Disease_outbreak_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\") \n",
    "import pandas as pd\n",
    "import calendar\n",
    "import numpy as np\n",
    "import pickle "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# function to extract month and year from the dataset\n",
    "\n",
    "def week2month(hep):\n",
    "    hep['LastDayWeek'] = pd.to_datetime((hep['week']-1).astype(str) + \"6\", format=\"%Y%U%w\")\n",
    "    hep['MonthMax'] = pd.DatetimeIndex(hep['LastDayWeek']).month\n",
    "    hep['Year'] = pd.DatetimeIndex(hep['LastDayWeek']).year\n",
    "    hep['MonthName'] = [calendar.month_name[i] for i in hep.MonthMax]\n",
    "    return hep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# function to load the dataset\n",
    "def get_input_transform(file_):\n",
    "    return week2month(pd.read_csv(file_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load the dataset from Kaggle\n",
    "hep = get_input_transform('data/hepatitis.csv')\n",
    "mea = get_input_transform('data/measles.csv')\n",
    "mum = get_input_transform('data/mumps.csv')\n",
    "per = get_input_transform('data/pertussis.csv')\n",
    "pol = get_input_transform('data/polio.csv')\n",
    "rub = get_input_transform('data/rubella.csv')\n",
    "sma = get_input_transform('data/smallpox.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>week</th>\n",
       "      <th>state</th>\n",
       "      <th>state_name</th>\n",
       "      <th>disease</th>\n",
       "      <th>cases</th>\n",
       "      <th>incidence_per_capita</th>\n",
       "      <th>LastDayWeek</th>\n",
       "      <th>MonthMax</th>\n",
       "      <th>Year</th>\n",
       "      <th>MonthName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196601</td>\n",
       "      <td>AL</td>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>5</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>196601</td>\n",
       "      <td>AR</td>\n",
       "      <td>ARKANSAS</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>11</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>196601</td>\n",
       "      <td>AZ</td>\n",
       "      <td>ARIZONA</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>6</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>196601</td>\n",
       "      <td>CA</td>\n",
       "      <td>CALIFORNIA</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>89</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>196601</td>\n",
       "      <td>CO</td>\n",
       "      <td>COLORADO</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     week state  state_name      disease cases  incidence_per_capita  \\\n",
       "0  196601    AL     ALABAMA  HEPATITIS A     5                  0.14   \n",
       "1  196601    AR    ARKANSAS  HEPATITIS A    11                  0.58   \n",
       "2  196601    AZ     ARIZONA  HEPATITIS A     6                  0.37   \n",
       "3  196601    CA  CALIFORNIA  HEPATITIS A    89                  0.47   \n",
       "4  196601    CO    COLORADO  HEPATITIS A     1                  0.05   \n",
       "\n",
       "  LastDayWeek  MonthMax  Year MonthName  \n",
       "0  1966-01-01         1  1966   January  \n",
       "1  1966-01-01         1  1966   January  \n",
       "2  1966-01-01         1  1966   January  \n",
       "3  1966-01-01         1  1966   January  \n",
       "4  1966-01-01         1  1966   January  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = hep\n",
    "for i in [mea,mum,per,pol,rub,sma]:\n",
    "    train_data = train_data.append(i)\n",
    "train_data = train_data.loc[(train_data['Year'] >= 1960) & (train_data['Year'] <=2011)]\n",
    "# examine the dataset \n",
    "train_data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# some data discrepancies must be resolved (issues like \\\\N )\n",
    "train_data.cases = train_data.cases.astype('str')\n",
    "train_data_bad = train_data[train_data.cases==train_data.cases.max()]\n",
    "ind = list(train_data_bad.index)\n",
    "train_data = train_data.drop(train_data.index[ind])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# will be using visulisation.csv for our visualization purposes later# will b \n",
    "\n",
    "train_data.to_csv('for_visulisation.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data.cases = train_data.cases.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>week</th>\n",
       "      <th>cases</th>\n",
       "      <th>incidence_per_capita</th>\n",
       "      <th>MonthMax</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>361935.000000</td>\n",
       "      <td>361935.000000</td>\n",
       "      <td>361935.000000</td>\n",
       "      <td>361935.000000</td>\n",
       "      <td>361935.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>198461.036059</td>\n",
       "      <td>15.238026</td>\n",
       "      <td>0.429124</td>\n",
       "      <td>6.933999</td>\n",
       "      <td>1984.325221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1330.980200</td>\n",
       "      <td>84.493216</td>\n",
       "      <td>2.233045</td>\n",
       "      <td>3.279924</td>\n",
       "      <td>13.298776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>196001.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1960.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>197349.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>198342.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1983.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>199524.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1995.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>201152.000000</td>\n",
       "      <td>7101.000000</td>\n",
       "      <td>209.160000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                week          cases  incidence_per_capita       MonthMax  \\\n",
       "count  361935.000000  361935.000000         361935.000000  361935.000000   \n",
       "mean   198461.036059      15.238026              0.429124       6.933999   \n",
       "std      1330.980200      84.493216              2.233045       3.279924   \n",
       "min    196001.000000       0.000000              0.000000       1.000000   \n",
       "25%    197349.000000       0.000000              0.000000       4.000000   \n",
       "50%    198342.000000       1.000000              0.030000       7.000000   \n",
       "75%    199524.000000       7.000000              0.190000      10.000000   \n",
       "max    201152.000000    7101.000000            209.160000      12.000000   \n",
       "\n",
       "                Year  \n",
       "count  361935.000000  \n",
       "mean     1984.325221  \n",
       "std        13.298776  \n",
       "min      1960.000000  \n",
       "25%      1973.000000  \n",
       "50%      1983.000000  \n",
       "75%      1995.000000  \n",
       "max      2011.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# examining the characteristics of the dataset\n",
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# converting non-numerical data to lowercase (to keep consistencies over all future datasets included)\n",
    "\n",
    "train_data.state_name = [i.lower() for i in train_data.state_name]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature additions\n",
    "Adding features like\n",
    "- Average temperature\n",
    "- Average precipitation\n",
    "- Average population\n",
    "- Polution standards\n",
    "#### These features have certain effect on the contagious disease spread\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "join_tavg = pd.read_excel('TemperaturesByState.xlsx', index_col= 0)\n",
    "# make sure non-nmerical data in lowercase format\n",
    "\n",
    "join_tavg['Date'] = pd.to_datetime(join_tavg['Date'],format = \"%Y%U%w\")\n",
    "join_tavg['Year'] = pd.DatetimeIndex(join_tavg['Date']).year\n",
    "join_tavg['Month'] = pd.DatetimeIndex(join_tavg['Date']).month\n",
    "join_tavg = join_tavg.loc[(join_tavg['Year'] >= 1960) & (join_tavg['Year'] <=2011)]\n",
    "join_tavg.State = [i.lower() for i in join_tavg.State]\n",
    "join_tavg = join_tavg.drop('Country',axis=1)\n",
    "join_tavg.head()\n",
    "\n",
    "# convert the column names for tavg dataset to match column names to our primary dataset\n",
    "join_tavg.columns = [ 'LastDayWeek','t_avg', 'tavg_anomaly', 'state_name','Year','MonthMax']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(31824, 6)\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LastDayWeek</th>\n",
       "      <th>t_avg</th>\n",
       "      <th>tavg_anomaly</th>\n",
       "      <th>state_name</th>\n",
       "      <th>Year</th>\n",
       "      <th>MonthMax</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1960-01-01</td>\n",
       "      <td>44.9114</td>\n",
       "      <td>0.219</td>\n",
       "      <td>alabama</td>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1960-01-02</td>\n",
       "      <td>44.6396</td>\n",
       "      <td>0.221</td>\n",
       "      <td>alabama</td>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1960-01-03</td>\n",
       "      <td>45.7358</td>\n",
       "      <td>0.099</td>\n",
       "      <td>alabama</td>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1960-01-04</td>\n",
       "      <td>64.1444</td>\n",
       "      <td>0.239</td>\n",
       "      <td>alabama</td>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1960-01-05</td>\n",
       "      <td>68.1278</td>\n",
       "      <td>0.305</td>\n",
       "      <td>alabama</td>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      LastDayWeek    t_avg  tavg_anomaly state_name  Year  MonthMax\n",
       "index                                                              \n",
       "0      1960-01-01  44.9114         0.219    alabama  1960         1\n",
       "0      1960-01-02  44.6396         0.221    alabama  1960         1\n",
       "0      1960-01-03  45.7358         0.099    alabama  1960         1\n",
       "0      1960-01-04  64.1444         0.239    alabama  1960         1\n",
       "0      1960-01-05  68.1278         0.305    alabama  1960         1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# examine the dataset\n",
    "\n",
    "print (join_tavg.shape)\n",
    "join_tavg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# join the avg temperature dataset with our priary dataset over common columns\n",
    "\n",
    "result = pd.merge(train_data, join_tavg, on=['LastDayWeek','Year','state_name','MonthMax'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5130, 12)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>state</th>\n",
       "      <th>state_name</th>\n",
       "      <th>disease</th>\n",
       "      <th>cases</th>\n",
       "      <th>incidence_per_capita</th>\n",
       "      <th>LastDayWeek</th>\n",
       "      <th>MonthMax</th>\n",
       "      <th>Year</th>\n",
       "      <th>MonthName</th>\n",
       "      <th>t_avg</th>\n",
       "      <th>tavg_anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196601</td>\n",
       "      <td>AL</td>\n",
       "      <td>alabama</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>5</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>40.4798</td>\n",
       "      <td>0.235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>196601</td>\n",
       "      <td>AL</td>\n",
       "      <td>alabama</td>\n",
       "      <td>RUBELLA</td>\n",
       "      <td>7</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>40.4798</td>\n",
       "      <td>0.235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>196601</td>\n",
       "      <td>AR</td>\n",
       "      <td>arkansas</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>11</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>35.2310</td>\n",
       "      <td>0.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>196601</td>\n",
       "      <td>AR</td>\n",
       "      <td>arkansas</td>\n",
       "      <td>MEASLES</td>\n",
       "      <td>2</td>\n",
       "      <td>0.11</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>35.2310</td>\n",
       "      <td>0.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>196601</td>\n",
       "      <td>AZ</td>\n",
       "      <td>arizona</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>6</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>37.9076</td>\n",
       "      <td>0.285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     week state state_name      disease  cases  incidence_per_capita  \\\n",
       "0  196601    AL    alabama  HEPATITIS A      5                  0.14   \n",
       "1  196601    AL    alabama      RUBELLA      7                  0.20   \n",
       "2  196601    AR   arkansas  HEPATITIS A     11                  0.58   \n",
       "3  196601    AR   arkansas      MEASLES      2                  0.11   \n",
       "4  196601    AZ    arizona  HEPATITIS A      6                  0.37   \n",
       "\n",
       "  LastDayWeek  MonthMax  Year MonthName    t_avg  tavg_anomaly  \n",
       "0  1966-01-01         1  1966   January  40.4798         0.235  \n",
       "1  1966-01-01         1  1966   January  40.4798         0.235  \n",
       "2  1966-01-01         1  1966   January  35.2310         0.225  \n",
       "3  1966-01-01         1  1966   January  35.2310         0.225  \n",
       "4  1966-01-01         1  1966   January  37.9076         0.285  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#examining the primary dataset\n",
    "\n",
    "print (result.shape)\n",
    "result.to_csv('for_temp_visualization.csv')\n",
    "train_data = result # make this your current working dataframe \n",
    "result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>state</th>\n",
       "      <th>state_name</th>\n",
       "      <th>disease</th>\n",
       "      <th>cases</th>\n",
       "      <th>incidence_per_capita</th>\n",
       "      <th>LastDayWeek</th>\n",
       "      <th>MonthMax</th>\n",
       "      <th>Year</th>\n",
       "      <th>MonthName</th>\n",
       "      <th>t_avg</th>\n",
       "      <th>tavg_anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196601</td>\n",
       "      <td>AL</td>\n",
       "      <td>alabama</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>5</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>40.4798</td>\n",
       "      <td>0.235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>196601</td>\n",
       "      <td>AL</td>\n",
       "      <td>alabama</td>\n",
       "      <td>RUBELLA</td>\n",
       "      <td>7</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>40.4798</td>\n",
       "      <td>0.235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>196601</td>\n",
       "      <td>AR</td>\n",
       "      <td>arkansas</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>11</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>35.2310</td>\n",
       "      <td>0.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>196601</td>\n",
       "      <td>AR</td>\n",
       "      <td>arkansas</td>\n",
       "      <td>MEASLES</td>\n",
       "      <td>2</td>\n",
       "      <td>0.11</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>35.2310</td>\n",
       "      <td>0.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>196601</td>\n",
       "      <td>AZ</td>\n",
       "      <td>arizona</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>6</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>37.9076</td>\n",
       "      <td>0.285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     week state state_name      disease  cases  incidence_per_capita  \\\n",
       "0  196601    AL    alabama  HEPATITIS A      5                  0.14   \n",
       "1  196601    AL    alabama      RUBELLA      7                  0.20   \n",
       "2  196601    AR   arkansas  HEPATITIS A     11                  0.58   \n",
       "3  196601    AR   arkansas      MEASLES      2                  0.11   \n",
       "4  196601    AZ    arizona  HEPATITIS A      6                  0.37   \n",
       "\n",
       "  LastDayWeek  MonthMax  Year MonthName    t_avg  tavg_anomaly  \n",
       "0  1966-01-01         1  1966   January  40.4798         0.235  \n",
       "1  1966-01-01         1  1966   January  40.4798         0.235  \n",
       "2  1966-01-01         1  1966   January  35.2310         0.225  \n",
       "3  1966-01-01         1  1966   January  35.2310         0.225  \n",
       "4  1966-01-01         1  1966   January  37.9076         0.285  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# examining the primary dataset now\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Normalize the statistical data\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler as mm\n",
    "\n",
    "scaler = mm()\n",
    "\n",
    "# avg temp deviations\n",
    "scaler.fit(train_data['tavg_anomaly'].reshape(-1,1))\n",
    "train_data['tavg_anomaly'] = scaler.transform(train_data['tavg_anomaly'].reshape(-1,1))\n",
    "\n",
    "# avg temp\n",
    "scaler.fit(train_data['t_avg'].reshape(-1,1))\n",
    "train_data['t_avg'] = scaler.transform(train_data['t_avg'].reshape(-1,1))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>state</th>\n",
       "      <th>state_name</th>\n",
       "      <th>disease</th>\n",
       "      <th>cases</th>\n",
       "      <th>incidence_per_capita</th>\n",
       "      <th>LastDayWeek</th>\n",
       "      <th>MonthMax</th>\n",
       "      <th>Year</th>\n",
       "      <th>MonthName</th>\n",
       "      <th>t_avg</th>\n",
       "      <th>tavg_anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196601</td>\n",
       "      <td>AL</td>\n",
       "      <td>alabama</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>5</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>0.536052</td>\n",
       "      <td>0.235911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>196601</td>\n",
       "      <td>AL</td>\n",
       "      <td>alabama</td>\n",
       "      <td>RUBELLA</td>\n",
       "      <td>7</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>0.536052</td>\n",
       "      <td>0.235911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>196601</td>\n",
       "      <td>AR</td>\n",
       "      <td>arkansas</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>11</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>0.482511</td>\n",
       "      <td>0.222805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>196601</td>\n",
       "      <td>AR</td>\n",
       "      <td>arkansas</td>\n",
       "      <td>MEASLES</td>\n",
       "      <td>2</td>\n",
       "      <td>0.11</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>0.482511</td>\n",
       "      <td>0.222805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>196601</td>\n",
       "      <td>AZ</td>\n",
       "      <td>arizona</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>6</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>0.509814</td>\n",
       "      <td>0.301442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     week state state_name      disease  cases  incidence_per_capita  \\\n",
       "0  196601    AL    alabama  HEPATITIS A      5                  0.14   \n",
       "1  196601    AL    alabama      RUBELLA      7                  0.20   \n",
       "2  196601    AR   arkansas  HEPATITIS A     11                  0.58   \n",
       "3  196601    AR   arkansas      MEASLES      2                  0.11   \n",
       "4  196601    AZ    arizona  HEPATITIS A      6                  0.37   \n",
       "\n",
       "  LastDayWeek  MonthMax  Year MonthName     t_avg  tavg_anomaly  \n",
       "0  1966-01-01         1  1966   January  0.536052      0.235911  \n",
       "1  1966-01-01         1  1966   January  0.536052      0.235911  \n",
       "2  1966-01-01         1  1966   January  0.482511      0.222805  \n",
       "3  1966-01-01         1  1966   January  0.482511      0.222805  \n",
       "4  1966-01-01         1  1966   January  0.509814      0.301442  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# examining the main dataset \n",
    "\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# save this primary dataset for future purposes\n",
    "\n",
    "result.to_csv('result.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# save the dataset in current working dataframe# save t \n",
    "train_data = result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nuncomment the line below when needed,\\nno need to perform above steps once you reach this step\\n\\n'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load this dataset for future purposes\n",
    "'''\n",
    "uncomment the line below when needed,\n",
    "no need to perform above steps once you reach this step\n",
    "\n",
    "'''\n",
    "\n",
    "#train_data = pd.read_csv('result.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load the working dataset for future purposes (no need to do the above steps)\n",
    "\n",
    "train_data = pd.read_csv('result.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>state</th>\n",
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       "      <th>disease</th>\n",
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       "      <th>LastDayWeek</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196601</td>\n",
       "      <td>AL</td>\n",
       "      <td>alabama</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>5</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>0.536052</td>\n",
       "      <td>0.235911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>196601</td>\n",
       "      <td>AL</td>\n",
       "      <td>alabama</td>\n",
       "      <td>RUBELLA</td>\n",
       "      <td>7</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>0.536052</td>\n",
       "      <td>0.235911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>196601</td>\n",
       "      <td>AR</td>\n",
       "      <td>arkansas</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>11</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>0.482511</td>\n",
       "      <td>0.222805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>196601</td>\n",
       "      <td>AR</td>\n",
       "      <td>arkansas</td>\n",
       "      <td>MEASLES</td>\n",
       "      <td>2</td>\n",
       "      <td>0.11</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>0.482511</td>\n",
       "      <td>0.222805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>196601</td>\n",
       "      <td>AZ</td>\n",
       "      <td>arizona</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>6</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>0.509814</td>\n",
       "      <td>0.301442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     week state state_name      disease  cases  incidence_per_capita  \\\n",
       "0  196601    AL    alabama  HEPATITIS A      5                  0.14   \n",
       "1  196601    AL    alabama      RUBELLA      7                  0.20   \n",
       "2  196601    AR   arkansas  HEPATITIS A     11                  0.58   \n",
       "3  196601    AR   arkansas      MEASLES      2                  0.11   \n",
       "4  196601    AZ    arizona  HEPATITIS A      6                  0.37   \n",
       "\n",
       "  LastDayWeek  MonthMax  Year MonthName     t_avg  tavg_anomaly  \n",
       "0  1966-01-01         1  1966   January  0.536052      0.235911  \n",
       "1  1966-01-01         1  1966   January  0.536052      0.235911  \n",
       "2  1966-01-01         1  1966   January  0.482511      0.222805  \n",
       "3  1966-01-01         1  1966   January  0.482511      0.222805  \n",
       "4  1966-01-01         1  1966   January  0.509814      0.301442  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# examining the dataset \n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for any duplicacy in the dataset (0: no duplicates)\n",
    "\n",
    "train_data.duplicated(subset=None, keep='first').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state_name</th>\n",
       "      <th>disease</th>\n",
       "      <th>cases</th>\n",
       "      <th>incidence_per_capita</th>\n",
       "      <th>MonthMax</th>\n",
       "      <th>Year</th>\n",
       "      <th>t_avg</th>\n",
       "      <th>tavg_anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alabama</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>5</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>0.536052</td>\n",
       "      <td>0.235911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alabama</td>\n",
       "      <td>RUBELLA</td>\n",
       "      <td>7</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>0.536052</td>\n",
       "      <td>0.235911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>arkansas</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>11</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>0.482511</td>\n",
       "      <td>0.222805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>arkansas</td>\n",
       "      <td>MEASLES</td>\n",
       "      <td>2</td>\n",
       "      <td>0.11</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>0.482511</td>\n",
       "      <td>0.222805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>arizona</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>6</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>0.509814</td>\n",
       "      <td>0.301442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state_name      disease  cases  incidence_per_capita  MonthMax  Year  \\\n",
       "0    alabama  HEPATITIS A      5                  0.14         1  1966   \n",
       "1    alabama      RUBELLA      7                  0.20         1  1966   \n",
       "2   arkansas  HEPATITIS A     11                  0.58         1  1966   \n",
       "3   arkansas      MEASLES      2                  0.11         1  1966   \n",
       "4    arizona  HEPATITIS A      6                  0.37         1  1966   \n",
       "\n",
       "      t_avg  tavg_anomaly  \n",
       "0  0.536052      0.235911  \n",
       "1  0.536052      0.235911  \n",
       "2  0.482511      0.222805  \n",
       "3  0.482511      0.222805  \n",
       "4  0.509814      0.301442  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove the useless attributes from the dataset \n",
    "\n",
    "# store unique state_name and diseases for label encoding (dont drop them!)\n",
    "states = np.asarray(train_data.state_name.unique())\n",
    "#dis = np.unique(train_data['disease'].values)\n",
    "\n",
    "week       = train_data.pop('week')\n",
    "monthN     = train_data.pop('MonthName')\n",
    "LOW        = train_data.pop('LastDayWeek')\n",
    "state     = train_data.pop('state')\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create test_train splits\n",
    "\n",
    "from sklearn.model_selection import train_test_split as tts\n",
    "\n",
    "y = train_data.pop('disease')\n",
    "train, test, y_train, y_test = tts(train_data, y,test_size = 0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3078, 7)\n",
      "(3078,)\n",
      "(2052, 7)\n",
      "(2052,)\n"
     ]
    }
   ],
   "source": [
    "print (train.shape)\n",
    "print (y_train.shape)\n",
    "\n",
    "print (test.shape)\n",
    "print (y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cases</th>\n",
       "      <th>incidence_per_capita</th>\n",
       "      <th>MonthMax</th>\n",
       "      <th>Year</th>\n",
       "      <th>t_avg</th>\n",
       "      <th>tavg_anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3078.000000</td>\n",
       "      <td>3078.000000</td>\n",
       "      <td>3078.0</td>\n",
       "      <td>3078.000000</td>\n",
       "      <td>3078.000000</td>\n",
       "      <td>3078.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>26.285900</td>\n",
       "      <td>0.968726</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1976.800845</td>\n",
       "      <td>0.671973</td>\n",
       "      <td>0.244941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>77.389468</td>\n",
       "      <td>3.385480</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.242372</td>\n",
       "      <td>0.182948</td>\n",
       "      <td>0.115559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1960.000000</td>\n",
       "      <td>0.028992</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1969.000000</td>\n",
       "      <td>0.533596</td>\n",
       "      <td>0.165138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1974.000000</td>\n",
       "      <td>0.686191</td>\n",
       "      <td>0.228047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1982.000000</td>\n",
       "      <td>0.833795</td>\n",
       "      <td>0.302752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1540.000000</td>\n",
       "      <td>75.860000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>0.990966</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             cases  incidence_per_capita  MonthMax         Year        t_avg  \\\n",
       "count  3078.000000           3078.000000    3078.0  3078.000000  3078.000000   \n",
       "mean     26.285900              0.968726       1.0  1976.800845     0.671973   \n",
       "std      77.389468              3.385480       0.0    11.242372     0.182948   \n",
       "min       1.000000              0.000000       1.0  1960.000000     0.028992   \n",
       "25%       2.000000              0.060000       1.0  1969.000000     0.533596   \n",
       "50%       5.000000              0.180000       1.0  1974.000000     0.686191   \n",
       "75%      19.000000              0.560000       1.0  1982.000000     0.833795   \n",
       "max    1540.000000             75.860000       1.0  2011.000000     0.990966   \n",
       "\n",
       "       tavg_anomaly  \n",
       "count   3078.000000  \n",
       "mean       0.244941  \n",
       "std        0.115559  \n",
       "min        0.000000  \n",
       "25%        0.165138  \n",
       "50%        0.228047  \n",
       "75%        0.302752  \n",
       "max        1.000000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# examine the test/train data now (uncomment the one you want to examine)\n",
    "\n",
    "train.describe()\n",
    "#test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# label encoding of useful non-numerical attributes\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "le = LabelEncoder()\n",
    "le.fit(y)\n",
    "\n",
    "y_train = le.transform(y_train)\n",
    "y_test = le.transform(y_test)\n",
    "\n",
    "le2 = LabelEncoder()\n",
    "le2.fit(states)\n",
    "\n",
    "train['state_name'] = le2.transform(train.state_name)\n",
    "test['state_name'] = le2.transform(test.state_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state_name</th>\n",
       "      <th>cases</th>\n",
       "      <th>incidence_per_capita</th>\n",
       "      <th>MonthMax</th>\n",
       "      <th>Year</th>\n",
       "      <th>t_avg</th>\n",
       "      <th>tavg_anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>48</td>\n",
       "      <td>32</td>\n",
       "      <td>1.83</td>\n",
       "      <td>1</td>\n",
       "      <td>1969</td>\n",
       "      <td>0.668803</td>\n",
       "      <td>0.277851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1</td>\n",
       "      <td>1969</td>\n",
       "      <td>0.871766</td>\n",
       "      <td>0.180865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4781</th>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1</td>\n",
       "      <td>1980</td>\n",
       "      <td>0.855921</td>\n",
       "      <td>0.153342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>44</td>\n",
       "      <td>5</td>\n",
       "      <td>0.49</td>\n",
       "      <td>1</td>\n",
       "      <td>1967</td>\n",
       "      <td>0.858528</td>\n",
       "      <td>0.521625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.493436</td>\n",
       "      <td>0.297510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      state_name  cases  incidence_per_capita  MonthMax  Year     t_avg  \\\n",
       "680           48     32                  1.83         1  1969  0.668803   \n",
       "708           11     10                  1.33         1  1969  0.871766   \n",
       "4781          24      4                  0.16         1  1980  0.855921   \n",
       "386           44      5                  0.49         1  1967  0.858528   \n",
       "896           30     12                  0.16         1  1970  0.493436   \n",
       "\n",
       "      tavg_anomaly  \n",
       "680       0.277851  \n",
       "708       0.180865  \n",
       "4781      0.153342  \n",
       "386       0.521625  \n",
       "896       0.297510  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check dtypes of each attribute (numst be numerical)\n",
    "\n",
    "train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training...%s LR\n",
      "LR\n",
      "testing_accuracy 38.7914230019\n",
      "training_accuracy 40.1884340481\n",
      "precision_test:  15.0226817313\n",
      "f1_score:  15.8660635217\n",
      "recall_score:  21.7657461265\n",
      "training...%s KNC\n",
      "KNC\n",
      "testing_accuracy 39.9122807018\n",
      "training_accuracy 96.4262508122\n",
      "precision_test:  41.2891711583\n",
      "f1_score:  40.1037639245\n",
      "recall_score:  39.3213538749\n",
      "training...%s SVC\n",
      "SVC\n",
      "testing_accuracy 42.3001949318\n",
      "training_accuracy 72.7745289149\n",
      "precision_test:  42.84939703\n",
      "f1_score:  36.1712032036\n",
      "recall_score:  34.5987536556\n",
      "training...%s QDA\n",
      "QDA\n",
      "testing_accuracy 26.6081871345\n",
      "training_accuracy 26.2833008447\n",
      "precision_test:  38.3235465594\n",
      "f1_score:  22.2305917203\n",
      "recall_score:  24.0412520365\n",
      "training...%s RFC\n",
      "RFC\n",
      "testing_accuracy 39.7173489279\n",
      "training_accuracy 95.0617283951\n",
      "precision_test:  40.9636950358\n",
      "f1_score:  37.6671815284\n",
      "recall_score:  36.0376899841\n",
      "training...%s ADC\n",
      "ADC\n",
      "testing_accuracy 42.9824561404\n",
      "training_accuracy 44.1520467836\n",
      "precision_test:  34.1252737699\n",
      "f1_score:  27.9969055269\n",
      "recall_score:  28.4405594984\n",
      "training...%s SGDC\n",
      "SGDC\n",
      "testing_accuracy 21.5886939571\n",
      "training_accuracy 22.6770630279\n",
      "precision_test:  3.59811565952\n",
      "f1_score:  5.91850367401\n",
      "recall_score:  16.6666666667\n",
      "training...%s DTC\n",
      "DTC\n",
      "testing_accuracy 49.3177387914\n",
      "training_accuracy 50.9421702404\n",
      "precision_test:  54.2057579728\n",
      "f1_score:  45.1697098733\n",
      "recall_score:  44.9887764381\n",
      "training...%s LDA\n",
      "LDA\n",
      "testing_accuracy 40.4970760234\n",
      "training_accuracy 41.3905133203\n",
      "precision_test:  27.2196267137\n",
      "f1_score:  26.1876763462\n",
      "recall_score:  29.8058391218\n",
      "training...%s MNB\n",
      "MNB\n",
      "testing_accuracy 24.6588693957\n",
      "training_accuracy 26.0883690708\n",
      "precision_test:  24.7477681917\n",
      "f1_score:  19.7246317124\n",
      "recall_score:  25.5397143986\n"
     ]
    }
   ],
   "source": [
    "#model training (try to load few classifiers for examining performance and chose the best)\n",
    "\n",
    "from sklearn.linear_model import SGDClassifier, LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB as MNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis, LinearDiscriminantAnalysis\n",
    "\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "\n",
    "classifiers=[\n",
    "    (LogisticRegression(penalty='l1'),\"LR\"),\n",
    "    (KNeighborsClassifier(n_neighbors=3, weights='distance', algorithm = 'auto'),\"KNC\"),\n",
    "    (SVC(),'SVC'),\n",
    "    (QuadraticDiscriminantAnalysis(),'QDA'),\n",
    "    (RandomForestClassifier(max_depth=50, n_estimators=10, max_features=1),'RFC'),\n",
    "    (AdaBoostClassifier(),'ADC'),\n",
    "    (SGDClassifier(),'SGDC'),\n",
    "    (DecisionTreeClassifier(max_depth=5),'DTC'),\n",
    "    (LinearDiscriminantAnalysis(),'LDA'),\n",
    "    (MNB(), 'MNB')\n",
    "]\n",
    "\n",
    "eval_ =[]\n",
    "train_acc, test_acc, name = [], [], []\n",
    "f1_, rec_, prec_ = [], [], []\n",
    "for clf in classifiers:\n",
    "    print (\"training...%s\",clf[1])\n",
    "    clf[0].fit(train, y_train)\n",
    "    y_preds = clf[0].predict(test)\n",
    "    y_preds_train = clf[0].predict(train)\n",
    "    name.append(clf[1])\n",
    "    train_acc.append( accuracy_score(y_train, y_preds_train)*100 )\n",
    "    test_acc.append( accuracy_score(y_test, y_preds)*100 )\n",
    "    f1_.append( f1_score(y_test, y_preds, average='macro')*100 )\n",
    "    rec_.append( recall_score(y_test, y_preds, average='macro')*100 )\n",
    "    prec_.append( precision_score(y_test, y_preds, average='macro')*100 )\n",
    "    print (clf[1])\n",
    "    print (\"testing_accuracy\", accuracy_score(y_test, y_preds)*100)\n",
    "    print (\"training_accuracy\", accuracy_score(y_train, y_preds_train)*100)\n",
    "    print (\"precision_test: \", precision_score(y_test, y_preds, average='macro')*100)\n",
    "    print (\"f1_score: \", f1_score(y_test, y_preds, average='macro')*100)\n",
    "    print (\"recall_score: \", recall_score(y_test, y_preds, average='macro')*100)\n",
    "eval_.append(train_acc)\n",
    "eval_.append(test_acc)\n",
    "eval_.append(f1_)\n",
    "eval_.append(rec_)\n",
    "eval_.append(prec_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OTrW5i+ImPDf5F9eQOjYEP2/3WtzVk7nJ0ejrWjhe0eDoUAalvL6V6sY2h/T0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\nTCV/ZcjmpkQD9LnBS1F1E01tRtXGeYg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/LiI2DNv/GvB7RLwqaQswOyI2j3GsXsZYb3IM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\na1VcMd8OAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21b057a8470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+8xteeumlI46RnJzMJZdcQlubaxGSiLB8+XJWrFjBxIkTmTp1KrfddhujRg2+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\nG0H+uES/mBL2QtFeROCivOB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BK486/fTT2bVrFwUFBb4OJSgVdC146sd0SYfTsKO2SRP8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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21b055866a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WArRCcMco96pjYkuZKTGcqapn1/Gzzh5Kh9Q2mCg4XeWWG5wuRwjB\n76el8vD4Afx723Ee/3g3jSazs4dld9b0ZD0lpdxp+dwIHABc/vfuLH0p6XHhblXq1NU8MLY/QsCb\nG62vbFhZ18hHPxRyfXovYroF2HF0rm1cchQ+GsFaN1umOVRSiVm6T5OPjhBC8MR1yTw2eSCf7Czi\nlx/m0ODhQb5Da/BCiHhgGLDN8tAjQog9Qoh3hRDd2zjnASFEthAiu2WNB3sqr65nz4lytTzTRb3D\nA7n5ijg+zC60ulv9x9mFGOsaueeaBDuPzrV1C/BlZP8efJPrXgH+fImC5J7ufYO1LUII5k9K4jdT\nB/H5nlP84oOdDm1Z6WhWB3ghRAiwAviVlLIC+DvQH9ABp4BFrZ0npVwspcyQUmZERTkm4G4+VIZZ\nwjhVPbLLHho3gEaTmbc2tT+LN5slS7ce44q+4ej6hDtgdK4tMyWaw6VVbtUjNM9gxE+r8fiNaQ+M\nHcAfZgzmmwMG7l+aTU29ZwZ5qwK8EMKXpuD+LynlJwBSSoOU0iSlNANvASPsN8yOydKXEhrgw9A4\nFWS6Kj4ymBuG9uZf245zpqr+ssdu0JdwtKzK62fv501q3tXqPrP4fEMl/aOC3b4hujXuHBXPizen\ns/lQGfe8t51KN923cDnWZNEI4B3ggJTy5RaPt+wMcROwz/bD6zgpJVn6MkYnRtq9x6i3mDshkep6\nE0va2RH47uYCenYLcMvcb3voExHEoJ6hrHWjZZq8YqPbb3DqiNuu7MOrM3X8UHCWO9/Zxrka68sa\nuANrIuA1wB3AxItSIl8UQuwVQuwBJgCP2nOg1jpUUklxRa1af7ehgTGhXDc4hve+K6CitvV/AHqD\nkc2Hyrjz6n5eMfuzVmZKDNnHznK2nd9+XIGxtoGi8hq3a9PXVTN0sbwxZxh7i87xs7e/d4u/K2tZ\nk0WzWUoppJTpLVMipZR3SCmHWB6fLqU85YgBt2ejpTyBCvC2NW9CEsbaRv659Virzy/ZUkCAr4bZ\nV/Z18MhcW2ZqDCazZIPe9YuP5Zc0bWrzphn8eVPSerH4jgz0hkpmLf6eUmOds4dkEx431crKL2NA\nVDCx4ap7ky0NiQtj3MAo3tl8lOr6C9cqz1bV88nOE9w0LJbuwSottaX02DCiQv3dogmI3tLFyRNT\nJK0xYVA0S+6+kuNnqpn55la3L/kMHhbgaxtMbDtyWs3e7WTexETOVNWzbHvhBY//7YttFH74LMse\nv4n+/fszb9486urq2LBhA2FhYQwbNozk5GTGjh3LZ599dsl1dTods2bNctTbcCiNRpCZEs1GfanL\np+PpDZUE+mqJ6+69k6NrEiN5/74RlBjruO3NrRSe6dgmP1fjUQF++9Ez1DWq7k32cmV8BCMSIlic\ndbg5WNU3mnjh0Z8zdPRkCo4cJj8/n5qaGp588kkAxowZw65du8jLy+O1115j3rx5rFu3rvmaBw4c\nwGQysWnTJqqq3CedsCMyU2KorGtk2xHbdcqyB73BSFJMiEf3zbXGlfER/Ov+q6ioaeS2N7e6VZrr\nxTwqwGfpS/Hz0TAyQZUnsJdHJiZiqKhjxY6mTjkvvvsxjcKH5/5nHgBarZZXXnmF999/n8rKCwuV\n6XQ6fv/73/P66683P7Zs2TLuuOMOrr32WlatWuW4N+JA1yRGEuCrcfl0yTyDd2XQXM7QPuEs+/lI\n6hrN3Pbm1uYNYO7GswJ8fikj4iMI9PO+AleOMjoxkqFxYfx94yEaTWb+s3YrUQkpjB8Y3XxMt27d\niI+P59ChS5uGtOx5CU1t0WbNmsXs2bPdrta2tQJ8tYxOjOKbAyUuW8nwTFU9pcY6r11/b01q7258\n+MBIBDBr8ffsKzrn7CF1mMcE+FPnatAbKhmjujfZlRCCuRMSKTxTwx8/y6XwbA2pvUKt/rW+ZYDL\nzs4mMjKSvn37MmnSJHbt2sWZM669jNFZk1OjKSqv4cAp15wJnp+hunObPntIignlowdHEeirZc5b\n37td8TiPCfCb9GWASo90hMyUGAb1DGXp1mOE9YqnsujCPpUVFRUUFxeTnJx8ybkX97w8ePAg8fHx\nDBgwgIqKClasWOGQ9+BoEwfFIAQuWyM+v7mLk2fWoOmK+MhgPnxwJOFBftz+9ja2HTnt7CFZzWMC\n/Mb8UqJD/RmkZiB2p9EIHp6QCMA9t02nrraG999/HwCTycSCBQuYN28egYEXZmPs2bOHP/7xj8yd\nOxez2cxHH33E3r17m3terlq1ymOXaaJC/dH1CXfZAJ9nMBIa4ENPL64Aejlx3YP46MFR9AwL4K4l\n29mcX+bsIVnFIwK8ySzZnF/GGNW9yWGuH9KL525KY/6kJD799FM+/vhjkpKS6NGjBxqNhqeffhqA\nTZs2NadJzp07l9dee41JkyaxadMmYmNj6d27d/M1x44dS25uLqdOucSeOZvLTIlhz4lzFJ+zrjKn\nI+mLm5p8qH8/besZFsCHD44ivkcw9y79weVvmoOH9GTdc6KcczUNjFXVIx1GqxH87Kqmhh7hQX1Y\nvXo1AN999x2zZ89m586djB8/nnPnWr8xNW7cOL7//vsLr6nVUlxcbN+BO9Hk1Bj+96s81h00NH/v\nXIGUkjyDkevTe7V/sJeLDPFn+QMjufPd7Tz4zx28NnsYU4e47vfNI2bwm/LLEALGJKn1d2e7+uqr\nOXbsGFdc0blm3Z4sKTqEvhFBrDvgWrtaS4x1nKtpUBk0VgoP8uOD+69iaJ9w5v17J5/uOuHsIbXJ\nIwJ8lr6UIbFhRKht8ooLE0KQmRLD5kNll5R7cKbzGTRJ6gar1boF+PL+vSO4KqEHj320m+Xbjzt7\nSK1y+wBfUdvArsJyxqrZu+IGMlOiqW80s8mFbtLleXkNms4K9vdhyT1XMjYpil9/spf32imn7Qxu\nH+C/O1SGySxVeqTiFq5MiCA0wMelWvnpDUYiQ/zoEeLv7KG4nQBfLYvvHM7k1BgWrsnlHxsPX/Z4\nIQS3335789eNjY1ERUUxbdo0AN577z00Gg179uxpPiYtLQ3Az3J+gaVMe47lzxmXez23D/Ab9WWE\n+PswrK/q3qS4Pl+thgnJ0Xx7sAST2TV2teYZKlWJgi7w99Hyt59dwbT0Xvz5y4O8slbf5o7l4OBg\n9u3bR01NU6XKtWvXEhsbe8ExcXFxPPfcc5d7yQlSSh1wC/Da5Q506wDf1L2plKsH9FBNJhS3kZka\nw+mqenIKnb8r0myW5KsaNF3mq9Xw11nDuGV4HH9dl8+f/3uwzSA/depUPv/8c6Bps9/s2bMveH7a\ntGns37+fvLy89l62G3DZHyJrWvb1EUKsF0LkCiH2CyF+aXk8QgixVgiRb/mze3vXsrUjZVUUldeo\n5RnFrYwbGIWPRrDWBWrEF5XXUF1vUgHeBrQawYs3p3P7yL68ufEIC1fvx9zKb2mzZs1i+fLl1NbW\nsmfPHq666qoLntdoNDz55JM8//zzbb3UeiHEPmAj8NvLjcmaaW8jsEBKmQqMBOYKIVKBXwPrpJRJ\nwDrL1w6VZeneNE4FeMWNhAX6clX/CJfYKHM+gya5p8qgsQWNRvDHGWncNzqBpVuP8ZtP916yFJee\nnk5BQQHLli1j6tSprV5nzpw5fP/99xw92uqN2wlSyjRgCPC6EKLNvzxrWvadklLutHxuBA4AscAM\nYKnlsKXAje1dy9ay9KUkRAbTJyLI0S+tKF2SmRJDfkklBU6uNZ7XnCKpZvC2IoTgt9enMG9CIst/\nKGTBRzk0mswXHDN9+nQef/zxS5ZnzvPx8WHBggX85S9/afN1pJSHAQOQ2tYxHVq4FkLEA8OAbUBM\niz6sxUBMG+c8IITIFkJkl5aWduTlLquu0cT3R86o6pGKW8pMafrn4uzaNPpiI73DAugW4OvUcXga\nIQSPX5fM49cOZGXOSR5Ztov6xh+D/L333sszzzzDkCFD2rzG3XffzTfffENbcVMIEQ0kAK03SqYD\nAd7ya8AK4FdSyoqWz8mmuwmt3lGQUi6WUmZIKTOiomy3lJJdcJaaBpPKf1fcUp+IIJJjQp0e4PMM\nlapEsB3Nm5jEb69P4ct9xaxtkRobFxfH/PnzL3uun58f8+fPp6Tkkns164UQOcB64NdSyjZ/iIQ1\nDQiEEL7AZ8BXUsqXLY/lAeOllKeEEL2ADVLKS+vDtpCRkSGzs7PbfT1rvPDFAd7dcpSc319LsL9H\nlNRRvMz/fnWQf2w8wo7fZhIe5Phd2I0mM6nPfMXdV8fzm6kpDn99b7LnRDnpcZ1P5RZC7JBSZnT0\nPGuyaATwDnDgfHC3WA3cZfn8LsCh/dY26ksZ3q+7Cu6K28pMicFklmzIs93SZUccO1NNfaNZZdA4\nQFeCe1dYs0RzDXAHMNGyeypHCDEV+DMwWQiRD2RavnaIkopaDhYbVXqk4taGxoUTGeLPWict0+hV\niQKP1+70V0q5GWirSPQk2w7HOufreKj1d8WdaTSCzJRoPttzivpGM34+jt2sl2cwIgQkRqsUSU/l\nlts/s/JLiQzxI7VXN2cPRVG6JDMlhsq6RrYfdXwvWr3BSL+IINWk3oO5XYA3myWbLN2brG30rCiu\n6prESAJ8NU7JptEbKlX+u4dzuwC//2QFZ6rqVfcmxSME+mkZnRjJ2lxDm7VL7KGu0cTRsiq1/u7h\n3C7AZ+U3ZRyo7k2Kp8hMiaGovIaDlpuejnCktAqTWaoceA/ndgF+o76Uwb27EalqVyseYmJKNIBD\na8Q316BRM3iP5lYB3ljbwM5jZ1V6pOJRokMD0PUJd+g6fF6xER+NICEy2GGvqTieWwX4rYdP02iW\nKj1S8TiTU2PYfeIchopah7ye3lBJQmSww1MzFcdyq7/drPxSgvy0DO/n8NLzimJX54uPfXvQMTXi\n9QajWn/3Am4V4F+YOYJR/XtcMOtYuHAhsbGx6HQ6UlNTWbZsmRNHqCidMzAmhD4RgQ5Zh6+ub+T4\nmWq1/u4F3CbAF5RVISWtrr8/+uij5OTksGrVKh588EEaGhqcMEJF6TwhBJMGxbD5UBnV9Y12fa18\nQyWAqkHjBdwmwG+ypEde7gZrUlISQUFBnD3r/F6XitJRk1NjqGs0s9lSisNe8pq7OKkA7+ncJsBv\n1JchBMT3aLt7086dO0lKSiI6OtqBI1MU2xiREEFogI/ds2nyDUb8fTT0VZ3QPJ5b1NqtbzSz9XAZ\nWo2gqXrxhV555RWWLFmCXq9nzZo1ThihonSdr1bD+ORo1h0owWSWaO1UiiPPUElidIjdrq+4DreY\nwe88fpaqehPaVoI7NK3B79+/nxUrVnDfffdRW+uYVDNFsbXMlGhOV9WTU1hut9fQFxvVDVYv4RYB\nPktfio9GtDvjmD59OhkZGSxduvSyxymKqxo/MBofjWCdnZZpzlU3UFxRq1IkvYRbBPghsWE8MLY/\n1dXVxMXFNX+8/PLLlxz7+9//npdffhmz2dzKlRTFtYUF+TIiIcJu6/D6ElWiwJu0uwYvhHgXmAaU\nSCnTLI8tBH4OnO819hsp5Rf2GuRPhvTiJ0N68aQVQXv48OHk5eXZayiKYneZKTH84bNcjp2uol8P\n25YSOF+DJilGNfnwBtbM4N8DprTy+CtSSp3lw27BXVG8zfldrd8csP2uVn2xkWA/LbHhgTa/tuJ6\n2g3wUsoswPHtZhTFS/XtEcTAmBC77GrNs5QoaC0bTfE8XVmDf0QIsUcI8a4Qos3iMEKIB4QQ2UKI\n7NJS53SPVxR3k5kSw/aCM5yrtt2ubCkleSqDxqt0NsD/HegP6IBTwKK2DpRSLpZSZkgpM6KiVBVI\nRbFGZmoMJrNkg952yzRllfWcrW5QJQq8SKcCvJTSIKU0SSnNwFvACNsOS1G8my4unMgQP5uuw+db\nbrCqAO89OhXghRC9Wnx5E7DPNsNRFAVAo2kqPrYhr4T6Rtuk/J6vQTOwp8qg8RbtBnghxDJgK5As\nhDghhLgPeFEIsVcIsQeYADxq53EqitfJTI3BWNvIDwW2yXHQG4x0D/IlSrW79Brt5sFLKWe38vA7\ndhiLoigtjE6MxN9Hw9pcA9ckRnb5ennFRgbGqAwab+IWO1kVxRsF+mkZnRjJNwcMSCm7dC0pJfmG\nSlUi2MuoAK8oLiwzNYYTZ2ua188769S5Wox1jSSpG6xeRQV4RXFhkwY19Tbo6qan5iYfKsB7FRXg\nFcWFRXcLYGif8C6nS+qLz6dIqgwab6ICvKK4uMkp0eQUllNi7HyfgzyDkZhu/oQH+dlwZIqrUwFe\nUVxcZmpT8bFvuzCL1xuMaoOTF1IBXlFcXHJMKHHdAztdI95klhwqqVQB3gupAK8oLk4IQWZKDJvy\ny6ipN3X4/MIz1dQ2mNUNVi+kAryiuIHMlBjqGs1sPlTW4XN/LFGgAry3UQFeUdzAiIQIQv19OpUu\neT6DJilaZdB4GxXgFcUN+PloGJccxbqDJZjNHdvVmmcw0icikGD/diuTKB5GBXhFcROTU2Moq6xj\n94nyDp2Xb6hkYLRanvFGKsAripsYPzAarUZ0KJumvtHM4dJKtf7upVSAVxQ3ERbky4j4CL7JtT4f\nvuB0FY1mqTJovJQK8IriRialRJNnMHL8dLVVx+cVqy5O3kwFeEVxI5Mtu1qtXabRG4xoBPSPCrbn\nsBQXZU1Hp3eFECVCiH0tHosQQqwVQuRb/uxu32EqigLQr0cwSdEhHQrw8ZHBBPhq7TwyxRVZM4N/\nD5hy0WO/BtZJKZOAdZavFUVxgMzUGLYfPcO5moZ2j9UbKtX6uxdrN8BLKbOAi5tCzgCWWj5fCtxo\n43EpitKGzJQYGs2SjfrSyx5X22Ci4HSVWn/3Yp1dg4+RUp6yfF4MxLR1oBDiASFEthAiu7T08j+Q\niqK0T9cnnMgQv3Z3tR4qqURKVJs+L9blm6yyqVlkm1vrpJSLpZQZUsqMqKiorr6cong9rUYwcVA0\n6/NKaDCZ2zwuTzX58HqdDfAGIUQvAMufXWs3oyhKh2SmxGCsbeSHoxevnv5IX2LET6uhXw+VQeOt\nOhvgVwN3WT6/C1hlm+EoimKN0UmR+PloWHuZbBp9sZH+UcH4alU2tLeyJk1yGbAVSBZCnBBC3Af8\nGZgshMgHMi1fK4riIEF+PoxOjOSbAwaaVkkvpTdUqvV3L9dueTkp5ew2nppk47EoitIBmSkxfHuw\nhPxWujUZaxsoKq9hTkxfJ41OcQXqdzdFcVOTUqIBWNtKNo3eUAmoEgXeTgV4RXFTMd0CGBoX1uqu\n1nxLFye1ycm7qQCvKG4sMyWGnMJySoy1FzyeZzAS6Kslrnugk0amuAIV4BXFjWWmxiAlrD94Yaay\n3j/nKjYAAAcpSURBVGBkYEwIGo1w0sgUV6ACvKK4sUE9Q4kND2TtRTXi84ovvfGqeB8V4BXFjQkh\nyEyJZvOhUmobTACcqaqnrLJOBXhFBXhFcXeZqTHUNpjZcqgMaFqeAVSbPkUFeEVxd1cl9CDE36c5\nm0avMmgUCxXgFcXN+floGJccxTcHSjCbJXnFRroF+PD2/73E4MGDSU9PR6fTsW3bNhobG/nNb35D\nUlISOp0OnU7Hc88913wtrVaLTqdj8ODBDB06lEWLFmE2/1jQbPv27YwdO5bk5GSGDRvG/fffT3W1\nde0DFcdrdyeroiiub3JKDJ/vOcWeonPoDUYiq4/x+abP2blzJ/7+/pSVlVFfX89vf/tbiouL2bt3\nLwEBARiNRhYtWtR8ncDAQHJycgAoKSlhzpw5VFRU8Oyzz2IwGLj11ltZvnw5o0aNAuDjjz/GaDQS\nFBTklPetXJ5oq46FPWRkZMjs7GyHvZ6ieIvy6nqG/+kbHhrXn39uPUZSTS71ud+yZs2a5mOqq6vp\n06cPBQUFhIa2vnwTEhJCZWVl89dHjhzhyiuvpKysjGeeeQaAP/zhD/Z9M8olhBA7pJQZHT1PLdEo\nigcID/Ljyvju/Cf7BBW1jVw7+VoKCwsZOHAgDz/8MBs3buTQoUP07du3zeDemv79+2MymSgpKWHf\nvn0MHz7cju9CsTUV4BXFQ2SmxFBirAMgPaEnO3bsYPHixURFRTFz5kw2bNhwwfFLlixBp9PRp08f\nCgsLnTBixd5UgFcUDzEp5cfOmQNjQtBqtYwfP55nn32W119/nTVr1nD8+HGMxqYsm3vuuYecnBzC\nwsIwmUytXvPIkSNotVqio6MZPHgwO3bscMh7UWxDBXhF8RAJkcEkRocQGeJPWVEB+fn5zc/l5OSQ\nnJzMfffdx7x586itbapdYzKZqK+vb/V6paWlPPTQQ8ybNw8hBPPmzWPp0qVs27at+ZhPPvkEg+Hy\nvWEV51FZNIriQZ6emkJZZR2VlSU88sgjlJeX4+PjQ2JiIosXLyYsLIzf/e53pKWlERoaSmBgIHfd\ndRe9e/cGoKamBp1OR0NDAz4+Ptxxxx089thjAMTExLB8+XIef/xxSkpK0Gg0jB07lilTpjjzLSuX\n0aUsGiFEAWAETEBje3d5VRaNoihKx3U2i8YWM/gJUsoyG1xHURRFsSG1Bq8oiuKhuhrgJfCNEGKH\nEOIBWwxIURRFsY2uLtGMllIWCSGigbVCiINSyqyWB1gC/wMAffuqBsCKoiiO0qUZvJSyyPJnCfAp\nMKKVYxZLKTOklBlRUVFdeTlFURSlAzod4IUQwUKI0POfA9cC+2w1MEVRFKVrurJEEwN8KoQ4f51/\nSyn/a5NRKYqiKF3W6QAvpTwCDLXhWBRFURQbcmi5YCFEKXCsk6dHAirf/kfq+/Ej9b24kPp+XMgT\nvh/9pJQdvonp0ADfFUKI7M7s5PJU6vvxI/W9uJD6flzIm78faqOToiiKh1IBXlEUxUO5U4Bf7OwB\nuBj1/fiR+l5cSH0/LuS13w+3WYNXFEVROub/27ufF5viOIzj76cZYihszUzNLESTDVkMygILIrYU\nC2u/U8LfILGQzQwbYjFmIQkL1hKjmBlqQowfsfEjG+SxOGdqslDI/dx7zue1uud0F0+n+3065/u9\n93tb6Q4+pZTSH8iCTymlimqJgpe0QdITSROSjkTniSKpW9JtSWOSRiXtj87UDCS1SRqRdDU6SzRJ\n8yUNSXosaVzSyuhMUSQdLMfJI0kXJc2KztRoTV/wktqA08BGoA/YLqkvNlWY78Ah231AP7C7xtdi\nuv3AeHSIJnEKuG57CcUvzWt5XSR1AvuAFbaXAm3AtthUjdf0BU+xQ+WE7ae2vwKXgK3BmULYfmP7\nfvn6M8Xg7YxNFUtSF7AJGIjOEk3SPGANMAhg+6vtD7GpQrUDsyW1Ax3A6+A8DdcKBd8JvJx2PEnN\nSw1AUg+wDLjz+3dW3kngMPAjOkgT6AXeA+fKKauBcqfX2im3Mj8OvADeAB9t34xN1XitUPDpF5Lm\nApeBA7Y/ReeJImkz8M72vegsTaIdWA6csb0M+ALUcs1K0gKKJ/1eYCEwR9KO2FSN1woF/wronnbc\nVZ6rJUkzKMr9gu3h6DzBVgNbJD2nmLpbK+l8bKRQk8Ck7amnuiGKwq+j9cAz2+9tfwOGgVXBmRqu\nFQr+LrBIUq+kmRQLJVeCM4VQsfn+IDBu+0R0nmi2j9rust1D8bm4Zbt2d2lTbL8FXkpaXJ5aB4wF\nRor0AuiX1FGOm3XUcMH5X/+T9b+z/V3SHuAGxUr4WdujwbGirAZ2Ag8lPSjPHbN9LTBTai57gQvl\nzdBTYFdwnhC270gaAu5TfPtshBpuWZBbFaSUUkW1whRNSimlv5AFn1JKFZUFn1JKFZUFn1JKFZUF\nn1JKFZUFn1JKFZUFn1JKFfUTCAJMhms39jwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21b05119828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2KDbzem4JPkYD12TG2fyx7784mdhQf/7fyi00NMsqjK6YBlgNmq4E+HiRPTqS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\n/JkzZzJz5kx7hXdKWmsKzRY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      "text/plain": [
       "<matplotlib.figure.Figure at 0x21b04e62668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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KrPVNresO7B3G8NhILh/dl6Q+kST16Ub/nmHHjffxq4uGsDS3mJdWmLnrvSxGx3Xn4WmJ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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21b04e56c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "titles = ['train_accuracy', 'test_accuracy', 'f1_score', 'recall', 'precision']\n",
    "\n",
    "for i in range(len(eval_)):\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    A = eval_[i][:]\n",
    "    plt.title(titles[i])\n",
    "    plt.plot(A)\n",
    "    for j, label in enumerate(name):\n",
    "        plt.text(j,A[j], label) \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# using plotly for the beautiful plots \n",
    "\n",
    "import plotly.plotly as py\n",
    "import plotly\n",
    "import pandas as pd\n",
    "\n",
    "# login api for plotly (dont forget to sign up to plotly)\n",
    "plotly.tools.set_credentials_file(username= 'Ubaid01', api_key = \"ip2KAG3NXA4zbbzIzbzY\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# for UI\n",
    "\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import plotly.offline as py\n",
    "\n",
    "def get_viz(the_yr_data, yr):\n",
    "    py.init_notebook_mode(connected=True)\n",
    "\n",
    "\n",
    "    for col in the_yr_data.columns:\n",
    "        the_yr_data[col] = the_yr_data[col].astype(str)\n",
    "\n",
    "    \n",
    "    scl = [\n",
    "            # Let first 10% (0.1) of the values have color rgb(0, 0, 0)\n",
    "            [0, 'rgb(0, 0, 0)'],\n",
    "            [0.1, 'rgb(0, 0, 0)'],\n",
    "\n",
    "            # Let values between 10-20% of the min and max of z\n",
    "            # have color rgb(20, 20, 20)\n",
    "            [0.1, 'rgb(20, 20, 20)'],\n",
    "            [0.2, 'rgb(20, 20, 20)'],\n",
    "\n",
    "            # Values between 20-30% of the min and max of z\n",
    "            # have color rgb(40, 40, 40)\n",
    "            [0.2, 'rgb(40, 40, 40)'],\n",
    "            [0.3, 'rgb(40, 40, 40)'],\n",
    "\n",
    "            [0.3, 'rgb(60, 60, 60)'],\n",
    "            [0.4, 'rgb(60, 60, 60)'],\n",
    "\n",
    "            [0.4, 'rgb(80, 80, 80)'],\n",
    "            [0.5, 'rgb(80, 80, 80)'],\n",
    "\n",
    "            [0.5, 'rgb(100, 100, 100)'],\n",
    "            [0.6, 'rgb(100, 100, 100)'],\n",
    "\n",
    "            [0.6, 'rgb(120, 120, 120)'],\n",
    "            [0.7, 'rgb(120, 120, 120)'],\n",
    "\n",
    "            [0.7, 'rgb(140, 140, 140)'],\n",
    "            [0.8, 'rgb(140, 140, 140)'],\n",
    "\n",
    "            [0.8, 'rgb(160, 160, 160)'],\n",
    "            [0.9, 'rgb(160, 160, 160)'],\n",
    "\n",
    "            [0.9, 'rgb(180, 180, 180)'],\n",
    "            [1.0, 'rgb(180, 180, 180)']\n",
    "        ]\n",
    "    data = [ dict(\n",
    "            type='choropleth',\n",
    "            colorscale = scl,\n",
    "            autocolorscale = True,\n",
    "            locations = the_yr_data['state'],\n",
    "            z = the_yr_data['cases'].astype(float),\n",
    "            zmin=0,\n",
    "            zmax=500,\n",
    "            locationmode = 'USA-states',\n",
    "            text = the_yr_data['text'],\n",
    "            marker = dict(\n",
    "                line = dict (\n",
    "                    color = 'rgb(255,255,255)',\n",
    "                    width = 2\n",
    "                )\n",
    "            ),\n",
    "            colorbar = dict(\n",
    "                title = \"Disease outbreak - cases in %d\" %(yr)\n",
    "            )\n",
    "        ) ]\n",
    "\n",
    "    layout = dict(\n",
    "        title = '%d US Diseases Cases Found by State<br>(Hover for breakdown)' %(yr),\n",
    "        geo = dict(\n",
    "            scope='usa',\n",
    "            projection=dict( type='albers usa' ),\n",
    "            showlakes = True,\n",
    "            lakecolor = 'rgb(255, 255, 255)',\n",
    "            ),\n",
    "    )\n",
    "\n",
    "    fig = dict( data=data, layout=layout )\n",
    "\n",
    "    url = py.iplot( fig, validate=False)\n",
    "\n",
    "def get_1yr_viz(yr):\n",
    "    data = pd.read_csv('for_visulisation.csv')\n",
    "    the_yr_data = data.loc[data['Year'] ==yr]\n",
    "    the_yr_data.head()\n",
    "    from collections import defaultdict\n",
    "    har = defaultdict(set)\n",
    "    a = list(the_yr_data.state_name)\n",
    "    b = list(the_yr_data.disease)\n",
    "    for i in range(len(a)):\n",
    "        har[a[i]].add(b[i])\n",
    "    print(len(a),len(b))\n",
    "    the_yr_data['disease_all'] = [' '.join(list(har[i])) for i in the_yr_data.state_name]\n",
    "    the_yr_data['text'] = the_yr_data['state_name'] + '<br>' +\\\n",
    "    'Disease '+the_yr_data['disease_all']\n",
    "    \n",
    "    tf = the_yr_data.filter(['state_name','state','cases'], axis=1)\n",
    "    tf.cases = tf.cases.astype(int)\n",
    "    the_yr_data_2 = tf.groupby(['state_name','state']).sum().reset_index()\n",
    "    the_yr_data_2['disease_all'] = [' '.join(list(har[i])) for i in the_yr_data_2.state_name]\n",
    "    the_yr_data_2['text'] = the_yr_data_2['state_name'] + '<br>' +\\\n",
    "    'Disease '+the_yr_data_2['disease_all']\n",
    "    \n",
    "    get_viz(the_yr_data_2, yr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9816 9816\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "autocolorscale": true,
         "colorbar": {
          "title": "Disease outbreak - cases in 1977"
         },
         "colorscale": [
          [
           0,
           "rgb(0, 0, 0)"
          ],
          [
           0.1,
           "rgb(0, 0, 0)"
          ],
          [
           0.1,
           "rgb(20, 20, 20)"
          ],
          [
           0.2,
           "rgb(20, 20, 20)"
          ],
          [
           0.2,
           "rgb(40, 40, 40)"
          ],
          [
           0.3,
           "rgb(40, 40, 40)"
          ],
          [
           0.3,
           "rgb(60, 60, 60)"
          ],
          [
           0.4,
           "rgb(60, 60, 60)"
          ],
          [
           0.4,
           "rgb(80, 80, 80)"
          ],
          [
           0.5,
           "rgb(80, 80, 80)"
          ],
          [
           0.5,
           "rgb(100, 100, 100)"
          ],
          [
           0.6,
           "rgb(100, 100, 100)"
          ],
          [
           0.6,
           "rgb(120, 120, 120)"
          ],
          [
           0.7,
           "rgb(120, 120, 120)"
          ],
          [
           0.7,
           "rgb(140, 140, 140)"
          ],
          [
           0.8,
           "rgb(140, 140, 140)"
          ],
          [
           0.8,
           "rgb(160, 160, 160)"
          ],
          [
           0.9,
           "rgb(160, 160, 160)"
          ],
          [
           0.9,
           "rgb(180, 180, 180)"
          ],
          [
           1,
           "rgb(180, 180, 180)"
          ]
         ],
         "locationmode": "USA-states",
         "locations": [
          "AL",
          "AK",
          "AZ",
          "AR",
          "CA",
          "CO",
          "CT",
          "DE",
          "DC",
          "FL",
          "GA",
          "HI",
          "ID",
          "IL",
          "IN",
          "IA",
          "KS",
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   ],
   "source": [
    " #get visualization for any year\n",
    "\n",
    "get_1yr_viz(1977)"
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