Suicide Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <p align = \"center\">Analysis of Suicide Rates From 1985 to 2016 </p>\n",
    "\n",
    "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",
    "\"Suicide is one of the leading causes of death among all American adults and rates are increasing in both men and women\" (SSM Health).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this mini-project, we will perform the following steps - \n",
    "\n",
    "    1.Data Wrangling\n",
    "    2.Exploratory Data Analysis\n",
    "    3.Machine Learning + Predictive Analytics \n",
    "    4.Conclusions\n",
    "    5.References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Questions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will try to find the answers for the following questions during our analysis - \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": "markdown",
   "metadata": {},
   "source": [
    "## Importing the Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "data = pd.read_csv('/Users/adityaagarwal/Aditya Ag/Jupyter Notebook/Data Science Projects/The-Suicide-Crisis-in-Data/data/suicides.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will explore all of the varaibles 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": [
    "# 1. Data Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>suicides/100k pop</th>\n",
       "      <th>country-year</th>\n",
       "      <th>HDI for year</th>\n",
       "      <th>gdp_for_year ($)</th>\n",
       "      <th>gdp_per_capita ($)</th>\n",
       "      <th>generation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>male</td>\n",
       "      <td>15-24 years</td>\n",
       "      <td>21</td>\n",
       "      <td>312900</td>\n",
       "      <td>6.71</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,156,624,900</td>\n",
       "      <td>796</td>\n",
       "      <td>Generation X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>male</td>\n",
       "      <td>35-54 years</td>\n",
       "      <td>16</td>\n",
       "      <td>308000</td>\n",
       "      <td>5.19</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,156,624,900</td>\n",
       "      <td>796</td>\n",
       "      <td>Silent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>female</td>\n",
       "      <td>15-24 years</td>\n",
       "      <td>14</td>\n",
       "      <td>289700</td>\n",
       "      <td>4.83</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,156,624,900</td>\n",
       "      <td>796</td>\n",
       "      <td>Generation X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>male</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>1</td>\n",
       "      <td>21800</td>\n",
       "      <td>4.59</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,156,624,900</td>\n",
       "      <td>796</td>\n",
       "      <td>G.I. Generation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>male</td>\n",
       "      <td>25-34 years</td>\n",
       "      <td>9</td>\n",
       "      <td>274300</td>\n",
       "      <td>3.28</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,156,624,900</td>\n",
       "      <td>796</td>\n",
       "      <td>Boomers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  year     sex          age  suicides_no  population  \\\n",
       "0  Albania  1987    male  15-24 years           21      312900   \n",
       "1  Albania  1987    male  35-54 years           16      308000   \n",
       "2  Albania  1987  female  15-24 years           14      289700   \n",
       "3  Albania  1987    male    75+ years            1       21800   \n",
       "4  Albania  1987    male  25-34 years            9      274300   \n",
       "\n",
       "   suicides/100k pop country-year  HDI for year  gdp_for_year ($)   \\\n",
       "0               6.71  Albania1987           NaN      2,156,624,900   \n",
       "1               5.19  Albania1987           NaN      2,156,624,900   \n",
       "2               4.83  Albania1987           NaN      2,156,624,900   \n",
       "3               4.59  Albania1987           NaN      2,156,624,900   \n",
       "4               3.28  Albania1987           NaN      2,156,624,900   \n",
       "\n",
       "   gdp_per_capita ($)       generation  \n",
       "0                 796     Generation X  \n",
       "1                 796           Silent  \n",
       "2                 796     Generation X  \n",
       "3                 796  G.I. Generation  \n",
       "4                 796          Boomers  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at 1st 5 data points\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 27820 entries, 0 to 27819\n",
      "Data columns (total 12 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   country             27820 non-null  object \n",
      " 1   year                27820 non-null  int64  \n",
      " 2   sex                 27820 non-null  object \n",
      " 3   age                 27820 non-null  object \n",
      " 4   suicides_no         27820 non-null  int64  \n",
      " 5   population          27820 non-null  int64  \n",
      " 6   suicides/100k pop   27820 non-null  float64\n",
      " 7   country-year        27820 non-null  object \n",
      " 8   HDI for year        8364 non-null   float64\n",
      " 9    gdp_for_year ($)   27820 non-null  object \n",
      " 10  gdp_per_capita ($)  27820 non-null  int64  \n",
      " 11  generation          27820 non-null  object \n",
      "dtypes: float64(2), int64(4), object(6)\n",
      "memory usage: 2.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# print the concise summery of the dataset\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27820, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape # 43776 Rows, 6 Columns"
   ]
  },
  {
   "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",
       "suicides/100k pop         0\n",
       "country-year              0\n",
       "HDI for year          19456\n",
       " gdp_for_year ($)         0\n",
       "gdp_per_capita ($)        0\n",
       "generation                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country               0\n",
       "year                  0\n",
       "sex                   0\n",
       "age                   0\n",
       "suicides_no           0\n",
       "population            0\n",
       "suicides/100k pop     0\n",
       "country-year          0\n",
       "HDI for year          0\n",
       " gdp_for_year ($)     0\n",
       "gdp_per_capita ($)    0\n",
       "generation            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['15-24 years', '35-54 years', '75+ years', '25-34 years',\n",
       "       '55-74 years', '5-14 years'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The different age groups\n",
    "data['age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Albania', 'Antigua and Barbuda', 'Argentina', 'Armenia', 'Aruba',\n",
       "       'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain',\n",
       "       'Barbados', 'Belarus', 'Belgium', 'Belize',\n",
       "       'Bosnia and Herzegovina', 'Brazil', 'Bulgaria', 'Cabo Verde',\n",
       "       'Canada', 'Chile', 'Colombia', 'Costa Rica', 'Croatia', 'Cuba',\n",
       "       'Cyprus', 'Czech Republic', 'Denmark', 'Dominica', 'Ecuador',\n",
       "       'El Salvador', 'Estonia', 'Fiji', 'Finland', 'France', 'Georgia',\n",
       "       'Germany', 'Greece', 'Grenada', 'Guatemala', 'Guyana', 'Hungary',\n",
       "       'Iceland', 'Ireland', 'Israel', 'Italy', 'Jamaica', 'Japan',\n",
       "       'Kazakhstan', 'Kiribati', 'Kuwait', 'Kyrgyzstan', 'Latvia',\n",
       "       'Lithuania', 'Luxembourg', 'Macau', 'Maldives', 'Malta',\n",
       "       'Mauritius', 'Mexico', 'Mongolia', 'Montenegro', 'Netherlands',\n",
       "       'New Zealand', 'Nicaragua', 'Norway', 'Oman', 'Panama', 'Paraguay',\n",
       "       'Philippines', 'Poland', 'Portugal', 'Puerto Rico', 'Qatar',\n",
       "       'Republic of Korea', 'Romania', 'Russian Federation',\n",
       "       'Saint Kitts and Nevis', 'Saint Lucia',\n",
       "       'Saint Vincent and Grenadines', 'San Marino', 'Serbia',\n",
       "       'Seychelles', 'Singapore', 'Slovakia', 'Slovenia', 'South Africa',\n",
       "       'Spain', 'Sri Lanka', 'Suriname', 'Sweden', 'Switzerland',\n",
       "       'Thailand', 'Trinidad and Tobago', 'Turkey', 'Turkmenistan',\n",
       "       'Ukraine', 'United Arab Emirates', 'United Kingdom',\n",
       "       'United States', 'Uruguay', 'Uzbekistan'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lists the different countries\n",
    "data['country'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101"
      ]
     },
     "execution_count": 10,
     "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1987, 1988, 1989, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999,\n",
       "       2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,\n",
       "       1985, 1986, 1990, 1991, 2012, 2013, 2014, 2015, 2011, 2016])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The different country groups\n",
    "data['year'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 13,
   "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": [
    "# 2. Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1: Which year has the most Suicides? Which year has the least Suicides ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>20996</th>\n",
       "      <th>3</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>male</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>22338.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>19044200</td>\n",
       "      <td>21800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suicides/100k pop</th>\n",
       "      <td>117.3</td>\n",
       "      <td>4.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country-year</th>\n",
       "      <td>Russian Federation1994</td>\n",
       "      <td>Albania1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HDI for year</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdp_for_year ($)</th>\n",
       "      <td>395,077,301,248</td>\n",
       "      <td>2,156,624,900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdp_per_capita ($)</th>\n",
       "      <td>2853</td>\n",
       "      <td>796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>generation</th>\n",
       "      <td>Boomers</td>\n",
       "      <td>G.I. Generation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     20996            3    \n",
       "country                 Russian Federation          Albania\n",
       "year                                  1994             1987\n",
       "sex                                   male             male\n",
       "age                            35-54 years        75+ years\n",
       "suicides_no                        22338.0              1.0\n",
       "population                        19044200            21800\n",
       "suicides/100k pop                    117.3             4.59\n",
       "country-year        Russian Federation1994      Albania1987\n",
       "HDI for year                           0.0              0.0\n",
       " gdp_for_year ($)          395,077,301,248    2,156,624,900\n",
       "gdp_per_capita ($)                    2853              796\n",
       "generation                         Boomers  G.I. Generation"
      ]
     },
     "execution_count": 14,
     "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "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('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 1995. This decrease could be due to awareness of suicide & mental health in the 90s, 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",
    "1. \"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",
    "2. elderly women, however, retain the highest rates throughout the life span (1)\n",
    "3. ratio of male to female elderly suicide deaths remains approximately 3:1\n",
    "4. These trends in suicide rates are probably due to:\n",
    "A) improved detection of those at risk\n",
    "B) aggressive treatment of mental illness in the elderly\" (Suicide in the elderly, General Practice Notebook).\n",
    "        \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "This confirms, that 1987 had the lowest sucide number, while 1994 had the highest suicide number."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2: Which country has the most Suicides ? Which country has the least Suicides ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>3</th>\n",
       "      <th>20996</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>male</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>1.0</td>\n",
       "      <td>22338.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>21800</td>\n",
       "      <td>19044200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suicides/100k pop</th>\n",
       "      <td>4.59</td>\n",
       "      <td>117.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country-year</th>\n",
       "      <td>Albania1987</td>\n",
       "      <td>Russian Federation1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HDI for year</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdp_for_year ($)</th>\n",
       "      <td>2,156,624,900</td>\n",
       "      <td>395,077,301,248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdp_per_capita ($)</th>\n",
       "      <td>796</td>\n",
       "      <td>2853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>generation</th>\n",
       "      <td>G.I. Generation</td>\n",
       "      <td>Boomers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              3                       20996\n",
       "country                     Albania      Russian Federation\n",
       "year                           1987                    1994\n",
       "sex                            male                    male\n",
       "age                       75+ years             35-54 years\n",
       "suicides_no                     1.0                 22338.0\n",
       "population                    21800                19044200\n",
       "suicides/100k pop              4.59                   117.3\n",
       "country-year            Albania1987  Russian Federation1994\n",
       "HDI for year                    0.0                     0.0\n",
       " gdp_for_year ($)     2,156,624,900         395,077,301,248\n",
       "gdp_per_capita ($)              796                    2853\n",
       "generation          G.I. Generation                 Boomers"
      ]
     },
     "execution_count": 29,
     "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": 30,
   "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": 31,
   "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>suicides/100k pop</th>\n",
       "      <th>country-year</th>\n",
       "      <th>HDI for year</th>\n",
       "      <th>gdp_for_year ($)</th>\n",
       "      <th>gdp_per_capita ($)</th>\n",
       "      <th>generation</th>\n",
       "      <th>suicide_per_pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27817</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>male</td>\n",
       "      <td>5-14 years</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2762158</td>\n",
       "      <td>2.17</td>\n",
       "      <td>Uzbekistan2014</td>\n",
       "      <td>0.675</td>\n",
       "      <td>63,067,077,179</td>\n",
       "      <td>2309</td>\n",
       "      <td>Generation Z</td>\n",
       "      <td>0.000022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27818</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>female</td>\n",
       "      <td>5-14 years</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2631600</td>\n",
       "      <td>1.67</td>\n",
       "      <td>Uzbekistan2014</td>\n",
       "      <td>0.675</td>\n",
       "      <td>63,067,077,179</td>\n",
       "      <td>2309</td>\n",
       "      <td>Generation Z</td>\n",
       "      <td>0.000017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27819</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>female</td>\n",
       "      <td>55-74 years</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1438935</td>\n",
       "      <td>1.46</td>\n",
       "      <td>Uzbekistan2014</td>\n",
       "      <td>0.675</td>\n",
       "      <td>63,067,077,179</td>\n",
       "      <td>2309</td>\n",
       "      <td>Boomers</td>\n",
       "      <td>0.000015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year     sex          age  suicides_no  population  \\\n",
       "27817  Uzbekistan  2014    male   5-14 years         60.0     2762158   \n",
       "27818  Uzbekistan  2014  female   5-14 years         44.0     2631600   \n",
       "27819  Uzbekistan  2014  female  55-74 years         21.0     1438935   \n",
       "\n",
       "       suicides/100k pop    country-year  HDI for year  gdp_for_year ($)   \\\n",
       "27817               2.17  Uzbekistan2014         0.675     63,067,077,179   \n",
       "27818               1.67  Uzbekistan2014         0.675     63,067,077,179   \n",
       "27819               1.46  Uzbekistan2014         0.675     63,067,077,179   \n",
       "\n",
       "       gdp_per_capita ($)    generation  suicide_per_pop  \n",
       "27817                2309  Generation Z         0.000022  \n",
       "27818                2309  Generation Z         0.000017  \n",
       "27819                2309       Boomers         0.000015  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 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": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Country Which Has Highest suicide_per_pop :  Kiribati\n",
      "Country Which Has Lowest suicide_per_pop  :  Thailand\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>25061</th>\n",
       "      <th>14059</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>Kiribati</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>1994</td>\n",
       "      <td>1991</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>1.0</td>\n",
       "      <td>286.691023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>5821000</td>\n",
       "      <td>278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suicides/100k pop</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country-year</th>\n",
       "      <td>Thailand1994</td>\n",
       "      <td>Kiribati1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HDI for year</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdp_for_year ($)</th>\n",
       "      <td>146,683,499,006</td>\n",
       "      <td>47,515,189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdp_per_capita ($)</th>\n",
       "      <td>2784</td>\n",
       "      <td>768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>generation</th>\n",
       "      <td>Millenials</td>\n",
       "      <td>G.I. Generation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suicide_per_pop</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.031263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              25061            14059\n",
       "country                    Thailand         Kiribati\n",
       "year                           1994             1991\n",
       "sex                            male             male\n",
       "age                      5-14 years        75+ years\n",
       "suicides_no                     1.0       286.691023\n",
       "population                  5821000              278\n",
       "suicides/100k pop              0.02              0.0\n",
       "country-year           Thailand1994     Kiribati1991\n",
       "HDI for year                    0.0              0.0\n",
       " gdp_for_year ($)   146,683,499,006       47,515,189\n",
       "gdp_per_capita ($)             2784              768\n",
       "generation               Millenials  G.I. Generation\n",
       "suicide_per_pop                 0.0         1.031263"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_minmax('suicide_per_pop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "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": [
    "# Question 3: Are certain age groups more inclined to suicide?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>suicides/100k pop</th>\n",
       "      <th>country-year</th>\n",
       "      <th>HDI for year</th>\n",
       "      <th>gdp_for_year ($)</th>\n",
       "      <th>gdp_per_capita ($)</th>\n",
       "      <th>generation</th>\n",
       "      <th>suicide_per_pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27220</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>1985</td>\n",
       "      <td>male</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>37.0</td>\n",
       "      <td>46900</td>\n",
       "      <td>78.89</td>\n",
       "      <td>Uruguay1985</td>\n",
       "      <td>0.667</td>\n",
       "      <td>4,732,017,873</td>\n",
       "      <td>1729</td>\n",
       "      <td>G.I. Generation</td>\n",
       "      <td>0.000789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20297</th>\n",
       "      <td>Republic of Korea</td>\n",
       "      <td>1990</td>\n",
       "      <td>female</td>\n",
       "      <td>55-74 years</td>\n",
       "      <td>153.0</td>\n",
       "      <td>2400600</td>\n",
       "      <td>6.37</td>\n",
       "      <td>Republic of Korea1990</td>\n",
       "      <td>0.000</td>\n",
       "      <td>279,349,355,714</td>\n",
       "      <td>7056</td>\n",
       "      <td>G.I. Generation</td>\n",
       "      <td>0.000064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20773</th>\n",
       "      <td>Romania</td>\n",
       "      <td>2003</td>\n",
       "      <td>male</td>\n",
       "      <td>25-34 years</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1803090</td>\n",
       "      <td>19.36</td>\n",
       "      <td>Romania2003</td>\n",
       "      <td>0.000</td>\n",
       "      <td>59,867,801,205</td>\n",
       "      <td>2898</td>\n",
       "      <td>Generation X</td>\n",
       "      <td>0.000194</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 country  year     sex          age  suicides_no  population  \\\n",
       "27220            Uruguay  1985    male    75+ years         37.0       46900   \n",
       "20297  Republic of Korea  1990  female  55-74 years        153.0     2400600   \n",
       "20773            Romania  2003    male  25-34 years        349.0     1803090   \n",
       "\n",
       "       suicides/100k pop           country-year  HDI for year  \\\n",
       "27220              78.89            Uruguay1985         0.667   \n",
       "20297               6.37  Republic of Korea1990         0.000   \n",
       "20773              19.36            Romania2003         0.000   \n",
       "\n",
       "       gdp_for_year ($)   gdp_per_capita ($)       generation  suicide_per_pop  \n",
       "27220      4,732,017,873                1729  G.I. Generation         0.000789  \n",
       "20297    279,349,355,714                7056  G.I. Generation         0.000064  \n",
       "20773     59,867,801,205                2898     Generation X         0.000194  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = data.sample(3)\n",
    "sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27817     5\n",
       "27818     5\n",
       "27819    55\n",
       "Name: AgeNum, dtype: int64"
      ]
     },
     "execution_count": 35,
     "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": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27817            Adolescent\n",
       "27818            Adolescent\n",
       "27819    Middle_Aged_Adults\n",
       "Name: AgeCategory, dtype: object"
      ]
     },
     "execution_count": 36,
     "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": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27817     5\n",
       "27818     5\n",
       "27819    55\n",
       "Name: AgeNum, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['AgeNum'].tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>suicides/100k pop</th>\n",
       "      <th>country-year</th>\n",
       "      <th>HDI for year</th>\n",
       "      <th>gdp_for_year ($)</th>\n",
       "      <th>gdp_per_capita ($)</th>\n",
       "      <th>generation</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>1987</td>\n",
       "      <td>male</td>\n",
       "      <td>15-24 years</td>\n",
       "      <td>21.0</td>\n",
       "      <td>312900</td>\n",
       "      <td>6.71</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2,156,624,900</td>\n",
       "      <td>796</td>\n",
       "      <td>Generation X</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>15</td>\n",
       "      <td>Adolescent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>male</td>\n",
       "      <td>35-54 years</td>\n",
       "      <td>16.0</td>\n",
       "      <td>308000</td>\n",
       "      <td>5.19</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2,156,624,900</td>\n",
       "      <td>796</td>\n",
       "      <td>Silent</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>35</td>\n",
       "      <td>Middle_Aged_Adults</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>female</td>\n",
       "      <td>15-24 years</td>\n",
       "      <td>14.0</td>\n",
       "      <td>289700</td>\n",
       "      <td>4.83</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2,156,624,900</td>\n",
       "      <td>796</td>\n",
       "      <td>Generation X</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>15</td>\n",
       "      <td>Adolescent</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  year     sex          age  suicides_no  population  \\\n",
       "0  Albania  1987    male  15-24 years         21.0      312900   \n",
       "1  Albania  1987    male  35-54 years         16.0      308000   \n",
       "2  Albania  1987  female  15-24 years         14.0      289700   \n",
       "\n",
       "   suicides/100k pop country-year  HDI for year  gdp_for_year ($)   \\\n",
       "0               6.71  Albania1987           0.0      2,156,624,900   \n",
       "1               5.19  Albania1987           0.0      2,156,624,900   \n",
       "2               4.83  Albania1987           0.0      2,156,624,900   \n",
       "\n",
       "   gdp_per_capita ($)    generation  suicide_per_pop  AgeNum  \\\n",
       "0                 796  Generation X         0.000067      15   \n",
       "1                 796        Silent         0.000052      35   \n",
       "2                 796  Generation X         0.000048      15   \n",
       "\n",
       "          AgeCategory  \n",
       "0          Adolescent  \n",
       "1  Middle_Aged_Adults  \n",
       "2          Adolescent  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(5.960000000000001, 0.5, 'Suicide Count')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "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",
    "plt.xticks(rotation=45)\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": [
    "# 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": [
    "\n",
    "\"In 2017, men died by suicide 3.54 times more often than women\" (SMM Health)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(24.936693749999996, 0.5, 'Number of Suicides')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "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)\n",
    "plt.xlabel('Sex',size=20)\n",
    "plt.ylabel('Number of Suicides',size=20)"
   ]
  },
  {
   "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": [
    "# 3. Machine Learning And 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": 45,
   "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>1987</td>\n",
       "      <td>male</td>\n",
       "      <td>15</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1987</td>\n",
       "      <td>male</td>\n",
       "      <td>35</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1987</td>\n",
       "      <td>female</td>\n",
       "      <td>15</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     sex  AgeNum  suicides_no\n",
       "0  1987    male      15         21.0\n",
       "1  1987    male      35         16.0\n",
       "2  1987  female      15         14.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newData= data.loc[:,['year','sex','AgeNum','suicides_no']]\n",
    "newData.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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\n",
    "\n",
    "Here we will perform `OneHotEncoding` on the Suicide_no Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0, 1.0, 1987, 15],\n",
       "       [0.0, 1.0, 1987, 35],\n",
       "       [1.0, 0.0, 1987, 15],\n",
       "       ...,\n",
       "       [0.0, 1.0, 2014, 5],\n",
       "       [1.0, 0.0, 2014, 5],\n",
       "       [1.0, 0.0, 2014, 55]], dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the Gender feature, is now encoded using 0's & 1's\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21., 16., 14., ..., 60., 44., 21.])"
      ]
     },
     "execution_count": 49,
     "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": 50,
   "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": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train data\n",
      " [[1.0 0.0 1996 25]\n",
      " [1.0 0.0 2003 35]\n",
      " [1.0 0.0 1996 25]\n",
      " ...\n",
      " [0.0 1.0 2008 35]\n",
      " [1.0 0.0 1994 25]\n",
      " [0.0 1.0 2008 75]]\n",
      "--------------------\n",
      "Test data\n",
      " [[1.0 0.0 1999 25]\n",
      " [0.0 1.0 1992 5]\n",
      " [1.0 0.0 1989 55]\n",
      " ...\n",
      " [1.0 0.0 1997 35]\n",
      " [0.0 1.0 1995 15]\n",
      " [0.0 1.0 1995 55]]\n"
     ]
    }
   ],
   "source": [
    "print(\"train data\\n\",x_train)\n",
    "print(\"-\"*20)\n",
    "print(\"Test data\\n\",x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y train\n",
      " [ 286.69102341   24.            7.         ... 1241.            9.\n",
      "    2.        ]\n",
      "Y test\n",
      " [ 117.   11. 1432. ...  219.   56.  473.]\n"
     ]
    }
   ],
   "source": [
    "print(\"Y train\\n\",y_train)\n",
    "print(\"Y test\\n\",y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training the Multiple Linear Regression model on the Training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 60,
     "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 model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[197.00413504]\n"
     ]
    }
   ],
   "source": [
    "# we are predicting the suicide count given certain demographics\n",
    "# A 55 year old male, in 2001 \n",
    "# suicide count of about 187.\n",
    "\n",
    "print(regressor.predict([[1,0,2001,55]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. 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.\n",
    "\n",
    "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. \n",
    "\n",
    "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.\n",
    "\n",
    "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": [
    "# 5. 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. "
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}