salary_PLR.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6ceb1c72",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "#We get our dataset with the help of pandas and put at the df.\n",
    "df = pd.read_csv(\"polynomial.csv\", sep= \";\")\n",
    "\n",
    "#We invite the PolynomialFeatures function to create a polynomial regression object.\n",
    "#We define the degree of polynom(N) during invite the function.\n",
    "polynomial_regression = PolynomialFeatures(degree = 2) # degree = N\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "efa18c62",
   "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>Experimentation</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>11000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>15000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Experimentation  Salary\n",
       "0                1    4500\n",
       "1                2    5000\n",
       "2                3    6000\n",
       "3                4    8000\n",
       "4                5   11000\n",
       "5                6   15000\n",
       "6                7   20000\n",
       "7                8   30000\n",
       "8                9   50000\n",
       "9               10  100000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "03ed1ba9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's see to our dataset\n",
    "plt.scatter(df['Experimentation'], df['Salary'])\n",
    "plt.xlabel('Experimentation(Years)')\n",
    "plt.ylabel('Salary')\n",
    "plt.savefig('sal_exp.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "df47adf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#As seen, the data is not distributed in a linear structure.\n",
    "#If we implement linear regression to this dataset, the result is failed.\n",
    "\n",
    "reg = LinearRegression()\n",
    "reg.fit(df[['Experimentation']],df['Salary'])\n",
    "plt.xlabel('Experimentation(Years)')\n",
    "plt.ylabel('Salary')\n",
    "\n",
    "plt.scatter(df['Experimentation'], df['Salary'])\n",
    "\n",
    "xaxis = df['Experimentation']\n",
    "yaxis = reg.predict(df[['Experimentation']])\n",
    "plt.plot(xaxis, yaxis, color = \"orange\", label = \"Linear Regression\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "174fcee4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We invite the PolynomialFeatures function to create a polynomial regression object.\n",
    "#We define the degree of polynom(N) during invite the function.\n",
    "polynomial_regression = PolynomialFeatures(degree = 7) # degree = N\n",
    "\n",
    "x_polynomial = polynomial_regression.fit_transform(df[['Experimentation']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "048bdb66",
   "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>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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" 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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We create our regression model subject and then invite the fit method so we are training our model with real data\n",
    "\n",
    "reg = LinearRegression()\n",
    "reg.fit(x_polynomial, df[\"Salary\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "adaab457",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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84+8FdAfamdkl9a1SR5vX017fOrVruczMCs2ssKSkpP7CRUSSWgGhg+asuAup0piup38GVrl7ibuXA7MJk6VviLqTiG43RssXEa7jVymH0FVVFN2v3V5jnah7qwOwqXYh7n6/u+e7e36XLl0a8ZJEROK0i/BRehYhLJJDY4JiDTDCzA6Nxg1GA8sIpxJOiZaZQjjGi6h9cnQkUy/CoPW7UffUFjMbEW3nu7XWqdzWBcCr0TiGiEgaehv4B8nU7QRhMLpB3P0dMysA3ieMviwC7icM1c8ys0sJYTIpWn6pmc0CPoqWv9rdd0WbuxJ4GMgiTL7+YtT+APCIma0k7ElMbmi9IiLJ72nCeRPj4i6kBku3L+j5+fleWFgYdxkiIgdpN9ATGA7MSfizm9lCd8+v6zGdmS0ikhTeJQzLxj+3U20KChGRpFBAmCX2nLgL2YuCQkQkdk4YnzgD6BhvKXVQUIiIxO59YDXJdrRTJQWFiEjsCggHoY6Pu5A6KShERGLlhKD4JuGyp8lHQSEiEqvFhDlSk7PbCRQUIiIxKyB8FE+IuY59U1CIiMSqABjFnom2k4+CQkQkNh8RpshLvpPsqlNQiIjEpoBwNYXz4i6kXgoKEZHYPA2MJFzSJ3kpKEREYvEJ8CHJfLRTJQWFiEgsno5uJ8ZaxYFQUIiIxKIAOImaF/5MTgoKEZGEW0WY3yn5u51AQSEiEoPKbqfkPiy2koJCRCThCoBhQK+4CzkgCgoRkYRaC7xDqnQ7gYJCRCTBZke3qdHtBAoKEZEEKwAGAcfFXcgBU1CIiCTMOuCvpFK3EygoREQS6E+ECxWlTrcTKChERBLoaaA/MCDuQg6KgkJEJCE2Aq+Tat1OoKAQEUmQOcBuFBQiIrIPBcCxQF7chRw0BYWISLP7AniVsDdhMddy8BQUIiLN7llgF6nY7QQKChGRBCgAcgnzO6UeBYWISLMqBV4mnDuRet1OoKAQEWlmfwbKSdVuJ1BQiIg0s6eBHODEuAtpsEYFhZl1NLMCM/vYzJaZ2clmdoSZvWxmK6LbTtWWn25mK81suZmNqdY+3MwWR4/dY2YWtR9iZk9F7e+YWW5j6hURSawtwF8I3U6p+728sZX/BviLu/cDBgPLgGnAPHfvA8yLfsfMBgCTgYHAWOC3ZpYRbec+4DKgT/QzNmq/FPjS3Y8F7gbubGS9IiIJ9Dywg1Sb26m2BgeFmR0OnAY8AODuO929FBgPzIwWmwlMiO6PB5509x3uvgpYCZxoZt2Aw919gbs78Mda61RuqwAYXbm3ISKS/AqAo4FT4i6kURqzR9EbKAEeMrNFZvYHM2sHHOXu6wGi267R8tmESztVKorasqP7tdtrrOPuFcBmoHPtQszsMjMrNLPCkpKSRrwkEZGm8jXwIjARyNjPssmtMUHRmnBQ8H3uPpTwrkyrZ/m69gS8nvb61qnZ4H6/u+e7e36XLl3qr1pEJCH+AmwjlY92qtSYoCgCitz9nej3yquFb4i6k4huN1Zbvke19XMIV/Eoiu7Xbq+xjpm1BjoAmxpRs4hIghQAXYB/iruQRmtwULj7P4C1ZtY3ahoNfEQ4V31K1DYFeCa6/ywwOTqSqRdh0PrdqHtqi5mNiMYfvltrncptXQC8Go1jiIgkse3Ac4Th1tbxltIEGvsKrgEeM7M2wKfAVEL4zDKzS4E1wCQAd19qZrMIYVIBXO3uu6LtXAk8DGQROvVejNofAB4xs5WEPYnJjaxXRCQBXgK2kg7dTgCWbl/Q8/PzvbCwMO4yRKRF+y5hj2IDkBlzLQfGzBa6e35dj6XuGSAiIklpB6HXfAKpEhL7o6AQEWlS8whH8qf2SXbVKShERJrU08DhwD/HXUiTUVCIiDSZcsK1sc8FDom3lCakoBARaTKvEQ7QTI+jnSopKEREmkwB0A44M+5CmpSCQkSkSewC/gScTTglLH0oKEREmsSbhHlS06vbCRQUIiJNpICwJ3FW3IU0OQWFiEij7SYcFnsWYYwivSgoREQa7W3gH6RjtxMoKEREmsDThPMmxsVdSLNQUIiINNCcRcWcescrrCt9lDdXDGfOoi1xl9QsFBQiIg0wZ1Ex02cv5sjDPqR7x8/506KTmD57MXMWFcddWpNTUIiINMCMucspK6/g8lFPs7OiNa8sO4my8l3MmLs87tKanIJCRKQB1pWWceWoAs46/m1+9fIlfFXWvqo93SgoREQa4PxhH3LjmD/y7Aen8bvX90wp3r1jep2VDQoKEZEG+ITbJ97Bxxt6cdPT1wIGQFZmBjeO6Rtvac1AQSEiclC+AsaT2boNa794nM7tOmFAdscsbp+Yx4Sh2XEX2ORax12AiEjq2A1cAqwAXmHM8Scx5viYS0oABYWIyAH7GfBn4H+A02OtJJHU9SQickBmA7cBU4GrY64lsRQUIiL7tRj4LnAScB+Vg9cthYJCRKRem4AJwOGEvYr0uRb2gdIYhYjIPlUAk4Ei4HWge7zlxERBISKyT9OAl4E/ACNiriU+6noSEanTY8B/EwauL425lngpKERE9rIQ+AEwCrg75lrip6AQEalhA2Hwuivwf0BmrNUkA41RiIhU2QlMAr4A3gK6xFtOklBQiIhUuR54E3gcGBZvKUlEXU8iIgD8nnAy3Y3ARTHXklwaHRRmlmFmi8zsuej3I8zsZTNbEd12qrbsdDNbaWbLzWxMtfbhZrY4euweM7Oo/RAzeypqf8fMchtbr4jI3t4mHN10JnB7zLUkn6bYo7gOWFbt92nAPHfvA8yLfsfMBhDOXBkIjAV+a2YZ0Tr3AZcBfaKfsVH7pcCX7n4s4dCDO5ugXhGRaoqB84FjgCeBjPoXb4EaFRRmlgOMI5yNUmk8MDO6P5Nw+EBl+5PuvsPdVwErgRPNrBtwuLsvcHcH/lhrncptFQCjK/c2REQabztwHrAVeAboVP/iLVRj9yh+DdxEmKS90lHuvh4guu0atWcDa6stVxS1ZUf3a7fXWMfdK4DNQOfaRZjZZWZWaGaFJSUljXxJItIyOHAF8B7wCKGzQ+rS4KAws7OBje6+8EBXqaPN62mvb52aDe73u3u+u+d36aLD2UTkQPwPocPiVvZ0YkhdGnN47EjgXDP7NtAWONzMHgU2mFk3d18fdSttjJYvAnpUWz8HWBe159TRXn2dIjNrDXQgTOUoItIIrwI/JvRu/2fMtSS/Bu9RuPt0d89x91zCIPWr7n4J8CwwJVpsCqHjj6h9cnQkUy/CoPW7UffUFjMbEY0/fLfWOpXbuiB6jr32KEREDtwq4ELgOMKQqM4S2J/mOOHuDmCWmV0KrCGc5oi7LzWzWcBHhLl7r3b3XdE6VwIPA1nAi9EPwAPAI2a2krAnMbkZ6hWRFuNrQjfTLsL30cNjrSZVWLp9Qc/Pz/fCwsK4yxCRpOOE75r/B7zAnqPwBcDMFrp7fl2PaQoPEWkh7gRmETo9FBIHQ51zItICvADcTNijuCnmWlKPgkJE0twnwL8CgwnDnjpn92ApKEQkjX1FOAQ2E5gDHBprNalKYxQikqZ2A5cAK4BXgJ7xlpPCFBQikqZ+BvwZuAc4PdZKUp26nkQkDc0GbgOmAj+KuZbUp6AQkTSzmDDBw0nAb9HgdeMpKEQkjWwinHl9GGGvom2s1aQLjVGISJqoIJwnUQS8BnSPtZp0oqAQkTQxDXiZcB21k2OuJb2o60lE0sBjwH8Trnt9acy1pB/tUYhIypmzqJgZc5ezrrSMb/Zdy++nXE9Gq9OAu+MuLS1pj0JEUsqcRcVMn72Y4tIyjmhXym3n/ScbvjqcFxbfQzgDW5qa9ihEJKXMmLucsvJdtMko57eX3M4Rh37FBb+7k9JtX/DtvLirS08KChFJKetKyxh13EL+8+zf842uRVz7xL+zdN2xGGVxl5a2FBQikkJW8silP+fUPgv4tKQ733voVl5bfgIA3TtmxVxb+lJQiEgK2Ar8HPgVJ/XO5K653+f+N85h564wJpGVmcGNY/rGWmE602C2iCQxBx4F+hKuTDeZzIwVHNv1v+hy2OEYkN0xi9sn5jFhaHaslaYz7VGISJJaCFwLvA3kA08DIwCYMBQFQwJpj0JEksxG4IfACcBK4EHgHSpDQhJPQSEiSaIc+DVwHPAwcAPhMqZT0UdVvNT1JCJJ4GXgOmAZMIYQGP3iLEiqUUyLSIw+Bc4DzgR2AM8CL6KQSC4KChGJwdfAT4EBhL2JXwBLgXPQhYaSj7qeRCSBHHgKuJFw3YiLgTsBHcGUzLRHISIJ8gEwCrgI6Aq8RThHQiGR7BQUItLMPgeuBIYTBqvvB94FRsZZlBwEdT2JSDOpAH4H3AJsAa4BbgU6xVmUNICCQkSawauEw12XAKOB3wADY61IGk5dTyLShD4DJhHCYSswm3BUk0IilWmPQkSawDbgl4QjmAy4Dfg3QFN/pwMFhYg0ggMFwL8Da4DJhMDoEWdR0sQa3PVkZj3MbL6ZLTOzpWZ2XdR+hJm9bGYrottO1daZbmYrzWy5mY2p1j7czBZHj91jZha1H2JmT0Xt75hZbiNeq4g0gTmLihl5x6uMvfte3v9sKHAhYYD6deAJFBLppzFjFBXAv7l7f8K0jleb2QBgGjDP3fsA86LfiR6bTOisHAv81swyom3dB1wG9Il+xkbtlwJfuvuxwN2E/VoRicmcRUU8/PYzXDHqv3n+umvJPXIVP3v2R8xZ9CxwWtzlSTNpcNeTu68H1kf3t5jZMsKZM+OB06PFZgKvAT+J2p909x3AKjNbCZxoZquBw919AYCZ/RGYQJjwZTzws2hbBcD/mpm5uze0bhE5WE6YXmMWQ495mAlD11KxqxWP/O3b3P3yxWwuO4zsjiuZMPSYuAuVZtIkYxRRl9BQwqTxR0UhgruvN7Ou0WLZwN+qrVYUtZVH92u3V66zNtpWhZltBjoTzuCp/vyXEfZIOOYY/WcVaRrLCNNtzIrut2Ltpjx+9/p45i49mU1fd6hacl1pWUw1SiI0OijMrD3h0lPXu/tX0fBCnYvW0eb1tNe3Ts0G9/sJp3uSn5+vvQ2RBltOCIZZhHMgjDDtxjXA+fzk6SUU1xEK3Tvq6KZ01qjzKMwskxASj7n77Kh5g5l1ix7vRrhcFYQ9heqjXDnAuqg9p472GuuYWWugA7CpMTWLSG0rCbO3DiFM71159vT/EP4U5xOm4OjKjWP6kpWZUWPtrMwMbhzTN5EFS4I15qgnAx4Alrn7r6o99CwwJbo/BXimWvvk6EimXoRB63ejbqotZjYi2uZ3a61Tua0LgFc1PiHSFD4F7gCGEf4U/wNoRziDei3wBvAj4Ogaa00Yms3tE/PI7piFAdkds7h9Yp6uX53mrKGfu2Z2KvAmsBjYHTXfTBinmAUcQziwepK7b4rW+Q/g+4Qjpq539xej9nzCtQ+zCIPY17i7m1lb4BHC+McmYLK7f1pfXfn5+V5YWNig1ySS3lYD/0f486z8GxlBOLz1AnRYa8tmZgvdPb/Ox9LtC7qCQqS6tewJh3eithMI4TAJ6BlTXZJs6gsKnZktknaKCUeTPwUsiNqGEbqaLgR6xVSXpCoFhUiKmLOomBlzl7OutIzuHbO4cUzfamMD6wnhMItwQSCAwcDPCeFwbOILlrShoBBJAXMWFTN99mLKyncBUFxaxl1z36DXkasY3OMlwuCzA3mECfkmAToSSZqGgkIkBcyYu5yy8l10blfKmIELOHvwm5zUawkZrXYD/QmHtE4CBsRbqKQlBYVI0nLCCXB/5drRT5Hfcxnf6BomMfh7STb/O/9CXvjwVObecHWsVUr6U1CIJI1thMNW/wq8Hf2E80vHDDyc91b3o2DhaF77ZDjL1vcCjGydES0JoKAQic169oTCX4H3CacYQThD+jzgFGAkry1vx/TZS6rGKEBnREviKChEEmIXYe6kylB4G1gVPdYWOJFw8Z+RwMmEuS/3mDAUwOo56kmk+SgoRJrFFsJkyZXB8LeoDcK0GCMJE+2NJMyx1Ga/W5wwNFvBILFQUIjsR/3nL0AYdP6MmnsLHxJmtjHCIauXEELhFCCXuidGFklOCgqRetR1/sItcxbR8dAPOL3vCvaEQ+WEx+0J8yfdQgiFEcDhiS9cpAkpKETqMWPux3Ruv47+3VYxOOcT8nM/YnDOCrLa7IiW6Em4oGMYdA57Dxn72JpIalJQiFT5ijAZ8odVP3+5fhGHtQ0X6inflcHSdb15/N2xvP9Zf+69+EfsuRijSPpSUEgLtItwsZ4Pa/2srrZMR2AQc5eOYeFnOXy8Ppdl/8hle3lbgOj8BYWEtAwKCklzn7N3ICwFtkePZxDmRBpBuOz6oOgnBzBatypmzqLFOn9BWjQFhaSJncDH7B0K66st05UQAlexJxD6E85jqFvl0U06f0FaMgWFJK26D0vtTjjCqHoYLAaWsees5jbAQOBM9gRCHnBUg+rQ+QvS0ikoJCnNWVTEr156g6M7FDPy2CL6d1tFtw6fsbNiDW1al1ZbsgchCM5mTyj0ATITX7RImlJQSIx2EAaQ/w58WuN27PErmTB0R9WSX+9oyycbevLikpGMHzKWPXsJnRJetUhLo6CQZuSE2U9rhsCe+0XRMpUOBXoD3+DRvx3Lmk1Hs+aLbqz6ojtrNh2NeysMGD9kXEJfhUhLp6CQvex/yorqKoC11LVXEG4311r+aEIYnE5lKOy5PYrKqS0e+uurFJeW7fVs3TWttkjCKSikhrqmrLjtuXfokNWeb/bbyt5B8Bl7BpEhjA30Inz4n0LNMOgNtDugOm4c07dGHaDDUkXioqBIIgf3Tf5g7CTMXLqFcPbxvm/NPubO87fQvu02Oh26hWOOWE/n9l/V2t4RhA/9fOBCau4VZNMUU1josFSR5GHuvv+lUkh+fr4XFhYe9HrN9yF94M9f/Rt061YVHNl+J/81vgdnDmzH/j7g67/dwYE5lJItbdiy/VC+3pHF5rL2YZxgUzfWbjqaey+eRAiEjk30qkUkWZjZQnfPr+sx7VFQd3fL9NmLAaLj9ncAZYSzeeu7PZBl6r49odeXvHbjDtpm7qBt650ckll+gNVnEWYnPazabY9avx/IbXugNRPurXtsIExZMewAaxKRdKKgIHRvtM3cxDM/ms4hrXfSNnNn9IFdTui2aYw2hA/ztvu47QBk8d6qLygrb8P28kPYUZHJ1zuy2LojfLP/5QX/RN0f8IfR1P+EGhsQkdoUFMC60jKy2rRh5cYcdlS0YXv0gb2zIpPLRx3Pvj/k93Vbeb8t0OqAapgxt75v8t9q5Cs8cBobEJHaFBSEQy6LS+Gqx26u0Z7dMYvLRyXmQzqZvslrygoRqe7Avu6muRvH9CUrs+aROon+kJ4wNJvbJ+aR3TELI4TU7RPz9IEtIrHTHgXJ092ib/IikowUFBF9SIuI1E1dTyIiUq+UCAozG2tmy81spZlNi7seEZGWJOmDwswygHuBs4ABwEVmNiDeqkREWo6kDwrgRGClu3/q7juBJ4HxMdckItJipEJQZBPmsa5UFLVVMbPLzKzQzApLSkoSWpyISLpLhaOerI62GjMZuvv9wP0AZlZiZp8lorBmdCTwedxFJBG9HzXp/dhD70VNjXk/eu7rgVQIiiLCLHeVcoB1+1rY3bs0e0XNzMwK9zWLY0uk96MmvR976L2oqbnej1ToenoP6GNmvcysDTAZeDbmmkREWoyk36Nw9woz+xEwl3BFnAfdfWnMZYmItBhJHxQA7v4C8ELcdSTQ/XEXkGT0ftSk92MPvRc1Ncv7kXZXuBMRkaaVCmMUIiISIwWFiIjUS0GRRMysh5nNN7NlZrbUzK6Lu6a4mVmGmS0ys+firiVuZtbRzArM7OPo/8jJcdcUJzO7Ifo7WWJmT5hZ27hrSiQze9DMNprZkmptR5jZy2a2Irrt1BTPpaBILhXAv7l7f2AEcLXmteI6YFncRSSJ3wB/cfd+wGBa8PtiZtnAtUC+ux9POCJycrxVJdzDwNhabdOAee7eB5gX/d5oCook4u7r3f396P4WwgdBi71IhpnlAOOAP8RdS9zM7HDgNOABAHff6e6lsRYVv9ZAlpm1Bg6lnhNx05G7vwFsqtU8HpgZ3Z8JTGiK51JQJCkzywWGAu/EXEqcfg3cBOyOuY5k0BsoAR6KuuL+YGbt4i4qLu5eDNwFrAHWA5vd/aV4q0oKR7n7eghfPIGuTbFRBUUSMrP2wNPA9e7+Vdz1xMHMzgY2uvvCuGtJEq2BYcB97j4U+Jom6lZIRVHf+3igF9AdaGdml8RbVfpSUCQZM8skhMRj7j477npiNBI418xWE6aW/5aZPRpvSbEqAorcvXIPs4AQHC3VPwOr3L3E3cuB2cApMdeUDDaYWTeA6HZjU2xUQZFEzMwIfdDL3P1XcdcTJ3ef7u457p5LGKR81d1b7DdGd/8HsNbM+kZNo4GPYiwpbmuAEWZ2aPR3M5oWPLhfzbPAlOj+FOCZpthoSkzh0YKMBL4DLDazD6K2m6MpTESuAR6LJsf8FJgacz2xcfd3zKwAeJ9wtOAiWth0Hmb2BHA6cKSZFQG3AncAs8zsUkKYTmqS59IUHiIiUh91PYmISL0UFCIiUi8FhYiI1EtBISIi9VJQiIhIvRQUIiJSLwWFiIjU6/8Do3b2H/7+9+sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Our model is trained. Let's see the results.\n",
    "y_head = reg.predict(x_polynomial)\n",
    "plt.plot(df['Experimentation'], y_head, color = 'yellow', label = \"polynomial regression\")\n",
    "plt.legend()\n",
    "plt.scatter(df['Experimentation'], df['Salary'])\n",
    "plt.show()\n",
    "# Try these: N = 2, N = 3, N = 4, N = 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "aa0616e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9496.57093489])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_polynomial_instance = polynomial_regression.fit_transform([[4.5]])\n",
    "reg.predict(x_polynomial_instance)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}