Random Forest Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Regression\n",
    "- Steps;\n",
    "    1. Pick at random K data points from the Training set.\n",
    "    2. Build the Decision Tree associated to these K data points.\n",
    "    3. Choose the number Ntree of trees you want to build and repeat step 1 and 2.\n",
    "    4. For a new data point, make each one of your Ntree trees predict the value of Y to for the data point in question and assign the new data point the average across all of the predict Y values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importing the Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Numpy allows us to work with array.\n",
    "import numpy as np\n",
    "\n",
    "# Maptplotlib which allows us to plot some chart.\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Pandas allows us to not only import the datasets but also create the matrix of features(independent) and \n",
    "# dependent variable.\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importing the dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(\"Position_Salaries.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The independent variable usally in the first columns of dataset and dependent variable usally in the last columns of the data sets.\n",
    "- X is Independent Variable.\n",
    "- Y is Dependent Variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1]\n",
      " [ 2]\n",
      " [ 3]\n",
      " [ 4]\n",
      " [ 5]\n",
      " [ 6]\n",
      " [ 7]\n",
      " [ 8]\n",
      " [ 9]\n",
      " [10]]\n"
     ]
    }
   ],
   "source": [
    "x = dataset.iloc[:, 1:-1].values      # iloc is index of location\n",
    "y = dataset.iloc[:, -1].values\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Training the Random Forest Regression Model on the whole dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(n_estimators=10, random_state=0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing Library\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "reg = RandomForestRegressor(n_estimators = 10, random_state = 0)\n",
    "\n",
    "# Fitting\n",
    "reg.fit(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Predicting a new Results.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([167000.])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[6.5]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualising the Random Forest Regression result (for higher resolution and smoother curve)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Increasing the density.\n",
    "x_grid = np.arange(min(x), max(x), 0.1)\n",
    "x_grid = x_grid.reshape(len(x_grid), 1)\n",
    "\n",
    "# Plotting\n",
    "plt.scatter(x, y, color = \"red\")\n",
    "plt.plot(x_grid, reg.predict(x_grid), color = \"blue\")\n",
    "\n",
    "plt.title(\"True or Bluff (Decision Tree Regression)\")\n",
    "plt.xlabel(\"Position Level\")\n",
    "plt.ylabel(\"Salary\")\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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}