RandomFRegresion.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Position_Salaries.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.iloc[:,1:2].values\n",
    "y = df.iloc[:,2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestRegressor(n_estimators=10,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=10,\n",
       "                      n_jobs=None, oob_score=False, random_state=0, verbose=0,\n",
       "                      warm_start=False)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgrid = np.arange(min(x),max(x),0.01)\n",
    "xgrid = xgrid.reshape((len(xgrid),1))\n",
    "pred = rf.predict(xgrid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jwgFiZlVj9254z3ty7tzcDK2t0NAAUrZsbfX8xyjyHIiZVY1du3KevurT3OzAGEMegZhZ1RjRCMTGnAPEzKrGiEcgNqYcIGZWNTwCqS0OEDOrGrt3ewRSSxwgZlY1fAqrtjhAzKxq+BRWbfFlvGZWERHw3HOwc2f+Y2zZAjNmVK5PNrocIGZWEY88AosWjfw4Z5018mPY2HCAmFlFvPxytmxthcMPz3+cj3+8Mv2x0ecAMbOK2L49W559NsydW2xfbGx4Et3MKqIvQGbOLLYfNnYcIGZWEdu3Z5fg+iqq8cMBYmYVsX07HHxw0b2wsZQ7QCQdKekRSeskPSPp8lQ/VNJqSevTcnaqS9KNkjZI+rWkE0qOtTRtv17S0pL6xyStTfvcKEmDtWFm+bz2GsyZAxMn5nxM6OWmm+CQLc9DYyO0tRX9I9kYGMkk+h7gKxHxS0kHA2skrQb+I/BQRFwraRmwDLgSOBNYmB4nATcDJ0k6FLgaaCL7Pso1klZFxLa0TQvwGHAfsBi4Px2zXBtmlsNLL8HWrXDeedn3kQ/L2rXwT/8Ee3r4BP8XNm6ElpZsnW+tXtdyB0hEvAq8mp7vkLQOmAcsAU5Lm90O/Izsl/sSYGVEBPCYpFmSjkjbro6IrQAphBZL+hkwMyIeTfWVwNlkATJQG2aWQ3d3trz4Yjj11GHu3PgZ2LPx3QdcvtwBUucqMgciqRH4KPA4cHgKl76QeW/abB6wqWS3zlQbrN5Zps4gbfTvV4ukdkntXV1deX88s7rX9+nx/t8IOyQdHcOrW90YcYBIOgi4G/iLiNg+2KZlapGjPmQR0RoRTRHRNNcXppsNqG8EMm1ajp0XLBhe3erGiAJE0mSy8GiLiHtS+fV0aoq03JzqncCRJbvPB145QH1+mfpgbZhZDn0BkmsEsmLFu3ecPj2rW10byVVYAm4F1kXEd0pWrQL6rqRaCtxbUr8gXY11MvBWOv30IHC6pNnpaqrTgQfTuh2STk5tXdDvWOXaMLMcRnQKq7k5u39JQwNI2bK11fMf48BIrsI6BfgCsFbSU6n2NeBa4E5JFwEdwDlp3X3Ap4ENQDdwIUBEbJX0TeCJtN03+ibUgS8B3wemkU2e35/qA7VhZjmM6BQWZGHhwBh3RnIV1r9Rfp4C4F335ExXX10ywLFuA24rU28Hji9T31KuDbPxqrsbfvvb/Pv3XWOSawRi45ZvpmhW43buhPnzYdu2kR1n6lSYPLkyfbLxwQFiVsva2thy5f9k27bHaJ7xYz7xuXlw4om5DvWBD1S4b1b3HCBmtaqtDVpa2N7dAMBnfncH/+FHP4V/5wlsGxu+maJZrVq+HLq72UF2B8OD2bHvE+BmY8ABYlar0ie9+wJkJtv3q5uNNgeIWa1Kn/TeTvYNTgezY7+62WjzHIhZwa69FtasybHj4T+HzjV07M1uETeT7f4EuI0pB4hZwf7qr+Cgg+Dww4e7ZwP83iGweTOLev438xZMgms8gW5jxwFiVqBdu7LH17+ed+57Vnp8AFhf0b6ZHYjnQMzyamvLvn1vwoTc38L31lvZ8pBDKtozszHhEYhZHukzGO/cRCrnt/A5QKyWeQRilkf6DMZ+cnwGwwFitcwjELM80mctLuMGVnHWvvpGoHHoh3n77WzpALFa5ACxceu552D16pw7z/o6bNvKLfxnPshv+DC/yuozDoLTGod1qIMPho9/PGc/zArkALFx64or4P77D7xdeX/9zrNrWcYZ/HP2GYy/bQVfRWvjhAPExq316+Gzn4W/+7ucB/jRj5h8zV8zs/NZWNCQfYDPn8GwccQBYrWnrY1bL/sVX9n6NUIT0hdZTBn2YbZvh3POgTlzcvbjv5yTPczGKQeI1ZZ0+ezD3a1MoJel8T3omQynLoJjjh3WoSZOhC9+cZT6aTYOOECstqTLZzuZz4dYy/VcAXuAdQ1w/0tF985sXHGA2Jj7+c/h0kthz54cO2+8D4DnOZp/z9376r6FudmYc4DY0LW1sWXZ/+CbnRfy9kGHZV+dunDhsA/z6KPw0kuweHGOPmx8Abq7+RBruZi/3Vf3LczNxpwDxIYmzT38uPs8buBy5v52MxMeDlizE6ZOG/bhLr00513H297a/xYi4FuYmxXEAVIL2tqyc/8dHdlf2jkvF92xAy68cN/tM4bl3xbA2z/heY7mIHbwGr/HBAJmNWTDibHS93NX4P0ws5HxvbAOpAJ3XB1x+y0t2c36IvbdtC9HP372M7j7bnjjjewP+GE93p5AN9M5gle5gu9k4QHFzD00N2eh1dubLR0eZoXwCGQwbW30fvFi3t7ZC0yFjZvhi5fDrolw7rnDOtQNN8BTT+Xow08Pge7b6GUC/4dT2MIc6AbOB/7T8A61dy9I8K//mn2B0bA0Nmfh1Z/nHszGLQfIYJYv5//tnM+xPLevthO4KD2GacYMOPLIYe7UffQ7Txt5ifP5eyayFxBcsWzYfTj22BzhAdlpIs89mFkJB8hgOjqYy2yu46v9Vgiuu25Yh5o7F77wBZg03He88czyf/k3NMB/H36A5Oa5BzPrRxFRdB/GRFNTU7S3tw9vp8bGgX95j9XEcf8vLoLsL/9Wf/e1mY0+SWsioqncOk+iD2bFiuyXdamxPm3T3JyFRUNDNoHR0ODwMLOq4FNYg6mW0zbNzQ4MM6s6DpAD8S9vM7OyfArLzMxyqdkAkbRY0m8kbZA0hpcjmZkZ1GiASJoIfBc4EzgOOE/SccX2ysxsfKnJAAFOBDZExAsRsRu4A1hScJ/MzMaVWg2QecCmktedqbYfSS2S2iW1d3V1jVnnzMzGg1q9Cktlau/6RGREtAKtAJK6JJX5VGBNOQx4o+hOVBG/H/v4vdif3499RvpeNAy0olYDpBMovavUfOCVwXaIiLmj2qMxIKl9oE+Ejkd+P/bxe7E/vx/7jOZ7UaunsJ4AFko6StIU4FxgVcF9MjMbV2pyBBIReyR9GXgQmAjcFhHPFNwtM7NxpSYDBCAi7gPuK7ofY6y16A5UGb8f+/i92J/fj31G7b0YN3fjNTOzyqrVORAzMyuYA8TMzHJxgNQASUdKekTSOknPSLq86D4VTdJESU9K+mnRfSmapFmS7pL0XPp/5I+K7lNRJP1l+jfytKQfSppadJ/GkqTbJG2W9HRJ7VBJqyWtT8vZlWrPAVIb9gBfiYhjgZOBS3zvLy4H1hXdiSpxA/BARBwDfJhx+r5ImgdcBjRFxPFkV2ieW2yvxtz3gcX9asuAhyJiIfBQel0RDpAaEBGvRsQv0/MdZL8g3nXrlvFC0nzgz4Bbiu5L0STNBP4YuBUgInZHxJvF9qpQk4BpkiYB0znAB4zrTUT8C7C1X3kJcHt6fjtwdqXac4DUGEmNwEeBx4vtSaH+Bvgq0Ft0R6rA7wNdwPfSKb1bJM0oulNFiIiXgW8DHcCrwFsR8c/F9qoqHB4Rr0L2xyjw3kod2AFSQyQdBNwN/EVEbC+6P0WQ9OfA5ohYU3RfqsQk4ATg5oj4KPA7KniKopakc/tLgKOA9wEzJJ1fbK/qmwOkRkiaTBYebRFxT9H9KdApwFmSXiK7jf+nJP19sV0qVCfQGRF9I9K7yAJlPPpT4MWI6IqIHuAe4BMF96kavC7pCIC03FypAztAaoAkkZ3jXhcR3ym6P0WKiKsiYn5ENJJNkD4cEeP2r8yIeA3YJOmDqbQIeLbALhWpAzhZ0vT0b2YR4/SCgn5WAUvT86XAvZU6cM3eymScOQX4ArBW0lOp9rV0OxezS4G2dGPRF4ALC+5PISLicUl3Ab8ku3LxScbZLU0k/RA4DThMUidwNXAtcKeki8hC9pyKtedbmZiZWR4+hWVmZrk4QMzMLBcHiJmZ5eIAMTOzXBwgZmaWiwPEzMxycYCYmVku/x9hsUupw8jq3AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y,color='red')\n",
    "plt.plot(xgrid,pred,color='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}