{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6f434067",
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.vision.all import *\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "072b5286",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "pd.plotting.register_matplotlib_converters()\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9688a933",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = r\"C:\\Users\\moham\\OneDrive\\Documents\\ML data\\world-data-2023.csv\"\n",
    "\n",
    "data = pd.read_csv(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0ab8ebde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Density\\n(P/Km2)</th>\n",
       "      <th>Abbreviation</th>\n",
       "      <th>Agricultural Land( %)</th>\n",
       "      <th>Land Area(Km2)</th>\n",
       "      <th>Armed Forces size</th>\n",
       "      <th>Birth Rate</th>\n",
       "      <th>Calling Code</th>\n",
       "      <th>Capital/Major City</th>\n",
       "      <th>Co2-Emissions</th>\n",
       "      <th>...</th>\n",
       "      <th>Out of pocket health expenditure</th>\n",
       "      <th>Physicians per thousand</th>\n",
       "      <th>Population</th>\n",
       "      <th>Population: Labor force participation (%)</th>\n",
       "      <th>Tax revenue (%)</th>\n",
       "      <th>Total tax rate</th>\n",
       "      <th>Unemployment rate</th>\n",
       "      <th>Urban_population</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>60</td>\n",
       "      <td>AF</td>\n",
       "      <td>58.10%</td>\n",
       "      <td>652,230</td>\n",
       "      <td>323,000</td>\n",
       "      <td>32.49</td>\n",
       "      <td>93.0</td>\n",
       "      <td>Kabul</td>\n",
       "      <td>8,672</td>\n",
       "      <td>...</td>\n",
       "      <td>78.40%</td>\n",
       "      <td>0.28</td>\n",
       "      <td>38,041,754</td>\n",
       "      <td>48.90%</td>\n",
       "      <td>9.30%</td>\n",
       "      <td>71.40%</td>\n",
       "      <td>11.12%</td>\n",
       "      <td>9,797,273</td>\n",
       "      <td>33.939110</td>\n",
       "      <td>67.709953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>105</td>\n",
       "      <td>AL</td>\n",
       "      <td>43.10%</td>\n",
       "      <td>28,748</td>\n",
       "      <td>9,000</td>\n",
       "      <td>11.78</td>\n",
       "      <td>355.0</td>\n",
       "      <td>Tirana</td>\n",
       "      <td>4,536</td>\n",
       "      <td>...</td>\n",
       "      <td>56.90%</td>\n",
       "      <td>1.20</td>\n",
       "      <td>2,854,191</td>\n",
       "      <td>55.70%</td>\n",
       "      <td>18.60%</td>\n",
       "      <td>36.60%</td>\n",
       "      <td>12.33%</td>\n",
       "      <td>1,747,593</td>\n",
       "      <td>41.153332</td>\n",
       "      <td>20.168331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>18</td>\n",
       "      <td>DZ</td>\n",
       "      <td>17.40%</td>\n",
       "      <td>2,381,741</td>\n",
       "      <td>317,000</td>\n",
       "      <td>24.28</td>\n",
       "      <td>213.0</td>\n",
       "      <td>Algiers</td>\n",
       "      <td>150,006</td>\n",
       "      <td>...</td>\n",
       "      <td>28.10%</td>\n",
       "      <td>1.72</td>\n",
       "      <td>43,053,054</td>\n",
       "      <td>41.20%</td>\n",
       "      <td>37.20%</td>\n",
       "      <td>66.10%</td>\n",
       "      <td>11.70%</td>\n",
       "      <td>31,510,100</td>\n",
       "      <td>28.033886</td>\n",
       "      <td>1.659626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>164</td>\n",
       "      <td>AD</td>\n",
       "      <td>40.00%</td>\n",
       "      <td>468</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.20</td>\n",
       "      <td>376.0</td>\n",
       "      <td>Andorra la Vella</td>\n",
       "      <td>469</td>\n",
       "      <td>...</td>\n",
       "      <td>36.40%</td>\n",
       "      <td>3.33</td>\n",
       "      <td>77,142</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67,873</td>\n",
       "      <td>42.506285</td>\n",
       "      <td>1.521801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>26</td>\n",
       "      <td>AO</td>\n",
       "      <td>47.50%</td>\n",
       "      <td>1,246,700</td>\n",
       "      <td>117,000</td>\n",
       "      <td>40.73</td>\n",
       "      <td>244.0</td>\n",
       "      <td>Luanda</td>\n",
       "      <td>34,693</td>\n",
       "      <td>...</td>\n",
       "      <td>33.40%</td>\n",
       "      <td>0.21</td>\n",
       "      <td>31,825,295</td>\n",
       "      <td>77.50%</td>\n",
       "      <td>9.20%</td>\n",
       "      <td>49.10%</td>\n",
       "      <td>6.89%</td>\n",
       "      <td>21,061,025</td>\n",
       "      <td>-11.202692</td>\n",
       "      <td>17.873887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country Density\\n(P/Km2) Abbreviation Agricultural Land( %)  \\\n",
       "0  Afghanistan               60           AF                58.10%   \n",
       "1      Albania              105           AL                43.10%   \n",
       "2      Algeria               18           DZ                17.40%   \n",
       "3      Andorra              164           AD                40.00%   \n",
       "4       Angola               26           AO                47.50%   \n",
       "\n",
       "  Land Area(Km2) Armed Forces size  Birth Rate  Calling Code  \\\n",
       "0        652,230           323,000       32.49          93.0   \n",
       "1         28,748             9,000       11.78         355.0   \n",
       "2      2,381,741           317,000       24.28         213.0   \n",
       "3            468               NaN        7.20         376.0   \n",
       "4      1,246,700           117,000       40.73         244.0   \n",
       "\n",
       "  Capital/Major City Co2-Emissions  ... Out of pocket health expenditure  \\\n",
       "0              Kabul         8,672  ...                           78.40%   \n",
       "1             Tirana         4,536  ...                           56.90%   \n",
       "2            Algiers       150,006  ...                           28.10%   \n",
       "3   Andorra la Vella           469  ...                           36.40%   \n",
       "4             Luanda        34,693  ...                           33.40%   \n",
       "\n",
       "  Physicians per thousand  Population  \\\n",
       "0                    0.28  38,041,754   \n",
       "1                    1.20   2,854,191   \n",
       "2                    1.72  43,053,054   \n",
       "3                    3.33      77,142   \n",
       "4                    0.21  31,825,295   \n",
       "\n",
       "   Population: Labor force participation (%) Tax revenue (%) Total tax rate  \\\n",
       "0                                     48.90%           9.30%         71.40%   \n",
       "1                                     55.70%          18.60%         36.60%   \n",
       "2                                     41.20%          37.20%         66.10%   \n",
       "3                                        NaN             NaN            NaN   \n",
       "4                                     77.50%           9.20%         49.10%   \n",
       "\n",
       "  Unemployment rate Urban_population   Latitude  Longitude  \n",
       "0            11.12%        9,797,273  33.939110  67.709953  \n",
       "1            12.33%        1,747,593  41.153332  20.168331  \n",
       "2            11.70%       31,510,100  28.033886   1.659626  \n",
       "3               NaN           67,873  42.506285   1.521801  \n",
       "4             6.89%       21,061,025 -11.202692  17.873887  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "50f52a71",
   "metadata": {},
   "outputs": [],
   "source": [
    "useless = [\"Abbreviation\",\"\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "534b2ea2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                                       object\n",
       "Density\\n(P/Km2)                              object\n",
       "Abbreviation                                  object\n",
       "Agricultural Land( %)                         object\n",
       "Land Area(Km2)                                object\n",
       "Armed Forces size                             object\n",
       "Birth Rate                                   float64\n",
       "Calling Code                                 float64\n",
       "Capital/Major City                            object\n",
       "Co2-Emissions                                 object\n",
       "CPI                                           object\n",
       "CPI Change (%)                                object\n",
       "Currency-Code                                 object\n",
       "Fertility Rate                               float64\n",
       "Forested Area (%)                             object\n",
       "Gasoline Price                                object\n",
       "GDP                                           object\n",
       "Gross primary education enrollment (%)        object\n",
       "Gross tertiary education enrollment (%)       object\n",
       "Infant mortality                             float64\n",
       "Largest city                                  object\n",
       "Life expectancy                              float64\n",
       "Maternal mortality ratio                     float64\n",
       "Minimum wage                                  object\n",
       "Official language                             object\n",
       "Out of pocket health expenditure              object\n",
       "Physicians per thousand                      float64\n",
       "Population                                    object\n",
       "Population: Labor force participation (%)     object\n",
       "Tax revenue (%)                               object\n",
       "Total tax rate                                object\n",
       "Unemployment rate                             object\n",
       "Urban_population                              object\n",
       "Latitude                                     float64\n",
       "Longitude                                    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e15d2fd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.select_dtypes(exclude=[\"object\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ad6cf0fe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
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",
      "text/plain": [
       "<Figure size 2000x10000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "corr = data.corr()\n",
    "\n",
    "\n",
    "plt.figure(figsize=(20,100))\n",
    "sns.heatmap(corr,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3b356a23",
   "metadata": {},
   "outputs": [],
   "source": [
    "useless = ['Longitude','Latitude','Calling Code']\n",
    "data.drop(useless,axis=1)\n",
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2dfd35dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data[\"Life expectancy\"]\n",
    "features = ['Birth Rate','Fertility Rate','Infant mortality','Maternal mortality ratio','Physicians per thousand']\n",
    "x = data[features]\n",
    "\n",
    "train_x, test_x, train_y, test_y = train_test_split(x,y,test_size=0.8,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d51607f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "40f08a99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(random_state=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor(random_state=1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = DecisionTreeRegressor(random_state=1)\n",
    "\n",
    "model.fit(train_x,train_y)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "00c19a23",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dff4f747",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5180555555555553"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "mean_absolute_error(test_y,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8457f3a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([82.7, 66.7, 65. , 65. , 78.5, 65. , 66.7, 69.8, 82.7, 72.8, 72.8,\n",
       "       68.7, 75. , 82.7, 75. , 81.9, 82.9, 54.3, 68.7, 64.1, 68.7, 82.7,\n",
       "       64.1, 76.1, 80.9, 82.5, 72.8, 65. , 69.8, 75.5, 76.5, 82.7, 82.7,\n",
       "       72.8, 75.4, 78.5, 72.8, 69.8, 69.8, 75.4, 75.4, 75.4, 70.9, 54.3,\n",
       "       78.5, 82.7, 61.2, 68.7, 54.3, 68.7, 61.2, 75.5, 78.5, 82.7, 69.8,\n",
       "       66.7, 80.9, 72.8, 82.7, 76.5, 69.8, 66.7, 65. , 69.8, 75.5, 61.2,\n",
       "       75.7, 73.1, 82.7, 80.9, 75.7, 72.8, 77.3, 72.8, 66.7, 72.8, 76.5,\n",
       "       68.7, 81. , 72.8, 72.8, 78.5, 75.7, 75.7, 64.1, 76.5, 82.7, 68.7,\n",
       "       69.8, 61.2, 66.7, 68.7, 66.7, 66.7, 68.7, 65. , 72.8, 61.2, 77.6,\n",
       "       72.8, 66.7, 78.5, 78.5, 54.3, 66.7, 65. , 75.4, 65. , 68.7, 64.1,\n",
       "       61.2, 78.5, 76.1, 65. , 75.7, 64.1, 76.5, 72.8, 69.8, 80.9, 72.8,\n",
       "       75.4, 66.7, 82.3, 66.7, 76.5, 82.7, 73.1, 75.8, 75.3, 72.8, 72.8,\n",
       "       68.7, 61.2, 70.9, 82.7, 78.5, 61.2, 75.4, 75.3, 78.5, 78.5, 65. ,\n",
       "       75.4])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bfc81da6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "filename = r\"C:\\Users\\moham\\OneDrive\\Documents\\abc.pickle\"\n",
    "pickle.dump(model, open(filename, \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6cb04ab9",
   "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>Birth Rate</th>\n",
       "      <th>Fertility Rate</th>\n",
       "      <th>Infant mortality</th>\n",
       "      <th>Maternal mortality ratio</th>\n",
       "      <th>Physicians per thousand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.49</td>\n",
       "      <td>4.47</td>\n",
       "      <td>47.9</td>\n",
       "      <td>638.0</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.78</td>\n",
       "      <td>1.62</td>\n",
       "      <td>7.8</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24.28</td>\n",
       "      <td>3.02</td>\n",
       "      <td>20.1</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.73</td>\n",
       "      <td>5.52</td>\n",
       "      <td>51.6</td>\n",
       "      <td>241.0</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15.33</td>\n",
       "      <td>1.99</td>\n",
       "      <td>5.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Birth Rate  Fertility Rate  Infant mortality  Maternal mortality ratio  \\\n",
       "0       32.49            4.47              47.9                     638.0   \n",
       "1       11.78            1.62               7.8                      15.0   \n",
       "2       24.28            3.02              20.1                     112.0   \n",
       "4       40.73            5.52              51.6                     241.0   \n",
       "5       15.33            1.99               5.0                      42.0   \n",
       "\n",
       "   Physicians per thousand  \n",
       "0                     0.28  \n",
       "1                     1.20  \n",
       "2                     1.72  \n",
       "4                     0.21  \n",
       "5                     2.76  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0cb2e34e",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_path = pd.read_csv(r\"C:\\Users\\moham\\OneDrive\\Documents\\ML data\\Tests\\imp.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4e18561b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_2 = model.predict(test_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "84dd5873",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([64.1])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_2"
   ]
  }
 ],
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