01.ipynb

{
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   "cell_type": "markdown",
   "id": "c4d46091-adb8-4e84-a1ff-80d9d2a06865",
   "metadata": {},
   "source": [
    "# Example 1-1 (Pg-21)\n",
    "\n",
    "Does happiness increases with increase in money?\n",
    "\n",
    "Training and running a linear model using Scikit-Learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ad30b9ee-3912-4600-88d5-95606caf658d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn.linear_model\n",
    "import sklearn.neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ba3cca84-6c14-4881-b3b9-5548f447bfeb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "oecd_bli = pd.read_csv(\"datasets/lifesat/oecd_bli_2015.csv\", thousands=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "76fb0db5-d122-4994-8240-9248cc56cc6e",
   "metadata": {
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   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LOCATION</th>\n",
       "      <th>Country</th>\n",
       "      <th>INDICATOR</th>\n",
       "      <th>Indicator</th>\n",
       "      <th>MEASURE</th>\n",
       "      <th>Measure</th>\n",
       "      <th>INEQUALITY</th>\n",
       "      <th>Inequality</th>\n",
       "      <th>Unit Code</th>\n",
       "      <th>Unit</th>\n",
       "      <th>PowerCode Code</th>\n",
       "      <th>PowerCode</th>\n",
       "      <th>Reference Period Code</th>\n",
       "      <th>Reference Period</th>\n",
       "      <th>Value</th>\n",
       "      <th>Flag Codes</th>\n",
       "      <th>Flags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AUS</td>\n",
       "      <td>Australia</td>\n",
       "      <td>HO_BASE</td>\n",
       "      <td>Dwellings without basic facilities</td>\n",
       "      <td>L</td>\n",
       "      <td>Value</td>\n",
       "      <td>TOT</td>\n",
       "      <td>Total</td>\n",
       "      <td>PC</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>0</td>\n",
       "      <td>units</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.1</td>\n",
       "      <td>E</td>\n",
       "      <td>Estimated value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AUT</td>\n",
       "      <td>Austria</td>\n",
       "      <td>HO_BASE</td>\n",
       "      <td>Dwellings without basic facilities</td>\n",
       "      <td>L</td>\n",
       "      <td>Value</td>\n",
       "      <td>TOT</td>\n",
       "      <td>Total</td>\n",
       "      <td>PC</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>0</td>\n",
       "      <td>units</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BEL</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>HO_BASE</td>\n",
       "      <td>Dwellings without basic facilities</td>\n",
       "      <td>L</td>\n",
       "      <td>Value</td>\n",
       "      <td>TOT</td>\n",
       "      <td>Total</td>\n",
       "      <td>PC</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>0</td>\n",
       "      <td>units</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>HO_BASE</td>\n",
       "      <td>Dwellings without basic facilities</td>\n",
       "      <td>L</td>\n",
       "      <td>Value</td>\n",
       "      <td>TOT</td>\n",
       "      <td>Total</td>\n",
       "      <td>PC</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>0</td>\n",
       "      <td>units</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CZE</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>HO_BASE</td>\n",
       "      <td>Dwellings without basic facilities</td>\n",
       "      <td>L</td>\n",
       "      <td>Value</td>\n",
       "      <td>TOT</td>\n",
       "      <td>Total</td>\n",
       "      <td>PC</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>0</td>\n",
       "      <td>units</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  LOCATION         Country INDICATOR                           Indicator  \\\n",
       "0      AUS       Australia   HO_BASE  Dwellings without basic facilities   \n",
       "1      AUT         Austria   HO_BASE  Dwellings without basic facilities   \n",
       "2      BEL         Belgium   HO_BASE  Dwellings without basic facilities   \n",
       "3      CAN          Canada   HO_BASE  Dwellings without basic facilities   \n",
       "4      CZE  Czech Republic   HO_BASE  Dwellings without basic facilities   \n",
       "\n",
       "  MEASURE Measure INEQUALITY Inequality Unit Code        Unit  PowerCode Code  \\\n",
       "0       L   Value        TOT      Total        PC  Percentage               0   \n",
       "1       L   Value        TOT      Total        PC  Percentage               0   \n",
       "2       L   Value        TOT      Total        PC  Percentage               0   \n",
       "3       L   Value        TOT      Total        PC  Percentage               0   \n",
       "4       L   Value        TOT      Total        PC  Percentage               0   \n",
       "\n",
       "  PowerCode  Reference Period Code  Reference Period  Value Flag Codes  \\\n",
       "0     units                    NaN               NaN    1.1          E   \n",
       "1     units                    NaN               NaN    1.0        NaN   \n",
       "2     units                    NaN               NaN    2.0        NaN   \n",
       "3     units                    NaN               NaN    0.2        NaN   \n",
       "4     units                    NaN               NaN    0.9        NaN   \n",
       "\n",
       "             Flags  \n",
       "0  Estimated value  \n",
       "1              NaN  \n",
       "2              NaN  \n",
       "3              NaN  \n",
       "4              NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oecd_bli.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2a449894-b521-4a09-8240-a5b90d79f952",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "gdp_per_capita = pd.read_csv(\"datasets/lifesat/gdp_per_capita.csv\" ,delimiter=\"\\t\", encoding=\"latin1\", thousands=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "813d0122-d281-47bf-be2a-1be1619d0b35",
   "metadata": {
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Subject Descriptor</th>\n",
       "      <th>Units</th>\n",
       "      <th>Scale</th>\n",
       "      <th>Country/Series-specific Notes</th>\n",
       "      <th>2015</th>\n",
       "      <th>Estimates Start After</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>599.994</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>3995.383</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>4318.135</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>4100.315</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>14414.302</td>\n",
       "      <td>2011.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Country                                 Subject Descriptor  \\\n",
       "0          Afghanistan  Gross domestic product per capita, current prices   \n",
       "1              Albania  Gross domestic product per capita, current prices   \n",
       "2              Algeria  Gross domestic product per capita, current prices   \n",
       "3               Angola  Gross domestic product per capita, current prices   \n",
       "4  Antigua and Barbuda  Gross domestic product per capita, current prices   \n",
       "\n",
       "          Units  Scale                      Country/Series-specific Notes  \\\n",
       "0  U.S. dollars  Units  See notes for:  Gross domestic product, curren...   \n",
       "1  U.S. dollars  Units  See notes for:  Gross domestic product, curren...   \n",
       "2  U.S. dollars  Units  See notes for:  Gross domestic product, curren...   \n",
       "3  U.S. dollars  Units  See notes for:  Gross domestic product, curren...   \n",
       "4  U.S. dollars  Units  See notes for:  Gross domestic product, curren...   \n",
       "\n",
       "        2015  Estimates Start After  \n",
       "0    599.994                 2013.0  \n",
       "1   3995.383                 2010.0  \n",
       "2   4318.135                 2014.0  \n",
       "3   4100.315                 2014.0  \n",
       "4  14414.302                 2011.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp_per_capita.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25b9d46a-0c5f-41b9-ad98-d7ab5cded5cb",
   "metadata": {},
   "source": [
    "This function just merges OECD's life satisfaction data and IMF's GDP per capita income."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1581dbd5-182c-466a-955c-7859683097fa",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def prepare_country_stats(oecd_bli,gdp_per_capita):\n",
    "    oecd_bli = oecd_bli[oecd_bli[\"INEQUALITY\"]==\"TOT\"]\n",
    "    # Modify the dataframe. Index will become Country, columns will be indicators and values will be \"Value\"\n",
    "    oecd_bli = oecd_bli.pivot(index=\"Country\", columns=\"Indicator\", values=\"Value\")\n",
    "    gdp_per_capita.rename(columns={\"2015\": \"GDP per capita\"}, inplace=True) # Renaming the column\n",
    "    gdp_per_capita.set_index(\"Country\", inplace=True)    \n",
    "    full_country_stats = pd.merge(left=oecd_bli, right=gdp_per_capita, left_index=True, right_index=True)\n",
    "    full_country_stats.sort_values(by=\"GDP per capita\", inplace=True)\n",
    "    remove_indices = [0, 1, 6, 8, 33, 34, 35]\n",
    "    keep_indices = list(set(range(36)) - set(remove_indices))\n",
    "    \n",
    "    return full_country_stats[[\"GDP per capita\", 'Life satisfaction']].iloc[keep_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "35fae58a-ddd8-499a-a90e-e0249325892f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Prepare the data\n",
    "country_stats = prepare_country_stats(oecd_bli, gdp_per_capita)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "32310327-3bc6-4378-934a-a240e3369454",
   "metadata": {
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   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Life satisfaction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>9054.914</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>9437.372</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>12239.894</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>12495.334</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovak Republic</th>\n",
       "      <td>15991.736</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                 GDP per capita  Life satisfaction\n",
       "Country                                           \n",
       "Russia                 9054.914                6.0\n",
       "Turkey                 9437.372                5.6\n",
       "Hungary               12239.894                4.9\n",
       "Poland                12495.334                5.8\n",
       "Slovak Republic       15991.736                6.1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eba0d566",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.c_[country_stats[\"GDP per capita\"]]\n",
    "y = np.c_[country_stats[\"Life satisfaction\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "055c46f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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WFqawsDA31xJoXbrHRWpoz7hLXqvBqYzWgf0IeIbbR27mz5+vBQsW6IUXXtAdd9yhO++8Uy+++KL+8pe/6JVXXnFrWQkJCbr66qud2q666ioVFxe7tZxBgwYpPz/frXkAq5k/Pk1DUmOd2oakxmr++DQfVYTmYD8CV87tIzelpaUaPHhwg/bBgwertLTUrWUNGTJEeXl5Tm1ffvmlunXr5tZydu3apYSEBLfmAazG3i5UWQ8MVGHFSR04fFLJMRH8n34rxH4ErlyznnOzfPlyzZw506n97bffVs+ePd1a1tSpUzV48GA999xzGjt2rLZt26ZFixZp0aJFjj4zZsxQSUmJ4zqczMxMJScnq3fv3qqpqdGSJUuUnZ2t7Oxsd1cFsKSUWH4MrYD9CDSf2+Fm7ty5+slPfqKcnBwNGTJENptN//jHP7Ru3TotX77crWVdd911WrlypWbMmKHf/va3SklJUWZmpiZMmODoU1pa6nSaqqamRtOnT1dJSYnCw8PVu3dvvf/++xozZoy7qwIAACzI7Yf4SdLOnTv18ssvKzc3V8YYXX311frlL3+ptDT/PyfMQ/wAAGh93Pn9bla4ac0INwAAtD4ef0JxVVWVY0GXe8IvgQEAAPiSS+GmQ4cOKi0tdQx5YLPZGvQxxshms6m2ttbjRQIAALjKpXDzySefqGPHjpLU5FAHAAAAvuZSuBk2bJjj3ykpKUpKSmpw9MYYw9AGAADA59x+QnFKSorKyxuOWnvkyBGlpKR4pCgAAIDmcjvc1F9bc7ETJ06obdu2HikKAACguVx+iN+0adMkSTabTU899ZTatfv36LW1tbXaunWrrr32Wo8XCAAA4A6Xw82uXbsknT9ys2fPHrVp08YxrU2bNurXr5+mT5/u+QoBAADc4HK4qb9L6v7779cf//hHnmcDAAD8ktvX3GRmZurcuXMN2o8cOXLZB/wBAAB4m9vhZty4cVq2bFmD9uXLl2vcuHEeKQoAAKC53A43W7du1fDhwxu033TTTdq6datHigIAAGgut8NNdXV1o6elzp49q9OnT3ukKAAAgOZyO9xcd911WrRoUYP2hQsXqn///h4pCgAAoLlcvluq3rPPPquRI0fqn//8p0aMGCFJWrdunbZv367Vq1d7vEAAAAB3uH3kZsiQIdq8ebOSkpK0fPlyvfvuu0pNTdXnn3+uG2+80Rs1AgAAuMxmjDG+LqIlVVVVyW63q7Kykmf1AADQSrjz++32aakLnT59WmfPnnVqIzAAAABfcvu01KlTpzR58mTFx8crMjJSHTp0cHoBAAD4ktvh5le/+pU++eQT/eUvf1FYWJhee+01zZ07V4mJicrKyvJGjQAAAC5z+7TUu+++q6ysLN10003KyMjQjTfeqNTUVHXr1k1///vfNWHCBG/UCQAA4BK3j9wcOXJEKSkpks5fX3PkyBFJ0g033KCcnBzPVgcAAOAmt8NN9+7ddeDAAUnS1VdfreXLl0s6f0Snffv2nqwNAADAbW6Hm/vvv1///Oc/JUkzZsxwXHszdepU/epXv/J4gQAAAO644ufcFBcXa8eOHerRo4f69evnqbq8hufcAADQ+rjz++3SkZuOHTuqoqJCkpSRkaHjx487pnXt2lV33313qwg2AADA+lwKNzU1NaqqqpIkvfXWWzpz5oxXiwIAAGgul24FT09P11133aX+/fvLGKMpU6YoPDy80b5vvPGGRwsEAABwh0vhZsmSJXr55Zf19ddfy2azqbKykqM3AADAL7l9QXFKSop27NihmJgYb9XkVVxQDABA6+PVgTMLCwsbtB07doxn3AAAAL/g9nNuXnjhBb399tuO92PHjlXHjh31ne98x/H8GwAAAF9xO9z89a9/VVJSkiRpzZo1WrNmjT766CONHj2ah/gBAACfc/u0VGlpqSPcvPfeexo7dqxuueUWJScn6/rrr/d4gQAAAO5w+8hNhw4ddPDgQUnSRx99pJEjR0qSjDGqra31bHUAAABucvvIzd13363/+I//UM+ePXX48GGNHj1akrR7926lpqZ6vEAAAAB3uB1uXn75ZSUnJ+vgwYN68cUXFRkZKen86apHH33U4wUCAAC444oHzmxteM5NyygoP6GiI6eUHBOhlNgIX5cDAGjlPP6cm1WrVmn06NEKDQ3VqlWrmux7xx13uF4pLOfYqRpNWbpbOfnljrahPeM0f3ya7O1CfVgZACBQuHTkJigoSGVlZYqPj1dQ0KWvQbbZbH5/UTFHbrxr4uvbtOmrCtVe8LUKttk0JDVWWQ8M9GFlAIDWzONHburq6hr9N3ChgvITTkds6tUao5z8chVWnOQUFQDA69y+FTwrK0vV1dUN2mtqapSVleWRotA6FR051eT0A4dPtlAlAIBA5na4uf/++1VZWdmg/fjx47r//vs9UhRap24d2zU5PTmGozYAAO9zO9wYY2Sz2Rq0f/PNN7Lb7R4pCq1T97hIDe0Zp+CLvh/BNpuG9ozjlBQAoEW4/JybtLQ02Ww22Ww2jRgxQiEh/561trZWhYWF+uEPf+iVItF6zB+fpseX7nK69mZIaqzmj0/zYVUAgEDicri56667JJ1/EvGoUaMcD++TpDZt2ig5OVn33HOPxwtE62JvF6qsBwaqsOKkDhw+yXNuAAAtzuVwM3v2bElScnKyfvKTn6ht27ZeKwqtX0osoQYA4BtuD78wadIkb9QBAADgEW6Hm9raWr388stavny5iouLVVNT4zT9yJEjHisOAADAXW7fLTV37lz94Q9/0NixY1VZWalp06bp7rvvVlBQkObMmeOFEgEAAFzndrj5+9//rldffVXTp09XSEiIxo8fr9dee02zZs3Sli1b3C6gpKREP/3pTxUTE6N27drp2muv1c6dO5ucZ+PGjerfv7/atm2r7t27a+HChW5/rjcUlJ/Q+rxDKqzgYXVAY/gbAdAS3D4tVVZWpr59+0qSIiMjHQ/0u+222/TUU0+5tayjR49qyJAhGj58uD788EPFx8fr66+/Vvv27S85T2FhocaMGaOHHnpIS5Ys0aZNm/Too48qLi7OZ3drMVgk0DT+RgC0JLeP3HTp0kWlpaWSpNTUVK1evVqStH37doWFhbm1rBdeeEFJSUlavHixBg4cqOTkZI0YMUI9evS45DwLFy5U165dlZmZqauuukoPPvigMjIy9NJLL7m7Kh4zZelubfqqwqlt01cVenzpLh9VBPgX/kYAtCS3w82PfvQjrVu3TpL0xBNP6KmnnlLPnj01ceJEZWRkuLWsVatWacCAAbr33nsVHx+vtLQ0vfrqq03Os3nzZt1yyy1ObaNGjdKOHTt09uzZBv2rq6tVVVXl9PKk+sEiay8aXP3CwSKBQMbfCICW5vZpqeeff97x7x//+Mfq0qWLPv30U6WmpuqOO+5wa1kFBQVasGCBpk2bppkzZ2rbtm2aMmWKwsLCNHHixEbnKSsrU6dOnZzaOnXqpHPnzqmiokIJCQlO0+bNm6e5c+e6VZc7XBkskue9IJDxNwKgpbkdbi42aNAgDRo0qFnz1tXVacCAAXruuecknR/iYd++fVqwYMElw42kBmNbmf/7P8LGxryaMWOGpk2b5nhfVVWlpKSkZtXbGAaLBJrG3wiAlub2aam33npL77//vuP9r3/9a7Vv316DBw9WUVGRW8tKSEjQ1Vdf7dR21VVXqbi4+JLzdO7cWWVlZU5thw4dUkhIiGJiYhr0DwsLU3R0tNPLkxgsEmgafyMAWprb4ea5555TeHi4pPPXv/zpT3/Siy++qNjYWE2dOtWtZQ0ZMkR5eXlObV9++aW6det2yXnS09O1Zs0ap7bVq1drwIABCg31zV0X88enaUhqrFMbg0UC/8bfCICWZDPmoqv8LqNdu3bav3+/unbtqt/85jcqLS1VVlaW9u3bp5tuuknl5eWXX8j/2b59uwYPHqy5c+dq7Nix2rZtmx566CEtWrRIEyZMkHT+tFJJSYmysrIknb8VvE+fPvrFL36hhx56SJs3b9bDDz+spUuXunQreFVVlex2uyorKz1+FIfBIoGm8TcCoLnc+f12+8hNZGSkDh8+LOn8EZORI0dKktq2bavTp0+7tazrrrtOK1eu1NKlS9WnTx89/fTTyszMdAQbSSotLXU6TZWSkqIPPvhAGzZs0LXXXqunn35ar7zyil+MSJ4SG6HhveL5jzZwCfyNAGgJbh+5mTBhgvbv36+0tDQtXbpUxcXFiomJ0apVqzRz5kzt3bvXW7V6hDeP3AAAAO/w6pGbP//5z0pPT1d5ebmys7MdF/Hu3LlT48ePb17FAAAAHuL2kZvWjiM3gLUVlJ9Q0ZFTXNcDWIw7v99X/JwbAPAHjF8FoJ7bp6UAwB8xfhWAeoQbAK0e41cBuBDhBkCr58r4VQACR7PCzblz57R27Vr99a9/1fHjxyVJ3377rU6cOOHR4gDAFYxfBeBCbl9QXFRUpB/+8IcqLi5WdXW1br75ZkVFRenFF1/UmTNntHDhQm/UCQCXVD9+1aavKpxOTQXbbBqSGstdU0CAcfvIzRNPPKEBAwbo6NGjjjGmJOlHP/qR1q1b59HiAMBVjF8FoJ7bR27+8Y9/aNOmTWrTpo1Te7du3VRSUuKxwgDAHfZ2ocp6YCDjVwFwP9zU1dWptra2Qfs333yjqKgojxQFAM2VEkuoAQKd26elbr75ZmVmZjre22w2nThxQrNnz9aYMWM8WRsAAIDb3B5+4dtvv9Xw4cMVHBys/Px8DRgwQPn5+YqNjVVOTo7i4+O9VatHMPwCAACtj1eHX0hMTNTu3bu1bNky7dy5U3V1dXrggQc0YcIEpwuM4RuMq4Pm4HsDwEpcOnLz/e9/X+vWrVOHDh3029/+VtOnT1e7dk0/V8JfWfXIDePqoDn43gBoLdz5/Xbpmpvc3FydPHn+CZ9z587lYX1+iHF10Bx8bwBYkUunpa699lrdf//9uuGGG2SM0UsvvaTIyMhG+86aNcujBeLy6sfVudiF4+pwqgEX43sDwKpcCjdvvvmmZs+erffee082m00ffvihQkIazmqz2Qg3PuDKuDr8SOFifG8AWJVL4aZXr15atmyZJCkoKEjr1q3z+7uiAgnj6qA5+N4AsCq3n3NTV1dHsPEz9ePqBNtsTu3BNpuG9ozj/77RKL43AKzKpbulVq1apdGjRys0NFSrVq1qsu8dd9zhseK8wap3S1WeOqvHl+7irhe4he8NgNbCnd9vl8JNUFCQysrKFB8fr6CgSx/ssdlsjQ7N4E+sGm7qMa4OmoPvDQB/5/GH+NXV1TX6b/gfxtVBc/C9AWAlbl9zcykHDx5URkaGpxYHAADQLB4LN0eOHNFbb73lqcUBAAA0i8fCDQAAgD9we+BMwMoYQBIAWj/CDSAGkAQAK3E53Nx9991NTj927NiV1gL4TFMDSGY9MNBHVQEAmsPlcGO32y87feLEiVdcENDSGEASAKzF5XCzePFib9YB+AwDSAKAtXC3FAIeA0gCgLUQbhDwGEASAKyFcANImj8+TUNSY53ahqTGav74NB9VBABoLm4FByTZ24Uq64GBDCAJABZAuAEuwACSAND6cVoKAABYCuEGAABYCqelvIhxilzHtgIAeArhxgsYp8h1bCsAgKdxWsoLmhqnCM7YVgAATyPceFj9OEW1xji1XzhOEc5jWwEAvIFw42GujFOE89hWAABvINx4GOMUuY5tBQDwBsKNhzFOkevYVgAAbyDceAHjFLmObQUA8DSbMRddzWlxVVVVstvtqqysVHR0tFc/i3GKXMe2AgA0xZ3fb55z40WMU+Q6thUAwFM4LQUAACyFcAMAACyFcAMAACzFp+Fmzpw5stlsTq/OnTtfsv+GDRsa9LfZbNq/f38LVg0AAPyZzy8o7t27t9auXet4HxwcfNl58vLynK6UjouL80ptAACg9fF5uAkJCWnyaE1j4uPj1b59e+8UBAAAWjWfX3OTn5+vxMREpaSkaNy4cSooKLjsPGlpaUpISNCIESO0fv36JvtWV1erqqrK6QUAAKzLp+Hm+uuvV1ZWlj7++GO9+uqrKisr0+DBg3X48OFG+yckJGjRokXKzs7WihUr1KtXL40YMUI5OTmX/Ix58+bJbrc7XklJSd5aHQAA4Af86gnFJ0+eVI8ePfTrX/9a06ZNc2me22+/XTabTatWrWp0enV1taqrqx3vq6qqlJSU1CJPKAYAAJ7hzhOKfX5a6kIRERHq27ev8vPzXZ5n0KBBTfYPCwtTdHS00wsAAFiXX4Wb6upq5ebmKiEhweV5du3a5VZ/AABgbT69W2r69Om6/fbb1bVrVx06dEjPPPOMqqqqNGnSJEnSjBkzVFJSoqysLElSZmamkpOT1bt3b9XU1GjJkiXKzs5Wdna2L1cDAAD4EZ+Gm2+++Ubjx49XRUWF4uLiNGjQIG3ZskXdunWTJJWWlqq4uNjRv6amRtOnT1dJSYnCw8PVu3dvvf/++xozZoyvVgEAAPgZv7qguCW4c0ESAADwD632gmIAAIArRbgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWEuLrAgBPKig/oaIjp5QcE6GU2AhflwMA8AHCDSzh2KkaTVm6Wzn55Y62oT3jNH98muztQn1YGQCgpXFaCpYwZelubfqqwqlt01cVenzpLh9VBADwFcINWr2C8hPKyS9XrTFO7bXGKCe/XIUVJ31UGQDAFwg3aPWKjpxqcvqBw4QbAAgkhBu0et06tmtyenIMFxYDQCAh3KDV6x4XqaE94xRsszm1B9tsGtozjrumACDAEG5gCfPHp2lIaqxT25DUWM0fn+ajigAAvsKt4LAEe7tQZT0wUIUVJ3Xg8EmecwMAAYxwA0tJiSXUAECg47QUAACwFMINAACwFJ+Gmzlz5shmszm9Onfu3OQ8GzduVP/+/dW2bVt1795dCxcubKFqAQBAa+Dza2569+6ttWvXOt4HBwdfsm9hYaHGjBmjhx56SEuWLNGmTZv06KOPKi4uTvfcc09LlAsAAPycz8NNSEjIZY/W1Fu4cKG6du2qzMxMSdJVV12lHTt26KWXXiLcAAAASX5wzU1+fr4SExOVkpKicePGqaCg4JJ9N2/erFtuucWpbdSoUdqxY4fOnj3b6DzV1dWqqqpyegEAAOvyabi5/vrrlZWVpY8//livvvqqysrKNHjwYB0+fLjR/mVlZerUqZNTW6dOnXTu3DlVVFQ0Os+8efNkt9sdr6SkJI+vBwAA8B8+DTejR4/WPffco759+2rkyJF6//33JUlvvfXWJeexXfSIffN/I0Ff3F5vxowZqqysdLwOHjzooeoBAIA/8vk1NxeKiIhQ3759lZ+f3+j0zp07q6yszKnt0KFDCgkJUUxMTKPzhIWFKSwszOO1AgAA/+Tza24uVF1drdzcXCUkJDQ6PT09XWvWrHFqW716tQYMGKDQ0NCWKBEAAPg5n4ab6dOna+PGjSosLNTWrVv14x//WFVVVZo0aZKk86eUJk6c6Oj/8MMPq6ioSNOmTVNubq7eeOMNvf7665o+fbqvVgEAAPgZn56W+uabbzR+/HhVVFQoLi5OgwYN0pYtW9StWzdJUmlpqYqLix39U1JS9MEHH2jq1Kn685//rMTERL3yyivcBg4AABxspv6K3ABRVVUlu92uyspKRUdH+7ocAADgAnd+v/3qmhsAAIAr5Vd3S6H5CspPqOjIKSXHRCglNsLX5QAA4DOEm1bu2KkaTVm6Wzn55Y62oT3jNH98muztuIMMABB4OC3Vyk1ZulubvnJ+OvOmryr0+NJdPqoIAADfIty0YgXlJ5STX67ai64JrzVGOfnlKqw46aPKAADwHcJNK1Z05FST0w8cJtwAAAIP4aYV69axXZPTk2O4sBgAEHgIN61Y97hIDe0Zp+CLBg0Nttk0tGccd00BAAIS4aaVmz8+TUNSY53ahqTGav74NB9VBACAb3EreCtnbxeqrAcGqrDipA4cPslzbgAAAY9wYxEpsYQaAAAkTksBAACLIdwAAABLIdwAAABL4ZobtAoMDAoAcBXhBn6NgUEBAO7itBT8GgODAgDcRbiB32JgUABAcxBu4LcYGBQA0ByEG/gtBgYFADQH4QZ+i4FBAQDNQbiBX2NgUACAu7gVHH6NgUEBAO4i3KBVYGBQAICrOC0FAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAsJeCGXzDGSJKqqqp8XAkAAHBV/e92/e94UwIu3Bw/flySlJSU5ONKAACAu44fPy673d5kH5txJQJZSF1dnb799ltFRUXJZrO1+OdXVVUpKSlJBw8eVHR0dIt/PtgHvsb29y22v++xD5rHGKPjx48rMTFRQUFNX1UTcEdugoKC1KVLF1+XoejoaL7UPsY+8C22v2+x/X2PfeC+yx2xqccFxQAAwFIINwAAwFIINy0sLCxMs2fPVlhYmK9LCVjsA99i+/sW29/32AfeF3AXFAMAAGvjyA0AALAUwg0AALAUwg0AALAUwg0AALAUws1l5OTk6Pbbb1diYqJsNpveeecdp+nGGM2ZM0eJiYkKDw/XTTfdpH379jn1qa6u1uOPP67Y2FhFRETojjvu0DfffOPU5+jRo/rZz34mu90uu92un/3sZzp27JhTn+LiYt1+++2KiIhQbGyspkyZopqaGm+stt+YN2+errvuOkVFRSk+Pl533XWX8vLynPqwD7xrwYIFuuaaaxwPHEtPT9eHH37omM72b1nz5s2TzWbTk08+6WhjH3jXnDlzZLPZnF6dO3d2TGf7+yGDJn3wwQfm//2//2eys7ONJLNy5Uqn6c8//7yJiooy2dnZZs+ePeYnP/mJSUhIMFVVVY4+Dz/8sPnOd75j1qxZYz777DMzfPhw069fP3Pu3DlHnx/+8IemT58+5tNPPzWffvqp6dOnj7ntttsc08+dO2f69Oljhg8fbj777DOzZs0ak5iYaCZPnuz1beBLo0aNMosXLzZ79+41u3fvNrfeeqvp2rWrOXHihKMP+8C7Vq1aZd5//32Tl5dn8vLyzMyZM01oaKjZu3evMYbt35K2bdtmkpOTzTXXXGOeeOIJRzv7wLtmz55tevfubUpLSx2vQ4cOOaaz/f0P4cYNF4eburo607lzZ/P888872s6cOWPsdrtZuHChMcaYY8eOmdDQULNs2TJHn5KSEhMUFGQ++ugjY4wxX3zxhZFktmzZ4uizefNmI8ns37/fGHM+ZAUFBZmSkhJHn6VLl5qwsDBTWVnplfX1R4cOHTKSzMaNG40x7ANf6dChg3nttdfY/i3o+PHjpmfPnmbNmjVm2LBhjnDDPvC+2bNnm379+jU6je3vnzgtdQUKCwtVVlamW265xdEWFhamYcOG6dNPP5Uk7dy5U2fPnnXqk5iYqD59+jj6bN68WXa7Xddff72jz6BBg2S325369OnTR4mJiY4+o0aNUnV1tXbu3OnV9fQnlZWVkqSOHTtKYh+0tNraWi1btkwnT55Ueno6278FPfbYY7r11ls1cuRIp3b2QcvIz89XYmKiUlJSNG7cOBUUFEhi+/urgBs405PKysokSZ06dXJq79Spk4qKihx92rRpow4dOjToUz9/WVmZ4uPjGyw/Pj7eqc/Fn9OhQwe1adPG0cfqjDGaNm2abrjhBvXp00cS+6Cl7NmzR+np6Tpz5owiIyO1cuVKXX311Y7/6LL9vWvZsmX67LPPtH379gbT+Bvwvuuvv15ZWVn67ne/q3/961965plnNHjwYO3bt4/t76cINx5gs9mc3htjGrRd7OI+jfVvTh8rmzx5sj7//HP94x//aDCNfeBdvXr10u7du3Xs2DFlZ2dr0qRJ2rhxo2M62997Dh48qCeeeEKrV69W27ZtL9mPfeA9o0ePdvy7b9++Sk9PV48ePfTWW29p0KBBktj+/obTUleg/mr5ixPzoUOHHOm6c+fOqqmp0dGjR5vs869//avB8svLy536XPw5R48e1dmzZxskeSt6/PHHtWrVKq1fv15dunRxtLMPWkabNm2UmpqqAQMGaN68eerXr5/++Mc/sv1bwM6dO3Xo0CH1799fISEhCgkJ0caNG/XKK68oJCTEse7sg5YTERGhvn37Kj8/n78BP0W4uQIpKSnq3Lmz1qxZ42irqanRxo0bNXjwYElS//79FRoa6tSntLRUe/fudfRJT09XZWWltm3b5uizdetWVVZWOvXZu3evSktLHX1Wr16tsLAw9e/f36vr6UvGGE2ePFkrVqzQJ598opSUFKfp7APfMMaourqa7d8CRowYoT179mj37t2O14ABAzRhwgTt3r1b3bt3Zx+0sOrqauXm5iohIYG/AX/Vghcvt0rHjx83u3btMrt27TKSzB/+8Aeza9cuU1RUZIw5fwug3W43K1asMHv27DHjx49v9BbALl26mLVr15rPPvvM/OAHP2j0FsBrrrnGbN682WzevNn07du30VsAR4wYYT777DOzdu1a06VLF8vfAvjII48Yu91uNmzY4HQb5qlTpxx92AfeNWPGDJOTk2MKCwvN559/bmbOnGmCgoLM6tWrjTFsf1+48G4pY9gH3vbLX/7SbNiwwRQUFJgtW7aY2267zURFRZkDBw4YY9j+/ohwcxnr1683khq8Jk2aZIw5fxvg7NmzTefOnU1YWJgZOnSo2bNnj9MyTp8+bSZPnmw6duxowsPDzW233WaKi4ud+hw+fNhMmDDBREVFmaioKDNhwgRz9OhRpz5FRUXm1ltvNeHh4aZjx45m8uTJ5syZM95cfZ9rbNtLMosXL3b0YR94V0ZGhunWrZtp06aNiYuLMyNGjHAEG2PY/r5wcbhhH3hX/XNrQkNDTWJiorn77rvNvn37HNPZ/v7HZowxvjlmBAAA4HlccwMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAXnTTTTfpySef9HUZQEAh3ACQdH5U4yeeeEKpqalq27atOnXqpBtuuEELFy7UqVOnHP2Sk5Nls9lks9kUHh6u5ORkjR07Vp988onT8g4cOODoZ7PZ1KFDBw0dOlQbN25s6VXzqRUrVujpp592vE9OTlZmZqbvCgICAOEGgAoKCpSWlqbVq1frueee065du7R27VpNnTpV7777rtauXevU/7e//a1KS0uVl5enrKwstW/fXiNHjtSzzz7bYNlr165VaWmpNm7cqOjoaI0ZM0aFhYUttWqSpLNnz7bo512oY8eOioqK8tnnAwHJ14NbAfC9UaNGmS5dupgTJ040Or2urs7x727dupmXX365QZ9Zs2aZoKAgs3//fmOMMYWFhUaS2bVrl6PPN998YySZhQsXNvo5ixcvNna73axcudL07NnThIWFmZEjRzYYYHDVqlXm+9//vgkLCzMpKSlmzpw55uzZs47pksyCBQvMHXfcYdq1a2dmzZrV6OedOXPG/OpXvzJdunQxbdq0Mampqea1114zxpwfgTkjI8MkJyebtm3bmu9+97smMzPTaf5JkyaZO++808yZM8fExcWZqKgo8/Of/9xUV1c7+lw4yOWwYcMaDAJrjDEVFRVm3Lhx5jvf+Y4JDw83ffr0Mf/93//daM0ALo8jN0CAO3z4sFavXq3HHntMERERjfax2WyXXc4TTzwhY4z+53/+55J92rVrJ6npIymnTp3Ss88+q7feekubNm1SVVWVxo0b55j+8ccf66c//ammTJmiL774Qn/961/15ptvNjhqNHv2bN15553as2ePMjIyGv2siRMnatmyZXrllVeUm5urhQsXKjIyUpJUV1enLl26aPny5friiy80a9YszZw5U8uXL3daxrp165Sbm6v169dr6dKlWrlypebOndvo561YsUJdunRxHPkqLS2VJJ05c0b9+/fXe++9p7179+rnP/+5fvazn2nr1q2X3E4AmuDrdAXAt7Zs2WIkmRUrVji1x8TEmIiICBMREWF+/etfO9ovdeTGGGM6depkHnnkEWNMwyM3J06cML/4xS9McHCw+fzzzxudf/HixUaS2bJli6MtNzfXSDJbt241xhhz4403mueee85pvr/97W8mISHB8V6SefLJJ5tc77y8PCPJrFmzpsl+F3r00UfNPffc43g/adIk07FjR3Py5ElH24IFC0xkZKSpra01xjgfuTGm6e13oTFjxphf/vKXLtcG4N9CfBmsAPiPi4/ObNu2TXV1dZowYYKqq6tdWoYxpsFyBg8erKCgIJ06dUoJCQl688031bdv30suIyQkRAMGDHC8/973vqf27dsrNzdXAwcO1M6dO7V9+3anIzW1tbU6c+aMTp065Tg6dOEyGrN7924FBwdr2LBhl+yzcOFCvfbaayoqKtLp06dVU1Oja6+91qlPv379HJ8pSenp6Tpx4oQOHjyobt26NVnDhfU///zzevvtt1VSUqLq6mpVV1df8kgagKYRboAAl5qaKpvNpv379zu1d+/eXZIUHh7u0nIOHz6s8vJypaSkOLW//fbbuvrqq9W+fXvFxMS4tKzGToPVt9XV1Wnu3Lm6++67G/Rp27at49+XCwaXW6/ly5dr6tSp+v3vf6/09HRFRUXpd7/7ncunilw5lVfv97//vV5++WVlZmaqb9++ioiI0JNPPqmamhqXlwHg3wg3QICLiYnRzTffrD/96U96/PHHm3204I9//KOCgoJ01113ObUnJSWpR48eLi/n3Llz2rFjhwYOHChJysvL07Fjx/S9731PkvT9739feXl5Sk1NbVad9fr27au6ujpt3LhRI0eObDD9f//3fzV48GA9+uijjravv/66Qb9//vOfOn36tCMsbdmyRZGRkerSpUujn9umTRvV1tY2+Kw777xTP/3pTyWdD3D5+fm66qqrmr1+QCDjgmIA+stf/qJz585pwIABevvtt5Wbm6u8vDwtWbJE+/fvV3BwsFP/48ePq6ysTAcPHlROTo5+/vOf65lnntGzzz57xaEjNDRUjz/+uLZu3arPPvtM999/vwYNGuQIO7NmzVJWVpbmzJmjffv2KTc3V2+//bb+67/+y63PSU5O1qRJk5SRkaF33nlHhYWF2rBhg+OC4dTUVO3YsUMff/yxvvzySz311FPavn17g+XU1NTogQce0BdffKEPP/xQs2fP1uTJkxUU1Ph/XpOTk5WTk6OSkhJVVFQ4PmvNmjX69NNPlZubq1/84hcqKytza30AXMDXF/0A8A/ffvutmTx5sklJSTGhoaEmMjLSDBw40Pzud79zumC2W7dujtuY27RpY7p27WrGjh1rPvnkE6flNXYr+OXU3wqenZ1tunfvbtq0aWN+8IMfmAMHDjj1++ijj8zgwYNNeHi4iY6ONgMHDjSLFi1yTJdkVq5cednPO336tJk6dapJSEhw3Ar+xhtvGGPO3yZ+3333Gbvdbtq3b28eeeQR85//+Z+mX79+jvnrbwWfNWuWiYmJMZGRkebBBx80Z86ccfS5+ILizZs3m2uuucaEhYU5bgU/fPiwufPOO01kZKSJj483//Vf/2UmTpxo7rzzTpe3HYB/sxljjE/TFQD8nzfffFNPPvmkjh075utSXHLffffp2LFjeuedd3xdCoALcFoKAABYCuEGAABYCqelAACApXDkBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWMr/Byj7UpSy0CJvAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the data\n",
    "country_stats.plot(kind=\"scatter\", x=\"GDP per capita\", y=\"Life satisfaction\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f1b524d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select a linear model\n",
    "lin_reg_model = sklearn.linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "11838d1a",
   "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>LinearRegression()</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\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model\n",
    "lin_reg_model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0db0da68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5.96242338]]\n"
     ]
    }
   ],
   "source": [
    "# Make prediction for Cyprus\n",
    "X_new = [[22587]] # Cyprus' GDP per Capita\n",
    "\n",
    "print(lin_reg_model.predict(X_new))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d44a24a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing the Linear Regression with k-Nearest Neighbours regression\n",
    "\n",
    "k_n_n = sklearn.neighbors.KNeighborsRegressor(n_neighbors=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "624099ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor(n_neighbors=3)</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor(n_neighbors=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsRegressor(n_neighbors=3)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_n_n.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d0223a5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5.76666667]]\n"
     ]
    }
   ],
   "source": [
    "# Make prediction for Cyprus\n",
    "print(k_n_n.predict(X_new))"
   ]
  }
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