Investigation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "from functools import reduce\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn import linear_model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import seaborn as sns\n",
    "from statsmodels.formula.api import ols\n",
    "sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read in suicide data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "deaths = pd.read_csv('suicides_2018.txt', delimiter='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### group by county code and sum deaths that used different codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "county_code_total_suicides = deaths.groupby(['County Code'])['Deaths'].sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>County Code</th>\n",
       "      <th>Deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1033.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1045.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1049.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   County Code  Deaths\n",
       "0       1003.0    33.0\n",
       "1       1015.0    19.0\n",
       "2       1033.0    10.0\n",
       "3       1045.0    10.0\n",
       "4       1049.0    10.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "county_code_total_suicides.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read in unemployment data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "unemployment = pd.read_excel('laucnty18.xlsx', header=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make column names mostly useful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "unemployment.rename(columns={'Unnamed: 0':'weird_code', 'State': 'state_code', 'County': 'county_code_segment', 'Unnamed: 3': 'county_and_state', 'Unnamed: 4': 'year', 'Unnamed: 5': '?', 'Unnamed: 6': 'total_labor_force', 'Unnamed: 7': 'employed', 'Unnamed: 8': 'unemployed', 'Unemploy-': 'unemployment_rate' }, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "unemployment.drop([0,1,2], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create 'County Code' (formated to match unemployment data) from current state and county code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "unemployment['County Code'] = unemployment['state_code'] + unemployment['county_code_segment']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "unemployment['County Code'] = pd.to_numeric(unemployment['County Code'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Isolate unemployment rate without county code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "unemployment_rate_county_code = unemployment[['County Code', 'unemployment_rate']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read in income and education data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "earnings_education = pd.read_csv('analytic_data2018_0.csv', low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3195 entries, 0 to 3194\n",
      "Columns: 508 entries, State FIPS Code to Population growth raw value\n",
      "dtypes: object(508)\n",
      "memory usage: 12.4+ MB\n"
     ]
    }
   ],
   "source": [
    "earnings_education.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### fuck that's a lot of columns..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State FIPS Code</th>\n",
       "      <th>County FIPS Code</th>\n",
       "      <th>5-digit FIPS Code</th>\n",
       "      <th>State Abbreviation</th>\n",
       "      <th>Name</th>\n",
       "      <th>Release Year</th>\n",
       "      <th>County Ranked (Yes=1/No=0)</th>\n",
       "      <th>Premature death raw value</th>\n",
       "      <th>Premature death numerator</th>\n",
       "      <th>Premature death denominator</th>\n",
       "      <th>...</th>\n",
       "      <th>Male population 18-44 raw value</th>\n",
       "      <th>Male population 45-64 raw value</th>\n",
       "      <th>Male population 65+ raw value</th>\n",
       "      <th>Total male population raw value</th>\n",
       "      <th>Female population 0-17 raw value</th>\n",
       "      <th>Female population 18-44 raw value</th>\n",
       "      <th>Female population 45-64 raw value</th>\n",
       "      <th>Female population 65+ raw value</th>\n",
       "      <th>Total female population raw value</th>\n",
       "      <th>Population growth raw value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>statecode</td>\n",
       "      <td>countycode</td>\n",
       "      <td>fipscode</td>\n",
       "      <td>state</td>\n",
       "      <td>county</td>\n",
       "      <td>year</td>\n",
       "      <td>county_ranked</td>\n",
       "      <td>v001_rawvalue</td>\n",
       "      <td>v001_numerator</td>\n",
       "      <td>v001_denominator</td>\n",
       "      <td>...</td>\n",
       "      <td>v013_rawvalue</td>\n",
       "      <td>v016_rawvalue</td>\n",
       "      <td>v017_rawvalue</td>\n",
       "      <td>v025_rawvalue</td>\n",
       "      <td>v026_rawvalue</td>\n",
       "      <td>v027_rawvalue</td>\n",
       "      <td>v031_rawvalue</td>\n",
       "      <td>v032_rawvalue</td>\n",
       "      <td>v035_rawvalue</td>\n",
       "      <td>v097_rawvalue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>00</td>\n",
       "      <td>000</td>\n",
       "      <td>00000</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6658.1139453</td>\n",
       "      <td>3537400</td>\n",
       "      <td>896862151</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>01</td>\n",
       "      <td>000</td>\n",
       "      <td>01000</td>\n",
       "      <td>AL</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9642.4120328</td>\n",
       "      <td>76851</td>\n",
       "      <td>13617223</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>01</td>\n",
       "      <td>001</td>\n",
       "      <td>01001</td>\n",
       "      <td>AL</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>9409.2947663</td>\n",
       "      <td>851</td>\n",
       "      <td>156697</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>01</td>\n",
       "      <td>003</td>\n",
       "      <td>01003</td>\n",
       "      <td>AL</td>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>7467.5966638</td>\n",
       "      <td>2740</td>\n",
       "      <td>554671</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 508 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  State FIPS Code County FIPS Code 5-digit FIPS Code State Abbreviation  \\\n",
       "0       statecode       countycode          fipscode              state   \n",
       "1              00              000             00000                 US   \n",
       "2              01              000             01000                 AL   \n",
       "3              01              001             01001                 AL   \n",
       "4              01              003             01003                 AL   \n",
       "\n",
       "             Name Release Year County Ranked (Yes=1/No=0)  \\\n",
       "0          county         year              county_ranked   \n",
       "1   United States         2018                        NaN   \n",
       "2         Alabama         2018                        NaN   \n",
       "3  Autauga County         2018                          1   \n",
       "4  Baldwin County         2018                          1   \n",
       "\n",
       "  Premature death raw value Premature death numerator  \\\n",
       "0             v001_rawvalue            v001_numerator   \n",
       "1              6658.1139453                   3537400   \n",
       "2              9642.4120328                     76851   \n",
       "3              9409.2947663                       851   \n",
       "4              7467.5966638                      2740   \n",
       "\n",
       "  Premature death denominator  ... Male population 18-44 raw value  \\\n",
       "0            v001_denominator  ...                   v013_rawvalue   \n",
       "1                   896862151  ...                             NaN   \n",
       "2                    13617223  ...                             NaN   \n",
       "3                      156697  ...                             NaN   \n",
       "4                      554671  ...                             NaN   \n",
       "\n",
       "  Male population 45-64 raw value Male population 65+ raw value  \\\n",
       "0                   v016_rawvalue                 v017_rawvalue   \n",
       "1                             NaN                           NaN   \n",
       "2                             NaN                           NaN   \n",
       "3                             NaN                           NaN   \n",
       "4                             NaN                           NaN   \n",
       "\n",
       "  Total male population raw value Female population 0-17 raw value  \\\n",
       "0                   v025_rawvalue                    v026_rawvalue   \n",
       "1                             NaN                              NaN   \n",
       "2                             NaN                              NaN   \n",
       "3                             NaN                              NaN   \n",
       "4                             NaN                              NaN   \n",
       "\n",
       "  Female population 18-44 raw value Female population 45-64 raw value  \\\n",
       "0                     v027_rawvalue                     v031_rawvalue   \n",
       "1                               NaN                               NaN   \n",
       "2                               NaN                               NaN   \n",
       "3                               NaN                               NaN   \n",
       "4                               NaN                               NaN   \n",
       "\n",
       "  Female population 65+ raw value Total female population raw value  \\\n",
       "0                   v032_rawvalue                     v035_rawvalue   \n",
       "1                             NaN                               NaN   \n",
       "2                             NaN                               NaN   \n",
       "3                             NaN                               NaN   \n",
       "4                             NaN                               NaN   \n",
       "\n",
       "  Population growth raw value  \n",
       "0               v097_rawvalue  \n",
       "1                         NaN  \n",
       "2                         NaN  \n",
       "3                         NaN  \n",
       "4                         NaN  \n",
       "\n",
       "[5 rows x 508 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "earnings_education.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "earnings_education.drop(axis=0, index=[0,1], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "earnings_education['County Code'] = pd.to_numeric(earnings_education['5-digit FIPS Code'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge all dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = [unemployment_rate_county_code, earnings_education, county_code_total_suicides]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = reduce(lambda left,right: pd.merge(left,right,on='County Code'), dfs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clear_low_columns(df):\n",
    "    for col in df.columns:\n",
    "        if df[col].isna().sum() >= 100:\n",
    "            df.drop(columns=col, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "clear_low_columns(merged)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_na_with_col_median(df):\n",
    "    for col in df.columns:\n",
    "        if df[col].isna().any():\n",
    "            df.fillna(merged[col].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "replace_na_with_col_median(merged)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.isna().any().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged['Population raw value'] = pd.to_numeric(merged['Population raw value'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create target variable (suicides in relation to population)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged['suicides_per_100k_pop'] = merged['Deaths'] / (merged['Population raw value']/100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "non_int_colums = ['State Abbreviation', 'Name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = np.array(merged.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "int_columns = [x for x in columns if x not in non_int_colums]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_df = merged[int_columns].apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check unemployment rate for outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "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": [
    "fix, ax = plt.subplots()\n",
    "\n",
    "ax.boxplot(full_df['unemployment_rate'])\n",
    "\n",
    "ax.set_ylabel('Unemployment Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "sns.distplot(full_df['unemployment_rate'], hist=True, kde=True, \n",
    "             bins=int(180/5), color = 'darkblue', \n",
    "             hist_kws={'edgecolor':'black'},\n",
    "             kde_kws={'linewidth': 4});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Replace values above 95%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_df['unemployment_rate'] = np.where((full_df['unemployment_rate'] > full_df['unemployment_rate'].quantile(.95)), full_df['unemployment_rate'].quantile(.95), full_df['unemployment_rate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Format columns to work with ols formula"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_df.columns = [col.replace(' ', '_').replace('%', 'percent').replace('-', '_').replace('(', \"\").replace(\")\", \"\") for col in full_df.columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create categorical variables from % of populations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_df['above_average_hispanic_population'] = np.where(full_df['percent_Hispanic_raw_value'] > full_df['percent_Hispanic_raw_value'].median(), 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_df['above_average_black_population'] = np.where(full_df['percent_Non_Hispanic_African_American_raw_value'] > full_df['percent_Non_Hispanic_African_American_raw_value'].median(), 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_df['above_average_child_population'] = np.where(full_df['percent_below_18_years_of_age_raw_value'] > full_df['percent_below_18_years_of_age_raw_value'].median(), 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_df['above_average_elderly_population'] = np.where(full_df['percent_65_and_older_raw_value'] > full_df['percent_65_and_older_raw_value'].median(), 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_df['above_average_native_population'] = np.where(full_df['percent_American_Indian_and_Alaskan_Native_raw_value'] > full_df['percent_American_Indian_and_Alaskan_Native_raw_value'].median(), 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_df['above_average_native_HI_population'] = np.where(full_df['percent_Native_Hawaiian/Other_Pacific_Islander_raw_value'] > full_df['percent_Native_Hawaiian/Other_Pacific_Islander_raw_value'].median(), 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_df['above_average_asian_population'] = np.where(full_df['percent_Asian_raw_value'] > full_df['percent_Asian_raw_value'].median(), 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_df['above_average_white_population'] = np.where(full_df['percent_Non_Hispanic_white_raw_value'] > full_df['percent_Non_Hispanic_white_raw_value'].median(), 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_df['above_average_female_population'] = np.where(full_df['percent_Females_raw_value'] > full_df['percent_Females_raw_value'].median(), 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_df['above_average_rural_population'] = np.where(full_df['percent_Rural_raw_value'] > full_df['percent_Rural_raw_value'].median(), 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = full_df['suicides_per_100k_pop']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_list = ['unemployment_rate', 'above_average_rural_population', 'above_average_female_population','above_average_white_population','above_average_black_population','above_average_hispanic_population','above_average_asian_population','above_average_native_population','above_average_native_HI_population','above_average_elderly_population', 'above_average_child_population']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = full_df[feature_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_from_feature_list(feature):\n",
    "    feature_list.remove(feature)\n",
    "    features = full_df[feature_list]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check for multicollinearity "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = features.corr()\n",
    "\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=1, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check correlations with target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>index</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>unemployment_rate</td>\n",
       "      <td>0.146660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>above_average_rural_population</td>\n",
       "      <td>0.255224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>above_average_female_population</td>\n",
       "      <td>-0.094560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>above_average_white_population</td>\n",
       "      <td>0.126033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>above_average_black_population</td>\n",
       "      <td>-0.160615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>above_average_hispanic_population</td>\n",
       "      <td>0.009953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>above_average_asian_population</td>\n",
       "      <td>-0.346510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>above_average_native_population</td>\n",
       "      <td>0.128209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>above_average_native_HI_population</td>\n",
       "      <td>0.094373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>above_average_elderly_population</td>\n",
       "      <td>0.206541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>above_average_child_population</td>\n",
       "      <td>-0.037123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 index         0\n",
       "0                    unemployment_rate  0.146660\n",
       "1       above_average_rural_population  0.255224\n",
       "2      above_average_female_population -0.094560\n",
       "3       above_average_white_population  0.126033\n",
       "4       above_average_black_population -0.160615\n",
       "5    above_average_hispanic_population  0.009953\n",
       "6       above_average_asian_population -0.346510\n",
       "7      above_average_native_population  0.128209\n",
       "8   above_average_native_HI_population  0.094373\n",
       "9     above_average_elderly_population  0.206541\n",
       "10      above_average_child_population -0.037123"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.corrwith(target).reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get data for each subpopulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "asian_data = full_df.loc[full_df['above_average_asian_population'] == 1]\n",
    "hispanic_data = full_df.loc[full_df['above_average_hispanic_population'] == 1]\n",
    "black_data = full_df.loc[full_df['above_average_black_population'] == 1]\n",
    "white_data = full_df.loc[full_df['above_average_white_population'] == 1]\n",
    "native_us_data = full_df.loc[full_df['above_average_native_population'] == 1]\n",
    "native_HI_data = full_df.loc[full_df['above_average_native_HI_population'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "populations = {'asian_data': asian_data, 'hispanic_data': hispanic_data, 'black_data': black_data, 'white_data': white_data, 'native_us_data': native_us_data, 'native_HI_data': native_HI_data}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot suicide rate for each population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "average_suicide_rate_full_population = full_df['suicides_per_100k_pop'].mean()\n",
    "\n",
    "race_average_values = [average_suicide_rate_full_population, asian_data['suicides_per_100k_pop'].mean(), hispanic_data['suicides_per_100k_pop'].mean(), black_data['suicides_per_100k_pop'].mean(), white_data['suicides_per_100k_pop'].mean(), native_us_data['suicides_per_100k_pop'].mean(), native_HI_data['suicides_per_100k_pop'].mean()]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.bar(['Full Population', 'Asian', 'Hispanic', 'Black', 'White', 'Native American', 'Native Hawaian'], race_average_values)\n",
    "plt.xticks(rotation=90)\n",
    "ax.set_ylabel('Suicides per 100k Population')\n",
    "ax.set_xlabel('Areas With Higher Than Average of Given Race')\n",
    "ax.set_title('Comparing Areas With Higher Than Average of Given Race to Suicide Rate')\n",
    "plt.savefig('images/suicide_rate_by_race.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ANOVA Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=13.040570272904802, pvalue=2.2806009350733902e-08)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(asian_data['suicides_per_100k_pop'], hispanic_data['suicides_per_100k_pop'], white_data['suicides_per_100k_pop'], black_data['suicides_per_100k_pop'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### T tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-1.7134083958760353, pvalue=0.08720590515609221)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(full_df['suicides_per_100k_pop'], white_data['suicides_per_100k_pop'], equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-1.5497003022116653, pvalue=0.12188566396714624)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(full_df['suicides_per_100k_pop'], native_us_data['suicides_per_100k_pop'], equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-0.12045103695937949, pvalue=0.9041771969605928)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(full_df['suicides_per_100k_pop'], hispanic_data['suicides_per_100k_pop'], equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-1.1609700274030244, pvalue=0.24623081841284825)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(full_df['suicides_per_100k_pop'], native_HI_data['suicides_per_100k_pop'], equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-1.5497003022116653, pvalue=0.12188566396714624)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(full_df['suicides_per_100k_pop'], native_us_data['suicides_per_100k_pop'], equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=5.486959842247142, pvalue=5.777456489578912e-08)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(full_df['suicides_per_100k_pop'], asian_data['suicides_per_100k_pop'], equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_from_feature_list('above_average_white_population')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_from_feature_list('above_average_native_HI_population')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_from_feature_list('above_average_native_population')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_from_feature_list('above_average_hispanic_population')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_from_feature_list('above_average_black_population')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Investigating unemployment rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14665959023997174"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_df['unemployment_rate'].corr(full_df['suicides_per_100k_pop'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01651989222486466"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asian_data['unemployment_rate'].corr(asian_data['suicides_per_100k_pop'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(full_df['unemployment_rate'], full_df['suicides_per_100k_pop'])\n",
    "ax.set_ylabel('Suicide Rate')\n",
    "ax.set_xlabel('Unemployment Rate')\n",
    "ax.set_title('Unemployment vs Suicide Rate')\n",
    "plt.savefig('images/unemployment_vs_suicide_rate.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Investigating Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "above_average_female_population\n",
       "0    12.161240\n",
       "1    11.136265\n",
       "Name: suicides_per_100k_pop, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_df.groupby('above_average_female_population')['suicides_per_100k_pop'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=1.3551017038048465, pvalue=0.17590638734917755)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(full_df['suicides_per_100k_pop'], full_df.loc[full_df['above_average_female_population'] == 1]['suicides_per_100k_pop'], equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_from_feature_list('above_average_female_population')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Investigating older populations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "above_average_elderly_population\n",
       "0    10.532405\n",
       "1    12.771177\n",
       "Name: suicides_per_100k_pop, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_df.groupby('above_average_elderly_population')['suicides_per_100k_pop'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-2.647865026609031, pvalue=0.00835353200689949)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(full_df['suicides_per_100k_pop'], full_df.loc[full_df['above_average_elderly_population'] == 1]['suicides_per_100k_pop'], equal_var=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Investigating younger populations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "above_average_child_population\n",
       "0    11.850527\n",
       "1    11.448137\n",
       "Name: suicides_per_100k_pop, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_df.groupby('above_average_child_population')['suicides_per_100k_pop'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=0.5109946246518865, pvalue=0.6095581837562389)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(full_df['suicides_per_100k_pop'], full_df.loc[full_df['above_average_child_population'] == 1]['suicides_per_100k_pop'], equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_from_feature_list('above_average_child_population')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Investigating rural populations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "above_average_rural_population\n",
       "0    10.269047\n",
       "1    13.035518\n",
       "Name: suicides_per_100k_pop, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_df.groupby('above_average_rural_population')['suicides_per_100k_pop'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-3.234951774096237, pvalue=0.0012975182372005182)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(full_df['suicides_per_100k_pop'], full_df.loc[full_df['above_average_rural_population'] == 1]['suicides_per_100k_pop'], equal_var=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(features, target, random_state=33,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training:  4 vs. Testing:  5\n"
     ]
    }
   ],
   "source": [
    "lm = LinearRegression()\n",
    "lm = lm.fit(X_train, y_train)\n",
    "y_train_pred = lm.predict(X_train)\n",
    "train_rmse = np.sqrt(metrics.mean_squared_error(y_train, y_train_pred))\n",
    "y_pred = lm.predict(X_test)\n",
    "test_rmse =np.sqrt(metrics.mean_squared_error(y_test, y_pred))\n",
    "\n",
    "print('Training: ', int(train_rmse), \"vs. Testing: \", int(test_rmse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scaling our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "scaler.fit(X_train)\n",
    "\n",
    "X_train_scale = pd.DataFrame(data=scaler.transform(X_train), columns=features.columns)\n",
    "\n",
    "X_test_scale = pd.DataFrame(data=scaler.transform(X_test), columns=features.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Root Mean Squared Error: 4.645248482275584\n"
     ]
    }
   ],
   "source": [
    "lm_scale = LinearRegression()\n",
    "lm_scale = lm.fit(X_train_scale, y_train)\n",
    "\n",
    "y_train_scale_pred = lm.predict(X_train_scale)\n",
    "\n",
    "train_scale_rmse = np.sqrt(metrics.mean_squared_error(y_train, y_train_scale_pred))\n",
    "print('Training Root Mean Squared Error:' , train_scale_rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>suicides_per_100k_pop</td> <th>  R-squared:         </th> <td>   0.137</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                     <td>OLS</td>          <th>  Adj. R-squared:    </th> <td>   0.131</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>Least Squares</td>     <th>  F-statistic:       </th> <td>   21.15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>               <td>Mon, 22 Jun 2020</td>    <th>  Prob (F-statistic):</th> <td>3.33e-16</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                   <td>02:45:51</td>        <th>  Log-Likelihood:    </th> <td> -1629.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>        <td>   537</td>         <th>  AIC:               </th> <td>   3270.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>            <td>   532</td>         <th>  BIC:               </th> <td>   3291.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>                <td>     4</td>         <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>nonrobust</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                  <td></td>                    <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                        <td>   10.3609</td> <td>    1.127</td> <td>    9.190</td> <td> 0.000</td> <td>    8.146</td> <td>   12.576</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>unemployment_rate</th>                <td>    0.4978</td> <td>    0.250</td> <td>    1.994</td> <td> 0.047</td> <td>    0.007</td> <td>    0.988</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>above_average_rural_population</th>   <td>    0.7908</td> <td>    0.536</td> <td>    1.476</td> <td> 0.140</td> <td>   -0.261</td> <td>    1.843</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>above_average_asian_population</th>   <td>   -2.8678</td> <td>    0.552</td> <td>   -5.193</td> <td> 0.000</td> <td>   -3.953</td> <td>   -1.783</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>above_average_elderly_population</th> <td>    0.7819</td> <td>    0.477</td> <td>    1.640</td> <td> 0.102</td> <td>   -0.155</td> <td>    1.719</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>180.322</td> <th>  Durbin-Watson:     </th> <td>   1.532</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 747.468</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.474</td>  <th>  Prob(JB):          </th> <td>4.89e-163</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 7.971</td>  <th>  Cond. No.          </th> <td>    23.2</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              OLS Regression Results                             \n",
       "=================================================================================\n",
       "Dep. Variable:     suicides_per_100k_pop   R-squared:                       0.137\n",
       "Model:                               OLS   Adj. R-squared:                  0.131\n",
       "Method:                    Least Squares   F-statistic:                     21.15\n",
       "Date:                   Mon, 22 Jun 2020   Prob (F-statistic):           3.33e-16\n",
       "Time:                           02:45:51   Log-Likelihood:                -1629.9\n",
       "No. Observations:                    537   AIC:                             3270.\n",
       "Df Residuals:                        532   BIC:                             3291.\n",
       "Df Model:                              4                                         \n",
       "Covariance Type:               nonrobust                                         \n",
       "====================================================================================================\n",
       "                                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------------------\n",
       "Intercept                           10.3609      1.127      9.190      0.000       8.146      12.576\n",
       "unemployment_rate                    0.4978      0.250      1.994      0.047       0.007       0.988\n",
       "above_average_rural_population       0.7908      0.536      1.476      0.140      -0.261       1.843\n",
       "above_average_asian_population      -2.8678      0.552     -5.193      0.000      -3.953      -1.783\n",
       "above_average_elderly_population     0.7819      0.477      1.640      0.102      -0.155       1.719\n",
       "==============================================================================\n",
       "Omnibus:                      180.322   Durbin-Watson:                   1.532\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              747.468\n",
       "Skew:                           1.474   Prob(JB):                    4.89e-163\n",
       "Kurtosis:                       7.971   Cond. No.                         23.2\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols(formula='suicides_per_100k_pop~'+'+'.join(feature_list), data=full_df).fit().summary()"
   ]
  }
 ],
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