Casualty Calculator for Boko Haram (2009-2018).ipynb

{
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  {
   "cell_type": "code",
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   "source": [
    "#Mulivariate linear regression\n",
    "#SOURCE: Youtube: https://github.com/codebasics/py/blob/master/ML/2_linear_reg_multivariate/2_linear_regression_multivariate.ipynb\n",
    "\n",
    "#Import needed libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>imonth</th>\n",
       "      <th>iday</th>\n",
       "      <th>nwound</th>\n",
       "      <th>nkill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>77</td>\n",
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       "    <tr>\n",
       "      <th>2650</th>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2651</th>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2652</th>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2653</th>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2654</th>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2655 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "      imonth  iday  nwound  nkill\n",
       "0          7    27     0.0      0\n",
       "1          7    27     0.0     77\n",
       "2          7    27     0.0      0\n",
       "3          7    27     0.0      0\n",
       "4          7    27     0.0      0\n",
       "...      ...   ...     ...    ...\n",
       "2650      12    27     0.0      0\n",
       "2651      12    28     0.0      0\n",
       "2652      12    28     0.0      0\n",
       "2653      12    28     0.0      0\n",
       "2654      12    30     0.0      0\n",
       "\n",
       "[2655 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This is the full 2665 data set\n",
    "df = pd.read_csv('boko_kill3.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.8790960451977403"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.nwound.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>imonth</th>\n",
       "      <th>iday</th>\n",
       "      <th>nwound</th>\n",
       "      <th>nkill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>27</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
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       "      <td>77</td>\n",
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       "      <th>2</th>\n",
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       "      <th>2652</th>\n",
       "      <td>12</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2653</th>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2654</th>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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       "<p>2655 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "      imonth  iday  nwound  nkill\n",
       "0          7    27     0.0      0\n",
       "1          7    27     0.0     77\n",
       "2          7    27     0.0      0\n",
       "3          7    27     0.0      0\n",
       "4          7    27     0.0      0\n",
       "...      ...   ...     ...    ...\n",
       "2650      12    27     0.0      0\n",
       "2651      12    28     0.0      0\n",
       "2652      12    28     0.0      0\n",
       "2653      12    28     0.0      0\n",
       "2654      12    30     0.0      0\n",
       "\n",
       "[2655 rows x 4 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nkill = df.nkill.fillna(df.nkill.median())\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = linear_model.LinearRegression()\n",
    "reg.fit(df.drop('nkill',axis='columns'),df.nkill)\n",
    "\n",
    "#dropped nkill and only kept the three variables of interest: day, month, and wounded\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.141116  , -0.0903381 ,  0.34868257])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Coefficients for three main variables\n",
    "\n",
    "reg.coef_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.155941345925843"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the intercept\n",
    "reg.intercept_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.97139722])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#MEDICAL TEAM'S TYPICAL WOUNDING FACTOR\n",
    "#If I had to provide aid on Oct(10) 24, and 4 people are injured I can expect 7 to die--NOT GOOD\n",
    "\n",
    "reg.predict([[10, 24, 4]])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.82109605])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#FOOD TEAM's TYPICAL WOUNDING FACTOR\n",
    "reg.predict([[7, 27, 6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.92437922])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DS TEAM's TYPICAL WOUNDING FACTOR\n",
    "#NOTE--THE IMPACT IS EVERYONE DIES--SEE VISUALIZATION AND STUDY RESULTS\n",
    "reg.predict([[1, 27, 1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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