Random Forest Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.formula.api as sm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
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       "      <td>g</td>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  class cap-shape cap-surface cap-color bruises odor gill-attachment  \\\n",
       "0     p         x           s         n       t    p               f   \n",
       "1     e         x           s         y       t    a               f   \n",
       "2     e         b           s         w       t    l               f   \n",
       "3     p         x           y         w       t    p               f   \n",
       "4     e         x           s         g       f    n               f   \n",
       "\n",
       "  gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n",
       "0            c         n          k  ...                        s   \n",
       "1            c         b          k  ...                        s   \n",
       "2            c         b          n  ...                        s   \n",
       "3            c         n          n  ...                        s   \n",
       "4            w         b          k  ...                        s   \n",
       "\n",
       "  stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "0                      w                      w         p          w   \n",
       "1                      w                      w         p          w   \n",
       "2                      w                      w         p          w   \n",
       "3                      w                      w         p          w   \n",
       "4                      w                      w         p          w   \n",
       "\n",
       "  ring-number ring-type spore-print-color population habitat  \n",
       "0           o         p                 k          s       u  \n",
       "1           o         p                 n          n       g  \n",
       "2           o         p                 n          n       m  \n",
       "3           o         p                 k          s       u  \n",
       "4           o         e                 n          a       g  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset=pd.read_csv('mushrooms.csv') \n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>freq</th>\n",
       "      <td>4208</td>\n",
       "      <td>3656</td>\n",
       "      <td>3244</td>\n",
       "      <td>2284</td>\n",
       "      <td>4748</td>\n",
       "      <td>3528</td>\n",
       "      <td>7914</td>\n",
       "      <td>6812</td>\n",
       "      <td>5612</td>\n",
       "      <td>1728</td>\n",
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       "      <td>4936</td>\n",
       "      <td>4464</td>\n",
       "      <td>4384</td>\n",
       "      <td>8124</td>\n",
       "      <td>7924</td>\n",
       "      <td>7488</td>\n",
       "      <td>3968</td>\n",
       "      <td>2388</td>\n",
       "      <td>4040</td>\n",
       "      <td>3148</td>\n",
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       "<p>4 rows × 23 columns</p>\n",
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      ],
      "text/plain": [
       "       class cap-shape cap-surface cap-color bruises  odor gill-attachment  \\\n",
       "count   8124      8124        8124      8124    8124  8124            8124   \n",
       "unique     2         6           4        10       2     9               2   \n",
       "top        e         x           y         n       f     n               f   \n",
       "freq    4208      3656        3244      2284    4748  3528            7914   \n",
       "\n",
       "       gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n",
       "count          8124      8124       8124  ...                     8124   \n",
       "unique            2         2         12  ...                        4   \n",
       "top               c         b          b  ...                        s   \n",
       "freq           6812      5612       1728  ...                     4936   \n",
       "\n",
       "       stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "count                    8124                   8124      8124       8124   \n",
       "unique                      9                      9         1          4   \n",
       "top                         w                      w         p          w   \n",
       "freq                     4464                   4384      8124       7924   \n",
       "\n",
       "       ring-number ring-type spore-print-color population habitat  \n",
       "count         8124      8124              8124       8124    8124  \n",
       "unique           3         5                 9          6       7  \n",
       "top              o         p                 w          v       d  \n",
       "freq          7488      3968              2388       4040    3148  \n",
       "\n",
       "[4 rows x 23 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [class, cap-shape, cap-surface, cap-color, bruises, odor, gill-attachment, gill-spacing, gill-size, gill-color, stalk-shape, stalk-root, stalk-surface-above-ring, stalk-surface-below-ring, stalk-color-above-ring, stalk-color-below-ring, veil-type, veil-color, ring-number, ring-type, spore-print-color, population, habitat]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 23 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verify that there are no null columns in the dataset\n",
    "dataset[dataset.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = dataset['class']\n",
    "x = dataset.drop(['class'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  cap-shape cap-surface cap-color bruises odor gill-attachment gill-spacing  \\\n",
      "0         x           s         n       t    p               f            c   \n",
      "1         x           s         y       t    a               f            c   \n",
      "2         b           s         w       t    l               f            c   \n",
      "3         x           y         w       t    p               f            c   \n",
      "4         x           s         g       f    n               f            w   \n",
      "\n",
      "  gill-size gill-color stalk-shape  ... stalk-surface-below-ring  \\\n",
      "0         n          k           e  ...                        s   \n",
      "1         b          k           e  ...                        s   \n",
      "2         b          n           e  ...                        s   \n",
      "3         n          n           e  ...                        s   \n",
      "4         b          k           t  ...                        s   \n",
      "\n",
      "  stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
      "0                      w                      w         p          w   \n",
      "1                      w                      w         p          w   \n",
      "2                      w                      w         p          w   \n",
      "3                      w                      w         p          w   \n",
      "4                      w                      w         p          w   \n",
      "\n",
      "  ring-number ring-type spore-print-color population habitat  \n",
      "0           o         p                 k          s       u  \n",
      "1           o         p                 n          n       g  \n",
      "2           o         p                 n          n       m  \n",
      "3           o         p                 k          s       u  \n",
      "4           o         e                 n          a       g  \n",
      "\n",
      "[5 rows x 22 columns]\n"
     ]
    }
   ],
   "source": [
    "print(x.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    p\n",
      "1    e\n",
      "2    e\n",
      "3    p\n",
      "4    e\n",
      "Name: class, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(y.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 0 1 0 0 0 0 1 0]\n"
     ]
    }
   ],
   "source": [
    "# use LabelEncoder to replace purchased (dependent variable) with 0 and 1 \n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "y= LabelEncoder().fit_transform(y)\n",
    "print(y[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   cap-shape_b  cap-shape_c  cap-shape_f  cap-shape_k  cap-shape_s  \\\n",
      "0            0            0            0            0            0   \n",
      "1            0            0            0            0            0   \n",
      "2            1            0            0            0            0   \n",
      "3            0            0            0            0            0   \n",
      "4            0            0            0            0            0   \n",
      "\n",
      "   cap-shape_x  cap-surface_f  cap-surface_g  cap-surface_s  cap-surface_y  \\\n",
      "0            1              0              0              1              0   \n",
      "1            1              0              0              1              0   \n",
      "2            0              0              0              1              0   \n",
      "3            1              0              0              0              1   \n",
      "4            1              0              0              1              0   \n",
      "\n",
      "   ...  population_s  population_v  population_y  habitat_d  habitat_g  \\\n",
      "0  ...             1             0             0          0          0   \n",
      "1  ...             0             0             0          0          1   \n",
      "2  ...             0             0             0          0          0   \n",
      "3  ...             1             0             0          0          0   \n",
      "4  ...             0             0             0          0          1   \n",
      "\n",
      "   habitat_l  habitat_m  habitat_p  habitat_u  habitat_w  \n",
      "0          0          0          0          1          0  \n",
      "1          0          0          0          0          0  \n",
      "2          0          1          0          0          0  \n",
      "3          0          0          0          1          0  \n",
      "4          0          0          0          0          0  \n",
      "\n",
      "[5 rows x 117 columns]\n"
     ]
    }
   ],
   "source": [
    "x = pd.get_dummies(x)\n",
    "print(x.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting the dataset into training and test set  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "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=0.3,random_state = 0) # func returns train and test data. It takes dataset and then split size test_size =0.3 means 30% data is for test and rest for training and random_state \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      cap-shape_b  cap-shape_c  cap-shape_f  cap-shape_k  cap-shape_s  \\\n",
      "5314            0            0            0            0            0   \n",
      "2075            0            0            0            0            0   \n",
      "5165            0            0            1            0            0   \n",
      "1155            1            0            0            0            0   \n",
      "4448            0            0            0            0            0   \n",
      "\n",
      "      cap-shape_x  cap-surface_f  cap-surface_g  cap-surface_s  cap-surface_y  \\\n",
      "5314            1              0              0              1              0   \n",
      "2075            1              0              0              1              0   \n",
      "5165            0              0              0              1              0   \n",
      "1155            0              0              0              0              1   \n",
      "4448            1              0              0              0              1   \n",
      "\n",
      "      ...  population_s  population_v  population_y  habitat_d  habitat_g  \\\n",
      "5314  ...             0             1             0          0          1   \n",
      "2075  ...             0             0             0          0          0   \n",
      "5165  ...             1             0             0          0          1   \n",
      "1155  ...             1             0             0          0          0   \n",
      "4448  ...             0             0             1          0          1   \n",
      "\n",
      "      habitat_l  habitat_m  habitat_p  habitat_u  habitat_w  \n",
      "5314          0          0          0          0          0  \n",
      "2075          0          1          0          0          0  \n",
      "5165          0          0          0          0          0  \n",
      "1155          0          1          0          0          0  \n",
      "4448          0          0          0          0          0  \n",
      "\n",
      "[5 rows x 117 columns]\n"
     ]
    }
   ],
   "source": [
    "print(x_train.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      cap-shape_b  cap-shape_c  cap-shape_f  cap-shape_k  cap-shape_s  \\\n",
      "380             0            0            0            0            0   \n",
      "3641            0            0            1            0            0   \n",
      "273             0            0            0            0            0   \n",
      "1029            0            0            0            0            0   \n",
      "684             0            0            0            0            0   \n",
      "\n",
      "      cap-shape_x  cap-surface_f  cap-surface_g  cap-surface_s  cap-surface_y  \\\n",
      "380             1              0              0              0              1   \n",
      "3641            0              0              0              0              1   \n",
      "273             1              0              0              0              1   \n",
      "1029            1              0              0              1              0   \n",
      "684             1              1              0              0              0   \n",
      "\n",
      "      ...  population_s  population_v  population_y  habitat_d  habitat_g  \\\n",
      "380   ...             1             0             0          0          0   \n",
      "3641  ...             0             0             1          1          0   \n",
      "273   ...             0             0             0          0          0   \n",
      "1029  ...             0             0             0          0          1   \n",
      "684   ...             0             1             0          1          0   \n",
      "\n",
      "      habitat_l  habitat_m  habitat_p  habitat_u  habitat_w  \n",
      "380           0          0          0          1          0  \n",
      "3641          0          0          0          0          0  \n",
      "273           0          1          0          0          0  \n",
      "1029          0          0          0          0          0  \n",
      "684           0          0          0          0          0  \n",
      "\n",
      "[5 rows x 117 columns]\n"
     ]
    }
   ],
   "source": [
    "print(x_test.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 1 0 1 0 1 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "print(y_train[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 0 0 0 0 1 1 0 1]\n"
     ]
    }
   ],
   "source": [
    "print(y_test[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(n_estimators=10, random_state=0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "regressor = RandomForestRegressor(n_estimators=10,random_state=0)\n",
    "regressor.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred= regressor.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9997205546026301"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error, confusion_matrix, classification_report\n",
    "r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00020508613617719443"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.972928630024612e-05"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "<text text-anchor=\"middle\" x=\"798\" y=\"-334.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">mse = 0.197</text>\r\n",
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     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import graphviz # Refer to https://forum.graphviz.org/t/new-simplified-installation-procedure-on-windows/224 for installation steps\n",
    "from sklearn import tree\n",
    "dot_data = tree.export_graphviz(regressor.estimators_[0]) # Extract and plot single tree\n",
    "graph = graphviz.Source(dot_data)\n",
    "graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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       "<graphviz.files.Source at 0x171512d8580>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import graphviz # Refer to https://forum.graphviz.org/t/new-simplified-installation-procedure-on-windows/224 for installation steps\n",
    "from sklearn import tree\n",
    "dot_data = tree.export_graphviz(regressor.estimators_[5]) # Extract and plot single tree\n",
    "graph = graphviz.Source(dot_data)\n",
    "graph"
   ]
  }
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