The Suicide Crisis in Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: Investigate Suicide Research Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of Contents\n",
    "    1.Introduction\n",
    "    2.Data Wrangling\n",
    "    3.Exploratory Data Analysis\n",
    "    4.Machine Learning + Predictive Analytics \n",
    "    5.Conclusions\n",
    "    6.References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From growing up in the heart of Silicon Valley, I have always wondered what factors play a role in suicide. There have been a plethora of suicide clusters from my High School in Palo Alto. This project seeks to explore the underlying factors. We will use a sample of 44,000 gather data from 141 different Countries, between the 80's to 2016.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Suicide is one of the leading causes of death among all American adults and rates are increasing in both men and women\"(SSM Health)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Questions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "  1. Which year has the most suicides ? \n",
    "   Which year has the least suicides ? \n",
    "   \n",
    "   \n",
    "  2. Which country has the most suicides ? \n",
    "   Which country has the least suicides ? \n",
    "   \n",
    "\n",
    "3. Are certain age groups more inclined to suicide?\n",
    "\n",
    "\n",
    "4. What is the relationship between gender and the number of suicides? \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "data = pd.read_csv('/Users/jarar_zaidi/Desktop/ML Projects/Practice Excel/who_suicide_statistics.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our dataset has 4 Features (Country, Year, Gender, and Age). We will explore all of these in detail. While the suicide_no is what we would like to predict (we will do this in Section 4 - Machine Learning + Predictive Analytics )."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Data Wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "note: we will peform more data wrangling outside this section. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets start with getting basic infomation about our Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>suicides_no</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1985</td>\n",
       "      <td>female</td>\n",
       "      <td>15-24 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>277900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1985</td>\n",
       "      <td>female</td>\n",
       "      <td>25-34 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>246800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1985</td>\n",
       "      <td>female</td>\n",
       "      <td>35-54 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>267500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1985</td>\n",
       "      <td>female</td>\n",
       "      <td>5-14 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>298300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1985</td>\n",
       "      <td>female</td>\n",
       "      <td>55-74 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>138700.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  year     sex          age  suicides_no  population\n",
       "0  Albania  1985  female  15-24 years          NaN    277900.0\n",
       "1  Albania  1985  female  25-34 years          NaN    246800.0\n",
       "2  Albania  1985  female  35-54 years          NaN    267500.0\n",
       "3  Albania  1985  female   5-14 years          NaN    298300.0\n",
       "4  Albania  1985  female  55-74 years          NaN    138700.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at 1st 5 data points\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 43776 entries, 0 to 43775\n",
      "Data columns (total 6 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   country      43776 non-null  object \n",
      " 1   year         43776 non-null  int64  \n",
      " 2   sex          43776 non-null  object \n",
      " 3   age          43776 non-null  object \n",
      " 4   suicides_no  41520 non-null  float64\n",
      " 5   population   38316 non-null  float64\n",
      "dtypes: float64(2), int64(1), object(3)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()# print the concise summery of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(43776, 6)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape # 43776 Rows, 6 Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country           0\n",
       "year              0\n",
       "sex               0\n",
       "age               0\n",
       "suicides_no    2256\n",
       "population     5460\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# counts total row in each col. that have null values\n",
    "# note: all the na columns are type Object\n",
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country        0\n",
       "year           0\n",
       "sex            0\n",
       "age            0\n",
       "suicides_no    0\n",
       "population     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# From above, we can see that, suicides_no & population, have null values.\n",
    "#Lets, fill the null values with zero using 'fillna'\n",
    "data= data.fillna(0)\n",
    "# Now, we have no null columns!\n",
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['15-24 years', '25-34 years', '35-54 years', '5-14 years',\n",
       "       '55-74 years', '75+ years'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The different age groups\n",
    "data['age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Albania', 'Anguilla', 'Antigua and Barbuda', 'Argentina',\n",
       "       'Armenia', 'Aruba', 'Australia', 'Austria', 'Azerbaijan',\n",
       "       'Bahamas', 'Bahrain', 'Barbados', 'Belarus', 'Belgium', 'Belize',\n",
       "       'Bermuda', 'Bolivia', 'Bosnia and Herzegovina', 'Brazil',\n",
       "       'British Virgin Islands', 'Brunei Darussalam', 'Bulgaria',\n",
       "       'Cabo Verde', 'Canada', 'Cayman Islands', 'Chile', 'Colombia',\n",
       "       'Costa Rica', 'Croatia', 'Cuba', 'Cyprus', 'Czech Republic',\n",
       "       'Denmark', 'Dominica', 'Dominican Republic', 'Ecuador', 'Egypt',\n",
       "       'El Salvador', 'Estonia', 'Falkland Islands (Malvinas)', 'Fiji',\n",
       "       'Finland', 'France', 'French Guiana', 'Georgia', 'Germany',\n",
       "       'Greece', 'Grenada', 'Guadeloupe', 'Guatemala', 'Guyana', 'Haiti',\n",
       "       'Honduras', 'Hong Kong SAR', 'Hungary', 'Iceland',\n",
       "       'Iran (Islamic Rep of)', 'Iraq', 'Ireland', 'Israel', 'Italy',\n",
       "       'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kiribati', 'Kuwait',\n",
       "       'Kyrgyzstan', 'Latvia', 'Lithuania', 'Luxembourg', 'Macau',\n",
       "       'Malaysia', 'Maldives', 'Malta', 'Martinique', 'Mauritius',\n",
       "       'Mayotte', 'Mexico', 'Monaco', 'Mongolia', 'Montenegro',\n",
       "       'Montserrat', 'Morocco', 'Netherlands', 'Netherlands Antilles',\n",
       "       'New Zealand', 'Nicaragua', 'Norway',\n",
       "       'Occupied Palestinian Territory', 'Oman', 'Panama', 'Paraguay',\n",
       "       'Peru', 'Philippines', 'Poland', 'Portugal', 'Puerto Rico',\n",
       "       'Qatar', 'Republic of Korea', 'Republic of Moldova', 'Reunion',\n",
       "       'Rodrigues', 'Romania', 'Russian Federation',\n",
       "       'Saint Kitts and Nevis', 'Saint Lucia',\n",
       "       'Saint Pierre and Miquelon', 'Saint Vincent and Grenadines',\n",
       "       'San Marino', 'Sao Tome and Principe', 'Saudi Arabia', 'Serbia',\n",
       "       'Seychelles', 'Singapore', 'Slovakia', 'Slovenia', 'South Africa',\n",
       "       'Spain', 'Sri Lanka', 'Suriname', 'Sweden', 'Switzerland',\n",
       "       'Syrian Arab Republic', 'TFYR Macedonia', 'Tajikistan', 'Thailand',\n",
       "       'Trinidad and Tobago', 'Tunisia', 'Turkey', 'Turkmenistan',\n",
       "       'Turks and Caicos Islands', 'Ukraine', 'United Arab Emirates',\n",
       "       'United Kingdom', 'United States of America', 'Uruguay',\n",
       "       'Uzbekistan', 'Venezuela (Bolivarian Republic of)',\n",
       "       'Virgin Islands (USA)', 'Zimbabwe'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lists the different countries\n",
    "data['country'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "141"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the Number of different Countries our dataset is from\n",
    "data['country'].nunique()\n",
    "# Our dataset is from 141 different Countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995,\n",
       "       1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,\n",
       "       2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 1983, 1984,\n",
       "       2016, 1979, 1980, 1981, 1982])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The different country groups\n",
    "data['year'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace 0 values with, NA\n",
    "data['suicides_no'] = data['suicides_no'].replace(0,np.NAN)\n",
    "\n",
    "# replace Na values with, mean value\n",
    "mean_value=data['population'].mean()\n",
    "\n",
    "data['population']=data['population'].fillna(mean_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# do same for Popualation\n",
    "# replace Na values with, mean value\n",
    "mean_value=data['suicides_no'].mean()\n",
    "\n",
    "data['suicides_no']=data['suicides_no'].fillna(mean_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ---------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question 1: Which year has the most Suicides ? Which year has the least Suicides ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Year Which Has Highest suicides_no :  1994\n",
      "Year Which Has Lowest suicides_no  :  1987\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>33128</th>\n",
       "      <th>29</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>1994</td>\n",
       "      <td>1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>male</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>35-54 years</td>\n",
       "      <td>75+ years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suicides_no</th>\n",
       "      <td>22338</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>1.90442e+07</td>\n",
       "      <td>35600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          33128      29   \n",
       "country      Russian Federation    Albania\n",
       "year                       1994       1987\n",
       "sex                        male     female\n",
       "age                 35-54 years  75+ years\n",
       "suicides_no               22338          1\n",
       "population          1.90442e+07      35600"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['suicides_no'] = data['suicides_no'].replace(0,np.NAN)\n",
    "\n",
    "mean_value=data['suicides_no'].mean()\n",
    "data['suicides_no']=data['suicides_no'].fillna(mean_value)\n",
    "\n",
    "def find_minmax(x):\n",
    "     #use the function 'idmin' to find the index of lowest suicide\n",
    "    min_index = data[x].idxmin()\n",
    "    #use the function 'idmax' to find the index of Highest suicide\n",
    "    high_index = data[x].idxmax()\n",
    "    \n",
    "    high = pd.DataFrame(data.loc[high_index,:])\n",
    "    low = pd.DataFrame(data.loc[min_index,:])\n",
    "    \n",
    "    #print the Year with high and low suicide\n",
    "    print(\"Year Which Has Highest \"+ x + \" : \",data['year'][high_index])\n",
    "    print(\"Year Which Has Lowest \"+ x + \"  : \",data['year'][min_index])\n",
    "    return pd.concat([high,low],axis = 1)\n",
    "\n",
    "find_minmax('suicides_no')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# year-wise analysis of mean number sucidies of each year\n",
    "            # x             #y\n",
    "data.groupby('year')['suicides_no'].mean().plot()\n",
    "\n",
    "#setup the title and labels of the figure.\n",
    "plt.title(\"Year vs. Suicide Count\",fontsize = 14)\n",
    "plt.xlabel('Year',fontsize = 13)\n",
    "plt.ylabel('Suicide Count',fontsize = 13)\n",
    "\n",
    "#setup the figure size.\n",
    "sns.set(rc={'figure.figsize':(10,5)})\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From observing our Time Series Line Plot, we can see a sharp drop in suicides in 1985. This decrease could be due to awareness of suicide & mental health in the 80s, as well as improved recogniztion of those at risk. This is indeed accurate, as the research,\"Suicide in the elderly\" supports this claim,\n",
    "\n",
    "    \"Female suicide rates have shown a similar overall decrease, reducing by between 45 and 60% during the years 1983–1995 in the 45–84 age group.\n",
    "\n",
    "    -elderly women, however, retain the highest rates throughout the life span (1)\n",
    "    -ratio of male to female elderly suicide deaths remains approximately 3:1\n",
    "    \n",
    "     These trends in suicide rates are probably due to:\n",
    "\n",
    "    improved detection of those at risk\n",
    "    aggressive treatment of mental illness in the elderly\" (Suicide in the elderly, General Practice Notebook).\n",
    "        \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This confirms, that 1987 had the lowest sucide number, while 1994 had the highest suicide number."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ---------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question 2: Which country has the most Suicides ? Which country has the least Suicides ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Country Which Has Highest suicides_no :  Russian Federation\n",
      "Country Which Has Lowest suicides_no  :  Albania\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>29</th>\n",
       "      <th>33128</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Russian Federation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>1987</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>female</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>75+ years</td>\n",
       "      <td>35-54 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suicides_no</th>\n",
       "      <td>1</td>\n",
       "      <td>22338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>35600</td>\n",
       "      <td>1.90442e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 29                  33128\n",
       "country        Albania  Russian Federation\n",
       "year              1987                1994\n",
       "sex             female                male\n",
       "age          75+ years         35-54 years\n",
       "suicides_no          1               22338\n",
       "population       35600         1.90442e+07"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def find_minmax(x):\n",
    "     #use the function 'idmin' to find the index of lowest suicide\n",
    "    min_index = data[x].idxmin()\n",
    "    #use the function 'idmax' to find the index of Highest suicide\n",
    "    high_index = data[x].idxmax()\n",
    "    \n",
    "    high = pd.DataFrame(data.loc[high_index,:])\n",
    "    low = pd.DataFrame(data.loc[min_index,:])\n",
    "    \n",
    "    #print the country with high and low suicide\n",
    "    print(\"Country Which Has Highest \"+ x + \" : \",data['country'][high_index])\n",
    "    print(\"Country Which Has Lowest \"+ x + \"  : \",data['country'][min_index])\n",
    "    return pd.concat([low,high],axis = 1)\n",
    "\n",
    "find_minmax('suicides_no')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets make a new column to calculate the Suicide per Population Size Ratio, to better understand our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate mean of suicides_no col\n",
    "meanSuicide = data['suicides_no'].mean()\n",
    "#calculate mean of pop. col\n",
    "meanPop = data['population'].mean()\n",
    "\n",
    "#NOTE: You may replace NA values with mean, OR Drop them, I showed both\n",
    "    \n",
    "# drops any Na rows\n",
    "data = data.dropna()    \n",
    "    \n",
    "# Replace 0 or NaN suicides_no, with the mean Suicide    \n",
    "data['suicides_no'] = data['suicides_no'].replace(np.NAN,meanSuicide)\n",
    "\n",
    "# Replace 0 or NaN populations, with the mean Populations\n",
    "data['population'] = data['population'].replace(np.NAN,meanPop)\n",
    "data['population'] = data['population'].replace(0,meanPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>suicides_no</th>\n",
       "      <th>population</th>\n",
       "      <th>suicide_per_pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43773</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1990</td>\n",
       "      <td>male</td>\n",
       "      <td>5-14 years</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.456536e+06</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43774</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1990</td>\n",
       "      <td>male</td>\n",
       "      <td>55-74 years</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.456536e+06</td>\n",
       "      <td>0.000051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43775</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1990</td>\n",
       "      <td>male</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.456536e+06</td>\n",
       "      <td>0.000009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country  year   sex          age  suicides_no    population  \\\n",
       "43773  Zimbabwe  1990  male   5-14 years          6.0  1.456536e+06   \n",
       "43774  Zimbabwe  1990  male  55-74 years         74.0  1.456536e+06   \n",
       "43775  Zimbabwe  1990  male    75+ years         13.0  1.456536e+06   \n",
       "\n",
       "       suicide_per_pop  \n",
       "43773         0.000004  \n",
       "43774         0.000051  \n",
       "43775         0.000009  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# peform operation\n",
    "data['suicide_per_pop'] = data['suicides_no']/data['population']\n",
    "\n",
    "        # another way of peforming the operation from above:\n",
    "# data['suicide_per_pop'] = data.apply(lambda row: row.suicides_no / row.population, axis = 1) \n",
    "\n",
    "data.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Country Which Has Highest suicide_per_pop :  Rodrigues\n",
      "Country Which Has Lowest suicide_per_pop  :  Egypt\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>12993</th>\n",
       "      <th>32351</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>Rodrigues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>2005</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>male</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>5-14 years</td>\n",
       "      <td>75+ years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suicides_no</th>\n",
       "      <td>1</td>\n",
       "      <td>249.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>9.54309e+06</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suicide_per_pop</th>\n",
       "      <td>1.04788e-07</td>\n",
       "      <td>0.9618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       12993      32351\n",
       "country                Egypt  Rodrigues\n",
       "year                    2005       2004\n",
       "sex                     male       male\n",
       "age               5-14 years  75+ years\n",
       "suicides_no                1    249.106\n",
       "population       9.54309e+06        259\n",
       "suicide_per_pop  1.04788e-07     0.9618"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_minmax('suicide_per_pop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# year-wise analysis of mean number sucidies of each year\n",
    "            # x             #y\n",
    "data.groupby('country')['suicides_no'].mean().plot()\n",
    "\n",
    "#info = pd.DataFrame(data['country'].sort_values(ascending = False))\n",
    "\n",
    "#setup the title and labels of the figure.\n",
    "plt.title(\"Country Vs. Suicide Count\",fontsize = 14)\n",
    "plt.xlabel('Country',fontsize = 13)\n",
    "plt.ylabel('Suicide Count',fontsize = 13)\n",
    "\n",
    "#setup the figure size.\n",
    "sns.set(rc={'figure.figsize':(10,5)})\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both the graph & find_minmax funciton above, confirm that Albania had the lowest suicide count, while Zimbabwe & Russian Federation, \n",
    "had the largest suicide count. A reason the Russian Federations may have a large suicide count may be that they have a very large population (144.5 million, while Albania only has about 3 million). It has been reported that Russian levels of alcohol consumption plays an immense role in it's large suicide count, but their is a lack of data to support this due to Soviet secrecy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From looking at the ratio's above, Rodrigues has the highest suicide per population ratio. \n",
    "While Egypt has the smallest ratio of Suicides per Population. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ---------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question 3: Are certain age groups more inclined to suicide?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = data.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>suicides_no</th>\n",
       "      <th>population</th>\n",
       "      <th>suicide_per_pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4664</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>2009</td>\n",
       "      <td>male</td>\n",
       "      <td>35-54 years</td>\n",
       "      <td>903.0</td>\n",
       "      <td>1390214.0</td>\n",
       "      <td>0.000650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7977</th>\n",
       "      <td>Canada</td>\n",
       "      <td>2003</td>\n",
       "      <td>male</td>\n",
       "      <td>5-14 years</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2087408.0</td>\n",
       "      <td>0.000009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41051</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>1986</td>\n",
       "      <td>male</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>513.0</td>\n",
       "      <td>634300.0</td>\n",
       "      <td>0.000809</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year   sex          age  suicides_no  population  \\\n",
       "4664   Belarus  2009  male  35-54 years        903.0   1390214.0   \n",
       "7977    Canada  2003  male   5-14 years         19.0   2087408.0   \n",
       "41051  Ukraine  1986  male    75+ years        513.0    634300.0   \n",
       "\n",
       "       suicide_per_pop  \n",
       "4664          0.000650  \n",
       "7977          0.000009  \n",
       "41051         0.000809  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43773     5\n",
       "43774    55\n",
       "43775    75\n",
       "Name: AgeNum, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # grabs first 2 chars from Age Column\n",
    "data['AgeNum'] = data['age'].str[:2]\n",
    "\n",
    "# remove all instances of dash -\n",
    "data['AgeNum'] = data['AgeNum'].map(lambda x: x.replace('-',''))\n",
    "\n",
    "# now, convert it to type int (not Object)\n",
    "data['AgeNum'] = data['AgeNum'].astype(int)\n",
    "\n",
    "data['AgeNum'].tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43773            Adolescent\n",
       "43774    Middle_Aged_Adults\n",
       "43775               Elderly\n",
       "Name: AgeCategory, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creates Age Categories\n",
    "def AgeGroup(x):\n",
    "    if(x >= 60):\n",
    "        return \"Elderly\"\n",
    "    elif(x >= 30):\n",
    "        return \"Middle_Aged_Adults\"\n",
    "    elif(x >= 18):\n",
    "        return \"Adults\"\n",
    "    else:\n",
    "        return \"Adolescent\"\n",
    "# Map each row in the Col to the AgeGroup Method\n",
    "data['AgeCategory'] = data['AgeNum'].map(lambda x: AgeGroup(x))\n",
    "# convert it back to type String\n",
    "data['AgeCategory'] = data['AgeCategory'].astype(str)\n",
    "data['AgeCategory'].tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43773     5\n",
       "43774    55\n",
       "43775    75\n",
       "Name: AgeNum, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['AgeNum'] .tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>suicides_no</th>\n",
       "      <th>population</th>\n",
       "      <th>suicide_per_pop</th>\n",
       "      <th>AgeNum</th>\n",
       "      <th>AgeCategory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1985</td>\n",
       "      <td>female</td>\n",
       "      <td>15-24 years</td>\n",
       "      <td>249.106328</td>\n",
       "      <td>277900.0</td>\n",
       "      <td>0.000896</td>\n",
       "      <td>15</td>\n",
       "      <td>Adolescent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1985</td>\n",
       "      <td>female</td>\n",
       "      <td>25-34 years</td>\n",
       "      <td>249.106328</td>\n",
       "      <td>246800.0</td>\n",
       "      <td>0.001009</td>\n",
       "      <td>25</td>\n",
       "      <td>Adults</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1985</td>\n",
       "      <td>female</td>\n",
       "      <td>35-54 years</td>\n",
       "      <td>249.106328</td>\n",
       "      <td>267500.0</td>\n",
       "      <td>0.000931</td>\n",
       "      <td>35</td>\n",
       "      <td>Middle_Aged_Adults</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  year     sex          age  suicides_no  population  \\\n",
       "0  Albania  1985  female  15-24 years   249.106328    277900.0   \n",
       "1  Albania  1985  female  25-34 years   249.106328    246800.0   \n",
       "2  Albania  1985  female  35-54 years   249.106328    267500.0   \n",
       "\n",
       "   suicide_per_pop  AgeNum         AgeCategory  \n",
       "0         0.000896      15          Adolescent  \n",
       "1         0.001009      25              Adults  \n",
       "2         0.000931      35  Middle_Aged_Adults  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(5.960000000000001, 0.5, 'Suicide Count')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"AgeCategory\", y=\"suicides_no\",palette=\"ch:.25\", kind=\"bar\",data=data);\n",
    "\n",
    "plt.title('Age vs. Suicide Count',size=25)\n",
    "plt.xlabel('Age Category',size=20)\n",
    "plt.ylabel('Suicide Count',size=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data illustrates that middle aged adults, between the ages of 30 through 60, have the highest suicide count. While elderly and adolescents have about half the amount as  middle aged adults."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ---------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question 4: What is the relationship between the gender and the number of suicides? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "female    21888\n",
       "male      21888\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# there is an equal number of Males & Females in our data\n",
    "data['sex'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: Hue, allows us to add another dimensionality to our analysis!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    \"In 2017, men died by suicide 3.54 times more often than women\"(SMM Health)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdsAAAFcCAYAAACAzxRUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3df3zN9f//8dvZdnb8Du+2iCU/CllRy49RG6oZs7DQmkze9S7e75C8V8xCWRGTkne/vRPvvDWMMQyhJQwpaVqSN2PFbH7Psh9n5/uHr/NpGZ2x18523K+XS5fjvM7r9Xo+Xsdy3/P14/k02Ww2GyIiImIYN2cXICIi4uoUtiIiIgZT2IqIiBhMYSsiImIwha2IiIjBqnzY2mw28vPz0U3VIiJSWVX5sC0oKCAtLY2CggJnlyIiIlKqKh+2IiIilZ3CVkRExGAKWxEREYMpbEVERAxmaNhu2LCBsLAwevbsSWxsLABbtmwhNDSUoKAgZs6caV83PT2dsLAwevTowfjx4ykqKjKyNBERkQpjWNgePnyYiRMn8s4777B8+XJ++OEHUlJSiI6O5p133mHVqlWkpaWRkpICQFRUFBMmTGDNmjXYbDbi4+ONKk1ERKRCGRa269ato1evXjRo0ACz2czMmTOpXr06TZo0wcfHBw8PD0JDQ0lOTuaXX37h/PnztGvXDoCwsDCSk5ONKk1ERKRCeRi144yMDMxmM8OGDePIkSN07dqV2267DS8vL/s63t7eZGVlcezYsRLLvby8yMrKKlN7aWlp5Va7iAiAn5+fs0sQF2FY2FqtVr7++mvmz59PjRo1GD58ONWqVcNkMtnXsdlsmEwmiouLS11eFr6+vlgslnKrX0REpLwYFrY33ngj/v7+1K9fH4AHH3yQ5ORk3N3d7etkZ2fj7e1NgwYNyM7Oti/PycnB29vbqNJEREQqlGHXbLt168ZXX33FmTNnsFqtbNq0ieDgYA4cOEBGRgZWq5WkpCQCAgJo1KgRFouFnTt3ApCYmEhAQIBRpYmIiFQow3q2bdu25amnniIiIoLCwkK6dOnCY489RrNmzRgxYgT5+fkEBgYSHBwMQFxcHDExMeTm5tKmTRsiIyONKk1ERKRCmWxVfLqc/Px80tLSdM1WpIKlpqYSHx/PwIED6dSpk7PLEanUDOvZiohrmzt3Lvv27SMvL09hK/InNFyjiFyVvLy8Eq8icnkKWxEREYMpbEVERAymsBURETGYwlZERMRgClsRERGDKWxFREQMprAVERExmMJWRETEYApbERERgylsRUREDKawFRERMZjCVkRExGAKWxEREYMpbEVERAymsBURETGYwlZEKCgqdOn2RJzNw9kFiIjzeXqYeeLjUWXaJutMtv21rNvOHfpWmdYXqerUsxURETGYwlZERMRgClsRERGDKWxFREQMprAVERExmMJWRETEYApbERERgylsRUREDKawFRERMZjCVkRExGAKWxEREYMpbEVERAymsBURETGYwlZERMRgClsRERGDKWxF5KqYzG4lXkXk8vR/iYhclRvuugnLTTW54a6bnF2KSKXn4ewCRKRqqt64NtUb13Z2GSJVgnq2IiIiBlPYioiIGExhKyIiYjBDr9kOHjyYEydO4OFxoZlXXnmFQ4cO8e6771JUVMSQIUMYNGgQAFu2bGHKlCnk5+fTs2dPRo8ebWRpIiIiFcawsLXZbBw8eJCNGzfawzYrK4vRo0eTkJCAp6cn4eHhdOzYkcaNGxMdHc38+fNp2LAhzzzzDCkpKQQGBhpVnoiISIUxLGz/97//AfDXv/6VU6dOMXDgQGrWrEmnTp2oW7cuAD169CA5OZkOHTrQpEkTfHx8AAgNDSU5OVlhKyIiLsGwsD1z5gz+/v689NJLFBYWEhkZSc+ePfHy8rKv4+3tze7duzl27Ngly7OyssrUXlpaWrnVLnK98fPzq/A2d+7cWeFtlpUzvhdxTYaF7d13383dd99tf9+/f3+mTJnC8OHD7ctsNhsmk4ni4mJMJtMly8vC19cXi8Vy7YWLSIVQkMn1xLC7kb/++mu2bt1qf2+z2WjUqBHZ2dn2ZdnZ2Xh7e9OgQYNSl4uIiLgCw8L27NmzTJs2jfz8fHJzc1m6dCnTp09n69atnDhxgt9++421a9cSEBBA27ZtOXDgABkZGVitVpKSkggICDCqNBERkQpl2Gnkbt268d1339G3b1+Ki4uJiIjAz8+P0aNHExkZSWFhIf379+euu+4CYOrUqYwYMYL8/HwCAwMJDg42qjQREZEKZbLZbDZnF3Et8vPzSUtL0zVbkWv0xMejKqytuUPfqrC2RCoDjSAlIiJiMIWtiIiIwRS2IiIiBlPYioiIGExhKyIiYjCFrYiIiMEUtiIiIgZT2IqIiBhMYSsiImIwha2IiIjBFLYiIiIGU9iKiIgYTGErIiJiMIWtiIiIwRS2IiIiBlPYioiIGExhKyIiYjCFrYiIiMEUtiIiIgZT2IqIiBhMYSsiImIwha2IiIjBFLYiIiIGU9iKiIgYTGErIiJiMIWtiIiIwRS2IiIiBlPYioiIGExhKyIiYjCFrYiIiMEUtiIiIgZT2IqIiBhMYSsiImIwha2IiIjBFLYiIiIGU9iKiIgYTGErIiJiMIWtiIiIwRS2IiIiBjM8bF9//XXGjh0LQHp6OmFhYfTo0YPx48dTVFQEwK+//sqgQYMIDg5m+PDhnDt3zuiyREREKoyhYbt161aWLl1qfx8VFcWECRNYs2YNNpuN+Ph4AF5++WUiIiJITk7G19eXd955x8iyREREKpRhYXvq1ClmzpzJsGHDAPjll184f/487dq1AyAsLIzk5GQKCwvZsWMHPXr0KLFcRETEVRgWthMmTGD06NHUqVMHgGPHjuHl5WX/3MvLi6ysLE6ePEmtWrXw8PAosVxERMRVeBix00WLFtGwYUP8/f1JSEgAoLi4GJPJZF/HZrNhMpnsr7/3x/eOSEtLu7aiRa5jfn5+Fd7mzp07K7zNsnLG9yKuyZCwXbVqFdnZ2fTp04fTp0+Tl5eHyWQiOzvbvk5OTg7e3t7Ur1+fs2fPYrVacXd3Jzs7G29v7zK36evri8ViKc/DEBEDKcjkemLIaeSPP/6YpKQkEhMTGTlyJN27d2fKlClYLBb7b7OJiYkEBARgNpu59957WbVqFQDLli0jICDAiLJEREScokKfs42Li2PKlCkEBweTl5dHZGQkABMnTiQ+Pp5evXrx9ddf89xzz1VkWSIiIoYy2Ww2m7OLuBb5+fmkpaXpNLLINXri41EV1tbcoW9VWFsilYFGkBIRETGYwlZERMRgClsRERGDKWxFREQMprAVERExmMJWRETEYApbEReRmprK888/T2pqqrNLEZE/MGS4RhGpeHPnzmXfvn3k5eXRqVMnZ5cjIr+jnq2Ii8jLyyvxKiKVh8JWRETEYA6dRi4uLmbOnDl8+eWXFBUV0aVLF4YNG2afg1ZEREQuz6Ge7YwZM0hNTWXIkCEMHTqUb7/9lmnTphldm4iIiEtwqGu6adMmlixZgtlsBqBr1648/PDDREdHG1qciIiIK3CoZ2uz2exBC+Dp6VnivYiIiFyeQ2HbqlUrXnvtNQ4dOsThw4eZMmUKt99+u9G1iYiIuASHwnbixImcPn2a8PBwBgwYwPHjx3nppZeMrk1ERMQlOHTNtlatWrz++utG1yIiIuKSHArbb7/9ljfeeIPTp09js9nsy1esWGFYYSIiIq7CobCdMGECYWFh3HHHHZhMJqNrEhERcSkOha2HhwdDhw41uhYRERGX5NANUrfddht79+41uhYR+f+KiwqdXYJUUfHx8bRr147c3Nyr2v7YsWOMHDmSPn360KtXL958880Slw//6OzZs4wdO/Zqy71uONSzPXz4MI888gg333wzFovFvlzXbEWM4eZhZue0p8q0Tf7JLPtrWbf1e+GjMq0vldeKFSvo1KkTycnJ9O/fv0zbFhYWMmzYMMaMGUOXLl0oKChg5MiRLFiwgEGDBpW6zenTp/npp5/Ko3SX5lDYjh49+rKfHTx4kFtvvbW86hERkauUlZVlHwth9uzZ9O/fnxMnTjBq1ChOnz7NnXfeyaFDh5g/fz5ff/0106ZNIz8/n7Zt2zJx4kS2bNlCkyZN6NKlC3BhAKPx48eTk5MDXJjGcdWqVZw8eZK77rqLuLg4ZsyYwf79+4mNjeXFF19k8uTJpKWl4ebmxrhx4/Dz8+Pbb79lwoQJeHh4cOutt9KsWTNGjBjB3LlzWbRoEW5ubvz1r3+lX79+jB07luPHj3Po0CHuuecefH19GTRoEEVFRYSEhJCUlFQlB1Vy6DRyhw4dSv0PrhzEIiJScVatWkXXrl1p3749+/fv5/Dhw8yePZugoCCWL19OvXr1ACgoKGD69Ol89NFHJCYm4unpybJly9i3b98lAxb5+Phw9913c+bMGXbs2MHChQtZu3YtmZmZpKWlMWbMGJo3b05MTAzx8fE0bdqUhIQEZs+ebR+PYcKECUybNo34+Hiysi6cgdmzZw8rVqxgyZIl/Pe//+WDDz7g8OHDwIVLl2vWrGHo0KGsXr0agK1bt9KhQ4cqGbRQDpPHX+lcvoiIVJykpCSef/55PDw86NatG0uXLmXHjh0MHz4cgN69e/Pdd99x4MABfv75ZwYPHgxAfn4+1apVo169epedza1OnTpER0fz2Wef2YP8t99+swc4QGpqKj/99BPLli0DIDc3l5ycHAoLC2ndujUAwcHBnD59mm+++YaHHnqIatWqARAQEMA333wDgK+vLwC33347Z8+eJSsri1WrVvHwww8b8K1VjGsOWz0KJCLifBkZGaSnpxMTE4PJZOL8+fNUq1aNWrVq2TtFF/+9Li4upk2bNsybNw+4EIomk4ldu3aRkJBQYr979uxh5cqVRERE8OSTT/Lkk0/St29fMjMzL+lsWa1WYmNjad++PXDhtLbZbC6x3sUarFZriW1tNpt9WfXq1e3LQ0JCSE5OZvfu3bz66qvX/D05iyaPFxFxAStWrGDAgAFs3LiRDRs22Ocft9lsrFu3DsD+2rRpUw4ePMj+/fux2WxER0ezatUqOnbsyL59+9i8eTMAv/32G2+88QY333wze/bs4a677mLgwIHUq1ePXbt2UVxcjIeHhz0k77nnHhYvXgzAjz/+yGOPPUbt2rWpVq0ae/fuxWq1smHDBkwmE35+fqxdu5bz58+Tm5tLSkoKbdu2veS4QkJCmDNnDv7+/ri5Vd3I0uzvIiIuYOXKlSWG1fXw8ODRRx9l9+7dfP755yxevJhbbrkFi8VCtWrVmDJlCmPGjKGgoIC2bdsSFhaGu7s7//rXv5g0aRKvv/46BQUF9O7dm8cff5zc3Fzmz59PaGgoZrOZtm3bcuTIEe69917MZjPjxo1j4sSJTJo0id69e2MymZg2bRpubm5MnjyZF198ETc3N+rVq4enpyd33nknISEh9O/fH6vVypAhQ2jevPklx9WoUSNuvPFGevfuXZFfZ7lT2IqIuICLNxL93j/+8Q+WLVuGr68vLVq0YP78+WRkZADQpUsX+13Hv+fj48OcOXMuWV6rVi3+85//lNr2xd4swNSpUy/5fNOmTfz3v/+levXqjBgxgqZNmwLYT0v/3u+3t9lsHDt2DJvNRrt27Uptu6q45rDVYz8iIpWXj4+P/amR+vXrExcXV+E11KtXjz59+mA2m7nnnnvo3r27Q9tt3bqV559/nilTphhcofEcCtv9+/fzzTff0L9/f0aPHk1aWhqxsbF06tSJmTNnGl2jiIhcJT8/P6cPQBQREUFERESZt+vcuTOpqakGVFTxHJ7P1mKx8MUXX5CVlcWrr76qkBUREXGQQ2Gbn5/Pww8/zFdffUXPnj3p2LEjhYUau1VERMQRDoVtQUEBOTk5fPHFF3Tu3JmcnBzy8/ONrk1ERMQlOBS2jz76KN26dcPPz48WLVrQv39/hgwZYnRtIiIiLsGhG6QiIiIIDw+3P1C8dOnSEkN0iYjzWTzcSryKSOXh0P+V586dIzY2liFDhnDq1ClmzpzJuXPnjK5NRMogqEU9mtWrRlAL/SJspIJC65+vVAH7/emnn2jZsiVr1qwp9fNt27bZxz6+nLFjx14yPGNFmjVrFl9//bXT2q9IDvVsY2Nj8fb25vjx41gsFnJzc5kwYQIzZswwuj4RcVBrrxq09qrh7DJcnqfZnYgXPi33/S6YVvp8sZezZMkSgoOD+eyzz+jRo0e511MRduzYQceOHZ1dRoVwqGebnp7O6NGj8fDwoHr16sTFxZGenm50bSIiUorCwkJWrFjBc889x549ezh06BAAX331FSEhIYSFhREfH29f/8CBAwwePJjQ0FD7EI5/tGzZMvr160efPn2Ijo4mPz+fwsJCoqKi6Nu3L3379rXv85dffiEyMpLevXvTv39/fvzxx8vuA+C+++5j8uTJ9O3bl0ceeYTDhw+zbNky0tLSiImJYe/evUZ/ZU7nUNj+cfBnq9VapQeEFhGpylJSUrj55ptp2rQpDz74IJ999hkFBQWMHTuWWbNmkZCQYJ+6DiAqKorBgwezYsUKxo0bx6hRoygoKLB/vm/fPuLj41m4cCGJiYn85S9/Yc6cOXz77becPn2aZcuW8f7779tP+b788sv06NGDpKQkRowYwbvvvnvZfQBkZ2fj7+/PsmXLaN++PZ9++il9+/bF19eX2NhYWrZsWbFfoBM4lJjt27dn+vTpnD9/nk2bNjFixAiHuv5vvfUWvXr1IiQkhI8//hiALVu2EBoaSlBQUImBMdLT0wkLC6NHjx6MHz+eoqKiqzwkERHXtmTJEvvA/L169SIhIYEff/wRb29v+2D+/fr1Ay7cc3Po0CGCgoIAaNeuHTfccAP/+9//7Pvbtm0bGRkZDBw4kD59+rB+/Xr+97//cdttt3HgwAGefPJJkpOTeeGFF4ALp3/79OkDQGBgIG+99dZl93HR/fffD1yYGP706dMGf0OVj0PXbP/5z3/ywQcfULt2bWbOnMn999/P3//+9ytus337dlJTU1m+fDlFRUX06tULf39/oqOjmT9/Pg0bNuSZZ54hJSWFwMBAoqKiiI2NpV27dkRHRxMfH39Vw3uJiLiy48ePs2nTJvbs2cO8efOw2WycOXOGzZs3l5g31t3dHeCSOWcvLvv9fLJWq5WePXsSExMDXAhoq9VKnTp1WLlyJZs3byYlJYV+/fqxcuXKEhPM22w29u/ff9l9XGSxWIAL89mWVpOrc6hnazab+cc//sGiRYtISEhg9OjR9i/ucjp06MC8efPw8PDg+PHjWK1Wzpw5Q5MmTfDx8cHDw4PQ0FCSk5P55ZdfOH/+vH1Wh7CwMJKTk6/96EREXExiYiKdOnXiyy+/ZMOGDWzcuJFhw4aRkpJCTk6O/frpypUrgQuz9TRu3Ji1a9cCsGvXLnJycrjtttvs++zYsSPr1q3j+PHj2Gw2Jk2axCeffML69euJioqia9euxMTEUKNGDfu0ehf3v2XLFl566aXL7uNK3N3dL5lE3lVdsWc7ePBgTCbTZT+fN2/eFXduNpuZNWsW//73vwkODubYsWN4eXnZP/f29iYrK+uS5V5eXmRlZTl6DACkpaWVaX2RyszPz8/ZJRhu586dzi7hT5X291BQaC3zncOOKCi04ml2/9P1li5dap/F56JBgwbx0Ucf8dFHHxEVFYWHhwd33HGH/fPp06czadIk3n77bcxmM2+//Taenp72z1u1asWzzz7LkCFDKC4upnXr1jz99NO4ubmxdu1aQkJCsFgsPPzww7Rs2ZIJEyYQExPDggULqF69OrGxsbRo0aLUfVzJ/fffz8SJE3n99de55557yviNVS0m2xX68xef31q3bh25ubk88sgjuLu7k5iYSJ06dZg8ebJDjfz2228MGzaM9u3bk5GRwfTp0wHYvHkz//73v/n73//OjBkzWLBgAQAHDx5k2LBhDvVu8/PzSUtLw9fX90972yJVyc5pT1VYW34vfMQTH4+qsPbmDn2rwtoSqQyu2LO9+OzWnDlzWLhwof0O5K5du/Loo49eccf79++noKCA1q1bU716dYKCgkhOTrZfR4ALd6h5e3vToEEDsrOz7ctzcnLw9va+6oMSERGpTBy6Znvy5MkSEw+cO3fuT+8my8zMJCYmhoKCAgoKCli/fj3h4eEcOHCAjIwMrFYrSUlJBAQE0KhRIywWi/20UmJiIgEBAddwWCIiIpWHQ3cj9+7dm4EDB/LQQw9hs9lITk5m4MCBV9wmMDCQ3bt307dvX9zd3QkKCiIkJIT69eszYsQI8vPzCQwMJDg4GIC4uDhiYmLIzc2lTZs2REZGXvvRiYiIVAJXvGb7e+vXr2fr1q3AhYvagYGBhhbmKF2zFVela7YiruOKPdv9+/fTvHlz9uzZQ4MGDewPSQPs2bOHNm3aGF6giIhIVXfFsJ02bRrvv/8+I0aMuOQzk8nE+vXrDStMRETEVVzxBqn3338fgA0bNrB8+XI2bNjA6tWrWbRokYJWRK5LxUWFlWK/5T3F3p+tK9fGoRukVq1axZtvvsnatWv59ddfiYiI4NVXX6V79+5G1yciUqm4eZgNuZ7u98JHZVq/vKfY2759+zXvQy7PoUd/3nvvPftoUU2bNiUhIYG3337b0MJERKR0ZZ1ib/DgwWzbtg248FjmHztKsbGxAAwYMOCy0+rJtXEobIuLi2nQoIH9fcOGDSkuLjasKBERubyyTrH3Zy5OHrBo0aLLTqsn18ahsK1fvz4LFy6kqKgIq9XK4sWLufHGG42uTURESlGWKfbK6nLT6sm1ceia7csvv8yYMWN45ZVXMJlMtGnThri4OKNrExGRPyjrFHsXXfzsz+YKr1evXqnT6tWpU6f8D+Y64lDYXrxOe/r0adzd3alVq5bRdYmISCkuTrH30Uf/d0PV22+/XWKKvVatWtmnwIMLAfrzzz/TqVMnPv/881L36+7uTlFRESkpKSxfvpw333yT+++/n61bt3LkyBGF7TW6Yth++OGH/O1vf2Py5MmlTrV38Ty/iIhUjKuZYu+pp55i7NixLFmyhAceeKDU/T7wwAP06dOHzz77rNRp9eTaXHG4xoULFxIeHs7s2bNL/fzZZ581rDBHabhGcVUarrFyKi4qxM3DXGX2K5XDFXu24eHhQOUIVRGRysCoQFTQujaHrtmGhoaWunzFihXlWoyIiIgrcihsX3rpJfufCwsLWblyJT4+PoYVJSIi4kocCtsOHTqUeN+5c2fCw8MZPny4IUWJiIi4EocGtfijkydPcuzYsfKuRURExCVd1TXbX3/9lUcffdSQgkRERFyNQ2E7fvx4Dh8+TJMmTdi+fTsmk4nIyEijaxMREXEJDp1GXrlyJd9//719jOTMzEyio6ONrk1EpNIpMGg+W0f3m5mZScuWLZkwYUKJ5enp6bRs2ZKEhAT69OlT6rbdu3cnMzPzkuUXZwVyZA7cPzN//nx8fX3Jzs6+pv38Xlnqmjp1Kp06daKgoOCy6/x+Ht/LuTiQx+7du5k+fbrjxV6GQz3bPXv2sHjxYj744AP69evHmDFjCAsLu+bGRUSqGk8PsyEDgJRloI+6deuyadMmrFarfQzkVatWUb9+feDCkI7OkpCQwAMPPMCSJUsYNmxYhbZdVFTE6tWrufvuu1mzZs1lH1sti59//pnjx49f834cClubzYabmxubN2+2f3nnz5+/5sZFRKTsatasSatWrdixYwedOnUCYPPmzXTu3Bm40Cvbu3cvp06dIioqiqNHj9K8eXPy8/MBKCgoYPz48aSlpdGoUSNOnjx5SRsZGRlMmjSJU6dOUa1aNV566aUSQ0CW5scff+T06dP87W9/Y+TIkTz99NO4uV04gTpv3jz+85//ULt2bZo1a8Ytt9zCiBEj+PLLL5k1axZFRUU0btyYyZMnU69ePb766iumTJmCxWKhadOmDn0vX3zxBbfccgt9+/Zl3rx59rC12WxMnTqVL774Am9vb6xWKx06dCAzM5PIyEg2bNgAYJ+nfcSIEQCcOXOGWbNmkZeXx7vvvku3bt2YMGECRUVFWCwWpkyZwq233upQbQ6dRr7lllv429/+RmZmJh06dGDMmDG0atXKoQZERKT89ezZkzVr1gAXTnW2bNkSs7nkKFSzZs3ijjvuYMWKFQwaNIicnBzgwqlegNWrVxMTE2OffP73XnzxRaKioli6dCmTJ0++ZDzm0ixZsoTg4GB8fX3x8PBg06ZNwIUQ/vTTT0lISGDBggVkZGQAcOLECWbMmMGcOXNYtmwZ9913H3FxcVc9N29CQgLBwcEEBgaSnp7Ozz//DMCaNWv44YcfSEpK4q233ir1eEtTp04dRo4cSffu3Rk+fDiffPIJQ4cOJSEhgYEDB7Jr1y6H9gMOhu2UKVPo3bs38+fPx2w2c++99/Lqq6863IiIiJSv7t278+WXX1JcXMzq1avp2bPnJets376dXr16AdC+fXv7YETbt2+3r3/rrbdy9913l9ju3LlzpKWlMW7cOPr06cOYMWPIy8srtQd8UWFhIStWrLDPs9uzZ08WLlwIwNatW+nWrRu1atXCYrEQEhICwHfffceRI0eIjIykT58+fPrpp2RkZLB3794yz817/PhxNm/eTM+ePalWrRrdunWzt799+3aCgoIwm83Ur1+fgICAP91faQIDA5k8eTLR0dHUrl27TKepHTqNXKNGjRIX3B977LGyVykiIuXm4qnknTt3kpqaypgxY1i1alWJdUwmU6lz3P5xuYdHySgoLi7G09OzxLXfo0ePUrdu3cvWs3HjRs6ePWsfS7+wsJDjx49z9OhR3NzcKC4uvmQbq9XKPffcw3vvvQdcmFjm3Llz/Prrr1ecm7c0y5cvx2az0b9/f+DCpc7CwkL++c9/XvZ4/7i8qKjoku/i94KDg7n77rvZuHEjc+fO5YsvviA2NvZPa4OrHNRCREScr2fPnsyYMcN+2vaP/P397YG5e/du++lTf39/VqxYQXFxMb/88gvffPNNie1q167Nrbfeat928+bNDBo06GPewOEAABMQSURBVIq1JCQkMGrUKDZs2MCGDRvYtGkTfn5+LFq0CH9/f1JSUsjNzaWgoIC1a9diMplo27Ytu3bt4sCBAwC88847TJs2jZYtW9rn5gVKzM17pfanTp1qb/+rr77ihhtuYNWqVfj7+7N69WoKCgo4ffq0/fR2nTp1OHXqFCdOnKCgoMC+/PcuzvML8Nxzz/H9998THh7OqFGj+OGHH/60rosc6tmKiMgFBUWFhkwRWFBUiGcZZ/7p1q0b48ePZ9So0u+OHjlyJGPHjiUkJIRmzZrZTyNHRESwb98+evbsSaNGjbj99tsv2Xb69OlMmjSJjz76CLPZzMyZM0ud1xwgJyeHbdu28dprr5VYPnToUCZNmsTf//53IiMjefTRR6lRowb16tXDYrHg5eXFa6+9xnPPPUdxcTE33XQT06dPx2w288Ybb5Q6N29pvv/+e06ePMlDDz1kX+bm5saQIUNYuHAh8fHxfP/99/Tu3Zsbb7zRfnq6du3aPPXUU/Tv358GDRpw5513XrLvu+66i9mzZxMXF8ewYcMYP348//rXvzCbzUyaNOmKdf3eFeezrQo0n624Ks1nK67iwIEDpKSk8MQTTwAwfPhwBgwYQPfu3Z1bWAVSz1ZERBwyd+5cli5deslyb29vPvzww8tu16hRI3vP0mQycd9999GtW7cyt//666+zZcuWS5b7+vpW+pt2FbZSQmpqKvHx8QwcOND+/J6ICMATTzxh752WhaenJzNmzLjm9l988cVr3oezKGylhLlz57Jv3z7y8vIUtiIi5UR3I0sJeXl5JV5FROTaKWxFREQMprAVERExmK7ZioiUgbWgEHfPsj0PW577zczMJDg42P6s6EUDBw7klVdeYe/evZds0717d+bNm0fjxo0dquWPA/LLtVPYurCreUi+KrUn4gzunmZWRQ4t9/32mvexw+t6e3uXOo3eK6+8Up4lSTlS2Lqwq5l3M+tMtv21rNtqoAIxkh5Lc9zlptazWq1MmzaN7du3Y7VaCQsL44knnmDbtm1Mnz6d4uJibrvtNnsPeNGiRaSmptof23n77bexWCw8/fTTTju2qkphKyJVgh5L+z/Hjh0rMTkMwLRp0+x/vji13ocffsiOHTtYvXo1APHx8QAsXbqUgoICnnzySXx9fQE4ePAgGzdupHbt2vbTyL169WLmzJnk5uZSq1YtkpKSmDdvXkUcostR2IpIlaDH0v7P5U4jX7R9+3Z7b/T3U+tt3bqV9PR0UlNTgQvf5d69e2nRogVNmzaldu3aJfZTs2ZNAgMDWbduHT4+Pvj4+HDTTTcZdFSuTWErIuJiLje1ntVqJSoqiqCgIODC5O01a9Zk165dl52g/ZFHHuHdd9+lcePGhIWFGV+8izL00Z/Zs2cTEhJCSEiI/RTHli1bCA0NJSgoiJkzZ9rXTU9PJywsjB49ejB+/Hj7lEZSsUxmtxKvIlL1XG5qvU6dOhEfH09hYSHnzp0jIiKCXbt2XXFf9957L0ePHmXbtm08+OCDhtfuqgzr2W7ZsoWvvvqKpUuXYjKZeOqpp0hKSiIuLo758+fTsGFDnnnmGVJSUggMDCQqKorY2FjatWtHdHQ08fHxREREGFWeXMYNd93E2fQcare+0dmliMhllHbNtn379vY/X25qvfDwcDIyMujXrx9FRUWEhYXRsWNHtm3bdsX2HnroIU6dOoWnp2f5H8x1wrCw9fLyYuzYsfa/nObNm3Pw4EGaNGli/4sPDQ0lOTmZFi1acP78edq1awdAWFgYs2bNUtg6QfXGtaneuPafryhyDYx6VrUi2rMWFJbpMZ2y7NeRGhs3bkxaWlqpn8XExABQq1YtZs+efcV1fq9jx4507NjR/v7i87U2m43CwkJ27NhBdHT0n9Yml2dY2N522232Px88eJDVq1fz+OOP4+XlZV/u7e1NVlYWx44dK7Hcy8uLrKwso0oTESe7mmdV845m2V/Lum15hqNRvyRU5C8fjsrOziYkJIQBAwbQpk0bZ5dTpRl+g9S+fft45plneOGFF3B3d+fgwYP2z2w2GyaTieLiYkwm0yXLy+Jyv+ldz/z8/Cq8zZ07d1Z4m67IGX93ru5qfjav978Hb29vduzY4ewyXIKhYbtz505GjhxJdHQ0ISEhbN++nezsbPvn2dnZeHt706BBgxLLc3Jy8Pb2LlNbvr6+WCyWcqtdrs71/o+TVF762RRnMuyW0yNHjvCPf/yDuLg4QkJCAGjbti0HDhwgIyMDq9VKUlISAQEBNGrUCIvFYv/NMzExkYCAAKNKExERqVCG9WznzJlDfn4+U6dOtS8LDw9n6tSpjBgxgvz8fAIDAwkODgYgLi6OmJgYcnNzadOmDZGRkUaVJlIhNLygiFxkWNjGxMSUetcbwPLlyy9Z1qpVKxYvXmxUOSIVTsMLli+zmwms//9VpIrRyAUiBtHwguWrS526+Hha6FKnrrNLESkzDdcoIlVCs2rVaVaturPLELkq6tmKiIgYTGErIiJiMIWtiIiIwRS2Ig4oKLQ6uwQRqcJ0g5SIAzzN7kS88GmZtsnJOQvA0ZyzZd52wbRBZVpfRCo39WxFREQMprAVERExmMJWnCo1NZXnn3+e1NRUZ5dS7kzu5hKvInL90jVbcSpXHtKw1s33cO7o99RscKezSxERJ1PYilO58pCGlht8sNzg4+wyRKQS0GlkERERgylsRUREDKawlXJjLSh06fZERK6WrtlKuXH3NLMqcmiZtsk7mmV/Leu2veZ9XKb1RUScRT1bERERgylsRUREDKawFacyu5lKvIqIuCKFrThVlzp18fG00KVOXWeXIiJiGN0gJU7VrFp1mlWr7uwyREQMpZ6tiIiIwRS2IiIiBlPYioiIGExhKyIiYjCFbSXnyvO9iohcL3Q3ciXnyvO9iohcL9SzreRceb5XEZHrhcJWRETEYArbClRcpCnhRESuR7pmW0apqanEx8czcODAMl9DdfMws3PaU2XaJv9klv21rNv6vfBRmdYXERFjKGzLSDcsiYhIWek0chlV9A1LFg+3Eq8iIlL1XNf/ghcUWp1dwp8KalGPZvWqEdSinrNLERGRq3Rdn0b2NLsT8cKnZdomJ+csAEdzzpZ52wXTBpVpfYDWXjVo7VWjzNuJiEjlcV33bK+Gyd1c4lVEROTPKGzLqNbN92Cu1YBaN9/j7FJERKSKuK5PI18Nyw0+WG7wcXYZIiJShahnKyIiYjCFrYiIiMEMD9vc3Fx69+5NZmYmAFu2bCE0NJSgoCBmzpxpXy89PZ2wsDB69OjB+PHjKSoqMro0ERGRCmFo2H733Xc89thjHDx4EIDz588THR3NO++8w6pVq0hLSyMlJQWAqKgoJkyYwJo1a7DZbMTHxxtZmoiISIUxNGzj4+OZOHEi3t7eAOzevZsmTZrg4+ODh4cHoaGhJCcn88svv3D+/HnatWsHQFhYGMnJyUaWJiIiUmEMvRv51VdfLfH+2LFjeHl52d97e3uTlZV1yXIvLy+ysrKMLE1ERKTCVOijP8XFxZhMJvt7m82GyWS67PKySEtLK3M9fn5+Zd5GKpedO3dWSDv6Wan6ruZnRX/vUl4qNGwbNGhAdna2/X12djbe3t6XLM/JybGfenaUr68vFoul3GqVqkH/GIqj9LMizlShj/60bduWAwcOkJGRgdVqJSkpiYCAABo1aoTFYrH/5pmYmEhAQEBFliYiImKYCu3ZWiwWpk6dyogRI8jPzycwMJDg4GAA4uLiiImJITc3lzZt2hAZGVmRpYmIiBimQsJ2w4YN9j/7+/uzfPnyS9Zp1aoVixcvrohyREREKpRGkBIRETGYwlZERMRgClsRERGDKWxFREQMprAVERExmMJWRETEYApbERERgylsRUREDKawFRERMZjCVkRExGAKWxEREYMpbEVERAymsBURETGYwlZERMRgClsRERGDKWxFREQMprAVERExmMJWRETEYApbERERgylsRUREDKawFRERMZjCVkRExGAKWxEREYMpbEVERAymsBURETGYwlZERMRgClsRERGDKWxFREQMprAVERExmMJWRETEYApbERERgylsRUREDKawFRERMZjCVkRExGAKWxEREYMpbEVERAymsBURETGYwlZERMRgClsRERGDKWxFREQMVqnCdsWKFfTq1YugoCA+/fRTZ5cjIiJSLjycXcBFWVlZzJw5k4SEBDw9PQkPD6djx460aNHC2aWJiIhck0oTtlu2bKFTp07UrVsXgB49epCcnMyzzz57xe1sNhsABQUFV9VunRrmq9ruauTn50O12hXaXm1zzQptz612xR5fRdLPSvm2V1V+Vjw9PTGZTOVYjVyPTLaLaeVk77//Pnl5eYwePRqARYsWsXv3biZPnnzF7c6ePctPP/1UESWKyHXI19cXi8Xi7DKkiqs0Pdvi4uISvz3abDaHfpusWbMmt99+O2azWb99iki58/T0dHYJ4gIqTdg2aNCAr7/+2v4+Ozsbb2/vP93Ozc2N2hV4OkpERKSsKs3dyJ07d2br1q2cOHGC3377jbVr1xIQEODsskRERK5ZpenZ3nTTTYwePZrIyEgKCwvp378/d911l7PLEhERuWaV5gYpERERV1VpTiOLiIi4KoWtiIiIwRS2IiIiBlPYioiIGExhW4mNGzeOBx54gKSkpHLf99ixY0lISCj3/Ypr6N69O5mZmc4uQ8RlVJpHf+RSS5cuZffu3RrBRkSkilPYVlLDhg3DZrMxYMAAhg4dyieffEJxcTFt2rRh4sSJWCwWunTpwgMPPMDu3bu58cYbeeSRR5g/fz5Hjx5l6tSpdOjQge3btzNz5kzOnz/PmTNnGDduHA8++GCJtpYtW1bq/qVq27ZtG++99x5ms5nMzEy6d+9OjRo1+PzzzwH44IMPSE5OJjExkd9++w2z2cyMGTNo1qyZfR9Wq5Vp06axfft2rFYrYWFhPPHEE046IpGqS6eRK6n33nsPgLi4OOLj41m4cCGJiYn85S9/Yc6cOQDk5OQQEBDAsmXLyM/P5/PPP2fBggWMGDGCTz75BID//Oc/xMbGsnTpUmJjY3nrrbdKtLNv377L7l+qvu+++46XX36ZJUuW8Omnn1K/fn0SEhJo2bIlK1eu5PPPP2f+/PkkJSXRtWvXS+aRjo+PBy6cZVm8eDHr168vMayqiDhGPdtKbtu2bWRkZDBw4EAACgsLueOOO+yfXxzSslGjRvj5+QFw8803c+bMGQCmT5/Oxo0bSU5O5rvvvuPcuXNl2r9UbbfffjsNGzYEoF69evj7+wP/9zMyY8YMVq5cycGDB9m0aROtW7cusf3WrVtJT08nNTUVgLy8PPbu3cu9995bsQciUsUpbCs5q9VKz549iYmJAeDcuXNYrVb757+/nuvu7n7J9hEREXTs2JGOHTvi7+/PP//5zzLtX6o2s7nkHLy//xk5cuQIjz76KI8//jgBAQHceOONpKenl1jfarUSFRVFUFAQACdOnKBmzYqb91bEVeg0ciXXsWNH1q1bx/Hjx7HZbEyaNMl+ivjPnDp1ioMHDzJq1CgCAgJYv379JUF6LfuXqu3777+nSZMmPPHEE9x55518/vnnl/x8dOrUifj4eAoLCzl37hwRERHs2rXLSRWLVF3q2VZyrVq14tlnn2XIkCEUFxfTunVrnn76aYe2rVu3Lv379yckJAQPDw86derE+fPnycvLK5f9S9V233338eOPP9KrVy9sNhvt27dn3759JdYJDw8nIyODfv36UVRURFhYGB07dnRSxSJVlyYiEBERMZhOI4uIiBhMYSsiImIwha2IiIjBFLYiIiIGU9iKiIgYTGErIiJiMIWtiIiIwRS24jLOnTvHyJEj6dOnD/369SMmJobi4mI2bNjAgAED6Nu3L+Hh4Xz77bfAhfmCR40aBVyYkMHf35/9+/c78xBExEVpBClxGevWrePcuXMkJiZitVqZOHEihw4dYubMmcybN4969eqxb98+hg4dytq1a3nppZfo168fS5cuZc6cOYwbN47mzZs7+zBExAVpBClxGYcPH+bxxx/nlltuoXPnzjz44INs376dWbNm0aBBA/t6J06c4MMPP6RVq1b88MMPDBw4kIcffpjXXnvNidWLiCtTz1Zcho+PD+vWrWPbtm2kpqYydOhQnnnmGfz9/XnzzTft6x05cgRvb28ADhw4QN26dUlPT6egoKDELEoiIuVF12zFZSxYsIBx48Zx3333ERUVxX333cfp06fZvHmz/VpsSkoKDz/8MOfPnyczM5NXX32Vf//73zRr1oy4uDgnH4GIuCr1bMVl9O3bl+3bt9OrVy+qV69Ow4YNGTx4MM2bN+f555/HZrPh4eHBu+++i8ViYcyYMTz55JPcfvvtTJgwgdDQUDp37kzXrl2dfSgi4mJ0zVZERMRgOo0sIiJiMIWtiIiIwRS2IiIiBlPYioiIGExhKyIiYjCFrYiIiMEUtiIiIgZT2IqIiBjs/wGCADg4pJTivQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 500.85x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"sex\", y=\"suicides_no\", hue=\"AgeCategory\", kind=\"bar\", data=data);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suicide is one of the leading causes of death among all Americans adults. Data show heightend differences in suicide for different sexes. It's evident that males are more inclined to suicide. For Females, the 4 age cateogries seem to level off at 150. We can't say the same for males. Male adults & male middle aged adults are at very high risk of suicide. Both genders show middle aged adults as the leading age group of suicide.\n",
    "\n",
    "        \"In 2017, men died by suicide 3.54 times more often than women. Middle-aged white men, in particular, are susceptible\"(SMM Health)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ---------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Machine Learning + Predictive Analytics "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our goal in this section is to build a multiple linear regression model that will be trained to \n",
    "\tunderstand correlation between our features and our predictor. \n",
    "\tWe want to predict Y (suicides count), given a a specific year, pertaining to a specfic age group & gender. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>suicides_no</th>\n",
       "      <th>population</th>\n",
       "      <th>suicide_per_pop</th>\n",
       "      <th>AgeNum</th>\n",
       "      <th>AgeCategory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1985</td>\n",
       "      <td>female</td>\n",
       "      <td>15-24 years</td>\n",
       "      <td>249.106328</td>\n",
       "      <td>277900.0</td>\n",
       "      <td>0.000896</td>\n",
       "      <td>15</td>\n",
       "      <td>Adolescent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1985</td>\n",
       "      <td>female</td>\n",
       "      <td>25-34 years</td>\n",
       "      <td>249.106328</td>\n",
       "      <td>246800.0</td>\n",
       "      <td>0.001009</td>\n",
       "      <td>25</td>\n",
       "      <td>Adults</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1985</td>\n",
       "      <td>female</td>\n",
       "      <td>35-54 years</td>\n",
       "      <td>249.106328</td>\n",
       "      <td>267500.0</td>\n",
       "      <td>0.000931</td>\n",
       "      <td>35</td>\n",
       "      <td>Middle_Aged_Adults</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  year     sex          age  suicides_no  population  \\\n",
       "0  Albania  1985  female  15-24 years   249.106328    277900.0   \n",
       "1  Albania  1985  female  25-34 years   249.106328    246800.0   \n",
       "2  Albania  1985  female  35-54 years   249.106328    267500.0   \n",
       "\n",
       "   suicide_per_pop  AgeNum         AgeCategory  \n",
       "0         0.000896      15          Adolescent  \n",
       "1         0.001009      25              Adults  \n",
       "2         0.000931      35  Middle_Aged_Adults  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>AgeNum</th>\n",
       "      <th>suicides_no</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1985</td>\n",
       "      <td>female</td>\n",
       "      <td>15</td>\n",
       "      <td>249.106328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1985</td>\n",
       "      <td>female</td>\n",
       "      <td>25</td>\n",
       "      <td>249.106328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1985</td>\n",
       "      <td>female</td>\n",
       "      <td>35</td>\n",
       "      <td>249.106328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     sex  AgeNum  suicides_no\n",
       "0  1985  female      15   249.106328\n",
       "1  1985  female      25   249.106328\n",
       "2  1985  female      35   249.106328"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newData= data.loc[:,['year','sex','AgeNum','suicides_no']]\n",
    "newData.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = newData.iloc[:, :-1].values # grab the every col except last\n",
    "y = newData.iloc[:, -1].values # grab last col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Encoding categorical data¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [1])], remainder='passthrough')\n",
    "X = np.array(ct.fit_transform(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.0, 0.0, 1985, 15],\n",
       "       [1.0, 0.0, 1985, 25],\n",
       "       [1.0, 0.0, 1985, 35],\n",
       "       ...,\n",
       "       [0.0, 1.0, 1990, 5],\n",
       "       [0.0, 1.0, 1990, 55],\n",
       "       [0.0, 1.0, 1990, 75]], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the Gender feature, is now encoded using 0's & 1's\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([249.10632817, 249.10632817, 249.10632817, ...,   6.        ,\n",
       "        74.        ,  13.        ])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting the dataset into the Training set and Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(X,y,test_size = 0.2, random_state = 1)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.0 0.0 2000 25]\n",
      " [1.0 0.0 1989 75]\n",
      " [0.0 1.0 2009 5]\n",
      " ...\n",
      " [0.0 1.0 1979 35]\n",
      " [1.0 0.0 1991 55]\n",
      " [1.0 0.0 1984 5]]\n"
     ]
    }
   ],
   "source": [
    "print(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.0 1.0 2003 5]\n",
      " [0.0 1.0 2010 55]\n",
      " [1.0 0.0 2002 25]\n",
      " ...\n",
      " [1.0 0.0 2012 25]\n",
      " [1.0 0.0 1995 75]\n",
      " [1.0 0.0 2010 75]]\n"
     ]
    }
   ],
   "source": [
    "print(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 17.          51.         249.10632817 ... 249.10632817   3.\n",
      " 249.10632817]\n"
     ]
    }
   ],
   "source": [
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 11. 389.   9. ...  20.  37.  11.]\n"
     ]
    }
   ],
   "source": [
    "print(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training the Multiple Linear Regression model on the Training set¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting the Test set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[186.81518101]\n"
     ]
    }
   ],
   "source": [
    "# we are predicting the suicide count given certain demographics\n",
    "\n",
    "# A 55 year old male, in 2001 \n",
    "# suicide count of about 187.\n",
    "print(regressor.predict([[1,0,2001,55]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ---------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. There was a decrease in suicides toward the 80's. This could be due to awareness of suicide & mental health in the 80s, as well as improved recogniztion of those at risk. But shortly after that their is a rise suicides that we are seeing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Russian levels of alcohol consumption plays an immense role in it's large suicide count, but their is a lack of data to support this due to Soviet secrecy. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. The data illustrates that middle aged adults, between the ages of 30 through 60, have the highest suicide count. While elderly and adolescents have about half the amount as middle aged adults."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Suicide is one of the leading causes of death among all Americans adults. Data show alarming differences in suicide for different sexes. It's evident that males are more inclined to suicide, than females. In addition, Mental health  is a major predictor for suicide. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ---------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. References\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "October 14, 2019 by SSM Health. “Why Are Middle-Aged White Men More Likely to Die by Suicide?” SSM Health, www.ssmhealth.com/blogs/ssm-health-matters/october-2019/middle-aged-men-more-likely-to-die-by-suicide. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "“Suicide in the Elderly.” Suicide in the Elderly - General Practice Notebook, gpnotebook.com/simplepage.cfm?ID=1711669253. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ---------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Where to get help\n",
    "\n",
    "Canada or US: If you're in an emergency, please call 911\n",
    "\n",
    "You can contact the US National Suicide Prevention Lifeline on 1-800-273-8255 or the Crisis Test Line by texting HOME to 741741\n",
    "\n",
    "Young people in need of help can call Kids Help Phone on 1-800-668-6868"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}