Exploring the Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# pandas for dataframes and reading excel data\n",
    "import pandas as pd\n",
    "\n",
    "# numpy for matrix calculations\n",
    "import numpy as np\n",
    "\n",
    "# matplotlob for plotting data\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_excel('data sheet.xlsx', sheetname='Sheet1')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>ASSO MEDICAL PROB</th>\n",
       "      <th>H/O INJURY/SURGERY</th>\n",
       "      <th>DRUG HISTORY</th>\n",
       "      <th>FREQUENCY</th>\n",
       "      <th>avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29</td>\n",
       "      <td>Male</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>115, 115, 113</td>\n",
       "      <td>114.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>Female</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>95,94,96</td>\n",
       "      <td>94.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>Male</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>80,82,81</td>\n",
       "      <td>81.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>Female</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>100, 101, 100</td>\n",
       "      <td>100.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38</td>\n",
       "      <td>female</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>9.91001e+07</td>\n",
       "      <td>99.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AGE     SEX ASSO MEDICAL PROB H/O INJURY/SURGERY DRUG HISTORY  \\\n",
       "0   29    Male                no                 no           no   \n",
       "1   36  Female                no                 no           no   \n",
       "2   37    Male                no                 no           no   \n",
       "3   37  Female                no                 no           no   \n",
       "4   38  female                no                 no           no   \n",
       "\n",
       "       FREQUENCY     avg  \n",
       "0  115, 115, 113  114.30  \n",
       "1       95,94,96   94.00  \n",
       "2       80,82,81   81.00  \n",
       "3  100, 101, 100  100.33  \n",
       "4    9.91001e+07   99.80  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop unnecessary details\n",
    "data.drop('DATE', axis=1, inplace=True)\n",
    "data.drop('SI.NO', axis=1, inplace=True)\n",
    "data.drop('NAME', axis=1, inplace=True)\n",
    "\n",
    "# recheck the data\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 7)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape of the data\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# we have 50 rows and 7 columns\n",
    "# our best bet would be to do One Shot learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>FREQUENCY</th>\n",
       "      <th>MEDIC_PROB</th>\n",
       "      <th>INJURY_SURGERY</th>\n",
       "      <th>DRUG_HISTORY</th>\n",
       "      <th>AVG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>72</td>\n",
       "      <td>male</td>\n",
       "      <td>68, 69, 68</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>68.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>72</td>\n",
       "      <td>male</td>\n",
       "      <td>69,70,71</td>\n",
       "      <td>yes(diabetes,blockage in heart)</td>\n",
       "      <td>shouler surgery</td>\n",
       "      <td>yes</td>\n",
       "      <td>70.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>74</td>\n",
       "      <td>female</td>\n",
       "      <td>63,62,62.6</td>\n",
       "      <td>yes(diabetes,bp)</td>\n",
       "      <td>knee surgery</td>\n",
       "      <td>yes</td>\n",
       "      <td>62.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>76</td>\n",
       "      <td>male</td>\n",
       "      <td>62,61,62.5</td>\n",
       "      <td>yes(diabetes,kidney stone)</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>61.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>85</td>\n",
       "      <td>male</td>\n",
       "      <td>62,63,64</td>\n",
       "      <td>yes (diabetes,heart blockage)</td>\n",
       "      <td>yes(open heart surgery)</td>\n",
       "      <td>yes</td>\n",
       "      <td>63.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    AGE     SEX   FREQUENCY                       MEDIC_PROB  \\\n",
       "45   72    male  68, 69, 68                               no   \n",
       "46   72    male    69,70,71  yes(diabetes,blockage in heart)   \n",
       "47   74  female  63,62,62.6                 yes(diabetes,bp)   \n",
       "48   76    male  62,61,62.5       yes(diabetes,kidney stone)   \n",
       "49   85    male    62,63,64    yes (diabetes,heart blockage)   \n",
       "\n",
       "             INJURY_SURGERY DRUG_HISTORY    AVG  \n",
       "45                       no           no  68.30  \n",
       "46          shouler surgery          yes  70.00  \n",
       "47             knee surgery          yes  62.53  \n",
       "48                       no           no  61.83  \n",
       "49  yes(open heart surgery)          yes  63.00  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we will convert data into lowercase\n",
    "data['SEX'] = data['SEX'].str.lower()\n",
    "data['MEDIC_PROB'] = data['ASSO MEDICAL PROB'].str.lower()\n",
    "data['INJURY_SURGERY'] = data['H/O INJURY/SURGERY'].str.lower()\n",
    "data['DRUG_HISTORY'] = data['DRUG HISTORY'].str.lower()\n",
    "data['AVG'] = data['avg']\n",
    "\n",
    "data.drop( ['ASSO MEDICAL PROB','H/O INJURY/SURGERY','DRUG HISTORY', 'avg' ], axis=1 ,inplace=True)\n",
    "\n",
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# now we need to cahnge our data into computer recognizable data.\n",
    "# from strings in sex and medical prob and others to integers\n",
    "\n",
    "# changing SEX\n",
    "data['SEX'] = data['SEX'] == 'female'\n",
    "data['SEX'] = data['SEX'].map({ True:1, False:0 })\n",
    "\n",
    "# changing medical probelm data\n",
    "data['MEDIC_PROB'] = data['MEDIC_PROB'] == 'no'\n",
    "data['MEDIC_PROB'] = data['MEDIC_PROB'].map({ True:0, False:1 })\n",
    "\n",
    "# changing Injury surgery data\n",
    "data['INJURY_SURGERY'] = data['INJURY_SURGERY'] == 'no'\n",
    "data['INJURY_SURGERY'] = data['INJURY_SURGERY'].map({ True:0, False:1 })\n",
    "\n",
    "# changing drug history data\n",
    "data['DRUG_HISTORY'] = data['DRUG_HISTORY'] == 'no'\n",
    "data['DRUG_HISTORY'] = data['DRUG_HISTORY'].map({ True:0, False:1 })\n",
    "\n",
    "# adding category for Osteoporosis, Osteopenia and Normal conditions\n",
    "# Normal 0 -- Osteopenia 1 -- Osteoporosis 2\n",
    "def categorize_data(freq):\n",
    "    # if frequency is less than 70, the subject is Osteoporotic\n",
    "    if(freq<60):\n",
    "        return 2\n",
    "    # if frequency is less than 100 and greate than 70, the subject is Osteopeniac\n",
    "    elif(freq<100):\n",
    "        return 1\n",
    "    # if frequency is greater than 100, the subject is Normal\n",
    "    else:\n",
    "        return 0\n",
    "        \n",
    "\n",
    "data['CATEGORY'] = data['AVG'].map(categorize_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>MEDIC_PROB</th>\n",
       "      <th>INJURY_SURGERY</th>\n",
       "      <th>DRUG_HISTORY</th>\n",
       "      <th>AVG</th>\n",
       "      <th>CATEGORY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>114.30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>81.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.33</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AGE  SEX  MEDIC_PROB  INJURY_SURGERY  DRUG_HISTORY     AVG  CATEGORY\n",
       "0   29    0           0               0             0  114.30         0\n",
       "1   36    1           0               0             0   94.00         1\n",
       "2   37    0           0               0             0   81.00         1\n",
       "3   37    1           0               0             0  100.33         0\n",
       "4   38    1           0               0             0   99.80         1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we know that some 'yes' had other details so we will fill \n",
    "# the Assoc medical problems and injury surgery as 1.\n",
    "data.fillna(-999999999)\n",
    "\n",
    "# removing the FREQUENCY column since it's average is stored in 'avg' column\n",
    "data.drop('FREQUENCY',axis= 1, inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# export unscaled data\n",
    "import pickle\n",
    "\n",
    "with open('data_processed_unscaled.pickle', 'wb') as file:\n",
    "    pickle.dump(data, file)\n",
    "\n",
    "# export unscaled data as csv\n",
    "data.to_csv('data_processed_unscaled.csv', index=False, encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# now, since the data is not scaled we will scale each column into a songle range 0 to 2\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "data_scaled = MinMaxScaler().fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>MEDIC_PROB</th>\n",
       "      <th>INJURY_SURGERY</th>\n",
       "      <th>DRUG_HISTORY</th>\n",
       "      <th>AVG</th>\n",
       "      <th>CATEGORY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.125000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.616764</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.371342</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.142857</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.736266</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.160714</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.726260</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        AGE  SEX  MEDIC_PROB  INJURY_SURGERY  DRUG_HISTORY       AVG  CATEGORY\n",
       "0  0.000000  0.0         0.0             0.0           0.0  1.000000       0.0\n",
       "1  0.125000  1.0         0.0             0.0           0.0  0.616764       1.0\n",
       "2  0.142857  0.0         0.0             0.0           0.0  0.371342       1.0\n",
       "3  0.142857  1.0         0.0             0.0           0.0  0.736266       0.0\n",
       "4  0.160714  1.0         0.0             0.0           0.0  0.726260       1.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scaled = pd.DataFrame(data_scaled)\n",
    "df_scaled.columns = data.columns\n",
    "df_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# export unscaled data\n",
    "import pickle\n",
    "\n",
    "with open('data_processed_scaled.pickle', 'wb') as file:\n",
    "    pickle.dump(df_scaled, file)\n",
    "\n",
    "# export scaled data as csv\n",
    "df_scaled.to_csv('data_processed_scaled.csv', index=False, encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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F6VQ6EbkBKAJ+6ux1Y8yTxpgiY0xRUlKS661UygMsFuHaeZk8d8sCYsKDPH79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\nAh3zdnuqCgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a17dfa358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the data\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.plot(df_scaled['AGE'] * 85, df_scaled['AVG'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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