Test Space.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5f225ba6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "82cd77e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load sample healthcare datasets\n",
    "ra_outbreak = pd.read_csv ('meningitis_dataset.csv')\n",
    "ra_insurance = pd.read_csv('insurance_data.csv')\n",
    "ra_cost = pd.read_csv('medical_cost.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "48dabe88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>surname</th>\n",
       "      <th>firstname</th>\n",
       "      <th>middlename</th>\n",
       "      <th>gender</th>\n",
       "      <th>gender_male</th>\n",
       "      <th>gender_female</th>\n",
       "      <th>state</th>\n",
       "      <th>settlement</th>\n",
       "      <th>rural_settlement</th>\n",
       "      <th>...</th>\n",
       "      <th>NmA</th>\n",
       "      <th>NmC</th>\n",
       "      <th>NmW</th>\n",
       "      <th>health_status</th>\n",
       "      <th>alive</th>\n",
       "      <th>dead</th>\n",
       "      <th>report_outcome</th>\n",
       "      <th>unconfirmed</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>null_serotype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Solade</td>\n",
       "      <td>Grace</td>\n",
       "      <td>Solape</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Rivers</td>\n",
       "      <td>Rural</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>alive</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>confirmed</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Eneche</td>\n",
       "      <td>Kure</td>\n",
       "      <td>Balogun</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Ebonyi</td>\n",
       "      <td>Rural</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Alive</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Confirmed</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id surname firstname middlename  gender  gender_male  gender_female  \\\n",
       "0   1  Solade     Grace     Solape  Female            0              1   \n",
       "1   2  Eneche      Kure    Balogun    Male            1              0   \n",
       "\n",
       "    state settlement  rural_settlement  ...  NmA NmC  NmW  health_status  \\\n",
       "0  Rivers      Rural                 1  ...    0   0    0          alive   \n",
       "1  Ebonyi      Rural                 1  ...    0   0    1          Alive   \n",
       "\n",
       "  alive dead  report_outcome  unconfirmed confirmed  null_serotype  \n",
       "0     1    0       confirmed            0         1              1  \n",
       "1     1    0       Confirmed            1         0              1  \n",
       "\n",
       "[2 rows x 40 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ra_outbreak.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ce43d10c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>surname</th>\n",
       "      <th>firstname</th>\n",
       "      <th>middlename</th>\n",
       "      <th>gender</th>\n",
       "      <th>gender_male</th>\n",
       "      <th>gender_female</th>\n",
       "      <th>state</th>\n",
       "      <th>settlement</th>\n",
       "      <th>rural_settlement</th>\n",
       "      <th>...</th>\n",
       "      <th>NmA</th>\n",
       "      <th>NmC</th>\n",
       "      <th>NmW</th>\n",
       "      <th>health_status</th>\n",
       "      <th>alive</th>\n",
       "      <th>dead</th>\n",
       "      <th>report_outcome</th>\n",
       "      <th>unconfirmed</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>null_serotype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99932</th>\n",
       "      <td>101795</td>\n",
       "      <td>Kwenzie</td>\n",
       "      <td>Christian</td>\n",
       "      <td>Atiku</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Sokoto</td>\n",
       "      <td>Urban</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Alive</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Confirmed</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39300</th>\n",
       "      <td>40033</td>\n",
       "      <td>Lawan</td>\n",
       "      <td>Sade</td>\n",
       "      <td>Sade</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Osun</td>\n",
       "      <td>Rural</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Dead</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Not Confirmed</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  surname  firstname middlename  gender  gender_male  \\\n",
       "99932  101795  Kwenzie  Christian      Atiku    Male            1   \n",
       "39300   40033    Lawan       Sade       Sade  Female            0   \n",
       "\n",
       "       gender_female   state settlement  rural_settlement  ...  NmA NmC  NmW  \\\n",
       "99932              0  Sokoto      Urban                 0  ...    0   0    1   \n",
       "39300              1    Osun      Rural                 1  ...    0   0    1   \n",
       "\n",
       "       health_status alive dead  report_outcome  unconfirmed confirmed  \\\n",
       "99932          Alive     1    0   Not Confirmed            1         0   \n",
       "39300           Dead     0    1   Not Confirmed            1         0   \n",
       "\n",
       "       null_serotype  \n",
       "99932              1  \n",
       "39300              0  \n",
       "\n",
       "[2 rows x 40 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the desired sample size\n",
    "sample_size = 1000 \n",
    "\n",
    "# Perform random sampling\n",
    "ra_outbreak1000 = ra_outbreak.sample(n=sample_size, random_state=42)  # Use a specific random state for reproducibility\n",
    "\n",
    "# Display the sampled DataFrame\n",
    "ra_outbreak1000.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "968cb99b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'surname', 'firstname', 'middlename', 'gender', 'gender_male',\n",
       "       'gender_female', 'state', 'settlement', 'rural_settlement',\n",
       "       'urban_settlement', 'report_date', 'report_year', 'age', 'age_str',\n",
       "       'date_of_birth', 'child_group', 'adult_group', 'disease', 'cholera',\n",
       "       'diarrhoea', 'measles', 'viral_haemmorrhaphic_fever', 'meningitis',\n",
       "       'ebola', 'marburg_virus', 'yellow_fever', 'rubella_mars', 'malaria',\n",
       "       'serotype', 'NmA', 'NmC', 'NmW', 'health_status', 'alive', 'dead',\n",
       "       'report_outcome', 'unconfirmed', 'confirmed', 'null_serotype'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ra_outbreak.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6f9ef962",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping a column\n",
    "ra_outbreak.drop (columns= ['middlename','serotype', 'gender_male','rural_settlement',\n",
    "       'urban_settlement', 'report_date', 'report_year', 'age_str','cholera',\n",
    "       'diarrhoea', 'measles', 'viral_haemmorrhaphic_fever', 'meningitis',\n",
    "       'ebola', 'marburg_virus', 'yellow_fever', 'rubella_mars', 'malaria',\n",
    "       'serotype', 'NmA', 'NmC', 'NmW','alive', 'dead', 'unconfirmed', 'confirmed', 'null_serotype',\n",
    "       'gender_female','child_group', 'adult_group'], axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c06bb44b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>surname</th>\n",
       "      <th>firstname</th>\n",
       "      <th>gender</th>\n",
       "      <th>state</th>\n",
       "      <th>settlement</th>\n",
       "      <th>age</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>disease</th>\n",
       "      <th>health_status</th>\n",
       "      <th>report_outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Solade</td>\n",
       "      <td>Grace</td>\n",
       "      <td>Female</td>\n",
       "      <td>Rivers</td>\n",
       "      <td>Rural</td>\n",
       "      <td>32</td>\n",
       "      <td>1986-01-17</td>\n",
       "      <td>Cholera</td>\n",
       "      <td>alive</td>\n",
       "      <td>confirmed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Eneche</td>\n",
       "      <td>Kure</td>\n",
       "      <td>Male</td>\n",
       "      <td>Ebonyi</td>\n",
       "      <td>Rural</td>\n",
       "      <td>32</td>\n",
       "      <td>1985-09-01</td>\n",
       "      <td>Marburg Virus</td>\n",
       "      <td>Alive</td>\n",
       "      <td>Confirmed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id surname firstname  gender   state settlement  age date_of_birth  \\\n",
       "0   1  Solade     Grace  Female  Rivers      Rural   32    1986-01-17   \n",
       "1   2  Eneche      Kure    Male  Ebonyi      Rural   32    1985-09-01   \n",
       "\n",
       "         disease health_status report_outcome  \n",
       "0        Cholera         alive      confirmed  \n",
       "1  Marburg Virus         Alive      Confirmed  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ra_outbreak.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "17770ba4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284484, 11)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ra_outbreak.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b98e20e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1340, 11)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ra_insurance.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "428ad9ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index            0\n",
       "PatientID        0\n",
       "age              5\n",
       "gender           0\n",
       "bmi              0\n",
       "bloodpressure    0\n",
       "diabetic         0\n",
       "children         0\n",
       "smoker           0\n",
       "region           3\n",
       "claim            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ra_insurance.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a419e38b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>27.90</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16884.9240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>33.77</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1725.5523</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  age     sex    bmi  children smoker     region     charges\n",
       "0   1   19  female  27.90         0    yes  southwest  16884.9240\n",
       "1   2   18    male  33.77         1     no  southeast   1725.5523"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ra_cost.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "92d27385",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1338, 8)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ra_cost.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0fb22855",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping a column\n",
    "ra_insurance.drop (columns= ['index'], axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ab0a3106",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientID</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bloodpressure</th>\n",
       "      <th>diabetic</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>claim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>male</td>\n",
       "      <td>23.2</td>\n",
       "      <td>91</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1121.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>male</td>\n",
       "      <td>30.1</td>\n",
       "      <td>87</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1131.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>23.166667</td>\n",
       "      <td>male</td>\n",
       "      <td>33.3</td>\n",
       "      <td>82</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1135.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>22.333333</td>\n",
       "      <td>male</td>\n",
       "      <td>33.7</td>\n",
       "      <td>80</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>northwest</td>\n",
       "      <td>1136.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>male</td>\n",
       "      <td>34.1</td>\n",
       "      <td>100</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>northwest</td>\n",
       "      <td>1137.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PatientID        age gender   bmi  bloodpressure diabetic  children smoker  \\\n",
       "0          1  39.000000   male  23.2             91      Yes         0     No   \n",
       "1          2  24.000000   male  30.1             87       No         0     No   \n",
       "2          3  23.166667   male  33.3             82      Yes         0     No   \n",
       "3          4  22.333333   male  33.7             80       No         0     No   \n",
       "4          5  21.500000   male  34.1            100       No         0     No   \n",
       "\n",
       "      region    claim  \n",
       "0  southeast  1121.87  \n",
       "1  southeast  1131.51  \n",
       "2  southeast  1135.94  \n",
       "3  northwest  1136.40  \n",
       "4  northwest  1137.01  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "ra_insurance['age'] = ra_insurance['age'].interpolate()\n",
    "\n",
    "# Display the first few rows of the DataFrame to verify the interpolation\n",
    "ra_insurance.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2b523fbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientID</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bloodpressure</th>\n",
       "      <th>diabetic</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
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       "      <th>claim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>male</td>\n",
       "      <td>23.2</td>\n",
       "      <td>91</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1121.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>male</td>\n",
       "      <td>30.1</td>\n",
       "      <td>87</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1131.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PatientID  age gender   bmi  bloodpressure diabetic  children smoker  \\\n",
       "0          1   39   male  23.2             91      Yes         0     No   \n",
       "1          2   24   male  30.1             87       No         0     No   \n",
       "\n",
       "      region    claim  \n",
       "0  southeast  1121.87  \n",
       "1  southeast  1131.51  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ra_insurance['age'] = ra_insurance['age'].astype(int)\n",
    "\n",
    "# Display the DataFrame to verify the changes\n",
    "ra_insurance.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3b12a761",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>PatientID</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bloodpressure</th>\n",
       "      <th>diabetic</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>claim</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
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       "      <td>23.2</td>\n",
       "      <td>91</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1121.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
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       "      <td>87</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1131.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>male</td>\n",
       "      <td>33.3</td>\n",
       "      <td>82</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
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       "      <td>southeast</td>\n",
       "      <td>1135.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>male</td>\n",
       "      <td>33.7</td>\n",
       "      <td>80</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>northwest</td>\n",
       "      <td>1136.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>male</td>\n",
       "      <td>34.1</td>\n",
       "      <td>100</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>northwest</td>\n",
       "      <td>1137.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PatientID  age gender   bmi  bloodpressure diabetic  children smoker  \\\n",
       "0          1   39   male  23.2             91      Yes         0     No   \n",
       "1          2   24   male  30.1             87       No         0     No   \n",
       "2          3   23   male  33.3             82      Yes         0     No   \n",
       "3          4   22   male  33.7             80       No         0     No   \n",
       "4          5   21   male  34.1            100       No         0     No   \n",
       "\n",
       "      region    claim  \n",
       "0  southeast  1121.87  \n",
       "1  southeast  1131.51  \n",
       "2  southeast  1135.94  \n",
       "3  northwest  1136.40  \n",
       "4  northwest  1137.01  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Forward fill missing values in the 'region' column and assign back to the original DataFrame\n",
    "ra_insurance['region'] = ra_insurance['region'].ffill()\n",
    "\n",
    "# Display the first few rows of the DataFrame to verify the forward fill\n",
    "ra_insurance.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "27d0041a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ra_insurance.rename(columns={\"PatientID\": \"Id\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1b5f4c17",
   "metadata": {},
   "outputs": [],
   "source": [
    "ra_cost.rename(columns={\"PatientID\": \"Id\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "04c5463f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ra_outbreak.rename(columns={\"id\": \"Id\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c5e9c69b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "# Merge datasets on common keys (Id)\n",
    "ra_merged = pd.merge(ra_outbreak, ra_insurance, on='Id')\n",
    "ra_merged = pd.merge(ra_merged, ra_cost, on='Id')\n",
    "\n",
    "# Cleanse and preprocess data: standardize formats\n",
    "ra_merged['date_of_birth'] = pd.to_datetime(ra_merged['date_of_birth'], errors='coerce')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bc4bee8",
   "metadata": {},
   "source": [
    "# Cleaning and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ce6589f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine 'age_x' and 'age_y' columns into a single 'age' column\n",
    "ra_merged['age'] = ra_merged[['age_x', 'age_y']].mean(axis=1)\n",
    "\n",
    "# Drop the individual 'age_x' and 'age_y' columns\n",
    "ra_merged.drop(['age_x', 'age_y'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6185eca5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing values in 'gender_x' column with values from 'gender_y' column\n",
    "ra_merged['gender_x'].fillna(ra_merged['gender_y'], inplace=True)\n",
    "\n",
    "# Rename 'gender_x' to 'gender'\n",
    "ra_merged.rename(columns={'gender_x': 'gender'}, inplace=True)\n",
    "\n",
    "# Drop 'gender_y' column\n",
    "ra_merged.drop('gender_y', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "63ee24a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing values in 'children_x' column with values from 'children_y' column\n",
    "ra_merged['children_x'].fillna(ra_merged['children_y'], inplace=True)\n",
    "\n",
    "# Rename 'children_x' to 'children'\n",
    "ra_merged.rename(columns={'children_x': 'children'}, inplace=True)\n",
    "\n",
    "# Drop 'children_y' column\n",
    "ra_merged.drop('children_y', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9bd958b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing values in 'smoker_x' column with values from 'smoker_y' column\n",
    "ra_merged['smoker_x'].fillna(ra_merged['smoker_y'], inplace=True)\n",
    "\n",
    "# Rename 'smoker_x' to 'smoker'\n",
    "ra_merged.rename(columns={'smoker_x': 'smoker'}, inplace=True)\n",
    "\n",
    "# Drop 'smoker_y' column\n",
    "ra_merged.drop('smoker_y', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9e6e8105",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing values in 'region_x' column with values from 'region_y' column\n",
    "ra_merged['region_x'].fillna(ra_merged['region_y'], inplace=True)\n",
    "\n",
    "# Rename 'region_x' to 'region'\n",
    "ra_merged.rename(columns={'region_x': 'region'}, inplace=True)\n",
    "\n",
    "# Drop 'region_y' column\n",
    "ra_merged.drop('region_y', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "eb8e8cca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine 'bmi_x' and 'bmi_y' columns into a single 'bmi' column\n",
    "ra_merged['bmi'] = ra_merged['bmi_x'].combine_first(ra_merged['bmi_y'])\n",
    "\n",
    "# Drop 'bmi_x' and 'bmi_y' columns\n",
    "ra_merged.drop(['bmi_x', 'bmi_y'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4b7a4c75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'surname', 'firstname', 'gender', 'state', 'settlement',\n",
       "       'date_of_birth', 'disease', 'health_status', 'report_outcome',\n",
       "       'bloodpressure', 'diabetic', 'children', 'smoker', 'region', 'claim',\n",
       "       'age', 'sex', 'charges', 'bmi'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ra_merged.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3314c5e9",
   "metadata": {},
   "source": [
    "# DATA AGGREGATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7f2721bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Age by Gender:\n",
      "gender\n",
      "Female    36.049048\n",
      "Male      36.772013\n",
      "Name: age, dtype: float64\n",
      "\n",
      "Insurance Coverage Distribution:\n",
      "claim\n",
      "1639.56     0.001516\n",
      "1704.57     0.001516\n",
      "1149.40     0.001516\n",
      "1121.87     0.000758\n",
      "12829.46    0.000758\n",
      "              ...   \n",
      "6313.76     0.000758\n",
      "6311.95     0.000758\n",
      "6289.75     0.000758\n",
      "6282.24     0.000758\n",
      "60021.40    0.000758\n",
      "Name: proportion, Length: 1316, dtype: float64\n",
      "\n",
      "Total Medical Costs for Meningitis Treatment:\n",
      "17498829.140809\n"
     ]
    }
   ],
   "source": [
    "# Calculate average age of meningitis patients\n",
    "average_age = ra_merged.groupby('gender')['age'].mean()\n",
    "print(\"Average Age by Gender:\")\n",
    "print(average_age)\n",
    "\n",
    "# Analyze insurance coverage among meningitis patients\n",
    "insurance_distribution = ra_merged['claim'].value_counts(normalize=True)\n",
    "print(\"\\nInsurance Coverage Distribution:\")\n",
    "print(insurance_distribution)\n",
    "\n",
    "# Calculate total medical costs associated with treating meningitis\n",
    "total_medical_costs = ra_merged['charges'].sum()\n",
    "print(\"\\nTotal Medical Costs for Meningitis Treatment:\")\n",
    "print(total_medical_costs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5c1bb671",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of Patients by Age Group:\n",
      "age_group\n",
      "10-19    133\n",
      "20-29    290\n",
      "30-49    567\n",
      "50-69    329\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Calculate distribution of patients by custom age group\n",
    "age_bins = [9, 20, 30, 46, 67]  # Adjusted age bins to cover the entire range\n",
    "age_labels = ['10-19', '20-29', '30-49', '50-69']  # Labels for age groups\n",
    "ra_merged['age_group'] = pd.cut(ra_merged['age'], bins=age_bins, labels=age_labels, right=False)\n",
    "age_distribution = ra_merged['age_group'].value_counts().sort_index()\n",
    "print(\"Distribution of Patients by Age Group:\")\n",
    "print(age_distribution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "47f3c1e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Average Age of Patients by Disease:\n",
      "disease\n",
      "Cholera                       35.718750\n",
      "Diarrhoea                     36.127737\n",
      "Ebola                         36.273913\n",
      "Malaria                       35.330935\n",
      "Marburg Virus                 36.617284\n",
      "Measles                       36.917323\n",
      "Meningitis                    37.463115\n",
      "Rubella Mars                  35.841463\n",
      "Viral Haemmorrhaphic Fever    37.076923\n",
      "Yellow Fever                  36.779167\n",
      "Name: age, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calculate average age of patients by disease\n",
    "average_age_by_disease = ra_merged.groupby('disease')['age'].mean()\n",
    "print(\"\\nAverage Age of Patients by Disease:\")\n",
    "print(average_age_by_disease)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "52e30165",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of Patients by State:\n",
      "state\n",
      "Nasarawa                     53\n",
      "Ebonyi                       45\n",
      "Yobe                         44\n",
      "Osun                         41\n",
      "Imo                          40\n",
      "Rivers                       39\n",
      "Kaduna                       39\n",
      "Adamawa                      39\n",
      "Katsina                      38\n",
      "Abia                         38\n",
      "Bauchi                       37\n",
      "Edo                          37\n",
      "Sokoto                       37\n",
      "Kogi                         37\n",
      "Ekiti                        36\n",
      "Akwa Ibom                    36\n",
      "Ogun                         36\n",
      "Niger                        36\n",
      "Delta                        35\n",
      "Cross River                  35\n",
      "Kwara                        35\n",
      "Oyo                          35\n",
      "Zamfara                      35\n",
      "Jigawa                       34\n",
      "Kebbi                        34\n",
      "Gombe                        34\n",
      "Bayelsa                      34\n",
      "Borno                        34\n",
      "Plateau                      33\n",
      "Ondo                         33\n",
      "Taraba                       31\n",
      "Federal Capital Territory    31\n",
      "Enugu                        31\n",
      "Kano                         31\n",
      "Anambra                      28\n",
      "Benue                        27\n",
      "Lagos                        21\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Determine distribution of patients by state\n",
    "state_distribution = ra_merged['state'].value_counts()\n",
    "print(\"Distribution of Patients by State:\")\n",
    "print(state_distribution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4198f037",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Average Healthcare Costs by Region:\n",
      "region\n",
      "northeast    14157.035302\n",
      "northwest    13668.451793\n",
      "southeast    12549.032938\n",
      "southwest    13164.640157\n",
      "Name: charges, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Analyze healthcare costs by region\n",
    "healthcare_cost_by_region = ra_merged.groupby('region')['charges'].mean()\n",
    "print(\"\\nAverage Healthcare Costs by Region:\")\n",
    "print(healthcare_cost_by_region)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7952fff6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Disease Count by Region:\n",
      "region     disease                   \n",
      "northeast  Diarrhoea                     29\n",
      "           Yellow Fever                  29\n",
      "           Measles                       28\n",
      "           Malaria                       27\n",
      "           Cholera                       23\n",
      "           Marburg Virus                 23\n",
      "           Meningitis                    20\n",
      "           Rubella Mars                  19\n",
      "           Ebola                         18\n",
      "           Viral Haemmorrhaphic Fever    14\n",
      "northwest  Diarrhoea                     40\n",
      "           Viral Haemmorrhaphic Fever    40\n",
      "           Marburg Virus                 40\n",
      "           Rubella Mars                  36\n",
      "           Meningitis                    35\n",
      "           Measles                       34\n",
      "           Yellow Fever                  31\n",
      "           Malaria                       31\n",
      "           Ebola                         31\n",
      "           Cholera                       29\n",
      "southeast  Marburg Virus                 60\n",
      "           Cholera                       52\n",
      "           Viral Haemmorrhaphic Fever    46\n",
      "           Yellow Fever                  44\n",
      "           Malaria                       44\n",
      "           Diarrhoea                     41\n",
      "           Rubella Mars                  40\n",
      "           Meningitis                    37\n",
      "           Ebola                         36\n",
      "           Measles                       36\n",
      "southwest  Cholera                       40\n",
      "           Marburg Virus                 39\n",
      "           Malaria                       37\n",
      "           Meningitis                    30\n",
      "           Viral Haemmorrhaphic Fever    30\n",
      "           Ebola                         30\n",
      "           Measles                       29\n",
      "           Rubella Mars                  28\n",
      "           Diarrhoea                     27\n",
      "           Yellow Fever                  16\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Analyze disease count by region\n",
    "disease_by_region = ra_merged.groupby('region')['disease'].value_counts()\n",
    "\n",
    "print(\"\\nDisease Count by Region:\")\n",
    "print(disease_by_region)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "23e4b626",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prevalence of Diseases Among Patients:\n",
      "disease\n",
      "Marburg Virus                 0.122820\n",
      "Cholera                       0.109174\n",
      "Malaria                       0.105383\n",
      "Diarrhoea                     0.103867\n",
      "Viral Haemmorrhaphic Fever    0.098560\n",
      "Measles                       0.096285\n",
      "Rubella Mars                  0.093252\n",
      "Meningitis                    0.092494\n",
      "Yellow Fever                  0.090978\n",
      "Ebola                         0.087187\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calculate prevalence of diseases among patients\n",
    "disease_prevalence = ra_merged['disease'].value_counts(normalize=True)\n",
    "print(\"Prevalence of Diseases Among Patients:\")\n",
    "print(disease_prevalence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0a5a62e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Disease Count per Gender:\n",
      "gender\n",
      "Female    683\n",
      "Male      636\n",
      "Name: disease, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Calculate disease count per gender\n",
    "disease_count_per_gender = ra_merged.groupby('gender')['disease'].count()\n",
    "\n",
    "# Display the result\n",
    "print(\"Disease Count per Gender:\")\n",
    "print(disease_count_per_gender)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7a88ead8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Distribution of Health Status by Settlement:\n",
      "settlement  health_status\n",
      "Rural       dead             339\n",
      "            alive            315\n",
      "Urban       dead             342\n",
      "            alive            323\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Convert health_status to lowercase\n",
    "ra_merged['health_status'] = ra_merged['health_status'].str.lower()\n",
    "\n",
    "# Distribution of health status by settlement\n",
    "health_status_distribution = ra_merged.groupby('settlement')['health_status'].value_counts()\n",
    "\n",
    "print(\"\\nDistribution of Health Status by Settlement:\")\n",
    "print(health_status_distribution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e91db726",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'surname', 'firstname', 'gender', 'state', 'settlement',\n",
       "       'date_of_birth', 'disease', 'health_status', 'report_outcome',\n",
       "       'bloodpressure', 'diabetic', 'children', 'smoker', 'region', 'claim',\n",
       "       'age', 'sex', 'charges', 'bmi', 'age_group'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ra_merged.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6cfc33f",
   "metadata": {},
   "source": [
    "# Data Visualization and Reporting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "230fd0f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Group the data by gender and disease, then count the occurrences\n",
    "disease_count_by_gender = ra_merged.groupby(['gender', 'disease']).size().unstack(fill_value=0)\n",
    "\n",
    "# Visualization: Stacked bar plot of disease count by gender\n",
    "plt.figure(figsize=(10, 6))\n",
    "ax = disease_count_by_gender.plot(kind='bar', stacked=True)\n",
    "\n",
    "# Adding data labels to each stacked bar\n",
    "for p in ax.patches:\n",
    "    width = p.get_width()\n",
    "    height = p.get_height()\n",
    "    x, y = p.get_xy() \n",
    "    ax.annotate(f'{height}', (x + width/2, y + height/2), ha='center', va='center')\n",
    "\n",
    "# Adding labels and title\n",
    "plt.title('Disease Count by Gender')\n",
    "plt.xlabel('Gender')\n",
    "plt.ylabel('Disease Count')\n",
    "plt.xticks(rotation=0)  # Rotate x-axis labels if needed\n",
    "\n",
    "# Shifting the legend to the far right of the chart\n",
    "plt.legend(title='Disease', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "\n",
    "# Showing the plot\n",
    "plt.tight_layout()  # Adjust layout to prevent clipping of labels\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "474c09ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "#ra_merged['disease'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d1bdfa4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'surname', 'firstname', 'gender', 'state', 'settlement',\n",
       "       'date_of_birth', 'disease', 'health_status', 'report_outcome',\n",
       "       'bloodpressure', 'diabetic', 'children', 'smoker', 'region', 'claim',\n",
       "       'age', 'sex', 'charges', 'bmi', 'age_group'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ra_merged.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d131a21a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization: Bar chart of patients by age group with data labels\n",
    "plt.figure(figsize=(8, 6))\n",
    "bars = age_distribution.plot(kind='bar', color='blue')\n",
    "\n",
    "# Add data labels to the bars\n",
    "for bar in bars.patches:\n",
    "    plt.annotate(round(bar.get_height(), 2), \n",
    "                 (bar.get_x() + bar.get_width() / 2, bar.get_height()), \n",
    "                 ha='center', va='bottom')\n",
    "    \n",
    "plt.title('Distribution of Patients by Age Group')\n",
    "plt.xlabel('Age Group')\n",
    "plt.ylabel('Number of Patients')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "38ac4d0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization: Bar chart of patients by state\n",
    "plt.figure(figsize=(12, 6))\n",
    "state_distribution.plot(kind='bar', color='blue')\n",
    "plt.title('Distribution of Patients by State')\n",
    "plt.xlabel('State')\n",
    "plt.ylabel('Number of Patients')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c39af877",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'healthcare_costs_by_region' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[43], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Visualization: Bar chart of patients by state\u001b[39;00m\n\u001b[0;32m      2\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[1;32m----> 3\u001b[0m healthcare_costs_by_region\u001b[38;5;241m.\u001b[39mplot(kind\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbar\u001b[39m\u001b[38;5;124m'\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mblue\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      5\u001b[0m \u001b[38;5;66;03m# Add data labels to the bars\u001b[39;00m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bar \u001b[38;5;129;01min\u001b[39;00m bars\u001b[38;5;241m.\u001b[39mpatches:\n",
      "\u001b[1;31mNameError\u001b[0m: name 'healthcare_costs_by_region' is not defined"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization: Bar chart of patients by state\n",
    "plt.figure(figsize=(12, 6))\n",
    "healthcare_costs_by_region.plot(kind='bar', color='blue')\n",
    "\n",
    "# Add data labels to the bars\n",
    "for bar in bars.patches:\n",
    "    plt.annotate(round(bar.get_height(), 2), \n",
    "                 (bar.get_x() + bar.get_width() / 2, bar.get_height()), \n",
    "                 ha='center', va='bottom')\n",
    "    \n",
    "plt.title('Healthcare Cost by Region')\n",
    "plt.xlabel('Region')\n",
    "plt.ylabel('healthcare Cost')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c627e42b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Unstack the grouped data to prepare for plotting\n",
    "health_status_distribution_unstacked = health_status_distribution.unstack()\n",
    "\n",
    "# Plotting the stacked bar chart\n",
    "ax = health_status_distribution_unstacked.plot(kind='bar', stacked=True, figsize=(10, 6))\n",
    "\n",
    "# Adding data labels to the stacked bars\n",
    "for p in ax.patches:\n",
    "    width = p.get_width()\n",
    "    height = p.get_height()\n",
    "    x, y = p.get_xy() \n",
    "    ax.annotate(f'{height}', (x + width/2, y + height/2), ha='center', va='center')\n",
    "\n",
    "# Adding labels and title\n",
    "plt.title('Distribution of Health Status by Settlement')\n",
    "plt.xlabel('Settlement')\n",
    "plt.ylabel('Number of Patients')\n",
    "plt.xticks(rotation=0)  # Rotate x-axis labels if needed\n",
    "\n",
    "# Showing the plot\n",
    "plt.legend(title='Health Status')\n",
    "plt.tight_layout()  # Adjust layout to prevent clipping of labels\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64489218",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Analyze disease count by region\n",
    "disease_by_region = ra_merged.groupby(['region', 'disease']).size().unstack(fill_value=0)\n",
    "\n",
    "# Visualization: Stacked bar plot of disease count by region\n",
    "plt.figure(figsize=(12, 8))\n",
    "disease_by_region.plot(kind='bar', stacked=True)\n",
    "\n",
    "# Adding labels and title\n",
    "plt.title('Disease Count by Region')\n",
    "plt.xlabel('Region')\n",
    "plt.ylabel('Count of Diseases')\n",
    "plt.xticks(rotation=45)  # Rotate x-axis labels if needed\n",
    "\n",
    "# Showing the plot\n",
    "plt.legend(title='Disease', bbox_to_anchor=(1.05, 1), loc='upper left')  # Adjust legend position\n",
    "plt.tight_layout()  # Adjust layout to prevent clipping of labels\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c76a9841",
   "metadata": {},
   "source": [
    "# USE CASE EXAMPLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c016ca53",
   "metadata": {},
   "outputs": [],
   "source": [
    "ra_merged.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a5de6f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Disease Distribution\n",
    "# Calculate the distribution of diseases\n",
    "disease_distribution = ra_merged['disease'].value_counts()\n",
    "disease_distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96aee4d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualize the distribution of diseases\n",
    "plt.figure(figsize=(10, 6))\n",
    "disease_distribution.plot(kind='bar', color='blue')\n",
    "plt.title('Distribution of Diseases by Regions')\n",
    "plt.xlabel('Disease')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c0d5f1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Health Outcomes Analysis\n",
    "# Calculate mortality rates and survival rates for each disease\n",
    "health_outcomes = ra_merged.groupby('disease')['health_status'].value_counts(normalize=True).unstack(fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0be28927",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualize health outcomes\n",
    "print(\"\\nHealth Outcomes Analysis:\")\n",
    "print(health_outcomes)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "0e7c7693",
   "metadata": {},
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns  # Import seaborn for color palette\n",
    "\n",
    "# Health Outcomes Analysis data\n",
    "health_outcomes = {\n",
    "    'Cholera': [0.472222, 0.527778],\n",
    "    'Diarrhoea': [0.445255, 0.554745],\n",
    "    'Ebola': [0.469565, 0.530435],\n",
    "    'Malaria': [0.532374, 0.467626],\n",
    "    'Marburg Virus': [0.506173, 0.493827],\n",
    "    'Measles': [0.480315, 0.519685],\n",
    "    'Meningitis': [0.483607, 0.516393],\n",
    "    'Rubella Mars': [0.463415, 0.536585],\n",
    "    'Viral Haemmorrhaphic Fever': [0.453846, 0.546154],\n",
    "    'Yellow Fever': [0.525000, 0.475000]\n",
    "}\n",
    "\n",
    "# Convert data to DataFrame for easier plotting\n",
    "import pandas as pd\n",
    "health_outcomes_df = pd.DataFrame(health_outcomes, index=['alive', 'dead'])\n",
    "\n",
    "# Define color palette\n",
    "colors = sns.color_palette('pastel')  # Choose any seaborn color palette\n",
    "\n",
    "# Visualization: Horizontal stacked bar plot of health outcomes analysis\n",
    "plt.figure(figsize=(10, 6))\n",
    "health_outcomes_df.plot(kind='barh', stacked=True, color=colors)\n",
    "\n",
    "# Adding labels and title\n",
    "plt.title('Health Outcomes Analysis by Disease')\n",
    "plt.xlabel('Proportion')\n",
    "plt.ylabel('Disease')\n",
    "plt.legend(title='Health Status', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "\n",
    "# Showing the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4a798a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Age Group Analysis\n",
    "# Analyze the distribution of diseases, mortality rates, and healthcare costs across different age groups\n",
    "age_group_analysis = ra_merged.groupby('age_group').agg({\n",
    "    'disease': 'count',\n",
    "    'health_status': lambda x: (x == 'Dead').mean(),  # Calculate mortality rate\n",
    "    'charges': 'mean'\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f8dee6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualize age group analysis\n",
    "print(\"\\nAge Group Analysis:\")\n",
    "print(age_group_analysis)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24ea52d1",
   "metadata": {},
   "source": [
    "Insights and Recommendations"
   ]
  },
  {
   "cell_type": "raw",
   "id": "9b22a5b8",
   "metadata": {},
   "source": [
    "1. Targeted Interventions for Marburg Virus:\n",
    "   Marburg Virus is associated with high mortality rates. To address this, targeted interventions should focus on:\n",
    "   - Strengthening public health surveillance systems to detect outbreaks early.\n",
    "   - Implementing strict infection control measures in healthcare settings.\n",
    "   - Conducting public awareness campaigns to educate communities about the symptoms, transmission, and prevention of \n",
    "Marburg Virus.\n",
    "\n",
    "2. Preventive Measures for Age Group 30-49:\n",
    "   Age group 30-49 has the highest patient count. To promote preventive healthcare and well-being among this age group, \n",
    "the following measures can be recommended:\n",
    "   - Encouraging regular health check-ups and screenings for early detection of diseases.\n",
    "   - Promoting healthy lifestyle behaviors, such as regular exercise, balanced diet, and stress management.\n",
    "   - Providing access to educational resources and support for managing chronic conditions, such as diabetes and hypertension.\n",
    "\n",
    "3. Resource Allocation Based on Disease Burden:\n",
    "   Allocate healthcare resources based on the disease burden and healthcare costs associated with each disease. \n",
    "Prioritize funding and support for:\n",
    "   - Diseases with high mortality rates and severe health outcomes.\n",
    "   - Regions and communities with a higher prevalence of diseases and greater healthcare needs.\n",
    "\n",
    "4. Focus on Nassarawa for Healthcare Provision and on Lagos healthacare system to replicate to other states:\n",
    "   Nassarawa has the highest patient count. Therefore, healthcare provision and resource allocation should be \n",
    "prioritized for this region. This may include:\n",
    "   - Strengthening healthcare infrastructure and facilities.\n",
    "   - Enhancing access to quality healthcare services, including medical facilities and personnel.\n",
    "\n",
    "5. Healthcare Disparities Between Urban and Rural Settlements:\n",
    "   Urban settlements have a higher death rate than rural areas. To address this disparity, efforts should focus on:\n",
    "   - Improving access to healthcare services in urban areas, including primary care facilities and emergency services.\n",
    "   - Addressing social determinants of health, such as poverty, housing conditions, and access to clean water and sanitation.\n",
    "\n",
    "6. Regional Disparities in Disease Burden:\n",
    "   Northeast region has the lowest disease count, while the South East has the highest. Strategies should be tailored \n",
    "to address regional disparities in disease burden:\n",
    "   - Conduct further research to understand the underlying factors contributing to regional differences in disease prevalence.\n",
    "   - Implement targeted interventions and health promotion programs to address specific health needs in each region.\n",
    "\n",
    "Conclusion:\n",
    "The analysis of the dataset `ra_merged` highlights significant patterns and trends in disease distribution, \n",
    "healthcare outcomes, and resource utilization. By leveraging these insights and recommendations, \n",
    "healthcare policymakers and stakeholders can develop targeted interventions, allocate resources effectively, \n",
    "and implement strategies to improve healthcare delivery, ultimately enhancing the overall health and well-being \n",
    "of the population."
   ]
  }
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