Polynomial_Linear_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Polynomial Linear Regression",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyOVLYpBUmPp4oDGwXcliCo0",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Lakshaypahuja21/Machine-Learning-Basics/blob/master/Polynomial_Linear_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4AdFEmsbfLfi"
      },
      "source": [
        "# Polynomial linear regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XtCUoTDpM_HW"
      },
      "source": [
        "## Import Library"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A4inHxkr202a"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gYp_a9SeM9tO"
      },
      "source": [
        "## Import dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gJPgNsUAM8iv",
        "outputId": "bb734aa0-5d41-4e83-d533-1005552756c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "df = pd.read_csv('Position_Salaries.csv')\n",
        "df.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Position</th>\n",
              "      <th>Level</th>\n",
              "      <th>Salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Business Analyst</td>\n",
              "      <td>1</td>\n",
              "      <td>45000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Junior Consultant</td>\n",
              "      <td>2</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Senior Consultant</td>\n",
              "      <td>3</td>\n",
              "      <td>60000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Manager</td>\n",
              "      <td>4</td>\n",
              "      <td>80000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Country Manager</td>\n",
              "      <td>5</td>\n",
              "      <td>110000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Position  Level  Salary\n",
              "0   Business Analyst      1   45000\n",
              "1  Junior Consultant      2   50000\n",
              "2  Senior Consultant      3   60000\n",
              "3            Manager      4   80000\n",
              "4    Country Manager      5  110000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gYJcAF1hNtGB"
      },
      "source": [
        "independent & dependent var"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iQFll6LvNsFw"
      },
      "source": [
        "x = df.iloc[:, 1:-1].values\n",
        "y = df.iloc[:, -1].values"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cptUGLS_hGZy"
      },
      "source": [
        "## Training linear regression model on full data "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "igQIJq4dhKsQ",
        "outputId": "f9935b81-b2d3-4dda-a1ef-6ea89f0264b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "linregressor = LinearRegression()\n",
        "linregressor.fit(x ,y)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RNNL9xZUav-7"
      },
      "source": [
        "## Training Polynomial regression model on full data set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aPL1LDwnavja",
        "outputId": "bfd612e5-7f9d-43d3-8e92-e210de16dc1a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "polyregressor = PolynomialFeatures(degree =4)\n",
        "x_poly = polyregressor.fit_transform(x)\n",
        "linregressor2 = LinearRegression()\n",
        "linregressor2.fit(x_poly,y)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xVZADRsFb73-"
      },
      "source": [
        "## visualizing the linear regression results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YKkW24ZVcBhR",
        "outputId": "7c749d83-1ea0-42c8-80db-96a8f7b57148",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.scatter(x, y, color = 'red')\n",
        "plt.plot(x, linregressor.predict(x), color = 'blue')\n",
        "plt.title('truth or bluff(linear model)')\n",
        "plt.xlabel('position level')\n",
        "plt.ylabel('salary')\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9xS-stY4cyml"
      },
      "source": [
        "## visualize polynomial regression results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CDI0u3iGcklN",
        "outputId": "8a81fee1-0621-4799-da66-e71f3791ddef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.scatter(x, y, color = 'red')\n",
        "plt.plot(x, linregressor2.predict(x_poly), color = 'blue')\n",
        "plt.title('truth or bluff(polynomial)')\n",
        "plt.xlabel('position level')\n",
        "plt.ylabel('salary')\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SgYhIA6xfDxMnwhlnQOvWSUezbZQIREQa4KmnwvpCudosBEoEIiINMnYsdOkCAwcmHcm2UyIQEdlGn34KzzwDw4dD8+ZJR7PtlAhERLbRhAmweXNuNwuBEoGIyDYrKoL994e+fZOOpGGUCEREtsFHH8Hrr+d+bQCUCEREtklxcVhqevjwpCNpuFgTgZkNMrMPzGyumV1RTZkhZjbLzN4zs7/FGY+ISCa4h2aho46Crl2TjqbhWsR1YTNrDtwBfBsoBd4ws0nuPiutTC/gSuBwd//MzHaJKx4RkUyZNg3mzIErr0w6ksyIs0bQD5jr7vPcfSMwHhhcqcwFwB3u/hmAuy+LMR4RkYwoKoJWreD005OOJDPiTARdgMVpz0ujY+n2BvY2s9fMbIqZDarqQmY2ysxKzKxk+fLlMYUrIlK7TZtg/PiwrlD79klHkxlJdxa3AHoBRwLDgHvMrEPlQu4+xt0L3b2wUy6u8SoiTcZzz4WJZE1htFBKnIlgCdAt7XnX6Fi6UmCSu29y9/nAh4TEICKSlYqKYOedw94DTUWcieANoJeZ9TCz7YChwKRKZR4n1AYws46EpqJ5McYkIrLN1qyBxx+HoUOhZcuko8mcOiWCaARQvbj7ZuAi4FlgNjDB3d8zs9+ZWWrV7meBFWY2C3gJ+IW7r6jva4mINIbHHoMNG5pWsxCAuXvthczmAY8AD6QP/0xCYWGhl5SUJBmCiOSpY4+FBQvC0FGzpKOpHzOb7u6FVZ2ra9PQAYT2+3uj0T2jzGzHjEUoIpLlliyBF18MtYFcSwK1qVMicPe17n6Pux8GXA78GlhqZg+ZWc9YIxQRyQLjxoUZxSNGJB1J5tW5j8DMTjWzx4DbgD8BewJPAk/HGJ+ISFYoKoL+/aFXExzXWNclJuYQOnP/6O7/STs+0cyOyHxYIiLZY+ZMeOcduP32pCOJR62JIBox9KC7/66q8+5+ScajEhHJIsXF0KIFDBmSdCTxqLVpyN23ACc3QiwiIlmnrCwkgkGDoKkubFDXpqHXzOx24GHgi9RBd38zlqhERLLE5MlQWgo335x0JPGpayI4MHpMbx5y4OjMhiMikl2KiqBdOzjllKQjiU+dEoG7HxV3ICIi2WbOnDBsdOhQaNMm6WjiU+eNaczsJODrQKvUseo6kEVEct3mzXD22WHfgd//Pulo4lWnRGBmdwFtgKOAe4EzgGkxxiUikqgbboCpU+Hhh6Fz56SjiVddl5g4zN3PAT5z998ChxJWChURaXJKSuC3v4URh81nyC8LoFkzKCgIw4eaoLo2Da2PHteZWWdgBbB7PCGJiCRn3brQJLR7+y+4/a3DYf3ScGLhQhg1KvzexNaZqGuN4Klo57A/Am8CC4BxcQUlIpKUK66A99+HB1tcQIdUEkhZtw6uvjqZwGJUp2WoK/wBs+2BVu6+Op6QaqZlqEUkLs8/D8cdB5deCrf+uVlYZa4yszDLLMfUtAx1jU1DZnZ6Dedw90cbGpyISDZYuRJGjoTeveH664HHuofmoMq6d2/s0GJXWx9BTVMoHFAiEJEm4cILYdkyePJJaN0aGD069AmsW1deqE2bcLyJqTERuPu5jRWIiEhSxo2D8ePDPf7gg6ODqQ7hq6+GRYtCTWD06CbXUQz16CPIlgll6iMQkUwqLYX99w9NQq+8ElYZbYoavFVlNKHsTOBiwIDvAXtkLEIRkQSUlYV+gU2b4K9/bbpJoDaaUCYieev22+GFF+DWW6FnHm+6W9dEsCF6TE0o24wmlIlIDps9Gy6/HE4+Gc4/P+loklXXitCTlSaUOXBPbFGJiMRo40Y46yxo2xbuuSdMDchndU0E7wNb3P0RM+sDHAw8Hl9YIiLxue46ePNNePRR2G23pKNJXl2bhn7l7mvN7JuEzWjuBf4SX1giIvGYMiVMGBs5Ek47LeloskNdE8GW6PEk4B53/wewXTwhiYjE4/PPw4Jy3brBn/+cdDTZo65NQ0vM7G7g28CN0XpDdU0iIiJZ4bLL4KOP4OWXYccdk44me9T1Zj4EeBY43t1XATsBv4gtKhGRDHv6abj77pAMjjgi6WiyS71XH02aZhaLSH19+instx/ssgu88QZsv33SETW+bV59VEQk17nDD38In30Gzz2Xn0mgNkoEItKkjR0bhonedBP07Zt0NNlJHb4i0mQtWAAXXRT6BH72s6SjyV6xJgIzG2RmH5jZXDO7ooZy3zUzN7Mq269EROpryxb4/vfD7w89BM2bJxtPNostEZhZc+AO4ASgDzAsmpVcuVw74KfA1LhiEZH8c+utYVnp//1fKChIOprsFmeNoB8w193nuftGYDwwuIpy1wE3Ur6wnYhIg8ycGfaT+c53ymsFUr04E0EXYHHa89Lo2FfM7GCgWzRTuVpmNsrMSsysZPny5ZmPVESajC+/DAvKdegAY8ZoQbm6SKyz2MyaAbcAP6+trLuPcfdCdy/s1KlT/MGJSM669lqYMQPuuw90u6ibOBPBEqBb2vOu0bGUdsB+wMtmtgAYAExSh7GIbKtXX4U//hEuuCDsMyB1E2cieAPoZWY9zGw7YCgwKXXS3Ve7e0d3L3D3AmAKcKq7a9qwiNTbmjVwzjnQowfcckvS0eSW2CaUuftmM7uIsEZRc+B+d3/PzH4HlLj7pJqvICJSd5deCosWhVpB27ZJR5NbYp1Z7O5PA09XOnZtNWWPjDMWEWm6Hn8cHngArroKDjss6Whyj2YWi0hO++ST0Cdw0EHw618nHU1uUiIQkZzlHjaeX7sWiopgO22XtU206JyI5Kz77oOnngqziPtstW6B1JVqBCKSkz76KHQQH300XHJJ0tHkNiUCEck5W7aEoaItWsCDD0Iz3ckaRE1DIpJzbroJ/vOf0C/QrVvt5aVmyqMiklPeeissIzFkCAwfnnQ0TYMSgYjkjA0bwoJynTrBX/6iBeUyRU1DIpIzrroKZs2Cf/4Tdtop6WiaDtUIRCQnvPBCGCZ64YVw/PFJR9O0KBGISNZbtQpGjoS99w4dxZJZSgQikn2Ki8P+ks2aQUEBF588n6VLYexYaNMm6eCaHvURiEh2KS6GUaNg3ToAJizsR9HCHvzm9Bn069c34eCaJtUIRCS7XH31V0ngY3bnR9zFN5jGVSWnJxxY06VEICLZZdEiABz4AfezgVaM5WxaLp6XbFxNmBKBiGSX7t1x4EYu51kG8Ud+wT58CN27Jx1Zk6U+AhHJKvN/ehs/vKwdz5cdw2Ae5yfcGXqIR49OOrQmSzUCEckKmzeHvYb3u+Y7vL7dEdy+0694lO9ie+wBY8bAiBFJh9hkqUYgIol7++2wwcz06XDyyXDnnS3p1u064LqkQ8sLqhGISGLWr4crr4TCQli8GB5+GCZN0oqijU01AhFJxEsvhekCc+fCuefCzTdr/aCkqEYgIo3qs8/CZvNHHw1lZfCvf8H99ysJJEmJQEQahTtMnAi9e8MDD8AvfwkzZ8IxxyQdmahpSERit2RJWDX0iSfgoIPg6afh4IOTjkpSVCMQkdiUlcFdd0GfPvDcc2Hl0GnTlASyjWoEIhKL998PfQH//ndo/rn7bthrr6SjkqqoRiAiGbVxI1x3HRxwALz3XugPeP55JYFsphqBiGTMlClhYth778HQoXDbbbDrrklHJbVRjUBEGmztWvjpT+Gww2D1anjySRg3TkkgV6hGICIN8vTT8OMfh5nBF14I118P7dolHZXUh2oEIrJNli2D4cPhpJOgbVt47TX4v/9TEshFsSYCMxtkZh+Y2Vwzu6KK8z8zs1lmNsPMXjCzPeKMR0Qazh0eeihMDHvkEfjtb+HNN+HQQ5OOTLZVbInAzJoDdwAnAH2AYWbWp1Kxt4BCd+8LTARuiiseEamDSpvGU1xc4fS8eXDccTByZEgEb78N114L22+fRLCSKXHWCPoBc919nrtvBMYDg9MLuPtL7r4uejoF6BpjPCJSk9Sm8QsXhq/9CxeG58XFbN4Mf/oT7LcfTJ0Kd94Jr7wSkoHkvjgTQRdgcdrz0uhYdc4DnokxHhGpSdqm8V9Zt463f1HMgAFw2WVw7LEwa1boHG6mHsYmIyv+Ks3sLKAQ+GM150eZWYmZlSxfvrxxgxPJF9Gm8SnracUV3EDh0kmUlsKECWGtoK6qtzc5cSaCJUD69hJdo2MVmNmxwNXAqe7+ZVUXcvcx7l7o7oWdOnWKJViRvJe2OfxLHElfZnAjVzCy7URmz4bvfQ/MEoxPYhNnIngD6GVmPcxsO2AoMCm9gJkdBNxNSALLYoxFRGrw+efw7Jn3c1WLmzicf3M0L+EYL2x/IvfetYWvfS3pCCVOsU0oc/fNZnYR8CzQHLjf3d8zs98BJe4+idAU1Bb4u4WvGovc/dS4YhKRYPXqsBjc5MnhZ/p02LLlaFo0P5LC7d7muo2/4ufdJtD6hmu1aXweMHdPOoZ6KSws9JKSkqTDEMkpK1fCq6+W3/jffjssEd2yJfTvDwMHhp9DDw2Tw6TpMbPp7l5Y1TktMSHSBC1bFoZ3pm78M2eG461awYAB8KtfhRv/gAHQunWysUrylAhEmoClS8tv+pMnw+zZ4XibNmEhuCFDwo2/Xz9N/pKtKRGIZIPi4jCOf9GiMHpn9Oga2+YXL654458zJxxv2xa++U0455xw4z/kENhuu0Z6D5KzlAhEkpaa0ZuazJWa0QswYgTuMH9+xRv/ggXhdPv28K1vheIDB4b9gFvof7XUk/7JiCSt0oxeB+as68LkS2Yy+Zlw4y8tDed23hmOOAIuvTTc+PffH5o3TyZsaTqUCEQSUlYW2vbnL+zGPI5gPj2YRR9e5VsspTOshF2eLx/RM3Bg2AReSztIpikRiMRo1aqwYuf8+eEn/fcFC+DLLwFe/ap8dxZyJC8zkMkM7DyXfUpf0GxeiZ0SgUgDbNgQmvSru9mvWlWxfIcOsOeeYRXPU04Jv/eY/yI9/u9n7LHhfVoRrbLSpg3cNAaUBKQRKBGI1KCsDJYsqfomP28efPxxxfLbbx+W8d9zzzA5q0eP6GbfI/x06FDVqxwNB/yiXqOGRDJJM4sl733xBXzwAXz00dY3/IULYePG8rJmYfXN1I09/Sa/556w225qw5fspJnFIoT1dWbPDuvpp/8sXFix3E5tv2TPfbfnwAPhtNMq3uy7d9eELGl6lAikyVmxYuub/axZFZtxWrWCffeFwzov4PyPH6L3phn0ZC49mM+OZVvg0jFqmpG8oUQgOckdPvmk4o0+9W1/WdqC5jvsEIZcHntseEz9FBRE4+8LjoRNlaoE6wjt9UoEkieUCCSruYfJVJVv9rNmwWeflZdr3z7c4E85peINv2vXWtrsK+3KVetxkSZIiUCyQllZGFdfuQ1/9mxYu7a8XKdOYcP0M8+seMPfbbdt3D2re/etOwlSx0XyhBKBNLpNm+Cdd2Dq1PDz7rvw/vuwfn15md13Dzf4kSPLb/a9e4dEkFGjR1dc5wfCGP7RozP8QiLZS4lAYpVq2pkyJfxMnRp2w9qwIZzfrfkyDtwynaPaldLn/P3p84MB9O5d3Xj7GKT6ATSGX/KY5hFIRn3xBZSUhBt+6ua/dGk416oVHHxw2AxlwKZX6X/P+XTb8GH55Nk2bWCMRuuIxEHzCCQWZWVhIlb6Tf/dd2HLlnC+Z0845phw4+/fH/r2TVsbv+Bs2FB5tM46jdYRSYASgdTZihUVb/rTpoVJWhBG7fTvD6eeGm78/fpBx441XEyjdUSyhhJBPqrDblgbN8KMGeXt+lOmwNy54VyzZuHb/dCh5d/299mnnksraLSOSNZQIsg3VeyG5ReMYvGnbZiy+2lf3fSnT08tkRxG8AwYABdcEB4POSRM1GoQjdYRyRpKBI2pnvvSxsGvupql69ozi0OZziFMYQBT1g/gv5fuDoQO3UMOgYsuCt/0BwwIk7Iyvia+RuuIZI38GDVUXMzCy+/k4yVOj66b2PWGS7GzGvmGU/mbOMQ6SqasLLS8pE/Qmj0bZk1ZzRraf1WuFx8ygCn0ZxoDSm6nb19o2TLj4YhIwmoaNdT0E0F0A75x3UVcwY0AtGYdPbpspMeBHbZaRrhHD2jXLobACwqqbhPfY4/ynckOTl0AAAgXSURBVMi3waZNYfnk1A0/9Vh5gtauu0YTs0oeovfaafRhFn2Zwc6szEgcIpLd8nv4aLQx+NmMZX9mMp8ezGNP5q/8OvOXDOLVV2HNmop/pGPHrZND6rF79238xtzAUTIbNsCHH1a82c+eHY5t2lRernv3cMM/8sjy2bi9e8NOO0UFilvAqAfVNi8iX2n6iSC60XZmKZ1ZWn58g8FbZbiHxcuq2mrwzTfhsccq3mibNYNu3arfmGTXXatpT6/jKJm1a8O3+co3/HnzQnNPKoa99go3+FNOCY99+oRlldu2reXzUNu8iFTS9JuGGtgks2VLWMe+qkQxb175rNmU1q3LE0OFGsXsf9Djuh/Qbn1YI3klX2PW9gcz+6zfM6vdgK9u+osXl1+rZcswLDN1o0899uoVOnVFROpKfQQxdtKuX19x8/LKCaNys9POzVbSvGwTy9i1Qjj77lvxZt+7d/jW36Lp19lEpBHkdx9BzE0hrVuHm/i++259LtXsVLEWsRObN1e84Xfvrn1uRSQ5Tb9GICIiNdYI9D1URCTPxZoIzGyQmX1gZnPN7Ioqzm9vZg9H56eaWUGc8YiIyNZiSwRm1hy4AzgB6AMMM7M+lYqdB3zm7j2BWyGa8SUiIo0mzhpBP2Cuu89z943AeGBwpTKDgYei3ycCx5hlfFUbERGpQZyJoAuQNiqe0uhYlWXcfTOwGti58oXMbJSZlZhZyfLly2MKV0QkP+VEZ7G7j3H3Qncv7JTx3ctFRPJbnIlgCdAt7XnX6FiVZcysBdAeWBFjTCIiUkmcieANoJeZ9TCz7YChwKRKZSYB349+PwN40XNtYoOISI6LdUKZmZ0I3AY0B+5399Fm9jugxN0nmVkrYCxwELASGOru82q55nKgisWDckpH4NOkg8gi+jzK6bOoSJ9HRQ35PPZw9yrb1nNuZnFTYGYl1c3wy0f6PMrps6hIn0dFcX0eOdFZLCIi8VEiEBHJc0oEyRiTdABZRp9HOX0WFenzqCiWz0N9BCIieU41AhGRPKdEICKS55QIGpGZdTOzl8xslpm9Z2Y/TTqmpJlZczN7y8yeSjqWpJlZBzObaGbvm9lsMzs06ZiSZGb/E/0/edfMxkXzjvKCmd1vZsvM7N20YzuZ2fNmNid6/FqmXk+JoHFtBn7u7n2AAcCFVSzNnW9+CsxOOogs8Wfgn+6+L3AAefy5mFkX4BKg0N33I0xKHZpsVI3qQWBQpWNXAC+4ey/gheh5RigRNCJ3X+rub0a/ryX8R6+8ImveMLOuwEnAvUnHkjQzaw8cAdwH4O4b3X1VslElrgXQOlqHrA3wccLxNBp3f4Ww2kK69GX7HwK+k6nXUyJISLQb20HA1GQjSdRtwC+BsqQDyQI9gOXAA1FT2b1mtkPSQSXF3ZcANwOLgKXAand/LtmoEreruy+Nfv8vsGumLqxEkAAzaws8Alzq7muSjicJZnYysMzdpycdS5ZoARwM/MXdDwK+IINV/1wTtX8PJiTIzsAOZnZWslFlj2hxzoyN/VciaGRm1pKQBIrd/dGk40nQ4cCpZraAsHvd0WZWlGxIiSoFSt09VUOcSEgM+epYYL67L3f3TcCjwGEJx5S0T8xsd4DocVmmLqxE0IiibTjvA2a7+y1Jx5Mkd7/S3bu6ewGhE/BFd8/bb3zu/l9gsZntEx06BpiVYEhJWwQMMLM20f+bY8jjzvNI+rL93weeyNSFlQga1+HA2YRvv29HPycmHZRkjYuBYjObARwIXJ9wPImJakYTgTeBmYR7Vd4sN2Fm44DXgX3MrNTMzgP+AHzbzOYQakx/yNjraYkJEZH8phqBiEieUyIQEclzSgQiInlOiUBEJM8pEYiI5DklApE0ZvYjMzsn+n2kmXVOO3dvJhYJjK57e0OvE/c1JX+0SDoAkWzi7nelPR0JvEu02Jm7n59ETCJxU41AmgQzK4jW8S+O1vKfaGZtonPHRAu5zYzWed8+Ov6HaG+IGWZ2c3TsN2Z2mZmdARQSJni9bWatzexlMyuMyg2Lrveumd2YFsfnZjbazN4xsylmVuPCYGbWycweMbM3op/DzayZmS0wsw5p5eaY2a5Vlc/8pyn5RolAmpJ9gDvdvTewBvhJtJnJg8CZ7r4/oRb8YzPbGTgN+Lq79wV+n34hd58IlAAj3P1Ad1+fOhc1F90IHE2YAfwNM0stCbwDMMXdDwBeAS6oJeY/A7e6+zeA7wL3unsZYfmA06LX6w8sdPdPqipf3w9JpDIlAmlKFrv7a9HvRcA3Cclhvrt/GB1/iLDu/2pgA3CfmZ0OrKvH63wDeDlaEG0zUBxdE2AjkNptbTpQUMu1jgVuN7O3CWvJ7BitTvswcGZUZmj0vKbyIttMfQTSlFReL6Xa9VPcfbOZ9SMsZnYGcBHhG35DbfLydVu2UPv/sWbAAHffkH7QzF4HeppZJ8IGJL+vpXyDA5f8pRqBNCXd0/b5HQ78G/gAKDCzntHxs4HJ0bfo9u7+NPA/hK0hK1sLtKvi+DRgoJl1NLPmwDBg8jbG/BxhsTkAzOxA+Gq9+ceAWwir1a6oqbxIQygRSFPyAWEf6NnA1wibvGwAzgX+bmYzCbuh3UW4wT8VrfT5b+BnVVzvQeCuVGdx6mC0S9QVwEvAO8B0d9/WJYEvAQqjDutZwI/Szj0MnEV5s1Bt5UW2iVYflSYh2vrzqWijcxGpB9UIRETynGoEIiJ5TjUCEZE8p0QgIpLnlAhERPKcEoGISJ5TIhARyXP/D2t7/Ac6rIG3AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ngHeUGK6dWLS"
      },
      "source": [
        "## visualize polynomial regression results (for smooth curve)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MOOolv7wdK5_",
        "outputId": "c78d71b5-bfa3-4c7d-e84d-88659dbe90e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "x_grid = np.arange(min(x), max(x), 0.1)\n",
        "x_grid = x_grid.reshape((len(x_grid), 1))\n",
        "plt.scatter(x, y, color = 'red')\n",
        "plt.plot(x, linregressor2.predict(x_poly), color = 'blue')\n",
        "plt.title('truth or bluff(polynomial)')\n",
        "plt.xlabel('position level')\n",
        "plt.ylabel('salary')\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SgYhIA6xfDxMnwhlnQOvWSUezbZQIREQa4KmnwvpCudosBEoEIiINMnYsdOkCAwcmHcm2UyIQEdlGn34KzzwDw4dD8+ZJR7PtlAhERLbRhAmweXNuNwuBEoGIyDYrKoL994e+fZOOpGGUCEREtsFHH8Hrr+d+bQCUCEREtklxcVhqevjwpCNpuFgTgZkNMrMPzGyumV1RTZkhZjbLzN4zs7/FGY+ISCa4h2aho46Crl2TjqbhWsR1YTNrDtwBfBsoBd4ws0nuPiutTC/gSuBwd//MzHaJKx4RkUyZNg3mzIErr0w6ksyIs0bQD5jr7vPcfSMwHhhcqcwFwB3u/hmAuy+LMR4RkYwoKoJWreD005OOJDPiTARdgMVpz0ujY+n2BvY2s9fMbIqZDarqQmY2ysxKzKxk+fLlMYUrIlK7TZtg/PiwrlD79klHkxlJdxa3AHoBRwLDgHvMrEPlQu4+xt0L3b2wUy6u8SoiTcZzz4WJZE1htFBKnIlgCdAt7XnX6Fi6UmCSu29y9/nAh4TEICKSlYqKYOedw94DTUWcieANoJeZ9TCz7YChwKRKZR4n1AYws46EpqJ5McYkIrLN1qyBxx+HoUOhZcuko8mcOiWCaARQvbj7ZuAi4FlgNjDB3d8zs9+ZWWrV7meBFWY2C3gJ+IW7r6jva4mINIbHHoMNG5pWsxCAuXvthczmAY8AD6QP/0xCYWGhl5SUJBmCiOSpY4+FBQvC0FGzpKOpHzOb7u6FVZ2ra9PQAYT2+3uj0T2jzGzHjEUoIpLlliyBF18MtYFcSwK1qVMicPe17n6Pux8GXA78GlhqZg+ZWc9YIxQRyQLjxoUZxSNGJB1J5tW5j8DMTjWzx4DbgD8BewJPAk/HGJ+ISFYoKoL+/aFXExzXWNclJuYQOnP/6O7/STs+0cyOyHxYIiLZY+ZMeOcduP32pCOJR62JIBox9KC7/66q8+5+ScajEhHJIsXF0KIFDBmSdCTxqLVpyN23ACc3QiwiIlmnrCwkgkGDoKkubFDXpqHXzOx24GHgi9RBd38zlqhERLLE5MlQWgo335x0JPGpayI4MHpMbx5y4OjMhiMikl2KiqBdOzjllKQjiU+dEoG7HxV3ICIi2WbOnDBsdOhQaNMm6WjiU+eNaczsJODrQKvUseo6kEVEct3mzXD22WHfgd//Pulo4lWnRGBmdwFtgKOAe4EzgGkxxiUikqgbboCpU+Hhh6Fz56SjiVddl5g4zN3PAT5z998ChxJWChURaXJKSuC3v4URh81nyC8LoFkzKCgIw4eaoLo2Da2PHteZWWdgBbB7PCGJiCRn3brQJLR7+y+4/a3DYf3ScGLhQhg1KvzexNaZqGuN4Klo57A/Am8CC4BxcQUlIpKUK66A99+HB1tcQIdUEkhZtw6uvjqZwGJUp2WoK/wBs+2BVu6+Op6QaqZlqEUkLs8/D8cdB5deCrf+uVlYZa4yszDLLMfUtAx1jU1DZnZ6Dedw90cbGpyISDZYuRJGjoTeveH664HHuofmoMq6d2/s0GJXWx9BTVMoHFAiEJEm4cILYdkyePJJaN0aGD069AmsW1deqE2bcLyJqTERuPu5jRWIiEhSxo2D8ePDPf7gg6ODqQ7hq6+GRYtCTWD06CbXUQz16CPIlgll6iMQkUwqLYX99w9NQq+8ElYZbYoavFVlNKHsTOBiwIDvAXtkLEIRkQSUlYV+gU2b4K9/bbpJoDaaUCYieev22+GFF+DWW6FnHm+6W9dEsCF6TE0o24wmlIlIDps9Gy6/HE4+Gc4/P+loklXXitCTlSaUOXBPbFGJiMRo40Y46yxo2xbuuSdMDchndU0E7wNb3P0RM+sDHAw8Hl9YIiLxue46ePNNePRR2G23pKNJXl2bhn7l7mvN7JuEzWjuBf4SX1giIvGYMiVMGBs5Ek47LeloskNdE8GW6PEk4B53/wewXTwhiYjE4/PPw4Jy3brBn/+cdDTZo65NQ0vM7G7g28CN0XpDdU0iIiJZ4bLL4KOP4OWXYccdk44me9T1Zj4EeBY43t1XATsBv4gtKhGRDHv6abj77pAMjjgi6WiyS71XH02aZhaLSH19+instx/ssgu88QZsv33SETW+bV59VEQk17nDD38In30Gzz2Xn0mgNkoEItKkjR0bhonedBP07Zt0NNlJHb4i0mQtWAAXXRT6BH72s6SjyV6xJgIzG2RmH5jZXDO7ooZy3zUzN7Mq269EROpryxb4/vfD7w89BM2bJxtPNostEZhZc+AO4ASgDzAsmpVcuVw74KfA1LhiEZH8c+utYVnp//1fKChIOprsFmeNoB8w193nuftGYDwwuIpy1wE3Ur6wnYhIg8ycGfaT+c53ymsFUr04E0EXYHHa89Lo2FfM7GCgWzRTuVpmNsrMSsysZPny5ZmPVESajC+/DAvKdegAY8ZoQbm6SKyz2MyaAbcAP6+trLuPcfdCdy/s1KlT/MGJSM669lqYMQPuuw90u6ibOBPBEqBb2vOu0bGUdsB+wMtmtgAYAExSh7GIbKtXX4U//hEuuCDsMyB1E2cieAPoZWY9zGw7YCgwKXXS3Ve7e0d3L3D3AmAKcKq7a9qwiNTbmjVwzjnQowfcckvS0eSW2CaUuftmM7uIsEZRc+B+d3/PzH4HlLj7pJqvICJSd5deCosWhVpB27ZJR5NbYp1Z7O5PA09XOnZtNWWPjDMWEWm6Hn8cHngArroKDjss6Whyj2YWi0hO++ST0Cdw0EHw618nHU1uUiIQkZzlHjaeX7sWiopgO22XtU206JyI5Kz77oOnngqziPtstW6B1JVqBCKSkz76KHQQH300XHJJ0tHkNiUCEck5W7aEoaItWsCDD0Iz3ckaRE1DIpJzbroJ/vOf0C/QrVvt5aVmyqMiklPeeissIzFkCAwfnnQ0TYMSgYjkjA0bwoJynTrBX/6iBeUyRU1DIpIzrroKZs2Cf/4Tdtop6WiaDtUIRCQnvPBCGCZ64YVw/PFJR9O0KBGISNZbtQpGjoS99w4dxZJZSgQikn2Ki8P+ks2aQUEBF588n6VLYexYaNMm6eCaHvURiEh2KS6GUaNg3ToAJizsR9HCHvzm9Bn069c34eCaJtUIRCS7XH31V0ngY3bnR9zFN5jGVSWnJxxY06VEICLZZdEiABz4AfezgVaM5WxaLp6XbFxNmBKBiGSX7t1x4EYu51kG8Ud+wT58CN27Jx1Zk6U+AhHJKvN/ehs/vKwdz5cdw2Ae5yfcGXqIR49OOrQmSzUCEckKmzeHvYb3u+Y7vL7dEdy+0694lO9ie+wBY8bAiBFJh9hkqUYgIol7++2wwcz06XDyyXDnnS3p1u064LqkQ8sLqhGISGLWr4crr4TCQli8GB5+GCZN0oqijU01AhFJxEsvhekCc+fCuefCzTdr/aCkqEYgIo3qs8/CZvNHHw1lZfCvf8H99ysJJEmJQEQahTtMnAi9e8MDD8AvfwkzZ8IxxyQdmahpSERit2RJWDX0iSfgoIPg6afh4IOTjkpSVCMQkdiUlcFdd0GfPvDcc2Hl0GnTlASyjWoEIhKL998PfQH//ndo/rn7bthrr6SjkqqoRiAiGbVxI1x3HRxwALz3XugPeP55JYFsphqBiGTMlClhYth778HQoXDbbbDrrklHJbVRjUBEGmztWvjpT+Gww2D1anjySRg3TkkgV6hGICIN8vTT8OMfh5nBF14I118P7dolHZXUh2oEIrJNli2D4cPhpJOgbVt47TX4v/9TEshFsSYCMxtkZh+Y2Vwzu6KK8z8zs1lmNsPMXjCzPeKMR0Qazh0eeihMDHvkEfjtb+HNN+HQQ5OOTLZVbInAzJoDdwAnAH2AYWbWp1Kxt4BCd+8LTARuiiseEamDSpvGU1xc4fS8eXDccTByZEgEb78N114L22+fRLCSKXHWCPoBc919nrtvBMYDg9MLuPtL7r4uejoF6BpjPCJSk9Sm8QsXhq/9CxeG58XFbN4Mf/oT7LcfTJ0Kd94Jr7wSkoHkvjgTQRdgcdrz0uhYdc4DnokxHhGpSdqm8V9Zt463f1HMgAFw2WVw7LEwa1boHG6mHsYmIyv+Ks3sLKAQ+GM150eZWYmZlSxfvrxxgxPJF9Gm8SnracUV3EDh0kmUlsKECWGtoK6qtzc5cSaCJUD69hJdo2MVmNmxwNXAqe7+ZVUXcvcx7l7o7oWdOnWKJViRvJe2OfxLHElfZnAjVzCy7URmz4bvfQ/MEoxPYhNnIngD6GVmPcxsO2AoMCm9gJkdBNxNSALLYoxFRGrw+efw7Jn3c1WLmzicf3M0L+EYL2x/IvfetYWvfS3pCCVOsU0oc/fNZnYR8CzQHLjf3d8zs98BJe4+idAU1Bb4u4WvGovc/dS4YhKRYPXqsBjc5MnhZ/p02LLlaFo0P5LC7d7muo2/4ufdJtD6hmu1aXweMHdPOoZ6KSws9JKSkqTDEMkpK1fCq6+W3/jffjssEd2yJfTvDwMHhp9DDw2Tw6TpMbPp7l5Y1TktMSHSBC1bFoZ3pm78M2eG461awYAB8KtfhRv/gAHQunWysUrylAhEmoClS8tv+pMnw+zZ4XibNmEhuCFDwo2/Xz9N/pKtKRGIZIPi4jCOf9GiMHpn9Oga2+YXL654458zJxxv2xa++U0455xw4z/kENhuu0Z6D5KzlAhEkpaa0ZuazJWa0QswYgTuMH9+xRv/ggXhdPv28K1vheIDB4b9gFvof7XUk/7JiCSt0oxeB+as68LkS2Yy+Zlw4y8tDed23hmOOAIuvTTc+PffH5o3TyZsaTqUCEQSUlYW2vbnL+zGPI5gPj2YRR9e5VsspTOshF2eLx/RM3Bg2AReSztIpikRiMRo1aqwYuf8+eEn/fcFC+DLLwFe/ap8dxZyJC8zkMkM7DyXfUpf0GxeiZ0SgUgDbNgQmvSru9mvWlWxfIcOsOeeYRXPU04Jv/eY/yI9/u9n7LHhfVoRrbLSpg3cNAaUBKQRKBGI1KCsDJYsqfomP28efPxxxfLbbx+W8d9zzzA5q0eP6GbfI/x06FDVqxwNB/yiXqOGRDJJM4sl733xBXzwAXz00dY3/IULYePG8rJmYfXN1I09/Sa/556w225qw5fspJnFIoT1dWbPDuvpp/8sXFix3E5tv2TPfbfnwAPhtNMq3uy7d9eELGl6lAikyVmxYuub/axZFZtxWrWCffeFwzov4PyPH6L3phn0ZC49mM+OZVvg0jFqmpG8oUQgOckdPvmk4o0+9W1/WdqC5jvsEIZcHntseEz9FBRE4+8LjoRNlaoE6wjt9UoEkieUCCSruYfJVJVv9rNmwWeflZdr3z7c4E85peINv2vXWtrsK+3KVetxkSZIiUCyQllZGFdfuQ1/9mxYu7a8XKdOYcP0M8+seMPfbbdt3D2re/etOwlSx0XyhBKBNLpNm+Cdd2Dq1PDz7rvw/vuwfn15md13Dzf4kSPLb/a9e4dEkFGjR1dc5wfCGP7RozP8QiLZS4lAYpVq2pkyJfxMnRp2w9qwIZzfrfkyDtwynaPaldLn/P3p84MB9O5d3Xj7GKT6ATSGX/KY5hFIRn3xBZSUhBt+6ua/dGk416oVHHxw2AxlwKZX6X/P+XTb8GH55Nk2bWCMRuuIxEHzCCQWZWVhIlb6Tf/dd2HLlnC+Z0845phw4+/fH/r2TVsbv+Bs2FB5tM46jdYRSYASgdTZihUVb/rTpoVJWhBG7fTvD6eeGm78/fpBx441XEyjdUSyhhJBPqrDblgbN8KMGeXt+lOmwNy54VyzZuHb/dCh5d/299mnnksraLSOSNZQIsg3VeyG5ReMYvGnbZiy+2lf3fSnT08tkRxG8AwYABdcEB4POSRM1GoQjdYRyRpKBI2pnvvSxsGvupql69ozi0OZziFMYQBT1g/gv5fuDoQO3UMOgYsuCt/0BwwIk7Iyvia+RuuIZI38GDVUXMzCy+/k4yVOj66b2PWGS7GzGvmGU/mbOMQ6SqasLLS8pE/Qmj0bZk1ZzRraf1WuFx8ygCn0ZxoDSm6nb19o2TLj4YhIwmoaNdT0E0F0A75x3UVcwY0AtGYdPbpspMeBHbZaRrhHD2jXLobACwqqbhPfY4/ynckOTl0AAAgXSURBVMi3waZNYfnk1A0/9Vh5gtauu0YTs0oeovfaafRhFn2Zwc6szEgcIpLd8nv4aLQx+NmMZX9mMp8ezGNP5q/8OvOXDOLVV2HNmop/pGPHrZND6rF79238xtzAUTIbNsCHH1a82c+eHY5t2lRernv3cMM/8sjy2bi9e8NOO0UFilvAqAfVNi8iX2n6iSC60XZmKZ1ZWn58g8FbZbiHxcuq2mrwzTfhsccq3mibNYNu3arfmGTXXatpT6/jKJm1a8O3+co3/HnzQnNPKoa99go3+FNOCY99+oRlldu2reXzUNu8iFTS9JuGGtgks2VLWMe+qkQxb175rNmU1q3LE0OFGsXsf9Djuh/Qbn1YI3klX2PW9gcz+6zfM6vdgK9u+osXl1+rZcswLDN1o0899uoVOnVFROpKfQQxdtKuX19x8/LKCaNys9POzVbSvGwTy9i1Qjj77lvxZt+7d/jW36Lp19lEpBHkdx9BzE0hrVuHm/i++259LtXsVLEWsRObN1e84Xfvrn1uRSQ5Tb9GICIiNdYI9D1URCTPxZoIzGyQmX1gZnPN7Ioqzm9vZg9H56eaWUGc8YiIyNZiSwRm1hy4AzgB6AMMM7M+lYqdB3zm7j2BWyGa8SUiIo0mzhpBP2Cuu89z943AeGBwpTKDgYei3ycCx5hlfFUbERGpQZyJoAuQNiqe0uhYlWXcfTOwGti58oXMbJSZlZhZyfLly2MKV0QkP+VEZ7G7j3H3Qncv7JTx3ctFRPJbnIlgCdAt7XnX6FiVZcysBdAeWBFjTCIiUkmcieANoJeZ9TCz7YChwKRKZSYB349+PwN40XNtYoOISI6LdUKZmZ0I3AY0B+5399Fm9jugxN0nmVkrYCxwELASGOru82q55nKgisWDckpH4NOkg8gi+jzK6bOoSJ9HRQ35PPZw9yrb1nNuZnFTYGYl1c3wy0f6PMrps6hIn0dFcX0eOdFZLCIi8VEiEBHJc0oEyRiTdABZRp9HOX0WFenzqCiWz0N9BCIieU41AhGRPKdEICKS55QIGpGZdTOzl8xslpm9Z2Y/TTqmpJlZczN7y8yeSjqWpJlZBzObaGbvm9lsMzs06ZiSZGb/E/0/edfMxkXzjvKCmd1vZsvM7N20YzuZ2fNmNid6/FqmXk+JoHFtBn7u7n2AAcCFVSzNnW9+CsxOOogs8Wfgn+6+L3AAefy5mFkX4BKg0N33I0xKHZpsVI3qQWBQpWNXAC+4ey/gheh5RigRNCJ3X+rub0a/ryX8R6+8ImveMLOuwEnAvUnHkjQzaw8cAdwH4O4b3X1VslElrgXQOlqHrA3wccLxNBp3f4Ww2kK69GX7HwK+k6nXUyJISLQb20HA1GQjSdRtwC+BsqQDyQI9gOXAA1FT2b1mtkPSQSXF3ZcANwOLgKXAand/LtmoEreruy+Nfv8vsGumLqxEkAAzaws8Alzq7muSjicJZnYysMzdpycdS5ZoARwM/MXdDwK+IINV/1wTtX8PJiTIzsAOZnZWslFlj2hxzoyN/VciaGRm1pKQBIrd/dGk40nQ4cCpZraAsHvd0WZWlGxIiSoFSt09VUOcSEgM+epYYL67L3f3TcCjwGEJx5S0T8xsd4DocVmmLqxE0IiibTjvA2a7+y1Jx5Mkd7/S3bu6ewGhE/BFd8/bb3zu/l9gsZntEx06BpiVYEhJWwQMMLM20f+bY8jjzvNI+rL93weeyNSFlQga1+HA2YRvv29HPycmHZRkjYuBYjObARwIXJ9wPImJakYTgTeBmYR7Vd4sN2Fm44DXgX3MrNTMzgP+AHzbzOYQakx/yNjraYkJEZH8phqBiEieUyIQEclzSgQiInlOiUBEJM8pEYiI5DklApE0ZvYjMzsn+n2kmXVOO3dvJhYJjK57e0OvE/c1JX+0SDoAkWzi7nelPR0JvEu02Jm7n59ETCJxU41AmgQzK4jW8S+O1vKfaGZtonPHRAu5zYzWed8+Ov6HaG+IGWZ2c3TsN2Z2mZmdARQSJni9bWatzexlMyuMyg2Lrveumd2YFsfnZjbazN4xsylmVuPCYGbWycweMbM3op/DzayZmS0wsw5p5eaY2a5Vlc/8pyn5RolAmpJ9gDvdvTewBvhJtJnJg8CZ7r4/oRb8YzPbGTgN+Lq79wV+n34hd58IlAAj3P1Ad1+fOhc1F90IHE2YAfwNM0stCbwDMMXdDwBeAS6oJeY/A7e6+zeA7wL3unsZYfmA06LX6w8sdPdPqipf3w9JpDIlAmlKFrv7a9HvRcA3Cclhvrt/GB1/iLDu/2pgA3CfmZ0OrKvH63wDeDlaEG0zUBxdE2AjkNptbTpQUMu1jgVuN7O3CWvJ7BitTvswcGZUZmj0vKbyIttMfQTSlFReL6Xa9VPcfbOZ9SMsZnYGcBHhG35DbfLydVu2UPv/sWbAAHffkH7QzF4HeppZJ8IGJL+vpXyDA5f8pRqBNCXd0/b5HQ78G/gAKDCzntHxs4HJ0bfo9u7+NPA/hK0hK1sLtKvi+DRgoJl1NLPmwDBg8jbG/BxhsTkAzOxA+Gq9+ceAWwir1a6oqbxIQygRSFPyAWEf6NnA1wibvGwAzgX+bmYzCbuh3UW4wT8VrfT5b+BnVVzvQeCuVGdx6mC0S9QVwEvAO8B0d9/WJYEvAQqjDutZwI/Szj0MnEV5s1Bt5UW2iVYflSYh2vrzqWijcxGpB9UIRETynGoEIiJ5TjUCEZE8p0QgIpLnlAhERPKcEoGISJ5TIhARyXP/D2t7/Ac6rIG3AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MAt7h3Fee69A"
      },
      "source": [
        "## Predicting new linear result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uEBxMlC5fSsM",
        "outputId": "73663f5e-2fa6-4a3f-870e-40ef80bde079",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "linregressor.predict([[6.5]])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([330378.78787879])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4OJzQYJXe6jD"
      },
      "source": [
        "## Predicting new polinomial result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8aBuSIxxeLyh",
        "outputId": "aa74ac69-5e5c-49ff-b374-a37f1ff4accc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "linregressor2.predict(polyregressor.fit_transform([[6.5]]))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([158862.45265155])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    }
  ]
}