Simple_linear_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Simple linear Regression.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNW4pEfOowVFNVG3Ne2/gjq"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**Import the libraries**"
      ],
      "metadata": {
        "id": "BL3RAXIsMO9-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "f3ICk8PycRE1"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " **Import the dataset**"
      ],
      "metadata": {
        "id": "5N0mWCD0MVzd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset =pd.read_csv('Salary_Data.csv')\n",
        "x=dataset.iloc[:,:-1].values\n",
        "y=dataset.iloc[:,-1].values"
      ],
      "metadata": {
        "id": "v_hQGeKiJ2jz"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Splitting the dataset into the training set & test set**"
      ],
      "metadata": {
        "id": "PJ3UBKC7Mb6V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test= train_test_split(x,y,test_size= 1/3, random_state=0)"
      ],
      "metadata": {
        "id": "sWPDizNOKJSn"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Traning the simple Linear regression model on the traning set**"
      ],
      "metadata": {
        "id": "3hhHOP9sMpy9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor =LinearRegression()\n",
        "regressor.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p2et6qAdKtFt",
        "outputId": "c7225f70-99a7-462a-8fd4-8cbc1d48fbd3"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Predicting the Test set results**"
      ],
      "metadata": {
        "id": "gge4qAqTMxLc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=regressor.predict(x_test)"
      ],
      "metadata": {
        "id": "5o8LFHbIL71_"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visulization the Traning set Results**"
      ],
      "metadata": {
        "id": "5EqcWcn8M7tU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(x_train,y_train, color='red')\n",
        "plt.plot(x_train,regressor.predict(x_train),color='blue')\n",
        "plt.title('salary vs experice (Traning set)')\n",
        "plt.xlabel('years of Experience')\n",
        "plt.ylabel('salary')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "NP5kW5ONM2xU",
        "outputId": "cf4039e8-d429-4ef5-a3af-fc181fd897e7"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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M37gRDjwQ5s1Ly69/fbpvZYst6n7qfsnBxcwGlzo+XbErt9ySmsEK7rwT3vWuhlah33FwMTPro1dfhbFj4Zn8EPV3vhN+/euBOx9YLfkSmJn1waRJqaO+EFja2+G3v3VgKXDmYmbWC0uXpgd3Fdu0aXDMB1ZLjrFmZlUaO7Y0sNxySxp27MDSmTMXM7MePPggvO1tpWV+iG/3nLmYmXVDKg0s7e0OLNVwcDEzq+DOO0ubu7bfPgWVgw5qXp0GEjeLmZmVKe9DWbiwdOJJ65kzFzOzbMaM0sBy2GEpW3Fg6T1nLmY25G3aBMOHl5atWjU0Zi+uF2cuZjakfeMbpYFl8uShMy1+PTm4mNmQ9OqrqQms+DliL78MV15Z5QH6+eOUm83BxcyGnNNPT1O3FHz1qylbKS7rVuFxyosWpR0Lj1N2gPkrhQdsA9DS0hLt7e3NroaZ1dFzz3Vu7tq4sQ/zgU2cWPmhZBMmwBNP9LF2A5OkuRHRUl7uzMXMhoTDDy8NLFdemZKOPk002c8ep9wfebSYmQ1qixZ1Hkq82Q02/eRxyv2ZMxczG7R23rk0sNx+e42mbukHj1Pu7xxczGzQaW9PI8GefbajLALe/e4anaC1FaZNS30sUvo9bVrDn4DZn7lZzMwGlfKpWx54APbbrw4nasLjlAcSZy5mNihcfHHnwBJRp8BiPXLmYmYDXnlQeegheMtbmlMXS5y5mNmAdeaZlbMVB5bmc+ZiZgPOhg2wxRalZStWwK67Nqc+1lndMhdJV0h6WtJDRWU7SbpD0mP592tyuSRdJGmBpHmSDizaZ3Le/jFJk4vKD5L0YN7nIil9f+nqHGY2OBxxRGlgGTMmZSsOLP1LPZvFrgSOKSv7HHBnROwF3JmXAd4D7JV/pgCXQAoUwJeBQ4CDgS8XBYtLgH8p2u+YHs5hZgPYmjWpCezXv+4oe+klWLq0eXWyrtUtuETEb4Bny4qPB6bn19OBE4rKr4pkDrCjpN2AScAdEfFsRDwH3AEck9dtHxFzIk2OdlXZsSqdw8wGqG22SY8ZLnj/+1O2Un4fo/Ufje5z2TUilufXK4BCIjsGWFK03dJc1l350grl3Z2jE0lTSJkS4z1tg1m/s2RJ5xlV+jTRpDVc0/5EOeOo65TMPZ0jIqZFREtEtIwePbqeVTEbvOr0XBOpNLB84QubMdGkNVyjM5enJO0WEctz09bTuXwZMK5ou7G5bBlwRFn5r3L52Arbd3cOM6u1wnNN1q5Ny4XnmkCf716fOxdayiZw95NBBp5GfweYDRRGfE0GbiwqPzWPGjsUeD43bd0GHC3pNbkj/2jgtrzuBUmH5lFip5Ydq9I5zKzWpk7tCCwFa9eWPt6xF6TSwFKYFt8GnrplLpKuIWUdu0haShr19U3gJ5JOAxYBH8qb3wK8F1gArAX+CSAinpX0NeAPebtzI6IwSOAM0oi0rYFb8w/dnMPMaq1GzzW58UY4oWzojYPKwOYnUWZ+EqVZH9TgiYzld9jfdVe6l8UGBj+J0sxqbzOea3L++ZWnbnFgGRw8/YuZ9V2h037q1NQUNn58CizddOZXGvH16KOw9951rKc1nIOLmW2eXjzX5PTT4fLLS8vcMj84ObiYWd2tXw9bbllatnIl7LJLc+pj9ec+FzOrq4MPLg0se++dshUHlsHNwcVsIKrTXfG1tHp16rD/wx86yl55JfWv2ODn4GI20BTuil+0KKUAhbvi+1GAkeA1RQ+7OPnkVNWRI5tXJ2ssBxezgabGd8XX0sKFnYcXb9oE11zTnPpY8zi4mA00NborvtYkeP3rO5bPPTdlK+XBxoYGjxYzG2jGj698V3yTHhtx881w7LGlZR5ebM5czAaazbgrvtd6GDgglQaWa65xYLHEmYvZQNOHu+L7pJvp9M9f0co555Ru7qBixTxxZeaJK83KdDEppcqevzd7Nhx3XIPqZP2OJ640s94pGyDwUa7qFFgiHFisMjeLmVlleeBAAMPKgsof/whve1tzqmUDgzMXM6vsvPN4o/7cKbDEjDYHFuuRg4uZdfLyy6BTWnks9vpr2VNjDyJmtNV+4IANSm4WM7MSlW56TON+5ja6KjaAOXMxMwCWL+8cWF55xUOMrW8cXMwMCXbfvWP5rW/1RJO2eRxczIaw+++vPNHkvHnNqY8NHg4uZkOUBAce2LF82mmeaNJqxx36ZkPMddfBSSeVlrlfxWrNmYvZECKVBpbvfc+BxeqjquAiaXi9K2Jm9fO1r3Vu7oqAT3yiOfWxwa/aZrHHJF0H/G9EPFzPCplZbZUHldtug6OPbk5dbOiotllsP+DPwGWS5kiaImn7OtbLzDbTBz5QOVtxYLFGqCq4RMSaiPhRRLwD+CzwZWC5pOmS9qxrDc2sVzZtSkHlhhs6yh55xH0r1lhVNYvlPpf3Af8ETATOB9qAvwVuAd5Yp/qZWS+87nXw1FOlZQ4q1gxV97kAdwHfjojfF5XPknR47atlZr3x4ouw3XalZatWwU47Nac+Zj0Gl5y1XBkR51ZaHxFn1rxWZla1rieaNGueHvtcImIjcGwD6mJmvbB4cefAsm6dA4v1D9U2i/1O0veBa4GXCoURcV9damVm3SoPKocdBr//feVtzZqh2uCyf/5d3DQWwLtqWx0z61JbG3f8560c/fSMkuLC6DCz/qSq4BIRf1/viphZN9ra0CmtQMdTIN877OfcfNUqkJ8Maf1P1RNXSnof8BZgq0JZV538ZlY7F1wAZ59dGkACwSZg6gQ/dtj6pWrnFrsU+DDwSUDAB4EJfT2ppLMkzZf0kKRrJG0laQ9J90haIOlaSVvmbUfm5QV5/cSi43w+l/9J0qSi8mNy2QJJn+trPc2aTYKzz+5YPpcvpsBSsHhx4ytlVoVqp395R0ScCjwXEV8FDqOPN05KGgOcCbRExL7AcOBk4FvAhRGxJ/AccFre5bR83j2BC/N2SNon7/cW4BjgB5KG56HTFwPvAfYBPpK3NRswTj65wtQtiC/y9dLC8eMbVymzXqg2uLycf6+VtDuwHthtM847Atha0ghgFLCcNDhgVl4/HTghvz4+L5PXHylJuXxmRLwaEQuBBcDB+WdBRPwlItYBM/O2ZgOCBNde27H8059CzGiDUaNKNxw1Cs47r7GVM6tStX0uN0naEfg2cB9ppNhlfTlhRCyT9B1gMSlo3Q7MBVZHxIa82VJgTH49BliS990g6Xlg51w+p+jQxfssKSs/pFJdJE0BpgCM9zdAa7LddoMVK0rLOu5Zyf0qU6emprDx41NgcX+L9VPVjhb7Wn55naSbgK0i4vm+nFDSa0iZxB7AauD/SM1aDRcR04BpAC0tLb71zJpiwwbYYovSsgcfhH33LduwtdXBxAaMboOLpA90s46IuL4P5zwKWBgRK/Nxrgf+BthR0oicvYwFluXtlwHjgKW5GW0HYFVReUHxPl2Vm/UrnrrFBqueMpfjulkXQF+Cy2LgUEmjSM1iRwLtpIkxTyL1kUwGbszbz87Ld+f1v4yIkDQbuFrSBcDuwF7AvaTRbHtJ2oMUVE4G/rEP9TSrm2efhZ13Li175pnOZWYDVbfBJSL+qdYnjIh7JM0i9d1sAO4nNU3dDMyU9PVcdnne5XLgx5IWAM+SggURMV/ST4CH83E+nudBQ9IngNtII9GuiIj5tX4fZn3lbMWGAkWV/6oH+02ULS0t0d7e3uxq2CA2f37nfpT162FE1bcym/U/kuZGREt5ebUPC7uUNGT470mjxE4iNUGZWRXKs5XXvrbzQ73MBpOG30RpNpTMnl35OfYOLDbYVRtcXsm/CzdRbmDzbqI0G/QkOL7o9t0Pf9h9KzZ0VBtcflZ2E+VC4Oq61cpsADvvvMrZysyZzamPWTNU25X4KLAxIq7L83QdCPy0ftUyG5jKg8r558N//mdz6mLWTNVmLl+MiDWS3kmaA+wy4JL6VctsYDnuuMrZigOLDVXVBpeN+ff7gB9FxM3AlvWpktnAEZGCyk03dZT9/OfuWzGrtllsmaQfAu8GviVpJNUHJrNByTdDmnWt2gDxIdId75MiYjWwE/DputXKrB979dXOgWXePAcWs2LVzoq8lqJ5xCJiOekZLGZDirMVs+q4acusCsuWdQ4sq1Y5sJh1xbMamfXA2YpZ7zlzMevC737XObBs2ODAYlYNZy5mFThbMds8zlzMilx6aeWbIR1YzHrHmYtZVh5UjjwSfvGL5tTFbKBz5mJD3uTJlbMVBxazvnNwsSFNgquu6lj+6lfdBGZWC24WsyFpt91gxYrSMgcVs9px5mJDSmGiyeLA8rOfVRFY2tpg4kQYNiz9bmurYy3NBj5nLjZk9Hl4cVsbTJkCa9em5UWL0jJAa2vN6mc2mDhzsUHvpZc6B5Y//akXzWBTp3YEloK1a1O5mVXkzMUGtZrcDLl4ce/KzcyZiw1OTzzRObA8/3wfO+3Hj+++3P0xZp04uNigI8Eee5SWRcD22/fxgOedB6NGlZaNGpXKC/0xixalkxT6YxxgbIhzcLFB4847O2crGzfWYIhxaytMmwYTJqQTTJiQlltb3R9j1gUHF6udJjYPSXDUUR3LW2+dgsqwWv0Lb21NbW2bNqXfhVFi7o8xq8jBxWqjSc1DF1xQeeqW8mSibnrqjzEbohxcrDaa0DwkwdlndyyfeGIT7rLvrj/GbAhzcLHaaGDz0Mc+Vjlbuf76mp+qZ931x5gNYb7PxWpj/PjUFFapvIbKg8oFF8BZZ9X0FL3X2upgYlbGmYvVRp2bh8aNq5ytND2wmFlFDi5WG3VqHtq0KR1u6dKOst/+1jMYm/V3bhaz2qlx85CfY282cDlzsX5nzZrOgWXx4hoGFk/XYlZ3zlysX6l7tuLp880aoimZi6QdJc2S9KikRyQdJmknSXdIeiz/fk3eVpIukrRA0jxJBxYdZ3Le/jFJk4vKD5L0YN7nIqnSR5b1WR2++T/+eOfAsnZtHZrBPF2LWUM0q1nsu8DPI+JNwH7AI8DngDsjYi/gzrwM8B5gr/wzBbgEQNJOwJeBQ4CDgS8XAlLe5l+K9jumAe9paKjDnfgS7LlnaVlEmsKl5jxdi1lDNDy4SNoBOBy4HCAi1kXEauB4YHrebDpwQn59PHBVJHOAHSXtBkwC7oiIZyPiOeAO4Ji8bvuImBMRAVxVdCzbXDX85n/77Z2zlU2b6txp7+lazBqiGZnLHsBK4H8l3S/pMknbALtGxPK8zQpg1/x6DLCkaP+luay78qUVyjuRNEVSu6T2lStXbubbGiJq9M1fgkmTOpbf+taO59vXladrMWuIZgSXEcCBwCURcQDwEh1NYADkjKPug04jYlpEtEREy+jRo+t9usFhM7/5f+c7lW+GnDdvM+tVLU/XYtYQzQguS4GlEXFPXp5FCjZP5SYt8u+n8/plwLii/cfmsu7Kx1Yot1rYjG/+Enz60x3Ln/xkk+5b6Wr6fDOrmYYHl4hYASyRtHcuOhJ4GJgNFEZ8TQZuzK9nA6fmUWOHAs/n5rPbgKMlvSZ35B8N3JbXvSDp0DxK7NSiY9nm6sM3/w98oHK2ctFFda6rmTVNs0aLfRJokzQP2B/4BvBN4N2SHgOOyssAtwB/ARYAPwLOAIiIZ4GvAX/IP+fmMvI2l+V9HgdubcB7Gjp68c1fghtu6FiePr2bbKXaIc6+CdKs/4sI/0Rw0EEHhXVhxoyICRMipPR7xowed9l224gURjp+ejzHqFGlO4wa1flc1W5nZg0BtEeFz1SFJ2sCoKWlJdrb25tdjf6n/I52SH0sXTSFbdwII8rmfbjnHjj44B7OM3Fi5Sn7J0xI2VFvtzOzhpA0NyJaOpU7uCQOLl3oxYf5QQfBffeVblb1P69hwypvLKXmt95uZ2YN0VVw8cSV1r0q7mt56aX02V4cWJYv7+VIsGqHOPsmSLMBwcHFutfDh7kE225bWhwBr3tdL89T7RBn3wRpNiA4uFj3uvgwX3b2BZ2GF6+ffjWLNLFvo7iqHeLsmyDNBgT3uWTuc+lGW1uaO2zxYhg/Hi16omT1P/wDzDqxdx3/ZjY4uEO/Bw4uPbvvvtRpX6zwGGKP4jIbmtyhb5tFKg0s555bNtGkp7I3syW1E50AAAxMSURBVCJ+EqV164474OijS8sqJrvjx1fOXDyKy2xIcuZiXZJKA8vs2d0ML/YoLjMr4uBinUybVnmiyeOO62Ynj+IysyJuFrMS5UHlgQdgv/2q3Lm11cHEzABnLpadc07lbKXqwGJmVsSZyxBXaaLJZctg992bUx8zGxycuQxhRx9dGlh22SVlKw4sZra5nLkMQS++CNtt17lsm22aUx8zG3ycuQwxO+1UGlgmTUrZigOLmdWSM5ch4sknYcyY0rING2D48ObUx8wGN2cuQ4BUGljOOSdlKw4sZlYvzlwGsQcegAMOKC3zPKVm1gjOXAYpqTSw/OhHDixm1jjOXAaZm27qPE2Lg4qZNZozl3ppa0vPOOnLUxn7SCoNLHfc4cBiZs3hzKUe2sqeyrhoUVqGusy9deut8N73lpY5qJhZMzlzqYepU0sf9wtpeerUmp6m8LCu4sCyZIkDi5k1n4NLPTTgqYyXXZZa3AqOOioFlbFja3YKM7M+c7NYPdTxqYyVJppcvRp22GGzD21mVjPOXOqhTk9l/NKXSgPLv/97ylYcWMysv3HmUg+FTvupU1NT2PjxKbD0sTN/7drOc3+9yki2vGU3aOv7cc3M6sWZS720tsITT8CmTel3HwNAa2tpYPn2Fl8gEFuyrmMUWgOGOZuZ9YYzl37qmWdg9OjSsk3jJ6LFZX05hVFozl7MrB9x5tIPHXhgaWCZOTMPO15S/1FoZma14MylH3n8cdhzz9KykntW6jgKzcyslpy59BMjR5YGll//usLNkHUahWZmVmsOLk12773pLvt16zrKIuDwwyts3NoK06bBhAlppwkT0rL7W8ysn3GzWBNJpcvz58M++/SwU2urg4mZ9XtNy1wkDZd0v6Sb8vIeku6RtEDStZK2zOUj8/KCvH5i0TE+n8v/JGlSUfkxuWyBpM81+r315Gc/Kw0se+6ZspUeA4uZ2QDRzGax/wAeKVr+FnBhROwJPAeclstPA57L5Rfm7ZC0D3Ay8BbgGOAHOWANBy4G3gPsA3wkb9t0hYkm3//+jrInn4THHqvRCZowzb+ZWSVNCS6SxgLvAy7LywLeBczKm0wHTsivj8/L5PVH5u2PB2ZGxKsRsRBYABycfxZExF8iYh0wM29be734MP/BD0onmjzuuBRsdtuthnWZMiWNJovwDZZm1lTN6nP5H+AzwHZ5eWdgdURsyMtLgTH59RhgCUBEbJD0fN5+DDCn6JjF+ywpKz+kUiUkTQGmAIzv7XDeKp/ZsmEDbLFF6a4vvADbbUdtdTfNv/tozKzBGp65SDoWeDoi5jb63OUiYlpEtEREy+jy2+F7UsUzWz7zmdLActZZKamoeWCBhkzzb2ZWrWZkLn8DvF/Se4GtgO2B7wI7ShqRs5exwLK8/TJgHLBU0ghgB2BVUXlB8T5dlddONx/m69bBuHHw9NMdxevWdc5gaso3WJpZP9LwzCUiPh8RYyNiIqlD/pcR0QrcBZyUN5sM3Jhfz87L5PW/jIjI5Sfn0WR7AHsB9wJ/APbKo8+2zOeYXfM30sWH9rU7n8HIkR2B5bvfTdlKXQML+AZLM+tX+tN9Lp8FZkr6OnA/cHkuvxz4saQFwLOkYEFEzJf0E+BhYAPw8YjYCCDpE8BtwHDgioiYX/PanndeSZ/Li2zDDjzPpmeGA6nD/sYbO9/LUjc1nubfzGxzKPzAdQBaWlqivb29dzu1tcHUqVy86Fg+wff/Wvzww/DmN9e4gmZm/ZCkuRHRUl7u6V82R2srl3/xib8GlilTUhOYA4uZDXX9qVlsQNp3X3jHO9K0+OPG9by9mdlQ4OCymQ45BH73u2bXwsysf3GzmJmZ1ZyDi5mZ1ZyDi5mZ1ZyDi5mZ1ZyDi5mZ1ZyDi5mZ1ZyDi5mZ1ZyDi5mZ1ZznFsskrQQqzFnfb+0CPNPsSjSZr4GvAfgaNPv9T4iITg/EcnAZoCS1V5osbijxNfA1AF+D/vr+3SxmZmY15+BiZmY15+AycE1rdgX6AV8DXwPwNeiX7999LmZmVnPOXMzMrOYcXMzMrOYcXAYYSeMk3SXpYUnzJf1Hs+vUDJKGS7pf0k3NrkszSNpR0ixJj0p6RNJhza5To0k6K/8feEjSNZK2anad6k3SFZKelvRQUdlOku6Q9Fj+/Zpm1rHAwWXg2QCcHRH7AIcCH5e0T5Pr1Az/ATzS7Eo00XeBn0fEm4D9GGLXQtIY4EygJSL2BYYDJze3Vg1xJXBMWdnngDsjYi/gzrzcdA4uA0xELI+I+/LrNaQPlTHNrVVjSRoLvA+4rNl1aQZJOwCHA5cDRMS6iFjd3Fo1xQhga0kjgFHAk02uT91FxG+AZ8uKjwem59fTgRMaWqkuOLgMYJImAgcA9zS3Jg33P8BngE3NrkiT7AGsBP43Nw1eJmmbZleqkSJiGfAdYDGwHHg+Im5vbq2aZteIWJ5frwB2bWZlChxcBihJ2wLXAZ+KiBeaXZ9GkXQs8HREzG12XZpoBHAgcElEHAC8RD9pCmmU3K9wPCnQ7g5sI+mU5taq+SLdW9Iv7i9xcBmAJG1BCixtEXF9s+vTYH8DvF/SE8BM4F2SZjS3Sg23FFgaEYWMdRYp2AwlRwELI2JlRKwHrgfe0eQ6NctTknYDyL+fbnJ9AAeXAUeSSG3tj0TEBc2uT6NFxOcjYmxETCR14P4yIobUN9aIWAEskbR3LjoSeLiJVWqGxcChkkbl/xNHMsQGNRSZDUzOrycDNzaxLn/l4DLw/A3wUdI39gfyz3ubXSlruE8CbZLmAfsD32hyfRoqZ22zgPuAB0mfZf1yGpRaknQNcDewt6Slkk4Dvgm8W9JjpIzum82sY4GnfzEzs5pz5mJmZjXn4GJmZjXn4GJmZjXn4GJmZjXn4GJmZjXn4GLWYJJGSvpFHkb+4bJ1V0paWDTM/PcNqE/dz2FDz4hmV8BsIJI0PCI29nH3AwAiYv8u1n86Imb18dhVkzQiIjZExFC9s93qyJmLDWqSzpX0qaLl8wrPwJH0aUl/kDRP0leLtvmppLn5WSFTispflHS+pD8Ch0n6Zn6uzjxJ36lw7p3yseZJmiPpbZJeC8wA3p4zkzdU+T6+K+lL+fUkSb+RNCxnOpdKapf05zz3WuF5N98uen//msuPkPRbSbPJd/VLerHoPJ2uiaSJ+ZkxP8rX5HZJW+d1e+Ys7I+S7iu8n66urQ0hEeEf/wzaH2AicF9+PQx4HNgZOJp0R7dy+U3A4Xm7nfLvrYGHgJ3zcgAfyq93Bv5Ex43IO1Y49/eAL+fX7wIeyK+PAG7qor5XAguBB/JPWy4fBcwH/j6f9w1F2/88v4e9SPOObQVMAf4rbzMSaCdN8ngEaaLLPYrO+WL+XfGa5Gu4Adg/b/cT4JT8+h7gxPx6q1zPLq+tf4bOj5vFbFCLiCckrZJ0AGkq8vsjYpWko0kfgvfnTbclfTj/BjhT0om5fFwuXwVsJE0YCvA88ApwudLTMCs9EfOdwD/kevxS0s6Stq+i2p2axSJiraR/yfU7KyIeL1r9k4jYBDwm6S/Am/J7e5ukk/I2O+T3sQ64NyIWVjhvV9dkMWmSyAdy+VxgoqTtgDERcUOu4ysAPVxbGyIcXGwouAz4GPA64IpcJuC/I+KHxRtKOoI0P9Nh+QP9V6Rv5ACvRO5niYgNkg4mTZh4EvAJUnZST28lBbndy8rL53AK0vv7ZETcVrwiv7+Xujh+V9dkIvBqUdFGUlbXlYrHsaHFfS42FNxAejTs24HCh+1twD/n5+IgaUzuD9kBeC4HljeRHiXdSd5vh4i4BTiL9Kjhcr8FWvP2RwDPRB+fvSNpAnA2aTDAeyQdUrT6g7n/5Q3A60nNZrcB/54fz4CkN6rnB4p1dU0qivQk1KWSTsjbj5Q0qrfHscHJmYsNehGxTtJdwOqizON2SW8G7k4ztvMicAqp/+LfJD1C+pCe08VhtwNulLQV6Zv6f1bY5ivAFXnm4rV0TIvek29L+q+i5UNIj1k4JyKezDPhXinp7Xn9YuBeYHvg3yLiFUmXkfublN7gSnp4/G0316S7UXEfBX4o6VxgPfDBbo7TL54zYo3hWZFt0JM0jDQ1+wcj4rFm16eWJF1JGhxQ96HLZr3hZjEb1CTtAywA7hxsgcWsP3PmYmZmNefMxczMas7BxczMas7BxczMas7BxczMas7BxczMau7/A7cDA6lzLBIsAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visulising the Test set Results**"
      ],
      "metadata": {
        "id": "Hfddyyi8NwpO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(x_test,y_test, color='red')\n",
        "plt.plot(x_train,regressor.predict(x_train),color='blue')\n",
        "plt.title('salary vs experice (Traning set)')\n",
        "plt.xlabel('years of Experience')\n",
        "plt.ylabel('salary')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "ozXN3lApN1gd",
        "outputId": "1493de97-2e68-463c-a67c-a01a19d577ce"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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OAwNpecKEqraz2py5mJlVwPr1cMghMGtWWn7ta+Gxx2DLLTeyYZm79XssbxDOXMzMNtNtt8Hgwe2B5e674fHHexFYoMxziTdS3iAcXMzM+mjNGhg2LF1fATjqqJTBHHPMJuwk363fwdChHW+GaUAOLmZmfTB2bLpQ/8wzabm1FX7/+z5MNNnSkqbaHz06DS0bPfrVqfcbmYOLmVmpjQwLXrQoxYA772wv27ABDj10M47Z0gLz5qUdzZvX8IEFHFzMzNq1DQuePz+NJ24bFpwDzIgR6YmQbW67LVXrDxNNVpqDi5lZm26GBf/l3KuRYPHi9uIIeOc7a9u8RuKhyGZmbcoM/xUBS9qXW1s3swtsgHDmYmbWpmT4790ckwJLtsMOKVtxYOkdBxczszZ5WLAIjuPuV4ufuOjnPP98ge1qQA4uZmbZNdGCVr/46vKRQ2YS10yh6ZOnFNiqxuRrLmY24G3YAIMGdSxL0+IfCrgfrC+cuZjZgPatb3UMLOPGdTMtvm0SZy5mNiCtWZPusC/10ktdy6xvnLmY2YBz9tkdg8jXv56yFQeWynHmYmYDxnPPde3uWr++D/OB2Ub5lJrZgPD2t3cMLFddlbIVB5bqcOZiZv3a/Plp/slSEWWrWgU5ZptZv7Xrrh0Dy513OrDUijMXM+t3WlvhLW/pWOagUlsOLmbWr3Se/v6hh+DAA4tpy0DmbjEz6xcuuaRrYIlwYCmKg4uZNYYenhApwTnntFd9+GF3gxXNwcXM6l83T4j8xNjHymYrb3xjMc20dr7mYmb1r9MTItcxiC1Xvwglz7FfuhT22KOAtllZVctcJF0p6WlJD5eU7SLpLklz8s+dc7kkXSxprqRZkg4p2WZcrj9H0riS8kMl/SVvc7GU/n7p7hhm1sBKnhB5NPewJeteXR4+PGUrDiz1pZrdYlcBJ3Qq+wJwd0TsA9ydlwHeCeyTX+OBSyEFCuBrwOHAYcDXSoLFpcC/lWx3wkaOYWaNatQoVrIdIvgtR79a/OLI17NoUXHNsu5VLbhExO+AZzsVnwxMzu8nA6eUlF8dyQxgJ0l7AmOBuyLi2Yh4DrgLOCGv2yEiZkREAFd32le5Y5hZg9p26Vx2YOWry+/hZmLotgz9r68U2CrrSa2vuewREUvy+6VAWyI7HFhYUm9RLuupfFGZ8p6O0YWk8aRMiVElz842s/qwcGHbY+3bv6rWM4gtRo+EiZOgpaWwtlnPChstljOOqg4W3NgxImJSRDRHRPOwYcOq2RQz20RSW2BJvvSlPNFkrId58xxY6lytM5enJO0ZEUty19bTuXwxMLKk3ohcthhKOlhT+W9y+Ygy9Xs6hpk1gJkzobm5Y5nvWWk8tc5cpgNtI77GATeXlJ+ZR40dATyfu7buAI6XtHO+kH88cEde94KkI/IosTM77avcMcyszkkdA0vbtPjWeKqWuUi6jpR17CZpEWnU17eBn0k6C5gPvD9Xvw14FzAXWA38C0BEPCvpG8Cfcr3zI6JtkMBHSSPStgFuzy96OIaZ1ambb4ZTOg29cVBpbAr/BgFobm6O1tbWopthNuB0vsP+nnvg6KMLaYr1gaSZEdHcudzTv5hZIb73vfITTTqw9A+e/sXMaqrco4Ufewz226+Y9lh1OHMxs5o5++yugSXCgaU/cuZiZlW3di1stVXHsmXLYLfdimmPVZ8zFzOrqsMO6xhY9tsvZSsOLP2bMxczq4oVK2DnTnOSv/wyDBlSTHustpy5mFnFSR0Dy+mnp2zFgWXgcOZiZhXzxBPw2td2LNuwoeuQY+v/nLmYWUVIHQPL+eenbMWBZWBy5mJmm+XWW+Hd7+5Y5ok/zJmLmfWZ1DGwXHedA4slDi5mtsm6m7rl9NOLaY/VH3eLmdkm6RxUpk+Hk04qpi1Wv5y5mFmvnHFG+WzFgcXKceZiZj0qN9Hkn/8Mb35zMe2xxuDgYmbd2ndfmDOnY5kv2FtvOLiYWRcvvQRDh3Yse+op2H33YtpjjcfBxcw6KHfTo7MV21S+oG9mACxZ0jWwvPyyA4v1jYOLmSHBXnu1L7/pTZ5o0jaPg4vZAPbgg12zlQ0bYNasYtpj/YeDi9kAJcEhh7Qvn3WWJ5q0yvEFfbMB5oYb4LTTOpb5uopVmjMXswFE6hhYfvADBxarjl4FF0mDqt0QM6ueb3yj/NQt55xTTHus/+ttt9gcSTcA/xsRj1SzQWZWWZ2Dyh13wPHHF9MWGzh62y12IPA34HJJMySNl7RDFdtlZpvpve8tn604sFgt9Cq4RMTKiPhJRLwV+DzwNWCJpMmSxlS1hWa2SdqeWX/TTe1ljz7qaytWW73qFsvXXE4E/gVoAr4HTAHeBtwG7Ful9pnZJnjNa9IcYKUcVKwIve0WmwOcDFwQEQdHxIUR8VRETAN+Wb3mmTWwKVOgqSnNV9/UlJarZNWqlK2UBpblyx1YrDgbzVxy1nJVRJxfbn1EfKLirTJrdFOmwPjxsHp1Wp4/Py0DtLRU9FCeaNLq0UYzl4hYD7y7Bm0x6z8mTGgPLG1Wr07lFbJgQdfA8sorDixWH3o7FPkPkn4IXA+82FYYEQ9UpVVmjW7Bgk0r30Sdg8qRR8If/1iRXZtVRG+Dy0H5Z2nXWADHVLY5Zv3EqFGpK6xc+Wa4666uQ4nbRoeZ1ZNeBZeI+KdqN8SsX5k4seM1F0iPdpw4sc+77BxA3vUuuPXWPu/OrKp6PXGlpBOBNwJbt5V1d5HfbMBru2g/YULqChs1KgWWPlzMv/BC+MxnOpb5uorVu97OLXYZ8AHg44CA9wGj+3pQSZ+SNFvSw5Kuk7S1pL0l3SdprqTrJW2V6w7Jy3Pz+qaS/Xwxl/9V0tiS8hNy2VxJX+hrO802S0sLzJuX+q3mzetTYJE6Bpbzz3dgscbQ2/tc3hoRZwLPRcTXgSPp442TkoYDnwCaI+IAYBBwOvAd4KKIGAM8B5yVNzkrH3cMcFGuh6T983ZvBE4AfiRpUB46fQnwTmB/4IO5rlnDOP308lO3fOUrxbTHbFP1Nri8lH+ulrQXsBbYczOOOxjYRtJgYCiwhDQ4YFpePxk4Jb8/OS+T1x8rSbl8akSsiYgngLnAYfk1NyL+HhGvAFNzXbOGIMH117cv//znzlas8fT2msstknYCLgAeII0Uu7wvB4yIxZK+CywgBa07gZnAiohYl6stAobn98OBhXnbdZKeB3bN5TNKdl26zcJO5YeXa4uk8cB4gFGbOYrHbHPtuScsXdqxzEHFGlVvJ678RkSsiIgbSNdaXh8RfUrQJe1MyiT2BvYCtiV1a9VcREyKiOaIaB42bFgRTTBj3bqUrZQGlr/8xYHFGluPmYuk9/awjoi4sQ/HPA54IiKW5f3cCPwDsJOkwTl7GQEszvUXAyOBRbkbbUdgeUl5m9Jtuis3qyueusX6q411i53Uw7oA+hJcFgBHSBpK6hY7FmgF7gFOI10jGQfcnOtPz8v35vW/joiQNB24VtKFpAxoH+B+0mi2fSTtTQoqpwMf6kM7zarm2Wdh1107lj3zTNcys0bVY3CJiH+p9AEj4j5J00jXbtYBDwKTgFuBqZK+mcuuyJtcAfxU0lzgWVKwICJmS/oZ8Ejez8fyPGhIOge4gzQS7cqImF3pz2HWV85WbCBQ9PJfdX+/ibK5uTlaW1uLbob1Y7NnwwEHdCxbuxYG9/pWZrP6I2lmRDR3Lu/tw8IuIw0Z/ifSKLHTSF1QZtYLnbOV3Xfv+lAvs/6k5jdRmg0k06eXvxnSgcX6u94Gl5fzz7abKNexeTdRmvV7EpxccvvuBz7gays2cPQ2uPyi002UTwDXVq1VZg1s4sTy2crUqcW0x6wIvb2U+BiwPiJuyPN0HQL8vHrNMmtMnYPK974Hn/50MW0xK1JvM5evRMRKSUeR5gC7HLi0es0yaywnnVQ+W3FgsYGqt8Flff55IvCTiLgV2Ko6TTJrHBEpqNxyS3vZL3/paytmve0WWyzpx8A7gO9IGkLvA5NZv+SbIc2619sA8X7SHe9jI2IFsAtwbtVaZVbH1qzpGlhmzXJgMSvVq8wlIlZTMo9YRCwhPYPFbEBxtmLWO+7aMuuFxYu7Bpblyx1YzLrjWY3MNsLZitmmc+Zi1o0//KFrYFm3zoHFrDecuZiV4WzFbPM4czErcdll5W+GdGAx2zTOXMyyzkHl2GPhV78qpi1mjc6Ziw1448aVz1YcWMz6zsHFBjQJrr66ffnrX3cXmFkluFvMBqQ994SlSzuWOaiYVY4zFxtQ2iaaLA0sv/iFA4tZpTlzsQHDw4vNaseZi/V7L77YNbD89a8OLGbV5MzF+jVnK2bFcOZi/dK8eV0Dy/PPO7CY1YozF+t3nK2YFc+Zi/Ubd9/dNbCsX+/AYlYEZy7WL3QOKttsA6tXF9MWM3PmYg3uwgvLT93iwGJWLGcu1rA6B5VTT4Ubbyxf18xqy5mLNYYpU6CpCbbYgo9sN61stuLAYlY/HFys/k2ZAuPHw/z5KDYw+cXTXl114YW+YG9Wj9wtZvVvwgRGrn6MRYzsUByjm+BT8wppkpn1zJmL1bUNG0Dz53UILL/nKALBggUFtszMeuLMxepW2ZshKSkcNap2jTGzTeLMxerOypVdA8uCrfftGFiGDoWJE2vbMDPrNQcXqysS7LBDx7IIGHn512D06FRh9GiYNAlaWopppJltVCHBRdJOkqZJekzSo5KOlLSLpLskzck/d851JeliSXMlzZJ0SMl+xuX6cySNKyk/VNJf8jYXS+U6WKyePP5412xl9eqSkWAtLWk2yg0b0k8HFrO6VlTm8n3glxHxeuBA4FHgC8DdEbEPcHdeBngnsE9+jQcuBZC0C/A14HDgMOBrbQEp1/m3ku1OqMFnsj6SYMyYjmURaQoXM2tMNQ8uknYE3g5cARARr0TECuBkYHKuNhk4Jb8/Gbg6khnATpL2BMYCd0XEsxHxHHAXcEJet0NEzIiIAK4u2ZfVkTvv7JqtbNjg+1bM+oMiMpe9gWXA/0p6UNLlkrYF9oiIJbnOUmCP/H44sLBk+0W5rKfyRWXKu5A0XlKrpNZly5Zt5seyTSHB2LHty296U/vz7c2s8RURXAYDhwCXRsTBwIu0d4EBkDOOqv/9GhGTIqI5IpqHDRtW7cMZ8N3vlp9octasYtpjZtVRRHBZBCyKiPvy8jRSsHkqd2mRfz6d1y+GDrdmj8hlPZWPKFNuBZPg3HPblz/+cXeBmfVXNQ8uEbEUWChpv1x0LPAIMB1oG/E1Drg5v58OnJlHjR0BPJ+7z+4Ajpe0c76QfzxwR173gqQj8iixM0v2ZQV473vLZysXX1xMe8ys+ooaLfZxYIqkWcBBwLeAbwPvkDQHOC4vA9wG/B2YC/wE+ChARDwLfAP4U36dn8vIdS7P2zwO3F6Dz2RlSHDTTe3LkydXOFspmS2Zpqa0bGaFU7hfAoDm5uZobW0tuhn9xvbbw6pVHcsq/k+tbbbk0ieDDR3qGyzNakjSzIho7lzuO/StotavT9lKaWC5774qXVuZMKHrIydXr07lZlYoBxermEMPhcGdpkKNgMMOq/CB2rrC5s8vv96zJZsVzrMi22Z78UXYbruOZUuWwGteU4WDlesK68yzJZsVzsHFNkvnUWCjRnWfUFREua6wUp4t2awuuFvM+mTx4q6BZe3aKgcW6LnLy7Mlm9UNZy62yToHlX/+Z5g2rUYH7y41Gj06zZZsZnXBmYv12gMPlJ9osmaBBVKX19ChHcvcFWZWdxxcrFekNBqszfnnFzTRZEtL6vryg8PM6pq7xaxHd90Fxx/fsazw+25bWhxMzOqcMxfrltQxsEyfXgeBxcwagoOLdTFpUvmJJk86qZj2mFnjcbeYddA5qDz0EBx4YDFtMbPG5czFAPjsZ8tnKw4sZtYXzlwGuPXru84Htngx7LVXMe0xs/7BmcsAdvzxHQPLbrulbMWBxcw2lzOXAWjVqvS8lc5l225bTHvMrP9x5jLA7LJLx8AydmzKVhxYzKySnLkMEE8+CcOHdyxbtw4GDSqmPWbWvzlzGQCkjoHls59N2YoDi5lVizOXfuyhh+DggzuW+Q57M6sFZy79lNQxsPzkJw4sZlY7zsKy5NoAAArhSURBVFz6mVtu6TpNi4OKmdWag0s/0vkO+7vuguOOK6YtZjawuVusH7j99vJTtziwmFlRnLk0sAjYotOfBwsXwogRxbTHzKyNM5cGdfnlHQPLccelYOPAYmb1wJlLgyk30eSKFbDjjsW0x8ysHGcuDeSrX+0YWP7jP1K24sBiZvXGmUsDWL2669xfa9bAVlsV0x4zs41x5lLnWlo6BpYLLkjZigOLmdUzZy516plnYNiwjmUbNnQdcmxmVo+cudShQw7pGFimTk3ZigOLmTUKZy515PHHYcyYjmWeusXMGpEzlzoxZEjHwPLb3zqwmFnjcuZSsPvvh8MP71jmoGJmjc7BpUCdr6HMng37719MW8zMKqmwbjFJgyQ9KOmWvLy3pPskzZV0vaStcvmQvDw3r28q2ccXc/lfJY0tKT8hl82V9IVaf7aN+cUvOgaWMWNStuLAYmb9RZHXXP4TeLRk+TvARRExBngOOCuXnwU8l8svyvWQtD9wOvBG4ATgRzlgDQIuAd4J7A98MNctXNuIr/e8p73sySdhzpzi2mRmVg2FBBdJI4ATgcvzsoBjgGm5ymTglPz+5LxMXn9srn8yMDUi1kTEE8Bc4LD8mhsRf4+IV4CpuW6hfvSjjhNNnnRSCjZ77llcm8zMqqWoay7/A3wO2D4v7wqsiIh1eXkRMDy/Hw4sBIiIdZKez/WHAzNK9lm6zcJO5Z0umSeSxgPjAUaNGrUZH6d769bBllt2LHvhBdh++/L1zcz6g5pnLpLeDTwdETNrfezOImJSRDRHRPOwzrfDV8DnPtcxsHzqUylbcWAxs/6uiG6xfwDeI2keqcvqGOD7wE6S2jKpEcDi/H4xMBIgr98RWF5a3mmb7spr5pVXYI890jxgpWUXXghMmQJNTamPrKkpLZuZ9TM1Dy4R8cWIGBERTaQL8r+OiBbgHuC0XG0ccHN+Pz0vk9f/OiIil5+eR5PtDewD3A/8Cdgnjz7bKh9jeg0+GgDXX59uiHz66bT8/e+nbGXLLUmBZPx4mD8/Fc6fn5YdYMysn6mn+1w+D0yV9E3gQeCKXH4F8FNJc4FnScGCiJgt6WfAI8A64GMRsR5A0jnAHcAg4MqImF3txq9alZ6rsmFDWj7pJLj55k73skyYkObPL7V6dSpvaal2E83Makbh28EBaG5ujtbW1j5te8klcM457cuPPAJveEOZiltsUf72e6k9KpmZNRBJMyOiuXO55xbbTFdc0R5Yxo9PsaNsYAHobkRalUaqmZkVxcFlMx1wALz1rbBgAfz4xxupPHEiDB3asWzo0FRuZtaPOLhspsMPhz/8AUaO3HhdWlpg0iQYPTp1hY0enZZ9vcXM+pl6uqA/MLS0OJiYWb/nzMXMzCrOwcXMzCrOwcXMzCrOwcXMzCrOwcXMzCrOwcXMzCrOwcXMzCrOc4tlkpYB84tuxybYDXim6EYUzOfA5wB8Dor+/KMjossDsRxcGpSk1nKTxQ0kPgc+B+BzUK+f391iZmZWcQ4uZmZWcQ4ujWtS0Q2oAz4HPgfgc1CXn9/XXMzMrOKcuZiZWcU5uJiZWcU5uDQYSSMl3SPpEUmzJf1n0W0qgqRBkh6UdEvRbSmCpJ0kTZP0mKRHJR1ZdJtqTdKn8v+BhyVdJ2nrottUbZKulPS0pIdLynaRdJekOfnnzkW2sY2DS+NZB3wmIvYHjgA+Jmn/gttUhP8EHi26EQX6PvDLiHg9cCAD7FxIGg58AmiOiAOAQcDpxbaqJq4CTuhU9gXg7ojYB7g7LxfOwaXBRMSSiHggv19J+lIZXmyrakvSCOBE4PKi21IESTsCbweuAIiIVyJiRbGtKsRgYBtJg4GhwJMFt6fqIuJ3wLOdik8GJuf3k4FTatqobji4NDBJTcDBwH3FtqTm/gf4HLCh6IYUZG9gGfC/uWvwcknbFt2oWoqIxcB3gQXAEuD5iLiz2FYVZo+IWJLfLwX2KLIxbRxcGpSk7YAbgE9GxAtFt6dWJL0beDoiZhbdlgINBg4BLo2Ig4EXqZOukFrJ1xVOJgXavYBtJX242FYVL9K9JXVxf4mDSwOStCUpsEyJiBuLbk+N/QPwHknzgKnAMZKuKbZJNbcIWBQRbRnrNFKwGUiOA56IiGURsRa4EXhrwW0qylOS9gTIP58uuD2Ag0vDkSRSX/ujEXFh0e2ptYj4YkSMiIgm0gXcX0fEgPqLNSKWAgsl7ZeLjgUeKbBJRVgAHCFpaP4/cSwDbFBDienAuPx+HHBzgW15lYNL4/kH4AzSX+wP5de7im6U1dzHgSmSZgEHAd8quD01lbO2acADwF9I32V1OQ1KJUm6DrgX2E/SIklnAd8G3iFpDimj+3aRbWzj6V/MzKzinLmYmVnFObiYmVnFObiYmVnFObiYmVnFObiYmVnFObiY1ZikIZJ+lYeRf6DTuqskPVEyzPyPNWhP1Y9hA8/gohtg1ogkDYqI9X3c/GCAiDiom/XnRsS0Pu671yQNjoh1ETFQ72y3KnLmYv2apPMlfbJkeWLbM3AknSvpT5JmSfp6SZ2fS5qZnxUyvqR8laTvSfozcKSkb+fn6syS9N0yx94l72uWpBmS3ixpd+Aa4C05M3ldLz/H9yV9Nb8fK+l3krbImc5lklol/S3Pvdb2vJsLSj7f/8vlR0v6vaTp5Lv6Ja0qOU6XcyKpKT8z5if5nNwpaZu8bkzOwv4s6YG2z9PdubUBJCL88qvfvoAm4IH8fgvgcWBX4HjSHd3K5bcAb8/1dsk/twEeBnbNywG8P7/fFfgr7Tci71Tm2D8AvpbfHwM8lN8fDdzSTXuvAp4AHsqvKbl8KDAb+Kd83NeV1P9l/gz7kOYd2xoYD3w51xkCtJImeTyaNNHl3iXHXJV/lj0n+RyuAw7K9X4GfDi/vw84Nb/fOrez23Pr18B5uVvM+rWImCdpuaSDSVORPxgRyyUdT/oSfDBX3Y705fw74BOSTs3lI3P5cmA9acJQgOeBl4ErlJ6GWe6JmEcB/5zb8WtJu0raoRfN7tItFhGrJf1bbt+nIuLxktU/i4gNwBxJfwdenz/bmyWdluvsmD/HK8D9EfFEmeN2d04WkCaJfCiXzwSaJG0PDI+Im3IbXwbYyLm1AcLBxQaCy4GPAK8BrsxlAv4rIn5cWlHS0aT5mY7MX+i/If1FDvBy5OssEbFO0mGkCRNPA84hZSfV9CZSkNurU3nnOZyC9Pk+HhF3lK7In+/Fbvbf3TlpAtaUFK0nZXXdKbsfG1h8zcUGgptIj4Z9C9D2ZXsH8K/5uThIGp6vh+wIPJcDy+tJj5LuIm+3Y0TcBnyK9Kjhzn4PtOT6RwPPRB+fvSNpNPAZ0mCAd0o6vGT1+/L1l9cBryV1m90B/Ed+PAOS9tXGHyjW3TkpK9KTUBdJOiXXHyJp6Kbux/onZy7W70XEK5LuAVaUZB53SnoDcG+asZ1VwIdJ1y/+XdKjpC/pGd3sdnvgZklbk/5S/3SZOucBV+aZi1fTPi36xlwg6csly4eTHrPw2Yh4Ms+Ee5Wkt+T1C4D7gR2Af4+IlyVdTr7epPQBl7GRx9/2cE56GhV3BvBjSecDa4H39bCfunjOiNWGZ0W2fk/SFqSp2d8XEXOKbk8lSbqKNDig6kOXzTaFu8WsX5O0PzAXuLu/BRazeubMxczMKs6Zi5mZVZyDi5mZVZyDi5mZVZyDi5mZVZyDi5mZVdz/B9bo54B/A8aMAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}