dischage model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statisitical analysis of Discharge data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import pylab as pl\n",
    "import \n",
    "numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sl no</th>\n",
       "      <th>Billing</th>\n",
       "      <th>Treating_Doctor</th>\n",
       "      <th>Pending_Evaluations</th>\n",
       "      <th>Discharge_TAT</th>\n",
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       "      <td>Insurance</td>\n",
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       "      <td>140</td>\n",
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       "      <td>Self</td>\n",
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      ],
      "text/plain": [
       "   sl no    Billing Treating_Doctor Pending_Evaluations  Discharge_TAT\n",
       "0      1  Insurance            Doc2                 yes            140\n",
       "1      2       Self            Doc4                  No            122\n",
       "2      3  Insurance            Doc3                 yes            153\n",
       "3      4       Self            Doc6                  No            132\n",
       "4      5       Self            Doc3                 yes            139"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('dischargedata.csv')\n",
    "\n",
    "# take a look at the dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
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       "      <td>21</td>\n",
       "      <td>122</td>\n",
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       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>85</td>\n",
       "      <td>196</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>86</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>87</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>88</td>\n",
       "      <td>225</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>89</td>\n",
       "      <td>210</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>90</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>91</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>92</td>\n",
       "      <td>123</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>93</td>\n",
       "      <td>230</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>94</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>95</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>96</td>\n",
       "      <td>205</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>97</td>\n",
       "      <td>230</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>98</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>99</td>\n",
       "      <td>142</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>100</td>\n",
       "      <td>110</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>101</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>102</td>\n",
       "      <td>122</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>103</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>104</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>105</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>106</td>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>107</td>\n",
       "      <td>208</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>108</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>108 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sl no  Discharge_TAT  Doc1  Doc2  Doc3  Doc4  Doc5  Doc6  No  yes\n",
       "0        1            140     0     1     0     0     0     0   0    1\n",
       "1        2            122     0     0     0     1     0     0   1    0\n",
       "2        3            153     0     0     1     0     0     0   0    1\n",
       "3        4            132     0     0     0     0     0     1   1    0\n",
       "4        5            139     0     0     1     0     0     0   0    1\n",
       "5        6            119     0     0     0     0     0     1   1    0\n",
       "6        7            225     1     0     0     0     0     0   0    1\n",
       "7        8            210     1     0     0     0     0     0   0    1\n",
       "8        9            155     0     0     1     0     0     0   0    1\n",
       "9       10            173     0     0     1     0     0     0   0    1\n",
       "10      11            130     0     0     1     0     0     0   1    0\n",
       "11      12            130     0     0     1     0     0     0   1    0\n",
       "12      13            105     0     0     1     0     0     0   1    0\n",
       "13      14             51     0     0     0     1     0     0   1    0\n",
       "14      15            199     1     0     0     0     0     0   0    1\n",
       "15      16            233     0     0     1     0     0     0   0    1\n",
       "16      17            222     0     0     1     0     0     0   0    1\n",
       "17      18            145     0     0     0     0     0     1   0    1\n",
       "18      19            110     0     0     0     0     1     0   0    1\n",
       "19      20            130     1     0     0     0     0     0   1    0\n",
       "20      21            122     1     0     0     0     0     0   1    0\n",
       "21      22            139     0     0     0     0     0     1   0    1\n",
       "22      23             17     0     0     0     0     1     0   1    0\n",
       "23      24             70     0     0     0     1     0     0   1    0\n",
       "24      25            120     1     0     0     0     0     0   1    0\n",
       "25      26            209     0     0     0     0     0     1   0    1\n",
       "26      27            159     1     0     0     0     0     0   1    0\n",
       "27      28             93     0     1     0     0     0     0   1    0\n",
       "28      29            240     0     0     0     1     0     0   0    1\n",
       "29      30            153     0     0     1     0     0     0   0    1\n",
       "..     ...            ...   ...   ...   ...   ...   ...   ...  ..  ...\n",
       "78      79            120     1     0     0     0     0     0   1    0\n",
       "79      80            209     0     0     0     0     0     1   0    1\n",
       "80      81            159     1     0     0     0     0     0   1    0\n",
       "81      82            149     0     1     0     0     0     0   0    1\n",
       "82      83            184     0     0     0     1     0     0   0    1\n",
       "83      84            153     0     0     1     0     0     0   0    1\n",
       "84      85            196     0     0     0     0     0     1   0    1\n",
       "85      86            115     0     0     1     0     0     0   1    0\n",
       "86      87            119     0     0     0     0     0     1   1    0\n",
       "87      88            225     1     0     0     0     0     0   0    1\n",
       "88      89            210     1     0     0     0     0     0   0    1\n",
       "89      90            155     0     0     1     0     0     0   0    1\n",
       "90      91            173     0     0     1     0     0     0   0    1\n",
       "91      92            123     0     0     1     0     0     0   1    0\n",
       "92      93            230     0     0     1     0     0     0   0    1\n",
       "93      94            178     0     0     1     0     0     0   0    1\n",
       "94      95             51     0     0     0     1     0     0   1    0\n",
       "95      96            205     1     0     0     0     0     0   0    1\n",
       "96      97            230     0     0     1     0     0     0   0    1\n",
       "97      98            192     0     0     1     0     0     0   0    1\n",
       "98      99            142     0     0     0     0     0     1   0    1\n",
       "99     100            110     0     0     0     0     1     0   0    1\n",
       "100    101             80     1     0     0     0     0     0   1    0\n",
       "101    102            122     1     0     0     0     0     0   1    0\n",
       "102    103             85     0     0     0     0     0     1   1    0\n",
       "103    104             17     0     0     0     0     1     0   1    0\n",
       "104    105             70     0     0     0     1     0     0   1    0\n",
       "105    106            120     1     0     0     0     0     0   1    0\n",
       "106    107            208     0     0     0     0     0     1   0    1\n",
       "107    108            132     1     0     0     0     0     0   1    0\n",
       "\n",
       "[108 rows x 10 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#one hot encoding for treaing doc and pending eva.. deleting billing coz not significant\n",
    "del df['Billing']\n",
    "for column in df.columns:\n",
    "    if df[column].dtype==object:\n",
    "        dummyCols=pd.get_dummies(df[column])\n",
    "        df=df.join(dummyCols)\n",
    "        del df[column]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(cdf.Treating_Doctor, cdf.Discharge_TAT,  color='blue')\n",
    "plt.xlabel(\"Treating Doctor\")\n",
    "plt.ylabel(\"Discharge TAT\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "msk = np.random.rand(len(df)) < 0.8\n",
    "train = df[msk]\n",
    "test = df[~msk]\n",
    "train.to_csv('dischargetrainset.csv')\n",
    "test.to_csv('dischargetestset.csv')\n",
    "train.describe(include='all')\n",
    "#train.to_csv('test_encoded.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[ 37.22850149 -17.49787949  21.51482885  -1.56004611 -59.01060861\n",
      "   19.32520387 -40.40675007  40.40675007]]\n",
      "intercept:  [131.36229613]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model_1 = linear_model.LinearRegression()\n",
    "x = np.asanyarray(train[['Doc1','Doc2','Doc3','Doc4','Doc5','Doc6','No','yes']])\n",
    "y = np.asanyarray(train[['Discharge_TAT']])\n",
    "#x=list(x)\n",
    "#y=list (y)\n",
    "model_1.fit (x, y)\n",
    "print ('Coefficients: ', model_1.coef_)\n",
    "print(\"intercept: \", model_1.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Residual sum of squares: 640.44\n",
      "Variance score: 0.80\n"
     ]
    }
   ],
   "source": [
    "y_hat= model_1.predict(x)\n",
    "print(\"Residual sum of squares: %.2f\"\n",
    "      % np.mean((y_hat - y) ** 2))\n",
    "\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "print('Variance score: %.2f' % model_1.score(x, y))\n",
    "#print(y_hat) predicted value of train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d98706fe80>,\n",
       " <matplotlib.lines.Line2D at 0x1d9867c8c18>,\n",
       " <matplotlib.lines.Line2D at 0x1d9867c8d68>,\n",
       " <matplotlib.lines.Line2D at 0x1d9867c8eb8>,\n",
       " <matplotlib.lines.Line2D at 0x1d986bf5048>,\n",
       " <matplotlib.lines.Line2D at 0x1d986bf5198>,\n",
       " <matplotlib.lines.Line2D at 0x1d986bf52e8>,\n",
       " <matplotlib.lines.Line2D at 0x1d986bf5438>]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#interval plot showing variance\n",
    "plt.scatter(train.yes, train.Discharge_TAT,  color='blue')\n",
    "plt.plot(x, model_1.coef_[0][0]*x + model_1.intercept_[0], '-r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Residual sum of squares: 717.40\n",
      "Variance score: 0.77\n"
     ]
    }
   ],
   "source": [
    "#checking in complete data dset\n",
    "w=np.asanyarray(df[['Doc1','Doc2','Doc3','Doc4','Doc5','Doc6','No','yes']])\n",
    "z=np.asanyarray(df[['Discharge_TAT']])\n",
    "z_hat= model_1.predict(w)\n",
    "print(\"Residual sum of squares: %.2f\"\n",
    "      % np.mean((z_hat - z) ** 2))\n",
    "\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "print('Variance score: %.2f' % model_1.score(w, z))\n",
    "#print(y_hat) predicted value of train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}