06. Decision Tree-Random-Forest-Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression with Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\Mohan\\\\Desktop\\\\Remote Internship-2020\\\\Python codes'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\Mohan\\\\Desktop\\\\Remote Internship-2020\\\\Data Sets'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.chdir(\"C:/Users/Mohan/Desktop/Remote Internship-2020/Data Sets\")\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1=pd.read_csv(\"Position_Salaries.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "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",
       "      <td>0</td>\n",
       "      <td>Business Analyst</td>\n",
       "      <td>1</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Junior Consultant</td>\n",
       "      <td>2</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Senior Consultant</td>\n",
       "      <td>3</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Manager</td>\n",
       "      <td>4</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Country Manager</td>\n",
       "      <td>5</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Region Manager</td>\n",
       "      <td>6</td>\n",
       "      <td>150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Partner</td>\n",
       "      <td>7</td>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Senior Partner</td>\n",
       "      <td>8</td>\n",
       "      <td>300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>C-level</td>\n",
       "      <td>9</td>\n",
       "      <td>500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\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"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Feature Engineering\n",
    "X = data1.iloc[:, 1:2].values\n",
    "y = data1.iloc[:, 2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1],\n",
       "       [ 2],\n",
       "       [ 3],\n",
       "       [ 4],\n",
       "       [ 5],\n",
       "       [ 6],\n",
       "       [ 7],\n",
       "       [ 8],\n",
       "       [ 9],\n",
       "       [10]], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  45000,   50000,   60000,   80000,  110000,  150000,  200000,\n",
       "        300000,  500000, 1000000], dtype=int64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fit Decision Tree Rgerssion Model to the dataset\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "#Create the Decision Tree regressor object \n",
    "regressor = DecisionTreeRegressor(random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "                      max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      presort=False, random_state=0, splitter='best')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fit the regressor object to the dataset.\n",
    "regressor.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predict a new result\n",
    "y_pred = regressor.predict([[5.5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([110000.])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "#Visualise the Decision Tree Regression results\n",
    "plt.scatter(X, y, color = 'red')\n",
    "plt.plot(X, regressor.predict(X), color = 'blue')\n",
    "plt.title('Check It (Regression Model)')\n",
    "plt.xlabel('Position level')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observation\n",
    "The Decision Tree Regression is both non-linear and non-continuous model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": [
    "#Visualise the Regression results(for higher resolution and smoother curve)\n",
    "X_grid = np.arange(min(X), max(X), 0.01)\n",
    "X_grid = X_grid.reshape((len(X_grid), 1))\n",
    "plt.scatter(X, y, color = 'red')\n",
    "plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')\n",
    "plt.title('Decision Regression Model')\n",
    "plt.xlabel('Position level')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predict a new result\n",
    "y_pred = regressor.predict([[5.5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([110000.])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The Decision Tree regression works better for more than one dimensions conditions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the salaries dataset\n",
    "data2=pd.read_csv(\"Position_Salaries.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "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",
       "      <td>0</td>\n",
       "      <td>Business Analyst</td>\n",
       "      <td>1</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Junior Consultant</td>\n",
       "      <td>2</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Senior Consultant</td>\n",
       "      <td>3</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Manager</td>\n",
       "      <td>4</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Country Manager</td>\n",
       "      <td>5</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Region Manager</td>\n",
       "      <td>6</td>\n",
       "      <td>150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Partner</td>\n",
       "      <td>7</td>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Senior Partner</td>\n",
       "      <td>8</td>\n",
       "      <td>300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>C-level</td>\n",
       "      <td>9</td>\n",
       "      <td>500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\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"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Select all rows and column 1 from dataset to x and all rows and column 2 as y\n",
    "x = data2.iloc[:, 1:2].values  \n",
    "y = data2.iloc[:, 2].values  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1],\n",
       "       [ 2],\n",
       "       [ 3],\n",
       "       [ 4],\n",
       "       [ 5],\n",
       "       [ 6],\n",
       "       [ 7],\n",
       "       [ 8],\n",
       "       [ 9],\n",
       "       [10]], dtype=int64)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  45000,   50000,   60000,   80000,  110000,  150000,  200000,\n",
       "        300000,  500000, 1000000], dtype=int64)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fit Random forest regressor to the dataset\n",
    "# import the regressor \n",
    "from sklearn.ensemble import RandomForestRegressor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create regressor object \n",
    "regressor = RandomForestRegressor(n_estimators = 100, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=0, verbose=0,\n",
       "                      warm_start=False)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit the regressor with x and y data \n",
    "regressor.fit(x, y)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predict a new result\n",
    "y_pred = regressor.predict([[6.5]])  # test the output by changing values \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([158300.])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Visualise the result\n",
    "# arange for creating a range of values \n",
    "# from min value of x to max  \n",
    "# value of x with a difference of 0.01  \n",
    "# between two consecutive values \n",
    "X_grid = np.arange(min(x), max(x), 0.01)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reshape for reshaping the data into a len(X_grid)*1 array,  \n",
    "# i.e. to make a column out of the X_grid value                   \n",
    "X_grid = X_grid.reshape((len(X_grid), 1)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f8da169808>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Scatter plot for original data \n",
    "plt.scatter(x, y, color = 'blue')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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": [
    "# plot predicted data \n",
    "plt.plot(X_grid, regressor.predict(X_grid),  \n",
    "         color = 'green')  \n",
    "plt.title('Random Forest Regression') \n",
    "plt.xlabel('Position level') \n",
    "plt.ylabel('Salary') \n",
    "plt.show()"
   ]
  }
 ],
 "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
}