PloynomialRegressionSalaryPrediction.ipynb

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  "metadata": {
    "colab": {
      "name": "Untitled77.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNEGPkmUkXIX8vLAEZdTexy",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DivyaWadehra/AI-LAB/blob/master/PloynomialRegressionSalaryPrediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4wX2ayhluwKb"
      },
      "source": [
        "#Importing Libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cY2U2iHOz3F9"
      },
      "source": [
        "#Import Dastaset\n",
        "dataset=pd.read_csv('Position_Salaries.csv')\n",
        "X=dataset.iloc[:,1:-1].values\n",
        "y=dataset.iloc[:,-1].values"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "Gqt1mwzP0ZyP",
        "outputId": "4d9803c2-896b-41cb-825d-617ffc720141"
      },
      "source": [
        "dataset"
      ],
      "execution_count": 14,
      "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",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Region Manager</td>\n",
              "      <td>6</td>\n",
              "      <td>150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Partner</td>\n",
              "      <td>7</td>\n",
              "      <td>200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Senior Partner</td>\n",
              "      <td>8</td>\n",
              "      <td>300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>C-level</td>\n",
              "      <td>9</td>\n",
              "      <td>500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>CEO</td>\n",
              "      <td>10</td>\n",
              "      <td>1000000</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\n",
              "5     Region Manager      6   150000\n",
              "6            Partner      7   200000\n",
              "7     Senior Partner      8   300000\n",
              "8            C-level      9   500000\n",
              "9                CEO     10  1000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H2WWa5ey0KAZ",
        "outputId": "f600afa1-1719-4d86-8ac3-f64eaead1739"
      },
      "source": [
        "#Training the model\n",
        "from sklearn.linear_model import LinearRegression\n",
        "lin_reg=LinearRegression()\n",
        "lin_reg.fit(X,y)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fA5ycXMM0KZx",
        "outputId": "0c9a2ba1-de4d-478f-f0d5-8a4f40e44b29"
      },
      "source": [
        "#Polynomial model\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly_reg=PolynomialFeatures(degree=2)\n",
        "x_poly=poly_reg.fit_transform(X)\n",
        "lin_reg2=LinearRegression()\n",
        "lin_reg2.fit(x_poly,y)\n"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F_i9uvvq2hD_",
        "outputId": "73317887-2796-4f12-9fd2-3b5c7a822e28"
      },
      "source": [
        "x_poly"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  1.,   1.,   1.],\n",
              "       [  1.,   2.,   4.],\n",
              "       [  1.,   3.,   9.],\n",
              "       [  1.,   4.,  16.],\n",
              "       [  1.,   5.,  25.],\n",
              "       [  1.,   6.,  36.],\n",
              "       [  1.,   7.,  49.],\n",
              "       [  1.,   8.,  64.],\n",
              "       [  1.,   9.,  81.],\n",
              "       [  1.,  10., 100.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "jOXihr9J0K9A",
        "outputId": "552eaa63-321c-479d-8845-7946a8325b94"
      },
      "source": [
        "#Visualize results\n",
        "plt.scatter(X,y,color='red')\n",
        "plt.plot(X,lin_reg.predict(X),color='blue')\n",
        "plt.title('Linear Regression Results')\n",
        "plt.xlabel('Position level')\n",
        "plt.ylabel('salary')\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NyaCtcCIwM6sQAX/60/LKn5Aqfw4cCN/9bsuWfmgpTgRmZqTKn3fdla7+nTw5Vf684IL8K39WAycCMyu1N95I3T233prqALV05c9q0Kgx7uwmM2ZmbcKyZfDYY3DEEdC9e+oG6tkT/ud/YOJEOPPMLAmMHp2aA2utlR5Hjy448nw0tkUwTdKDwG2VdxgzM2tNPvwQRo1KLYBp01ZR+XP0aOjXDxYtSq9nzEivAfr0acmwc9fYWa/fBF4HRkp6XlI/SZvkGJeZWbOZOjVN9ezYEc47DzbfPH3Pz5wJl13WQPnnQYOWJ4Faixal5W1MoxJBRHwcESMiYl/gIuBnwFxJt0vaPtcIzcyaYOnSNOvn4INh551Tv/9xx8H48TBuHJx88irKP8+cuXrLW7FGdQ1lYwTfA84AugHXAKOBbwNjyW45aWZWtLqVPzt3hl/+sgmVP7t0STuob3kb0+gxAuAp4OqIeK5i+QOSvtP8YZmZrZ5Jk9LUz9rKnwceCNdeC0cd1cTKn1dcseIYAaSKcldc0WwxV4tVfjxZa2BURFxe3/qI+GGzR2Vm1ggNVf4cMAB22WUNd147IDxoUOoO6tIlJYE2NlAMjbwxjaT/jYg9WyCeVfKNacysbuXP7bZLg8FnnAGbbVZ0dNWpOW5M86ykIcB9wN9qF0bExGaIz8xslRqq/DlyZHpsrZU/q0FjE8Gu2WNl91AA323ecMzMVlS38ucmm6TbPvbvny4GszXXqEQQEQfmHYiZWaW6lT932inNBDr1VNhoo6Kja1saPZYu6XvAzsD6tcsaGkA2M2uK+ip/HnVUqvx54IFts/JnNWjsdQQ3Ae2BA4GRwHHA/+YYl5mVSH2VPy+8MFX+7Nq16Ojavsa2CPaNiG9IeikiLpN0DfBonoGZWdtXt/Lnbrul5717l6fyZzVobCL4JHtcJGkb4D1g63xCMrO2bNky+MMfUvfPo49Cu3ap9MPAgbDPPu7+KUJjE8EjkjYDrgYmkmYMjcwtKjNrc+pW/txqK7jkklT5c2ufVhaqsbOGfp49fVDSI8D6EfFhfmGZWVsxdWrq+7/jjjQWsM8+qeLnsceuouibtZiVJgJJx6xkHRHxUPOHZGat3dKl8Pvfp+6fJ5+E9dZLd/waMAD22KPo6KyuVbUIjlzJugBWmggk9QSuB9oBIyPiyga2OxZ4APjHiHD9CLNW6r330pW+w4al8jydO8OvfpUqf3boUHR01pCVJoKIOKOpO86K1Q0FDgFmA+Mljal7hzNJGwM/Al5o6rHMrFiTJqWz/3vuWV7587rr4Mgjm1j501pUnheU7QlMj4g3s9+/F+gF1L3V5c+Bq4ALGxuLmRVv8WJ48MGUAJ57LlX+PP301P2z885FR2ero7E3r78JOBEYCAg4HljVZR4dgVkVr2dnyyr3uzvQOSL+exXH7yepRlLNvHnzGhOymeXknXfSYG/Xrqnf/9134Te/gTlzUkkIJ4HWp7ALyiStBVwLnL6qbSNiODAcUhnqNTmuma2+CHj++XT2/8ADqTVw2GHpTmD/9E+u/NnaNTYRfJo91l5Q9j6rvqBsDtC54nWnbFmtjYFdgKeVriDZChgj6SgPGJtVh08/hXvvTdM/ayt/9u+fqn+68mfb0dhE8Pt6LigbsYrfGQ90l7QtKQH0Bk6uXZldh/D5PAJJTwMXOAmYFW/mzOWVP997L1X+vPFGOOUUV/5sixqbCF4FlkbEg5J2AnYHHl7ZL0TEEkkDgMdJ00dvjYgpki4HaiJizJoEbmbNKwKefjqd/ddW/uzVK5V+OOAAl35oyxqbCH4aEfdL+hbpZjS/Bm4E9lrZL0XEWGBsnWWXNLDtAY2Mxcya0cKFyyt/TpkCm2/uyp9l09hEsDR7/B4wIiL+W9IvcorJzFrA9Omp7s9tt6U6QLvvnp6feKIrf5ZNYxPBHEk3ky4Ou0rSejRy6qmZVY9ly+Dxx9PZf23lz+OPT3P/XfmzvBqbCE4AegK/jogFkrbGF4CZtRoffpjO9ocOTS2BrbaCn/0M+vVz5U9rfPXRRVTUFYqIucDcvIIys+YxZUo6+7/zzlT5c9994fLLXfnTVuQqIGZtzJIlqfLnkCHLK3+efHLq/tl996Kjs2rkRGDWRsyfnyp/3nijK3/a6nEiMGvlJk5MZ/933w1//7srf9rq85+JWSv02Wep8ueQIcsrf55xhit/WtN4CqhZKzJ3Llx6abrQ6+ST23Dlz9GjoVu3VM2uW7f02nLjFoFZlYuAcePS2f/996fB4MMOS6Uf2mTlz9Gj07zWRYvS6xkz0muAPn2Ki6sNU0Trqurco0ePqKlxXTpr+z75ZHnlz4kTU+XPM89M1T+3377o6HLUrVv68q+ra1d4662WjqbNkDQhInrUt84tArMqM2NG6uYZOTJV/tx555JV/pw5c/WW2xpzIjCrAhHw1FPp7P93v0vLSlv5s0uX+lsEXbq0fCwl0dZ6F81alYUL09n+LrvAQQfBM8/AT34Cb74JDz2UpoKWKgkAXHFFmgZVqX37tNxy4RaBWQGmTYNhw1z5s161A8KDBqXuoC5dUhLwQHFunAjMWsiyZfDYY8srf669dqr8OXAg7L13Cc/8V6ZPH3/xtyAnArOcLVgAo0atWPnz0ktd+dOqhxOBWU7qq/z585/DMce48qdVFycCs2ZUW/lz8OA0C8iVP601cCIwawZ1K3926QJXXgl9+7ryp1U/JwKzNTBxYjr7v+eeVPnzu9915U9rffynaraaait/Dh6cagBtuOHy0g9tpuiblYoTgVkjzZ0LN9+cft55J9X7ue46OO002GyzoqMzazonArOVqK/y5+GHp8HfNln500rJicCsHnUrf266abrw69xz23jlTyslJwKzCjNnppk/I0Ysr/x5003pItdSVP60Usq1YSupp6TXJE2XdHE9638saaqklyQ9IalrnvGY1ae28ucxx8C228J//ifsvz88+SS8/DL84AclSgK+M1gp5dYikNQOGAocAswGxksaExFTKzabBPSIiEWSzgH+Ezgxr5jMKi1cCHfdlbp/pkyBzTeHiy6Cs88uacVj3xmstPJsEewJTI+INyPiM+BeoFflBhHxVERkf3U8D3TKMR4zIFX+/Nd/hU6d4Jxz0tW/t90Gs2fDL39Z0iQAqdpnbRKotWhRWm5tWp5jBB2BWRWvZwN7rWT7vsCj9a2Q1A/oB9CltP9LbU3Urfy5zjqp8ueAAa78+TnfGay0qmKwWNIpQA9g//rWR8RwYDikexa3YGjWyrny52rwncFKK8+uoTlA54rXnbJlK5B0MDAIOCoi/p5jPFYikyenbp9OnVI30JZbpjIQM2bAz37mJFAv3xmstPJsEYwHukvalpQAegMnV24gaTfgZqBnRLybYyxWAkuWwJgxqfvHlT+bwHcGK63cEkFELJE0AHgcaAfcGhFTJF0O1ETEGOBqYCPgfqVO2pkRcVReMVnb5Mqfzch3BiulXMcIImIsMLbOsksqnh+c5/GtbZswIZ39V1b+vP76VPmzXbuiozNrPVwpxVqVzz6Du+9Od/vq0SPV/znzzHQdwBNPwNFHt9Ik4Au5rEBVMWvIbFXefhuGD/9i5c/TT091gFo1X8hlBVNE65qN2aNHj6ipqSk6DGsBEfDcc6n754EHYOlSOOywVPzt0EPbUOXPbt3qn7bZtSu89VZLR2NtlKQJEdGjvnVuEVjVqa38OXgwTJpUgsqfvpDLCuZEYFVjxow082fkyJJV/vSFXFawttK4tlYqIg3yfv/78JWvwNVXl7Dypy/ksoK5RWCFWLgQ7rwz9f9PnVryyp++kMsK5haBtahp0+C886Bjx9Tnv/76VVD5sxqmbvbpkwaGly1Lj04C1oLcIrDc1Vb+HDw4PVZV5U9P3TTz9FHLz4IF6Wx/6FB4441U6O3ss+Gss6qo6JunblpJePqotajJk1Pf/513phPt/faDX/wi3Qpy3XWLjq4OT900cyKw5lFb+XPwYHj66dT3f/LJ0L9/lVf+9NRNMw8W25qZPx9+9as09fPYY+Evf4GrrkqDv7fcsookUA2DtJ66aeYWgTXNhAnp7P/ee1Plz4MOghtuWI3Kn9UySOupm2YeLLbG++yzVPNnyBAYNw423BBOOy11/+y002ruzIO0Zi3Kg8W2Rt5+O1X9vPlm+OtfoXv3Zqj86UFas6rhMYIyakTffAQ8+yycdFI6Sf/5z1P9/0cfhVdfhR/9aA3LPzc0GOtBWrMW50RQNrV98zNmpG/72r75LBl88kma+7/HHvCtb6Uv/oED4fXX4ZFHoGfPZir/7EFas6rhRNCSqmGWzKBBywdoay1axIyLhnHxxdC5c7rj1+LFqfLnnDlw7bU5lH/u0yfdaaZr13Rpcdeu6bUHac1aXkS0qp899tgjVttdd0V07Rohpce77lr9faypu+6KaN8+Ip2Hp5/27Vs+Funz4y+DeIID42geirVYEmutFXHMMRFPPRWxbFnLhmVm+QJqooHv1bY/a6juNEVIXRAtffZZLbNkunVj4Yz53MmpDGEAU9mZDszjrE1+y9kv93cXvVkbtbJZQ22/a6iBrhAGDWrZOKpglsy0aXDe1x6jI3M4lxvZgE8YxWnM2uCr/HLYZk4CZiXV9hNBFXwBA4XNklm2DMaOTff63WEHGPbk1zhi3/cZt9X3Gc+enNb1T6w/YrD75s1KrO1fR1AttWSuuKL+LqqcZsnUV/nzsstSCFtttS3wX7kc18xan7bfIqiWaYotNEtm8uRU6rljR/jxj2GrrVIZiLfegksuSa/NzCq1/RZBNdWS6dMnl+M2VPlzwADYbbdmP5yZtTG5JgJJPYHrgXbAyIi4ss769YA7gD2A94ATI+KtZg8kpy/gos2fDyNGwI03wqxZqZFx1VXQt2+6B7CZWWPklggktQOGAocAs4HxksZExNSKzfoCH0TE9pJ6A1cBJ+YVU1tRX+XPwYPhiCMaWfnTzKxCni2CPYHpEfEmgKR7gV5AZSLoBVyaPX8AGCJJ0doubmgBtZU/Bw+G559PlT/79m1i5U8zswp5JoKOwKyK17OBvRraJiKWSPoQ2ByYn2NcrUp9lT+vvz6Vf16jom9mZplWMVgsqR/QD6BLCa56ioDnnktn/w8+CEuXwuGHp8HfQw9tpqJvZmaZPBPBHKBzxetO2bL6tpktaW1gU9Kg8QoiYjgwHFKJiVyirQKffAL33JNu/DJpUjrj/+EP4Zxzcij6ZmaWyTMRjAe6S9qW9IXfGzi5zjZjgNOAccBxwJNlHB+YMQOGDYORI+H992GXXVLlz1NOSWMBZmZ5yi0RZH3+A4DHSdNHb42IKZIuJ1XBGwPcAtwpaTrwPilZlEIEPPlk6v75/e/TNWZHH526f/bfP702M2sJuY4RRMRYYGydZZdUPP8UOD7PGKrNwoVwxx2p++eVV6BDB7jootT907nzqn/fzKy5tYrB4rbg9ddT3Z9Ro+Cjj9IdwEaNghNPTFcCm5kVxYkgR8uWpVs9DhkCjz0G66wDxx+fbv24117u/jGz6uBEkIOVV/4sOjozsxU5ETSjyZPT2f+dd6Zq0/vtl+rbHXNMag2YmVUjJ4I1tGQJ/O53KQG48qeZtUZOBE00b16a9+/Kn2bW2jkRrKaamnT278qfZtZWOBE0QkOVPwcMgB13LDo6M7M140SwEq78aWZl4ERQR0OVPwcOhEMOceVPM2t7nAgytZU/Bw+GF1+EzTZLlT/PPRe2267o6MzM8lP6RFBf5c+bb063OHblTzMrg1ImgoYqfw4cCN/5jks/mFm5lCoR1Ff58+KL4eyzXfnTzMqrNIngllvgxz9OlT979IDbb4cTTnDlTzOz0iSCrl3hyCNT98+ee7r7x8ysVmkSwcEHpx8zM1uRZ8WbmZWcE4GZWck5EZiZlZwTgZlZyTkRmJmVnBOBmVnJORGYmZWcE4GZWckpIoqOYbVImgfMKDqONdQBmF90EFXEn8dy/ixW5M9jRWvyeXSNiC3qW9HqEkFbIKkmInoUHUe18OexnD+LFfnzWFFen4e7hszMSs6JwMys5JwIijG86ACqjD+P5fxZrMifx4py+Tw8RmBmVnJuEZiZlZwTgZlZycoKVFIAAATjSURBVDkRtCBJnSU9JWmqpCmSflR0TEWT1E7SJEmPFB1L0SRtJukBSa9KekXSPkXHVCRJ/5r9P5ks6R5JpbmxrKRbJb0raXLFsn+Q9EdJ07LHLzXX8ZwIWtYS4PyI2AnYG+gvaaeCYyraj4BXig6iSlwPPBYRXwO+SYk/F0kdgR8CPSJiF6Ad0LvYqFrUKKBnnWUXA09ERHfgiex1s3AiaEERMTciJmbPPyb9R+9YbFTFkdQJ+B4wsuhYiiZpU+A7wC0AEfFZRCwoNqrCrQ1sIGltoD3wdsHxtJiIeAZ4v87iXsDt2fPbgaOb63hOBAWR1A3YDXih2EgKdR3wE2BZ0YFUgW2BecBtWVfZSEkbFh1UUSJiDvBrYCYwF/gwIv5QbFSF2zIi5mbP3wG2bK4dOxEUQNJGwIPAeRHxUdHxFEHSEcC7ETGh6FiqxNrA7sCNEbEb8Deasenf2mT9371ICXIbYENJpxQbVfWINO+/2eb+OxG0MEnrkJLA6Ih4qOh4CrQfcJSkt4B7ge9KuqvYkAo1G5gdEbUtxAdIiaGsDgb+EhHzImIx8BCwb8ExFe2vkrYGyB7fba4dOxG0IEki9QG/EhHXFh1PkSLi3yKiU0R0Iw0CPhkRpT3ji4h3gFmSvpotOgiYWmBIRZsJ7C2pffb/5iBKPHieGQOclj0/Dfhdc+3YiaBl7QecSjr7fTH7ObzooKxqDARGS3oJ2BX4ZcHxFCZrGT0ATAReJn1XlabchKR7gHHAVyXNltQXuBI4RNI0UovpymY7nktMmJmVm1sEZmYl50RgZlZyTgRmZiXnRGBmVnJOBGZmJedEYG2KpKXZtNzJku6X1H41f38bSQ9kz3etnN4r6ShJzXK1r6SFzbGfvPdp5eDpo9amSFoYERtlz0cDE5p68Z6k00nVLwc0Y4i1+/48zmrep5WDWwTWlv0Z2D6r4/6wpJckPS/pGwCS9q+4sG+SpI0ldctaE+sClwMnZutPlHS6pCHZ73aT9GS2zyckdcmWj5J0g6TnJL0p6bhVBSnpQknjs31dli27UlL/im0ulXRBQ9ubrQknAmuTstLFh5GuSr0MmBQR3wD+Hbgj2+wCoH9E7Ap8G/ik9vcj4jPgEuC+iNg1Iu6rc4jBwO3ZPkcDN1Ss2xr4FnAEq7j6U9KhQHdgT9LVxHtI+g5wH3BCxaYnAPetZHuzJnMisLZmA0kvAjWkejW3kL6U7wSIiCeBzSVtAjwLXCvph8BmEbFkNY6zD3B39vzO7Bi1Ho6IZRExlVWXCj40+5lEKqfwNaB7REwCvpyNWXwT+CAiZjW0/WrEbfYFaxcdgFkz+yQ7w/9cqln2RRFxpaT/Bg4HnpX0T8CnzRDD3ysPv4ptBfwqIm6uZ939wHHAVqQWwqq2N2sStwisDP4M9AGQdAAwPyI+krRdRLwcEVcB40ln15U+BjZuYJ/PsfzWiX2yYzTF48CZ2T0qkNRR0pezdfdlxziOlBRWtb1Zk7hFYGVwKXBrVtVzEctL+Z4n6UDSHdKmAI+S+vdrPQVcnHU1/arOPgeS7iZ2IenOYmc0JbCI+IOkHYFxWctlIXAK6aY9UyRtDMypvTPVyrZvyvHNwNNHzcxKz11DZmYl50RgZlZyTgRmZiXnRGBmVnJOBGZmJedEYGZWck4EZmYl9/8B/gV5quW2DngAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "aY_VFAjo2DB_",
        "outputId": "431931de-4d45-4fbe-d005-dc6e70046ef1"
      },
      "source": [
        "#Visualize results Polynomial Regression\n",
        "plt.scatter(X,y,color='red')\n",
        "plt.plot(X,lin_reg2.predict(x_poly),color='blue')\n",
        "plt.title('Polynomial Regression Results')\n",
        "plt.xlabel('Position level')\n",
        "plt.ylabel('salary')\n",
        "plt.show()"
      ],
      "execution_count": 21,
      "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": "ATlJ9K1b2ZjR"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}