Decision Tree Regression Company Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import numpy package for arrays and stuff \n",
    "import numpy as np \n",
    "\n",
    "# import matplotlib.pyplot for plotting our result \n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "# import pandas for importing csv files \n",
    "import pandas as pd \n",
    "\n",
    "# import the regressor \n",
    "from sklearn.tree import DecisionTreeRegressor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import dataset \n",
    "dataset = pd.read_csv('~/Downloads/Data Science/data set/Company_Data.csv') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Sales</th>\n",
       "      <th>CompPrice</th>\n",
       "      <th>Income</th>\n",
       "      <th>Advertising</th>\n",
       "      <th>Population</th>\n",
       "      <th>Price</th>\n",
       "      <th>ShelveLoc</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Urban</th>\n",
       "      <th>US</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.50</td>\n",
       "      <td>138</td>\n",
       "      <td>73</td>\n",
       "      <td>11</td>\n",
       "      <td>276</td>\n",
       "      <td>120</td>\n",
       "      <td>Bad</td>\n",
       "      <td>42</td>\n",
       "      <td>17</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.22</td>\n",
       "      <td>111</td>\n",
       "      <td>48</td>\n",
       "      <td>16</td>\n",
       "      <td>260</td>\n",
       "      <td>83</td>\n",
       "      <td>Good</td>\n",
       "      <td>65</td>\n",
       "      <td>10</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.06</td>\n",
       "      <td>113</td>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>269</td>\n",
       "      <td>80</td>\n",
       "      <td>Medium</td>\n",
       "      <td>59</td>\n",
       "      <td>12</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.40</td>\n",
       "      <td>117</td>\n",
       "      <td>100</td>\n",
       "      <td>4</td>\n",
       "      <td>466</td>\n",
       "      <td>97</td>\n",
       "      <td>Medium</td>\n",
       "      <td>55</td>\n",
       "      <td>14</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.15</td>\n",
       "      <td>141</td>\n",
       "      <td>64</td>\n",
       "      <td>3</td>\n",
       "      <td>340</td>\n",
       "      <td>128</td>\n",
       "      <td>Bad</td>\n",
       "      <td>38</td>\n",
       "      <td>13</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>12.57</td>\n",
       "      <td>138</td>\n",
       "      <td>108</td>\n",
       "      <td>17</td>\n",
       "      <td>203</td>\n",
       "      <td>128</td>\n",
       "      <td>Good</td>\n",
       "      <td>33</td>\n",
       "      <td>14</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>6.14</td>\n",
       "      <td>139</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>120</td>\n",
       "      <td>Medium</td>\n",
       "      <td>55</td>\n",
       "      <td>11</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>7.41</td>\n",
       "      <td>162</td>\n",
       "      <td>26</td>\n",
       "      <td>12</td>\n",
       "      <td>368</td>\n",
       "      <td>159</td>\n",
       "      <td>Medium</td>\n",
       "      <td>40</td>\n",
       "      <td>18</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>5.94</td>\n",
       "      <td>100</td>\n",
       "      <td>79</td>\n",
       "      <td>7</td>\n",
       "      <td>284</td>\n",
       "      <td>95</td>\n",
       "      <td>Bad</td>\n",
       "      <td>50</td>\n",
       "      <td>12</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>9.71</td>\n",
       "      <td>134</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>120</td>\n",
       "      <td>Good</td>\n",
       "      <td>49</td>\n",
       "      <td>16</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sales  CompPrice  Income  Advertising  Population  Price ShelveLoc  Age  \\\n",
       "0     9.50        138      73           11         276    120       Bad   42   \n",
       "1    11.22        111      48           16         260     83      Good   65   \n",
       "2    10.06        113      35           10         269     80    Medium   59   \n",
       "3     7.40        117     100            4         466     97    Medium   55   \n",
       "4     4.15        141      64            3         340    128       Bad   38   \n",
       "..     ...        ...     ...          ...         ...    ...       ...  ...   \n",
       "395  12.57        138     108           17         203    128      Good   33   \n",
       "396   6.14        139      23            3          37    120    Medium   55   \n",
       "397   7.41        162      26           12         368    159    Medium   40   \n",
       "398   5.94        100      79            7         284     95       Bad   50   \n",
       "399   9.71        134      37            0          27    120      Good   49   \n",
       "\n",
       "     Education Urban   US  \n",
       "0           17   Yes  Yes  \n",
       "1           10   Yes  Yes  \n",
       "2           12   Yes  Yes  \n",
       "3           14   Yes  Yes  \n",
       "4           13   Yes   No  \n",
       "..         ...   ...  ...  \n",
       "395         14   Yes  Yes  \n",
       "396         11    No  Yes  \n",
       "397         18   Yes  Yes  \n",
       "398         12   Yes  Yes  \n",
       "399         16   Yes  Yes  \n",
       "\n",
       "[400 rows x 11 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# seprate categorical and numerical value\n",
    "categorical = [col for col in dataset.columns if dataset[col].dtype==object]\n",
    "numerical = [col for col in dataset.columns if dataset[col].dtype!=object]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     ShelveLoc  Urban  US\n",
      "0            0      0   0\n",
      "1            1      0   0\n",
      "2            2      0   0\n",
      "3            2      0   0\n",
      "4            0      0   1\n",
      "..         ...    ...  ..\n",
      "395          1      0   0\n",
      "396          2      1   0\n",
      "397          2      0   0\n",
      "398          0      0   0\n",
      "399          1      0   0\n",
      "\n",
      "[400 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "# for convert categorical to int\n",
    "for col in categorical:\n",
    "    temp = {}\n",
    "    count = 0\n",
    "    for val in dataset[col].values:\n",
    "        try:\n",
    "            temp[val]\n",
    "        except:\n",
    "            temp[val] = count\n",
    "            count += 1\n",
    "    dataset[col] = [temp[x] for x in dataset[col].values]\n",
    "print(dataset[categorical])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print the dataset \n",
    "\n",
    "X = dataset.iloc[0:199].values \n",
    "y = dataset.iloc[200:399].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
       "                      max_features=None, 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, presort='deprecated',\n",
       "                      random_state=0, splitter='best')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a decisiontreeregressor model \n",
    "regressor = DecisionTreeRegressor(random_state = 0) \n",
    "\n",
    "# fit the regressor with X and Y data \n",
    "regressor.fit(X, y) "
   ]
  }
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