sunkay/regression-linear-simple.ipynb

## regression-linear-simple.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Regression-linear-simple.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyN/PMu30dV/ku0iAKIjrOzF",
      "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/gist/sunkay/b43c0014650186d320536bb86bef8951/regression-linear-simple.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tmNZ2_k9H0aw"
      },
      "source": [
        "Simple linear regression using salary data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QyyDcMBLI-Kt"
      },
      "source": [
        "Data PreProcessing\n",
        "\n",
        "\n",
        "1.   Import Libraries\n",
        "2.   Read Data Set\n",
        "3.   Split Training & Test Sets\n",
        "4.   (No need to encode since there are no text categoriees/features\n",
        "5.   No need to address missing values\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VYFU5v0bH5I7"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r5nyZLJXIFJQ"
      },
      "source": [
        "dataset = pd.read_csv('Salary_Data.csv')\n",
        "X = dataset.iloc[:, :-1].values  \n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RY_S8NGWIkmt"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = \\\n",
        "        train_test_split(X, y, test_size = 0.2, random_state = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sj5xTkoRInO3"
      },
      "source": [
        "print(X_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qkuk4gAXJLky"
      },
      "source": [
        "print(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ix80DyvPJOMn",
        "outputId": "7f3d79d1-3368-4700-d73c-abd5dd6478f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydIrJQmHKQQN"
      },
      "source": [
        "y_pred = regressor.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xXsJ0Ps-LqOE",
        "outputId": "85fcb8b2-f64f-40ec-a439-9170cc678a1a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[75074.50510972 91873.8056381  62008.38247653 81607.56642631\n",
            " 67608.14931932 89073.92221671]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rpPr4nOGL-OW"
      },
      "source": [
        "Evalute of the predicted results are good enough\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b_40PR87MDv8",
        "outputId": "04cd6e43-7142-4d14-a26e-9dc6a9b99d86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.scatter(X_train, y_train, color = 'red')\n",
        "plt.plot(X_train, regressor.predict(X_train))\n",
        "plt.title('Salary vs. Experience (Training)')\n",
        "plt.xlabel('Years of Experience')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "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": "code",
      "metadata": {
        "id": "FIS8e-DZMlJ0",
        "outputId": "ff65fbb7-ed67-4cc9-a1ab-5daab573b3f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.scatter(X_test, y_test, color = 'red')\n",
        "plt.plot(X_train, regressor.predict(X_train))\n",
        "plt.title('Salary vs. Experience (Test)')\n",
        "plt.xlabel('Years of Experience')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAEWCAYAAACqitpwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3debxd473H8c83CSKKxFiZJLeCBjWlqHkWUY2rA6qEq81tSym3rWhKYwhxtaYONIYiQrTolQoiqKEIEmqIKZGZGDNQQabf/WM9J/beOSfn5GSfvc45+/t+vfbrrPVba6/125uc33nW86xnKSIwMzMrpzZ5J2BmZq2Pi4uZmZWdi4uZmZWdi4uZmZWdi4uZmZWdi4uZmZWdi4s1S5KmSzow7zxaOkl7SXot7zxKSXpc0o4VOtdvJf2oEueyz7m4WJORtKekJyQtkDQ3/UL5at55VUIqjp9I+nfB6/eVziMiHouIrSp93pWRdDjwUUQ8J+nqgu9nkaTFBev3NuLYJ0j6Z0n4N8AvJa1Zlg9gDeLiYk1C0nrA3cDvgA2ALsC5wGdNfN52TXn8VXR4RHyh4HVKJU/ezL6LQj8ERgBExA9rvh/gQuC2gu/r0HKcLCLmAK8C3yjH8axhXFysqWwJEBG3RsTSiPgkIu6PiBcAJH1J0kOSPpD0vqSRkjrWdiBJu0h6UtJ8SXMk/b7wr1BJIelkSZOByZL+IOm3JccYLen0Wo59laTflMTuknRGWj5T0puSPpL0mqQDVveLSee8o2D9YkkPKrOvpNmSfpm+l+mSji3Ydy1Jv5E0U9I76S//tdO2mveeKelt4M81sYL3d5Z0h6T3JE2TdGrBtiGS/iLppvR5J0nqU7C9m6Q703s/KGyJSfovSa9ImidprKTN6/jsawL7A4804HvaLbV850t6XtK+BdtOkDQ15TlN0rGSvgxcDXwttXzmFxzuYeCw+s5pZRQRfvlV9hewHvABcCNwKNCpZPsWwEHAWsDGwKPA5QXbpwMHpuWdgd2AdkAP4BXgpwX7BjCOrIW0NrAL8BbQJm3fCFgIbFpLnnsDswCl9U7AJ0BnYKu0rXPa1gP4UgM///L8a9nWAXgdOAHYC3gf6Jq27QssAS5N380+wMfAVmn7ZcDo9FnXBf4OXFTy3ovTe9dOsdlpextgInAOsCbwH8BU4JC0fQjwKdAPaAtcBIxP29oCz6fzrwO0B/ZM2/oDU4Avp/9GvwKeqOOzbwN8XMe2IcDNablL+v+nX8r7oLS+cTr/hwXfyWbANmn5BOCftRz7SODZvP9dVNMr9wT8ar2v9MvmBmB2+qU3urZf8GnfI4DnCtZX9sv5p8DfCtYD2L9kn1eAg9LyKcA9dRxLwExg77T+A+ChtLwF8C5wILDGKn726cC/gfkFrx8UbN8VmAvMAI4piNcUiHUKYn8Bzk65fkxBgQO+BkwreO8ioH3J8WYXnHNmSZ5nAX9Oy0OABwq29QY+KTjPe0C7Wj7rvcBJBettyIr55rXsuwfwdh3fWWFxORMYUbJ9LDAgFZf5wDeBtUv2qau4HARMzfvfRDW9fFnMmkxEvBIRJ0REV2BbstbA5QCSNpU0Kl1y+hC4mayFsQJJW0q6W9Lbad8La9l3Vsn6jcD30vL3SNf4a8kxgFHAMSn0XWBk2jaFrJANAd5N+XZu2KcH4IiI6FjwuqbgvE+RtRpEVjwKzYuIjwvWZ5B9dxuTtXompktF84H7UrzGexHxaR35bA50rnlvev8vgU0L9nm7YHkh0D713XQDZkTEkjqOe0XBMeemz9Wlln3nkbW46rM58O2SXPcENkvfzVFkfTdzJI2RtHU9x1uXrCBZhbi4WEVExKtkrZhtU+hCshbHdhGxHlkBUB1vv4qsQ7ZX2veXtexbOr33zUB/SduTtaD+byXp3Qp8K/UT7Aos7w+JiFsiYk+yX3ZBdslptUk6mezS1VvAL0o2d5K0TsF697Tf+2SX7LYpKFjrR9YZvjzllZx2Flkrp7DgrRsR/RqQ8iygex2DBGYB/11y3LUj4ola9p0CSFJthaf0mCNKjrlORAwDiIixEXEQ2SWxV4Gawl3X5/8y2WU9qxAXF2sSkraW9D+Suqb1bmStg/Fpl3XJLhstSL9ofr6Sw61Ldo393+kv1HrvWYiI2cAzZC2WOyLik5Xs+xzZL+5rgbERMT/lvJWk/SWtRdYX8QmwrL5z10fSlsAFZAX1OOAXknYo2e1cSWtK2gv4OvDXiFhG9kv0MkmbpGN1kXRIA0/9NPBR6vBfW1JbSduqYcPDnwbmAMMkrSOpvaQ90rargbMkbZNyWl/St2s7SEQsAh4g60tamZuBwyUdkvJsnwYndE2t3v6pAH9G9v9RzX+Xd4CuWnHY8T5kl++sQlxcrKl8RNYKeErSx2RF5SXgf9L2c4GdgAXAGODOlRzrZ2SXqz4i++V6WwNzuBHYjjouiZW4haxv5ZaC2FrAMLLC8zawCVkfBWl00qR6jvl3Fd/n8rf0l//NwMUR8XxETCZriY1IRYx0rnlkrZWRwA9Tyw+yvogpwPh0ifABsoEH9YqIpWSFagdgGp8X1PUb+N7DyfqhZpL1ox2Vtv2NrEU3KuX0Etkgjrr8iayorux8s8gGCvySrK9nFtkfIG3S6wyy72cuWeGo+YPjIWAS8Lak9wEkbUbWf7Sy1quVWc0IGbNWR9LeZL/IN48W8j96Gm57c+qnarUkPQ6cklqNTX2u3wJvRMQfm/pc9rnmepOV2WqRtAZwGnBtSyks1SQi9qh/r7Kd63/q38vKzZfFrNVJN9PNJ+vsvTzndMyqki+LmZlZ2bnlYmZmZec+l2SjjTaKHj165J2GmVmLMnHixPcjYuPSuItL0qNHDyZMmJB3GmZmLYqkGbXFfVnMzMzKzsXFzMzKzsXFzMzKzsXFzMzKzsXFzMzKzsXFzMzKzsXFzMzKzsXFzKxKTX3v3/zuwcksXrrajylagW+iNDOrMhHBybc8yz0vZk+1/s+dutC1U4eynsPFxcysirw4ewGH//6fy9cvO2r7shcWcHExM6sKy5YF3/7Tk0ycMQ+Ajb6wFo8P2o+12rVtkvO5uJiZtXKPT3mfY699avn6DSd+lX232qRJz+niYmbWSi1euox9L3mYN+d/AsA2nddj9Cl70raNmvzcTTZaTNL1kt6V9FJB7BJJr0p6QdLfJHUs2HaWpCmSXpN0SEG8b4pNkTSoIN5T0lMpfpukNVN8rbQ+JW3v0VSf0cysuRrzwhx6Db53eWG540e7M+bUvSpSWKBphyLfAPQtiY0Dto2IrwCvA2cBSOoNHA1sk97zR0ltJbUF/gAcCvQGjkn7AlwMXBYRWwDzgJNS/CRgXopflvYzM6sKCxctodfgezj5lmcB2H/rTZh2UT923rxTRfNosuISEY8Cc0ti90fEkrQ6HuialvsDoyLis4iYBkwBdkmvKRExNSIWAaOA/pIE7A/cnt5/I3BEwbFuTMu3Awek/c3MWrUR42fQ+5yxLF6aPb5+3Ol7c/0JXyWPX4F59rn8F3BbWu5CVmxqzE4xgFkl8V2BDYH5BYWqcP8uNe+JiCWSFqT93y9NQNJAYCBA9+7dV/PjmJnlY97Hi9jx/HHL14/+ajeGffMrOWaUU3GRNBhYAozM4/w1ImI4MBygT58+kWcuZmaNcfkDr3P5A5OXrz8+aH+6dFw7x4wyFS8ukk4Avg4cEBE1v9DfBLoV7NY1xagj/gHQUVK71Hop3L/mWLMltQPWT/ubmbUab83/hN2HPbR8/dQDenHGQVvmmFGxihYXSX2BXwD7RMTCgk2jgVskXQp0BnoBTwMCeknqSVY0jga+GxEh6R/At8j6YQYAdxUcawDwZNr+UEERMzNr8X71fy9y8/iZy9efPfsgNlhnzRwzWlGTFRdJtwL7AhtJmg38mmx02FrAuNTBND4ifhgRkyT9BXiZ7HLZyRGxNB3nFGAs0Ba4PiImpVOcCYySdAHwHHBdil8HjJA0hWxAwdFN9RnNzCppyrsfceCljy5fP/cb2zBg9x75JbQS8h/1mT59+sSECRPyTsPMbAURwcARExn38jsASPDSkENYZ63874OXNDEi+pTG88/MzMzq9K9Z8zniD48vX7/ymB35xvadc8yoYVxczMyaoWXLgiP++DgvzF4AwGbrt+eRn+/Hmu1axmO4XFzMzJqZR15/jwHXP718fcRJu7BXr41zzGjVubiYmTUTi5YsY8+LH+Ldjz4DYIduHbnzR7vTpkLzgZWTi4uZWTPw/Rsn8MAr7yxf/7+T92CHbh1X8o7mzcXFzCxH7374Kbtc+ODy9X223JgbTsxnPrBycnExM8tJj0FjitaHH7czB2/zxZyyKS8XFzOzCit9jj3A9GGH5ZRN03BxMTOroNLWSkscCdYQLi5mZhXwwMvv8P2bimcBaW2tlUIuLmZmTay0tXLfT/di6y+ul1M2leHiYmbWRG56cjrn3DWpKNaaWyuFXFzMzMosIuh51j1Fsad+eQCbrtc+p4wqz8XFzKyMhoyexA1PTF++/uXN1uPe0/bKL6GcuLiYmZXBZ0uWstWv7iuKTTq3eUyLn4fq/NRmZmV03HVP8djk95evH7FDZy4/esccM8qfi4uZWSPNX7iIHc4bVxSbMvRQ2rVtGdPiNyUXFzOzRigdXnz6gVty2oG9csqm+XFxMTNbBZPeWsBhVxZP3TLton4tfqLJcnNxMTNroNLWymkH9OL0g7bMKZvmzcXFzKwe9096m4EjJhbFquVmyMZycTEzW4nS1soVR+9A/x265JRNy+HiYmZWi+GPvsGF97xaFHNrpeFcXMzMSpS2Vu788e7s1L1TTtm0TC4uZmbJYVc+xqS3PiyKubXSOC4uZlb1lixdxhaD7y2KPXDGPmyxyRdyyqjlc3Exs6pWegkM3FopBxcXM6tKCz5ZzPbn3l8U+9c5B9Gxw5o5ZdS6uLiYWdVxa6XpubiYWdWY+t6/2f+3jxTFPNFk03BxMbOqUNpa6bXJFxh3xj45ZdP6ubiYWav22OT3OO66p4tivgTW9FxczKzVKm2tHLNLNy468is5ZVNdXFzMrNW59rGpXDDmlaKYWyuV5eJiZq1KaWvl/CO25bjdNs8pm+rl4mJmrcK3r36CZ6bPK4q5tZKfJht/J+l6Se9KeqkgtoGkcZImp5+dUlySrpQ0RdILknYqeM+AtP9kSQMK4jtLejG950qlx8DVdQ4za50igh6DxhQVluHH7ezCkrOmHNx9A9C3JDYIeDAiegEPpnWAQ4Fe6TUQuAqyQgH8GtgV2AX4dUGxuAr4QcH7+tZzDjNrZXoMGkPPs+4pik0fdhgHb/PFnDKyGk1WXCLiUWBuSbg/cGNavhE4oiB+U2TGAx0lbQYcAoyLiLkRMQ8YB/RN29aLiPEREcBNJceq7Rxm1kosWrJshb6Vcafv7dZKM1LpPpdNI2JOWn4b2DQtdwFmFew3O8VWFp9dS3xl51iBpIFkLSW6d+++qp/FzHLgqVtahtzmPEgtjsjzHBExPCL6RESfjTfeuClTMWu9Ro6EHj2gTZvs58iRTXKaeR8vWqGwPH/OwS4szVSlWy7vSNosIuakS1vvpvibQLeC/bqm2JvAviXxh1O8ay37r+wcZlZuI0fCwIGwcGG2PmNGtg5w7LFlO41bKy1PpVsuo4GaEV8DgLsK4senUWO7AQvSpa2xwMGSOqWO/IOBsWnbh5J2S6PEji85Vm3nMLNyGzz488JSY+HCLF4Gr7/z0QqF5Y0L+7mwtABN1nKRdCtZq2MjSbPJRn0NA/4i6SRgBvCdtPs9QD9gCrAQOBEgIuZKOh94Ju13XkTUDBL4MdmItLWBe9OLlZzDzMpt5sxVi6+C0qKyY/eO/O3He6z2ca0ylHVLWJ8+fWLChAl5p2HWsvTokV0KK7X55jB9eqMO+cDL7/D9m4r/Lbql0nxJmhgRfUrjvkPfzBpv6NDiPheADh2yeCOUtlZO2rMnZ3+99+pkaDlxcTGzxqvptB88OLsU1r17VlhWsTP/qoff4OL7Xi2KubXSsrm4mNnqOfbY1RoZVtpaueRbX+HbfbrVsbe1FC4uZpaLH4+cyD0vvl0Uc2ul9XBxMbOKiogV5gO740dfY+fNN8gpI2sKud2hb2aroUJ3xZdbnwvG1TrRpAtL6+OWi1lLU6G74svp08VL2frs+4pijw/any4d184pI2tqvs8l8X0u1mI0wb0lTclTt7Ruvs/FrLVowrviy+ndjz5ll6EPFsVePu8QOqzpXzvVwP+VzVqa7t1rb7k0o8dGlLZW2rURUy7sl1M2lgd36Ju1NEOHZnfBF1qNu+JXahUHDrz05oIVCsvUC/u5sFQht1zMWpoy3RVfr1UcOFBaVPbYYkNGfn+38uZkLYY79BN36JuVaODAgTEvzOHkW54t2sUd9tXDHfpmtmoaMHCgtLVyyn5b8LNDtmrKrKyFcHExs9qtZODApeNe58oHJxeF3VqxQu7QN7Pa1TFwoMfRfygqLFces6MLi63ALRczq13JwIEB3xvGI523KdrFRcXq4uJiZnU79ljiu99dYT6wv5+yJ9t1XT+npKwlcHExszptffa9fLp4WVHMrRVrCBcXM1vBJ4uW8uVziieafPqXB7DJeu1zyshaGhcXMyviiSatHFxczAyAOQs+4WsXPVQUe/X8vrRfo21OGVlL5uJiZiu0Vjp2WIN/nXNwTtlYa+DiYlbFnp05jyP/+ERRbNpF/ZCUU0bWWri4mFWp0tbKwb03ZfjxK0wRZdYoLi5mVebOZ2dzxl+eL4q5w97KzcXFrIqUtlZ+fshWnLzfFjllY61Zg4qLpLYRsbSpkzGzpjF0zMtc89i0ophbK9aUGtpymSzpDuDPEfFyUyZkZuVV2loZftzOHLzNF3PKxqpFQ4vL9sDRwLWS2gDXA6Mi4sMmy8zMVsu3rnqCCTPmFcXcWrFKadCU+xHxUURcExG7A2cCvwbmSLpRki/YmtVmFZ8/Xy7LlgU9Bo0pKiz3/XQvFxarqAb3uQCHAScCPYDfAiOBvYB7gC2bKD+zlmkVnz9fLp66xZqLBve5AP8ALomIwjuubpe0d/nTMmvhBg/+vLDUWLgwizdBcfn3Z0vY9tdji2ITf3UgG35hrbKfy6wh6i0uqdVyQ0ScV9v2iDi17FmZtXQNeP58ubi1Ys1RvcUlIpZK+jpQa3Exs1qs5Pnz5TJr7kL2+t9/FMVev+BQ1mznp5db/hp6WexxSb8HbgM+rglGxLNNkpVZSzd0aHGfC2TPox86tCyHL22tdNtgbR77xf5lObZZOTS0uOyQfha2XgJo1P/Nkk4Hvp+O8SLZQIHNgFHAhsBE4LiIWCRpLeAmYGfgA+CoiJiejnMWcBKwFDg1IsameF/gCqAtcG1EDGtMnmaNVvL8ebp3zwrLava3jJ/6AUcPH18U80ST1hwpIip7QqkL8E+gd0R8IukvZCPO+gF3RsQoSVcDz0fEVZJ+DHwlIn4o6WjgPyPiKEm9gVuBXYDOwAN8PmrtdeAgYDbwDHBMfTd/9unTJyZMmFD+D2xWJqWtlf/csQuXHbVDHXubVYakiRGxwoynDZ5bTNJhwDbA8uec1tXJ3wDtgLUlLQY6AHPIWkHfTdtvBIYAVwH90zLA7cDvlf2Z1p/sRs7PgGmSppAVGoApETE15T0q7euZBaxFuvLByVw67vWimDvsrblr6H0uV5MVgf2Aa4FvAU835oQR8aak3wAzgU+A+8kug82PiCVpt9lAl7TcBZiV3rtE0gKyS2ddgMLrA4XvmVUS37WOzzUQGAjQvYwdrWblUtpa+e99/oOzDv1yTtmYNVxDWy67R8RXJL0QEedK+i1wb2NOKKkTWUuiJzAf+CvQtzHHWl0RMRwYDtllsTxyMKvN8dc/zaOvv1cUc2vFWpKGFpdP0s+FkjqTdaxv1shzHghMi4j3ACTdCewBdJTULrVeugJvpv3fBLoBsyW1A9ZP56+J1yh8T11xs2avtLXyu2N25PDtO+eUjVnjNLS43C2pI3AJ8CzZKK9rG3nOmcBukjqQFa0DgAlkMwB8i2zE2ADgrrT/6LT+ZNr+UESEpNHALZIuJevQ70V2qU5AL0k9yYrK0Xzel2PWbPlmSGtNGlRcIuL8tHiHpLuB9hGxoDEnjIinJN1OVqSWAM+RXZoaA4ySdEGKXZfech0wInXYzyUrFkTEpDTS7OV0nJNrnjkj6RRgLNlQ5OsjYlJjcjWrhMVLl9FrcPFV5jGn7sk2ndfPKSOz1bfSociSjlzZmyPizrJnlBMPRbY8uLViLV1jhyIfvpJtAbSa4mJWSXM/XsRO548rinmiSWtNVlpcIuLESiViVi3cWrFqkNdNlGZV5+W3PqTflY8VxSYPPZQ12nqiSWt9Kn4TpVk1cmvFqk3Fb6I0qyZ3v/AWp9zyXFHMRcWqQWNvopxL42+iNKsKpa2VvXptxIiTap2JyKzVWdWbKP+XbB4waPxNlGat2rB7X+XqR94oirm1YtVmpcVF0leBWTU3UUr6AtnzV14FLmv69MxaltLWyukHbslpB/bKKRuz/NTXcvkT2VxgSNobGAb8hOzhYcPJOvbNqt43r3qCiTPmFcXcWrFqVl9xaRsRc9PyUcDwiLiDbBqYfzVtambNX0TQ86x7imLXHN+Hg3pvmlNGZs1DvcWlYKbiA0jPPmnge81aNQ8vNqtbfQXiVuARSe+TjRh7DEDSFkCjJq40a+k+XbyUrc++ryh290/2ZNsunmjSrEZ9078MlfQg2bDj++PzWS7bkPW9mFUVt1bMGqbeS1sRMb6W2Ou17WvWWr01/xN2H/ZQUezZsw9ig3XWzCkjs+bN/SZm9XBrxWzVubiY1eHJNz7gmGuKG+5Thh5KO080aVYvFxezWri1YrZ6XFzMClz3z2mcf/fLRTEXFbNV5+JilpS2Vrbrsj5//8meOWVj1rK5uFjV++8RExg76Z2imFsrZqvHxcWqWmlr5Yf7fIlBh26dUzZmrYeLi1WlXoPvYfHSKIq5tWJWPi4uVlVqm2jyqmN34tDt/Ow7s3JycbGq4eHFZpXj4mKt3sJFS+h9ztii2LjT96bXpuvmlJFZ6+fiYq2aWytm+XBxsVZpxgcfs88lDxfFXhhyMOu1XyOfhMyqjIuLtTpurZjlz8XFWo2HX3uXE/78TFFs6oX9aNNGOWVkVr08vau1DCNHQo8e0KZN9nPkyKLNPQaNWaGwTB92mAuLWU7ccrHmb+RIGDgQFi7M1mfMyNaB331xF347rvjZdb4EZpY/Fxdr/gYP/ryw1Fi4kB4vdoQXPy8su39pQ275wW4VTs7MauPiYs3fzJlFq/t//2qmbti1KObWilnz4uJizV/37tmlMKDHmXcXbfrZwVtyyv698sjKzFbCHfrW/A0dSo8z716hsEzfbn7jCks9gwPMbPW5uFiztmxZZH0rBf74z2uZvt18OPbYVT9gzeCAGTMg4vPBAS4wZmWliKh/r3KfVOoIXAtsCwTwX8BrwG1AD2A68J2ImCdJwBVAP2AhcEJEPJuOMwD4VTrsBRFxY4rvDNwArA3cA5wW9XzQPn36xIQJE8r3IW21NcnNkD16LL/EVmTzzWH69NU7tlkVkjQxIvqUxvNquVwB3BcRWwPbA68Ag4AHI6IX8GBaBzgU6JVeA4GrACRtAPwa2BXYBfi1pE7pPVcBPyh4X98KfCYrkwULF69QWB44Y5/ydNqXDA6oN25mjVLxDn1J6wN7AycARMQiYJGk/sC+abcbgYeBM4H+wE2p5TFeUkdJm6V9x0XE3HTccUBfSQ8D60XE+BS/CTgCuLcCH89WU5NP3VIwOGCFuJmVTR4tl57Ae8CfJT0n6VpJ6wCbRsSctM/bwKZpuQswq+D9s1NsZfHZtcStGZv01oIVCstL5x5S/iHGQ4dChw7FsQ4dsriZlU0eQ5HbATsBP4mIpyRdweeXwACIiJDU5J1BkgaSXWqju/9yzU1FJ5qsGQQweHB2Kax796ywNGZwgJnVKY/iMhuYHRFPpfXbyYrLO5I2i4g56bLXu2n7m0C3gvd3TbE3+fwyWk384RTvWsv+K4iI4cBwyDr0G/+RrDFunzibn/31+aLYtIv6kY3haELHHutiYtbEKn5ZLCLeBmZJ2iqFDgBeBkYDA1JsAHBXWh4NHK/MbsCCdPlsLHCwpE6pI/9gYGza9qGk3dJIs+MLjmXNRI9BY1YoLNOHHdb0hcXMKiKvO/R/AoyUtCYwFTiRrND9RdJJwAzgO2nfe8iGIU8hG4p8IkBEzJV0PlAzFe55NZ37wI/5fCjyvbgzv9n4+V+f568TZxfFPHWLWeuTy30uzZHvc2l6pX0rO3bvyN9+vEdO2ZhZOdR1n4vnFrMmt+N59zNv4eKimFsrZq2bi4s1qdLWyqkH9OKMg7bMKRszqxQXF2sSfo69WXVzcbGyWrJ0GVsMLh4/8ecTvsp+W2+SU0ZmlgcXFysbt1bMrIaLi622BQsXs/159xfFHvvFfnTboEMd7zCz1s7FxVaLWytmVhsXF2uUye98xEGXPVoUe+2CvqzVrm1OGZlZc+LiYqvMrRUzq4+LizXYE2+8z3eveaooVpGJJs2sxXFxsQYpba0cuVMXLv3ODjllY2bNnYuLrdTfnpvN6betOHuxmdnKuLhYnUpbKxcduR3H7OKHqplZ/VxcbAUX3/cqVz38RlHMrRUzWxUuLlaktLUyauBu7PYfG+aUjZm1VC4uBsCx147n8SkfFMXcWjGzxnJxqXK1TTT56M/3o/uGnrrFzBrPxaWKbfmre1m0ZFlRzK0VMysHF5cq9OGni/nKkOKJJl8YcjDrtV8jp4zMrLVxcakypR32a6/RllfO75tTNmbWWrm4VIkZH3zMPpc8XBSbMvRQ2rVtk09CZtaqubhUgdLWyl69NmLESbvmlI2ZVQMXl1bsyTc+4JhrxhfF3GFvZpXg4tJKlbZWTt7vS/z8kK1zysbMqo2LSytz69MzOevOF4tibq2YWaW5uLQipa2Vy4/agSN27JJTNmZWzVxcWoEhoydxwxPTi2JurZhZnlxcWrCIoOdZ9xTF7vzx7uzUvVNOGSNWDfYAAApPSURBVJmZZVxcWqj//OPjPDdzflHMrRUzay5cXFqY2iaafHzQ/nTpuHZOGZmZrcjFpQX59tVP8Mz0eUUxt1bMrDlycWkBPvp0MduVTDT56vl9ab9G25wyMjNbOReXZq50WvxDttmUPx3XJ8eMzMzq5+LSTM2et5A9L/5HUWzqhf1o00Y5ZWRm1nAuLs1Q6c2Qp+6/BWccvFVO2ZiZrToXl2bk+Vnz6f+Hx4ti7rA3s5Yot4d5SGor6TlJd6f1npKekjRF0m2S1kzxtdL6lLS9R8Exzkrx1yQdUhDvm2JTJA2q9GdrjB6DxhQVlku/s70Li5m1WHk+Keo04JWC9YuByyJiC2AecFKKnwTMS/HL0n5I6g0cDWwD9AX+mApWW+APwKFAb+CYtG+zdN9Lb69wGWz6sMM4cqeuOWVkZrb6crksJqkrcBgwFDhDkoD9ge+mXW4EhgBXAf3TMsDtwO/T/v2BURHxGTBN0hRgl7TflIiYms41Ku37chN/rFVWWlRuG7gbu/7HhjllY2ZWPnn1uVwO/AJYN61vCMyPiCVpfTZQM51vF2AWQEQskbQg7d8FKHwSVuF7ZpXEm9VjF//8+DTO/XtxrfMlMDNrTSpeXCR9HXg3IiZK2rfS5y/JZSAwEKB79+5Nfr7aJpr8x8/2pedG6zT5uc3MKimPlssewDck9QPaA+sBVwAdJbVLrZeuwJtp/zeBbsBsSe2A9YEPCuI1Ct9TV7xIRAwHhgP06dMnVv+j1W3Ek9M5+65Jy9d7b7Ye95y2V1Oe0swsNxUvLhFxFnAWQGq5/CwijpX0V+BbwChgAHBXesvotP5k2v5QRISk0cAtki4FOgO9gKcBAb0k9SQrKkfzeV9OxdU20eRL5x7CF9byKHAza72a02+4M4FRki4AngOuS/HrgBGpw34uWbEgIiZJ+gtZR/0S4OSIWAog6RRgLNAWuD4iJpGDi+55hT89OnX5+kl79uTsrzfbgWtmZmWjiCa9GtRi9OnTJyZMmFCWYy1asozdhz3I+/9etDw2eeihrNE2z5HfZmblJ2liRKww4WFzarm0Cn9//i1+cutzy9d/fXhvTtyjZ44ZmZlVnotLmXz82RK2GzKWZakheOCXN+Ga4/uQ3ZJjZlZdXFzK4KYnp3NOwUiwB87Ymy02WbfuN5iZtXIuLqvptmdmLi8sx+zSnYuO3C7njMzM8ufispq23HRddt68E787Zkc6+zn2ZmaAi8tq27F7J+740e55p2Fm1qx4bKyZmZWdi4uZmZWdi4uZmZWdi4uZmZWdi4uZmZWdi4uZmZWdi4uZmZWdi4uZmZWdp9xPJL0HzMg7j1WwEfB+3knkzN+BvwPwd5D35988IjYuDbq4tFCSJtT2DIVq4u/A3wH4O2iun9+XxczMrOxcXMzMrOxcXFqu4Xkn0Az4O/B3AP4OmuXnd5+LmZmVnVsuZmZWdi4uZmZWdi4uLYykbpL+IellSZMknZZ3TnmQ1FbSc5LuzjuXPEjqKOl2Sa9KekXS1/LOqdIknZ7+Dbwk6VZJ7fPOqalJul7Su5JeKohtIGmcpMnpZ6c8c6zh4tLyLAH+JyJ6A7sBJ0vqnXNOeTgNeCXvJHJ0BXBfRGwNbE+VfReSugCnAn0iYlugLXB0vllVxA1A35LYIODBiOgFPJjWc+fi0sJExJyIeDYtf0T2S6VLvllVlqSuwGHAtXnnkgdJ6wN7A9cBRMSiiJifb1a5aAesLakd0AF4K+d8mlxEPArMLQn3B25MyzcCR1Q0qTq4uLRgknoAOwJP5ZtJxV0O/AJYlnciOekJvAf8OV0avFbSOnknVUkR8SbwG2AmMAdYEBH355tVbjaNiDlp+W1g0zyTqeHi0kJJ+gJwB/DTiPgw73wqRdLXgXcjYmLeueSoHbATcFVE7Ah8TDO5FFIpqV+hP1mh7QysI+l7+WaVv8juLWkW95e4uLRAktYgKywjI+LOvPOpsD2Ab0iaDowC9pd0c74pVdxsYHZE1LRYbycrNtXkQGBaRLwXEYuBO4Hdc84pL+9I2gwg/Xw353wAF5cWR5LIrrW/EhGX5p1PpUXEWRHRNSJ6kHXgPhQRVfUXa0S8DcyStFUKHQC8nGNKeZgJ7CapQ/o3cQBVNqihwGhgQFoeANyVYy7Lubi0PHsAx5H9xf6v9OqXd1JWcT8BRkp6AdgBuDDnfCoqtdpuB54FXiT7XdYsp0EpJ0m3Ak8CW0maLekkYBhwkKTJZC26YXnmWMPTv5iZWdm55WJmZmXn4mJmZmXn4mJmZmXn4mJmZmXn4mJmZmXn4mKtmjL/lHRoQezbku7LKZ+t0/Dx5yR9qWTbdEkvFgwxv7KJc+nT1Oew6uWhyNbqSdoW+CvZPGztgOeAvhHxRiOO1S4ilqxGLoOAdhFxQS3bppPN8vt+Y4+/Cnms1ucwq49bLtbqRcRLwN+BM4FzgJuBwZKeTi2I/pBNBCrpMUnPptfuKb5vio8GXpa0jqQxkp5PzxI5qvScknaQNF7SC5L+JqlTutn1p8CPJP2jIblLaifpGUn7pvWLJA1Ny9Ml/W9q7TwtaYsU31jSHel9z0jaI8WHSBoh6XFgRPpcd6dt66RnhZR+JydIulPSfel5If9bkFvf9D09L+nBlR3HqlBE+OVXq38B6wCvkd3NfRHwvRTvCLyetncA2qd4L2BCWt6XbHLInmn9m8A1Bcdev5bzvQDsk5bPAy5Py0OAn9WR4/SU37/S6/QU34ZsapMDyVpdaxbsPzgtHw/cnZZvAfZMy93JpgqqOfdEYO2Cz1Xzngvr+E5OAKYC6wPtgRlAN2BjYFbBd7LByo6T939/vyr/ald/+TFr+SLiY0m3Af8GvgMcLulnaXN7sl/CbwG/l7QDsBTYsuAQT0fEtLT8IvBbSReT/XJ+rPBc6XkrHSPikRS6keyyXEPsFyWXxSJikqQRwN3A1yJiUcHmWwt+XpaWDwR6Z1NuAbBemkUbYHREfFLLeQ8mmxC09DuB7EFUC9JnexnYHOgEPFrznUTE3HqOU63zflUtFxerJsvSS8A3I+K1wo2ShgDvkD3ZsQ3wacHmj2sWIuJ1STsB/YALJD0YEec1ce7bAfOBTUriUctyG2C3iCjMn1RsPqZ2dX0nuwKfFYSWsvLfG7Uex6qP+1ysGo0FfpJm00XSjim+PjAnIpaRTQ7atrY3S+oMLIyIm4FLKJnuPv2VP0/SXil0HPAIjSTpSGADsqdP/k5Sx4LNRxX8fDIt3082sWXN+3dowGnq+k7qMh7YW1LPtP8GjTyOtVJuuVg1Op/saZYvSGoDTAO+DvwRuEPS8cB91P1X/nbAJZKWAYuBH9WyzwDgakkdyPosTmxgbv+QtDQtvwCcQTbL7QERMUvS74Er+HyK9U7KZkb+DDgmxU4F/pDi7YBHgR/Wc966vpNaRcR7kgYCd6b93wUOWtXjWOvlochmLVQlhy6brSpfFjMzs7Jzy8XMzMrOLRczMys7FxczMys7FxczMys7FxczMys7FxczMyu7/wfRD57SAlBf3gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ub2zTGzLO4zO"
      },
      "source": [
        "Making a single prediction (for example the salary of an employee with 12 years of experience)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wuPfWj78O5_k",
        "outputId": "9d754c15-74f6-47cd-e050-9ca504e6a57b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(regressor.predict([[12]]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[137605.23485427]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r2Hm2CIAPUbf"
      },
      "source": [
        "Getting the final linear regression equation with the values of the coefficients\n",
        "y = b0 + b1 x"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LsWum86GPioK",
        "outputId": "b6467936-7981-45be-dad9-f93adcf95b8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(regressor.coef_)\n",
        "print(regressor.intercept_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[9332.94473799]\n",
            "25609.89799835482\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UT6tW5pvPmFv"
      },
      "source": [
        "Salary=9345.94×YearsExperience+26816.19"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "43eM-E7uP6t7"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}