqm8li5dyk033cRpp53G66+/riIgpSrxa7GZHQccDzQ0s0HFnqoNpGy/SkFE1xFI5bfnnnvy8ccfc/DBB1OlioZTkdKVtjfMBGoSKxa1it3WA2cmP1py/HL6qGYnk0rG3Rk8eDBDhw4FoFWrVioCkpASWwTuPhmYbGYj3f37nXlzM+sKPESsBfGUu9+zjXXOAm4DHPjK3fvtzGclKhItuo5AhUAqD3fnxhtv5N5772XAgAG/DCYnkohEjphuMrN/AYcCv3Q0unt2aS8ys3RgKNAJWAx8bmZj3P2bYuscCNwInODua8xs953Yhh3y65XF6hqSysHdGTRoEA8++CB/+ctfGDp0qIqA7JBE9oYvEBteYj/gdmAR8HkCrzsGmO/uC909H3gZOHWrdS4Ghrr7GgB3X5Fg7p1WNNaQCoFUBu7OX//6Vx588EGuvPJKHnvsMdJ0/Et2UCK/Mbu5+9NAgbtPdvcLgVJbA3FNgOJXJC+OP1Zcc6C5mX1kZp/Eu5J+x8wuMbNpZjZt5cqVCXx0ydZtjp0+WitLp49K6jMzmjVrxnXXXceDDz6oloDslET2hgXxn8vMrAewFCirQUoygAOBdsBewBQza+nua4uv5O7DgeEArVu39l35wDWb8qlVNUMtAklpkUiEb7/9loMPPpirr7466DiS4hLZG/7DzOoA1wDXAk8BAxN43RJg72LLe8UfK24xMMbdC9z9O+BbYoUhadZuyqduDZ1JIamrsLCQc889lzZt2rBkydZ/UiI7bruFwN3Huvs6d5/l7u3d/Wjg5wTe+3PgQDPbz8wygT7AmK3WeYNYawAza0Csq2jhjmzAjlq7uYC61TKT+REiSVNQUECfPn146aWXuPnmm2nSZOveVpEdV9oFZenAWcT69ce5+ywzOwW4CagGHFnaG7t7oZldAYwndvroCHefbWZDgGnuPib+XOf4fAcR4Dp3X10WG1aS/MKoriqWlJSXl8dZZ53FmDFjuP/++9UlJGWmtGMETxPr2vkMeNjMlgKtgRvc/Y1E3tzd3wHe2eqxW4vdd2BQ/FYuCqNOui4mkxT0yCOPMGbMGB599FENICdlqrRC0Bpo5e5RM8sClgPNkv2NPdkiUSezSsqOkCEhdtVVV9GyZUu6dOkSdBSpZErrI8l39yiAu28BFqZ6EQC1CCS15ObmctFFF7F8+XKqVKmiIiBJUVohaGFmX8dvM4stzzSzr8srYFmLRKMaZ0hSwvr16+natSsjR47k888TuYZTZOeU1jV0cLmlKEeRKGoRSIW3Zs0aunbtyvTp03nllVfo2bNn0JGkEitt0LmdGmiuossrjFAlQ2cNScW1evVqOnfuzMyZM3nttdfo1atX0JGkkgvdOAvrNxdQp5ouKJOKKxKJzav95ptv0q1bt4DTSBiEqhC4O2s3FVBXhUAqoJUrV1K3bl123313Pv/8cw0eJ+Umod80M6tmZgclO0yybcyPUBh16lZXIZCKZcmSJbRt25ZLLrkEQEVAytV2f9vMrCcwAxgXXz7CzLYeKiIlrN2UD6AhJqRC+f777znppJNYtmwZF110UdBxJIQS+dpxG7G5BdYCuPsMYnMTpJy1m2IDqdZW15BUEAsXLuSkk05i9erVvP/++5xwwglBR5IQSmgYandft9U457s0FHRQNuZpLgKpOCKRCL169SI3N5eJEydy1FFHBR1JQiqRPeJsM+sHpMenlrwS+Di5sZKjID5xveYikIogPT2dJ598kho1atCqVaug40iIJbJH/Cux+YrzgBeBdSQ2H0GFUzRfsSaulyDNnDmToUOHAnDcccepCEjgEmkRtHD3m4Gbkx0m2QrjLYJMtQgkIF9++SWdOnUiKyuLc845hzp16gQdSSShFsF9ZjbHzO4ws8OSniiJCiJqEUhwPvvsM7Kzs6lRowaTJ09WEZAKI5EZytoD7YGVwBPxQeduSXqyJPilEOgcbSlnH3/8MR07dqRevXpMmTKFZs2aBR1J5BcJ7RHdfbm7Pwz8hdg1Bbdu5yUVUn5hrBBU1VhDUs5mz55N48aNmTJlCvvuu2/QcUR+I5ELyg42s9viQ1E/QuyMob2SniwJdNaQlLcNGzYAcPHFFzNjxgz22isl/3SkkktkjziC2MVkXdy9nbsPc/cVSc6VFEVdQ5lqEUg5GDduHPvttx9Tp04FoFq1agEnEtm27Z415O7HlUeQ8lDUNVRFB4slyd566y3OPPNMDj30UJo3bx50HJFSlVgIzOzf7n5WvEuo+JXERmze+ZQ7+Tk/UlQI1CKQ5Hnttdfo06cPRx55JOPHj6devXpBRxIpVWktgqviP08pjyDl4ZeuIRUCSZKpU6dy9tln06ZNG9555x2dIiopocQ9orsvi9+9zN2/L34DLiufeGUrvzA2X3GapqqUJDnmmGO44447GDdunIqApIxEvhp32uF/6UIAAB1CSURBVMZjKTltUkEkqm4hSYqXXnqJxYsXk56ezo033kitWrWCjiSSsBL3imZ2afz4wEFm9nWx23fA1+UXsewURFxnDEmZGzZsGP369eOuu+4KOorITintGMGLwLvA3cANxR7f4O4/JzVVkuQVqkUgZeuhhx5i4MCB9OzZkwceeCDoOCI7pbS9orv7IuByYEOxG2ZWP/nRyl5BJKqriqXM/Otf/2LgwIGcfvrpjB49mqpVqwYdSWSnbK9FcArwBbHTR4sfYXVg/yTmSor8wqiuIZAysWXLFkaNGkWfPn147rnnqFJFs95J6iqxELj7KfGfKTkt5bYURKI6RiC7xN2JRCJkZWWRk5NDrVq1yMjQjHeS2hIZa+gEM6sRv3+Omd1vZvskP1rZy9cxAtkF7s4NN9zAaaedRkFBAfXq1VMRkEohkb3iMGCTmR0OXAMsAJ5PaqokyVeLQHaSu3P11Vfzz3/+k7333pv09PSgI4mUmUT2ioXu7sCpwKPuPhRIyZOkCyNOhi4mkx0UjUa5/PLLeeihh7jqqqsYOnQoaZrTQiqRRH6bN5jZjcC5wNtmlgak7JExQ4VAdsx1113HsGHD+Nvf/sYDDzyAmX6HpHJJpIPzbKAfcKG7L48fH/hXcmOJVBz9+vWjfv363HTTTSoCUiklMlXlcuAFoI6ZnQJscffnkp5MJEAFBQW8/vrrABx99NHcfPPNKgJSaSVy1tBZwGdAb+As4FMzOzPZwUSCkp+fT58+fTjjjDP49NNPg44jknSJdA3dDPyhaFYyM2sITABGJzOYSBDy8vLo3bs3b731Fg8++CBt2rQJOpJI0iVSCNK2mppyNQlOel/RRNxR615KsnnzZk477TTGjx/PY489xqWXXhp0JJFykUghGGdm44GX4stnA+8kL1Ly5BdGqZWlC4Bk23JycpgwYQJPPfUUAwYMCDqOSLlJZM7i68zsdKBt/KHh7v6f5MZKjoJIVLOTye+4O2ZGt27dmDt3LgcccEDQkUTKVWnzERxoZm+a2SxiB4rvc/dBqVoEINYi0JXFUty6devo2LEjEyZMAFARkFAqba84AhgLnEFsBNJHyiVREmmICSluzZo1dOrUiSlTprB+/fqg44gEprSuoVru/mT8/jwzm14egZIpv1BdQxKzatUqOnXqxDfffMPrr79Oz549g44kEpjSCkGWmR3Jr/MQVCu+7O4pVxjUNSQAa9eupX379syfP58333yTrl27Bh1JJFClFYJlwP3FlpcXW3YgO1mhkiVfk9cLULt2bU4++WQefPBBOnToEHQckcCVNjFN+/IMUh7cIV2jj4bW4sWLKSwspGnTpjz66KNBxxGpMHRSvYTCokWLyM7Opk6dOnzxxRcaRlqkGBUCqfQWLFhAdnY269ev55VXXlERENmKCoFUavPmzSM7O5u8vDwmTpzIkUceGXQkkQonkdFHLT5X8a3x5X3M7JjkRxPZdddddx2FhYVMmjRJRUCkBIm0kR8DjgP6xpc3AEMTeXMz62pm88xsvpndUMp6Z5iZm1nrRN5XJFHPPvssU6ZMoWXLlkFHEamwEikEbdz9cmALgLuvATK39yIzSydWMLoBhwB9zeyQbaxXC7gK0MDvUiamT59O37592bJlC/Xq1eOggw4KOpJIhZZIISiI79QdfpmPIJrA644B5rv7QnfPB14GTt3GencA9xIvNCK74tNPPyU7O5upU6eycuXKoOOIpIRECsHDwH+A3c3sTuBD4K4EXtcE+LHY8uL4Y78ws6OAvd397dLeyMwuMbNpZjZtV/643X2nXysV30cffUSnTp3YbbfdmDx5MnvvvXfQkURSQiLDUL9gZl8AHYgNL/FHd5+zqx9sZmnErlTun0CG4cBwgNatW+/03rwg6mSk64KyymjKlCl0796dJk2aMHHiRJo0abL9F4kIkNhZQ/sAm4C3gDHAxvhj27MEKP6VbK/4Y0VqAYcBOWa2CDgWGJPMA8aFkShVdA55pVSvXj2OPvpoJk+erCIgsoMSuY7gbWLHBwzIAvYD5gGHbud1nwMHmtl+xApAH6Bf0ZPuvg5oULRsZjnAte4+bQfyJywadaKOWgSVzLx582jevDktW7YkJycH01ykIjtsu1+P3b2lu7eK/zyQ2EHgqQm8rhC4AhgPzAH+7e6zzWyImfXa1eA7qiAaO76tQecqjzFjxtCqVSuGDx8OoCIgspN2+Mpid59uZm0SXPcdtprf2N1vLWHddjuaZUcURmKHFjI06FylMHr0aPr27ctRRx3F2WefHXQckZS23UJgZoOKLaYBRwFLk5YoSX4pBGoRpLyXXnqJc889lzZt2vDuu+9Su3btoCOJpLREWgS1it0vJHbM4LXkxEmeoq4htQhS2w8//MD5559P27ZtGTt2LDVr1gw6kkjKK7UQxC8kq+Xu15ZTnqSJRItaBCoEqWyfffZhzJgxnHTSSVSvXj3oOCKVQon9JGaW4e4R4IRyzJM0BZH4wWKdPpqShg0bxtixYwHo2rWrioBIGSptr/hZ/OcMMxtjZuea2elFt/IIV5Z+PUagFkGqeeCBB7jssst4/vnng44iUiklcowgC1hNbI7iousJHHg9ibnKXGG8a0hTVaaWe+65hxtvvJEzzzyTUaNGBR1HpFIqrRDsHj9jaBa/FoAiKTdoT9E4QyoEqWPIkCEMHjyYvn378txzz5GRoXmURJKhtL+sdKAmvy0ARVKuEMQbBKTpoqOU4O789NNPnH/++Tz99NOkp6cHHUmk0iqtECxz9yHlliTJovEWgRoEFZu7s3LlSnbffXceeeQRAM0xLJJkpf2FVapdZlEh0DAEFZe7M3DgQI4++mhWrFhBWlqaioBIOSjtr6xDuaUoB66uoQotGo1y6aWX8vDDD9O7d28aNmwYdCSR0CixELj7z+UZJNnUNVRxRSIRLrroIp544gluuOEG7rvvPrXcRMpRaNrdOlhccd17770888wzDB48mLvuuktFQKScheZ8vF+PEQQcRH7niiuuoHHjxlxwwQVBRxEJpdC0CPyXriFVgoogPz+fIUOGsGnTJmrXrq0iIBKg0BSCoq4h1YHgbdmyhTPOOIPBgwczbty4oOOIhF5ouoZ01lDFsHnzZv74xz/y3nvv8fjjj3P66Sk3bJVIpROaQqBjBMHbuHEjPXv2JCcnhxEjRqg7SKSCCE8hiPcNWeW6Ti6lLFu2jHnz5vHcc89xzjnnBB1HROLCUwjiXUMadK78bdq0iWrVqnHAAQfw7bffUqNGjaAjiUgxITpYXDT6aMBBQubnn3/mpJNO4pZbbgFQERCpgEKzW9RYQ+Vv1apVZGdnM3PmTI477rig44hICULUNaTrCMrTTz/9RIcOHViwYAFvvfUWnTt3DjqSiJQgPIUgNmUx6SoESVdYWEinTp347rvvePvtt8nOzg46koiUIjyFQKePlpuMjAxuvfVW9thjD0488cSg44jIdoSuEKhrKHkWLVrEzJkz6dmzJ2eeeWbQcUQkQSEqBLGfOn00OebPn092djZ5eXksWLCAmjVrBh1JRBIUokKg+QiSZe7cuXTo0IG8vDwmTJigIiCSYkJTCCJRnT6aDLNmzaJjx464Ozk5ORx22GFBRxKRHRSaQvDroHPB5qhsXnvtNdLS0pg4cSItWrQIOo6I7ITQXVCmYwRlIxKJAHDrrbfy5ZdfqgiIpLAQFYLYTw06t+s++eQTDj30UObNm4eZscceewQdSUR2QWgKges6gjLx4Ycf0qlTJwoLC6lWrVrQcUSkDISoEMR+qhDsvJycHLp06UKTJk2YPHky++yzT9CRRKQMhKcQoAvKdsUnn3xC9+7dadq0KTk5OTRp0iToSCJSRkJTCKKaqnKXtGzZkvPOO4+cnBwaNWoUdBwRKUMhKgQ6RrAzJk6cyIYNG6hRowaPP/44DRs2DDqSiJSx0BQCHSPYca+++ipdunTh5ptvDjqKiCRRiAqB5izeES+88AJ9+vTh2GOP5R//+EfQcUQkicJTCOI/dT3Z9o0cOZJzzz2Xk08+mXfffZfatWsHHUlEkig0hSAa1VlDidi4cSN///vf6dixI2PHjtUAciIhEJqxhqI6RrBd7k6NGjWYMmUKjRs3JisrK+hIIlIOQtMiKOoa0uij23bfffcxcOBA3J399ttPRUAkRMJTCDQfQYnuvvturr32WpYtW/bLYHIiEh6hKQS/XkegSlDE3bn99tu56aab6NevHy+++CIZGaHpLRSRuNAUgl+uIwg2RoVy2223cdttt9G/f3+ee+45FQGRkApNISiiBsGvWrduzWWXXcbTTz9Nenp60HFEJCChKwRhF41G+fzzzwHo2bMnQ4cOJS1NvwYiYaY9QIhEo1EuvfRSjj32WGbMmBF0HBGpINQpHBKRSISLLrqIkSNHctNNN3H44YcHHUlEKoiktgjMrKuZzTOz+WZ2wzaeH2Rm35jZ12b2gZntm8w8YVVYWMh5553HyJEjuf322/nHP/6hs6dE5BdJKwRmlg4MBboBhwB9zeyQrVb7Emjt7q2A0cA/k5UnzF5//XVefPFF7rrrLm699VYVARH5jWR2DR0DzHf3hQBm9jJwKvBN0QruPqnY+p8A5yQxT2j17t2bxo0bc+KJJwYdRUQqoGR2DTUBfiy2vDj+WEkGAO8mMU+obNmyhfPOO49Zs2ZhZioCIlKiCnHWkJmdA7QG/lXC85eY2TQzm7Zy5cryDZeCNm3aRK9evXj++eeZNm1a0HFEpIJLZiFYAuxdbHmv+GO/YWYdgZuBXu6et603cvfh7t7a3VtrqsTS5ebm0qNHDyZMmMCIESPo379/0JFEpIJL5jGCz4EDzWw/YgWgD9Cv+ApmdiTwBNDV3VckMUsobNiwgW7dujF16lSef/55/vSnPwUdSURSQNIKgbsXmtkVwHggHRjh7rPNbAgwzd3HEOsKqgm8Gj+T5Qd375WsTJVdRkYGtWvX5uWXX6Z3795BxxGRFJHUC8rc/R3gna0eu7XY/Y7J/Pyw+Pnnn0lLS6Nu3bq8/fbbOj1URHZIhThYLDtv5cqVtG/fnlNPPRV3VxEQkR2mISZS2PLly+nQoQMLFy5kzJgxKgIislNUCFLUkiVLyM7OZvHixbzzzju0b98+6EgikqJUCFLU+eefz7Jlyxg/fjxt27YNOo6IpDAVghQ1fPhwVq1axTHHHBN0FBFJcTpYnEL+97//cdNNNxGNRtl///1VBESkTKgQpIg5c+Zw8skn8+STT7J48eKg44hIJaJCkAJmzZpFu3btiEaj5OTksM8++wQdSUQqERWCCm7GjBm0a9eOjIwMJk+ezKGHHhp0JBGpZFQIKrgVK1ZQv359Jk+ezEEHHRR0HBGphHTWUAW1evVqdtttNzp37szs2bOpUqVK0JFEpJJSi6ACmjJlCvvvvz+jR48GUBEQkaRSIahgJk6cSLdu3dhzzz05/vjjg44jIiGgQlCBjB8/nh49erD//vuTk5PDnnvuGXQkEQkBFYIKYsGCBfTq1YsWLVowadIk9thjj6AjiUhIqBBUEM2aNePRRx/lgw8+oEGDBkHHEZEQUSEI2OjRo5k+fToAF198MfXr1w84kYiEjQpBgEaNGsXZZ5/NHXfcEXQUEQkxFYKAjBgxgvPOO4927doxatSooOOISIipEATgiSeeYMCAAXTq1ImxY8dSo0aNoCOJSIipEJQzd2fs2LH06NGDN998k2rVqgUdSURCTkNMlKMtW7aQlZXFq6++SlpaGpmZmUFHEhFRi6C83HnnnRx33HGsW7eOrKwsFQERqTBUCJLM3Rk8eDC33HILhx12mI4HiEiFo66hJHJ3brzxRu69914uuOACnnzySdLT04OOJSLyG2oRJNE///lP7r33Xv7yl7/w1FNPqQiISIWkFkES9evXj4KCAm6++WbMLOg4IiLbpBZBGYtGozzzzDNEIhH23ntvbrnlFhUBEanQVAjKUCQS4cILL+TCCy9kzJgxQccREUmIuobKSGFhIeeddx4vvfQSQ4YM4bTTTgs6kohIQlQIykB+fj79+vXjtdde45577uH6668POpKISMJUCMrAN998w7vvvsv999/P1VdfHXQcEZEdokKwC6LRKGlpaRxxxBF8++23NGnSJOhIIiI7TAeLd9KmTZvo2rUrw4cPB1AREJGUpUKwE3Jzc+nevTsffPABWVlZQccREdkl6hraQevWraN79+58+umnjBo1ir59+wYdSURkl6gQ7ID8/Hw6d+7M9OnTeeWVVzjjjDOCjiQissvUNbQDMjMz6d27N6+99pqKgIhUGmoRJGDFihX8+OOPHH300Vx77bVBxxERKVMqBNuxbNkyOnTowNq1a1mwYIGmlhSRSkeFoBRLliwhOzubJUuW8Pbbb6sIiEilpEJQgu+//57s7GxWrlzJ+PHjOeGEE4KOJCKSFCoEJfi///s/Vq9ezfvvv0+bNm2CjiMikjQ6a6gE9913H5988omKgIhUeioExcyZM4dOnTqxcuVKMjMzadGiRdCRRESSTl1DcTNnzqRDhw6kp6ezevVqGjZsGHQkEZFyoRYB8OWXX9K+fXsyMzOZPHmyWgIiEiqhLwTTp08nOzubGjVqMHnyZJo3bx50JBGRchX6QtC4cWOOPfZYpkyZQrNmzYKOIyJS7kJbCGbOnElhYSGNGzfm3XffZd999w06kohIIEJZCD744APatGnD3//+96CjiIgELqmFwMy6mtk8M5tvZjds4/mqZvZK/PlPzaxpMvMATJgwgVNOOYVmzZoxcODAZH+ciEiFl7RCYGbpwFCgG3AI0NfMDtlqtQHAGnc/AHgAuDdZeYqc1fssWrRowaRJk9hjjz2S/XEiIhVeMlsExwDz3X2hu+cDLwOnbrXOqcCz8fujgQ5mZskIsyUvD4CWLVsyceJEGjRokIyPERFJOcksBE2AH4stL44/ts113L0QWAfstvUbmdklZjbNzKatXLlyp8Ic0Kgeh++eydtvj6VevXo79R4iIpVRSlxZ7O7DgeEArVu39p15jx6tGtOjVeMyzSUiUhkks0WwBNi72PJe8ce2uY6ZZQB1gNVJzCQiIltJZiH4HDjQzPYzs0ygDzBmq3XGAOfH758JTHT3nfrGLyIiOydpXUPuXmhmVwDjgXRghLvPNrMhwDR3HwM8DTxvZvOBn4kVCxERKUdJPUbg7u8A72z12K3F7m8Beiczg4iIlC6UVxaLiMivVAhEREJOhUBEJORUCEREQk6FQEQk5FQIRERCToVARCTkVAhEREJOhUBEJOQs1Yb2MbOVwPc7+fIGwKoyjJMKtM3hoG0Oh13Z5n3dveG2nki5QrArzGyau7cOOkd50jaHg7Y5HJK1zeoaEhEJORUCEZGQC1shGB50gABom8NB2xwOSdnmUB0jEBGR3wtbi0BERLZSKQuBmXU1s3lmNt/MbtjG81XN7JX485+aWdPyT1m2EtjmQWb2jZl9bWYfmNm+QeQsS9vb5mLrnWFmbmYpf4ZJIttsZmfF/69nm9mL5Z2xrCXwu72PmU0ysy/jv9/dg8hZVsxshJmtMLNZJTxvZvZw/N/jazM7apc/1N0r1Y3YtJgLgP2BTOAr4JCt1rkMeDx+vw/wStC5y2Gb2wPV4/cvDcM2x9erBUwBPgFaB527HP6fDwS+BOrFl3cPOnc5bPNw4NL4/UOARUHn3sVtPgk4CphVwvPdgXcBA44FPt3Vz6yMLYJjgPnuvtDd84GXgVO3WudU4Nn4/dFABzOzcsxY1ra7ze4+yd03xRc/AfYq54xlLZH/Z4A7gHuBLeUZLkkS2eaLgaHuvgbA3VeUc8aylsg2O1A7fr8OsLQc85U5d59CbA73kpwKPOcxnwB1zazxrnxmZSwETYAfiy0vjj+2zXXcvRBYB+xWLumSI5FtLm4AsW8UqWy72xxvMu/t7m+XZ7AkSuT/uTnQ3Mw+MrNPzKxruaVLjkS2+TbgHDNbTGyO9L+WT7TA7Ojf+3YldfJ6qXjM7BygNXBy0FmSyczSgPuB/gFHKW8ZxLqH2hFr9U0xs5buvjbQVMnVFxjp7veZ2XHA82Z2mLtHgw6WKipji2AJsHex5b3ij21zHTPLINacXF0u6ZIjkW3GzDoCNwO93D2vnLIly/a2uRZwGJBjZouI9aWOSfEDxon8Py8Gxrh7gbt/B3xLrDCkqkS2eQDwbwB3nwpkERuTp7JK6O99R1TGQvA5cKCZ7WdmmcQOBo/Zap0xwPnx+2cCEz1+FCZFbXebzexI4AliRSDV+41hO9vs7uvcvYG7N3X3psSOi/Ry92nBxC0Tifxuv0GsNYCZNSDWVbSwPEOWsUS2+QegA4CZHUysEKws15TlawxwXvzsoWOBde6+bFfesNJ1Dbl7oZldAYwndsbBCHefbWZDgGnuPgZ4mljzcT6xgzJ9gku86xLc5n8BNYFX48fFf3D3XoGF3kUJbnOlkuA2jwc6m9k3QAS4zt1TtrWb4DZfAzxpZlcTO3DcP5W/2JnZS8SKeYP4cY/BQBUAd3+c2HGQ7sB8YBNwwS5/Zgr/e4mISBmojF1DIiKyA1QIRERCToVARCTkVAhEREJOhUBEJORUCKRCMrOImc0odmtayrq5ZfB5I83su/hnTY9fobqj7/GUmR0Sv3/TVs99vKsZ4+9T9O8yy8zeMrO621n/iFQfjVOST6ePSoVkZrnuXrOs1y3lPUYCY919tJl1Bv7P3Vvtwvvtcqbtva+ZPQt86+53lrJ+f2Kjrl5R1lmk8lCLQFKCmdWMz6Mw3cxmmtnvRho1s8ZmNqXYN+YT4493NrOp8de+ambb20FPAQ6Iv3ZQ/L1mmdnA+GM1zOxtM/sq/vjZ8cdzzKy1md0DVIvneCH+XG7858tm1qNY5pFmdqaZpZvZv8zs8/gY839O4J9lKvHBxszsmPg2fmlmH5vZQfErcYcAZ8eznB3PPsLMPouvu60RWyVsgh57WzfdtnUjdlXsjPjtP8Sugq8df64Bsasqi1q0ufGf1wA3x++nExtvqAGxHXuN+OPXA7du4/NGAmfG7/cGPgWOBmYCNYhdlT0bOBI4A3iy2GvrxH/mEJ/zoChTsXWKMp4GPBu/n0lsFMlqwCXALfHHqwLTgP22kTO32Pa9CnSNL9cGMuL3OwKvxe/3Bx4t9vq7gHPi9+sSG4uoRtD/37oFe6t0Q0xIpbHZ3Y8oWjCzKsBdZnYSECX2TXgPYHmx13wOjIiv+4a7zzCzk4lNVvJRfGiNTGLfpLflX2Z2C7FxagYQG7/mP+6+MZ7hdeBEYBxwn5ndS6w76b87sF3vAg+ZWVWgKzDF3TfHu6NamdmZ8fXqEBss7rutXl/NzGbEt38O8H6x9Z81swOJDbNQpYTP7wz0MrNr48tZwD7x95KQUiGQVPEnoCFwtLsXWGxE0aziK7j7lHih6AGMNLP7gTXA++7eN4HPuM7dRxctmFmHba3k7t9abK6D7sA/zOwDdx+SyEa4+xYzywG6AGcTm2gFYrNN/dXdx2/nLTa7+xFmVp3Y+DuXAw8Tm4BnkrufFj+wnlPC6w04w93nJZJXwkHHCCRV1AFWxItAe+B3cy5bbB7mn9z9SeApYtP9fQKcYGZFff41zKx5gp/5X+CPZlbdzGoQ69b5r5ntCWxy91HEBvPb1pyxBfGWyba8QmygsKLWBcR26pcWvcbMmsc/c5s8NtvclcA19utQ6kVDEfcvtuoGYl1kRcYDf7V488hio9JKyKkQSKp4AWhtZjOB84C521inHfCVmX1J7Nv2Q+6+ktiO8SUz+5pYt1CLRD7Q3acTO3bwGbFjBk+5+5dAS+CzeBfNYOAf23j5cODrooPFW3mP2MRAEzw2/SLECtc3wHSLTVr+BNtpscezfE1sYpZ/AnfHt7346yYBhxQdLCbWcqgSzzY7viwhp9NHRURCTi0CEZGQUyEQEQk5FQIRkZBTIRARCTkVAhGRkFMhEBEJORUCEZGQUyEQEQm5/wepCV2/cy8+/wAAAABJRU5ErkJggg=="
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
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    "# RandomForestClassifier algorithm using Pipeline\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "pipeline = Pipeline(steps=[(\"preprocessor\",preprocessor),\n",
    "                   (\"logregressor\",RandomForestClassifier(n_estimators=100))])\n",
    "model = pipeline.fit(X_train,y_train)\n",
    "print(model)\n",
    "\n",
    "predictions= model.predict(X_test)\n",
    "y_scores = model.predict_proba(X_test)\n",
    "cm = confusion_matrix(y_test, predictions)\n",
    "print ('Confusion Matrix:\\n',cm, '\\n')\n",
    "print('Accuracy:', accuracy_score(y_test, predictions))\n",
    "print(\"Overall Precision:\",precision_score(y_test, predictions))\n",
    "print(\"Overall Recall:\",recall_score(y_test, predictions))\n",
    "auc = roc_auc_score(y_test,y_scores[:,1])\n",
    "print('\\nAUC: ' + str(auc))\n",
    "\n",
    "# calculate ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])\n",
    "\n",
    "# plot ROC curve\n",
    "fig = plt.figure(figsize=(6, 6))\n",
    "# Plot the diagonal 50% line\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "# Plot the FPR and TPR achieved by our model\n",
    "plt.plot(fpr, tpr)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.show()"
   ]
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    "# save prepared model\n",
    "\n",
    "import joblib\n",
    "\n",
    "# Save the model as a pickle file\n",
    "filename = './diabetes_model.pkl'\n",
    "joblib.dump(model, filename)"
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       "<div class=\"ansiout\">New sample: [2.0, 180.0, 74.0, 24.0, 21.0, 23.9091702, 1.488172308, 22.0]\n",
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    "# Load the model from the file\n",
    "model = joblib.load(filename)\n",
    "\n",
    "# predict on a new sample\n",
    "# The model accepts an array of feature arrays (so you can predict the classes of multiple patients in a single call)\n",
    "# We'll create an array with a single array of features, representing one patient\n",
    "X_new = np.array([[2,180,74,24,21,23.9091702,1.488172308,22]])\n",
    "print ('New sample: {}'.format(list(X_new[0])))\n",
    "\n",
    "# Get a prediction\n",
    "pred = model.predict(X_new)\n",
    "\n",
    "# The model returns an array of predictions - one for each set of features submitted\n",
    "# In our case, we only submitted one patient, so our prediction is the first one in the resulting array.\n",
    "print('Predicted class is {}'.format(pred[0]))"
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   "source": [
    "############## Thank You ##############################"
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