Decision Tree Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import library\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import confusion_matrix \n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "from sklearn.metrics import accuracy_score \n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import data\n",
    "fraud_data = pd.read_csv(\"~/Downloads/Data Science/data set/Fraud_check.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Undergrad</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>TaxableIncome</th>\n",
       "      <th>CityPopulation</th>\n",
       "      <th>WorkExperience</th>\n",
       "      <th>Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NO</td>\n",
       "      <td>Single</td>\n",
       "      <td>68833</td>\n",
       "      <td>50047</td>\n",
       "      <td>10</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>YES</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>33700</td>\n",
       "      <td>134075</td>\n",
       "      <td>18</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NO</td>\n",
       "      <td>Married</td>\n",
       "      <td>36925</td>\n",
       "      <td>160205</td>\n",
       "      <td>30</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YES</td>\n",
       "      <td>Single</td>\n",
       "      <td>50190</td>\n",
       "      <td>193264</td>\n",
       "      <td>15</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NO</td>\n",
       "      <td>Married</td>\n",
       "      <td>81002</td>\n",
       "      <td>27533</td>\n",
       "      <td>28</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>YES</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>76340</td>\n",
       "      <td>39492</td>\n",
       "      <td>7</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>YES</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>69967</td>\n",
       "      <td>55369</td>\n",
       "      <td>2</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>NO</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>47334</td>\n",
       "      <td>154058</td>\n",
       "      <td>0</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>YES</td>\n",
       "      <td>Married</td>\n",
       "      <td>98592</td>\n",
       "      <td>180083</td>\n",
       "      <td>17</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>NO</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>96519</td>\n",
       "      <td>158137</td>\n",
       "      <td>16</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>600 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Undergrad MaritalStatus  TaxableIncome  CityPopulation  WorkExperience  \\\n",
       "0          NO        Single          68833           50047              10   \n",
       "1         YES      Divorced          33700          134075              18   \n",
       "2          NO       Married          36925          160205              30   \n",
       "3         YES        Single          50190          193264              15   \n",
       "4          NO       Married          81002           27533              28   \n",
       "..        ...           ...            ...             ...             ...   \n",
       "595       YES      Divorced          76340           39492               7   \n",
       "596       YES      Divorced          69967           55369               2   \n",
       "597        NO      Divorced          47334          154058               0   \n",
       "598       YES       Married          98592          180083              17   \n",
       "599        NO      Divorced          96519          158137              16   \n",
       "\n",
       "    Urban  \n",
       "0     YES  \n",
       "1     YES  \n",
       "2     YES  \n",
       "3     YES  \n",
       "4      NO  \n",
       "..    ...  \n",
       "595   YES  \n",
       "596   YES  \n",
       "597   YES  \n",
       "598    NO  \n",
       "599    NO  \n",
       "\n",
       "[600 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Good\n",
       "1      Good\n",
       "2      Good\n",
       "3      Good\n",
       "4      Good\n",
       "       ... \n",
       "595    Good\n",
       "596    Good\n",
       "597    Good\n",
       "598    Good\n",
       "599    Good\n",
       "Name: TaxableIncome, Length: 600, dtype: category\n",
       "Categories (2, object): [Risk < Good]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert cetogory value\n",
    "\n",
    "bins = [0,30000, np.inf]\n",
    "names = ['Risk', 'Good']\n",
    "\n",
    "fraud_data['TaxableIncome'] = pd.cut(fraud_data['TaxableIncome'], bins, labels=names)\n",
    "\n",
    "fraud_data['TaxableIncome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Undergrad</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>TaxableIncome</th>\n",
       "      <th>CityPopulation</th>\n",
       "      <th>WorkExperience</th>\n",
       "      <th>Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NO</td>\n",
       "      <td>Single</td>\n",
       "      <td>Good</td>\n",
       "      <td>50047</td>\n",
       "      <td>10</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>YES</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Good</td>\n",
       "      <td>134075</td>\n",
       "      <td>18</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NO</td>\n",
       "      <td>Married</td>\n",
       "      <td>Good</td>\n",
       "      <td>160205</td>\n",
       "      <td>30</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YES</td>\n",
       "      <td>Single</td>\n",
       "      <td>Good</td>\n",
       "      <td>193264</td>\n",
       "      <td>15</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NO</td>\n",
       "      <td>Married</td>\n",
       "      <td>Good</td>\n",
       "      <td>27533</td>\n",
       "      <td>28</td>\n",
       "      <td>NO</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>YES</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Good</td>\n",
       "      <td>39492</td>\n",
       "      <td>7</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>YES</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Good</td>\n",
       "      <td>55369</td>\n",
       "      <td>2</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>NO</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Good</td>\n",
       "      <td>154058</td>\n",
       "      <td>0</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>YES</td>\n",
       "      <td>Married</td>\n",
       "      <td>Good</td>\n",
       "      <td>180083</td>\n",
       "      <td>17</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>NO</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Good</td>\n",
       "      <td>158137</td>\n",
       "      <td>16</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>600 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Undergrad MaritalStatus TaxableIncome  CityPopulation  WorkExperience  \\\n",
       "0          NO        Single          Good           50047              10   \n",
       "1         YES      Divorced          Good          134075              18   \n",
       "2          NO       Married          Good          160205              30   \n",
       "3         YES        Single          Good          193264              15   \n",
       "4          NO       Married          Good           27533              28   \n",
       "..        ...           ...           ...             ...             ...   \n",
       "595       YES      Divorced          Good           39492               7   \n",
       "596       YES      Divorced          Good           55369               2   \n",
       "597        NO      Divorced          Good          154058               0   \n",
       "598       YES       Married          Good          180083              17   \n",
       "599        NO      Divorced          Good          158137              16   \n",
       "\n",
       "    Urban  \n",
       "0     YES  \n",
       "1     YES  \n",
       "2     YES  \n",
       "3     YES  \n",
       "4      NO  \n",
       "..    ...  \n",
       "595   YES  \n",
       "596   YES  \n",
       "597   YES  \n",
       "598    NO  \n",
       "599    NO  \n",
       "\n",
       "[600 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Undergrad  MaritalStatus  Urban\n",
      "0            0              0      0\n",
      "1            1              1      0\n",
      "2            0              2      0\n",
      "3            1              0      0\n",
      "4            0              2      1\n",
      "..         ...            ...    ...\n",
      "595          1              1      0\n",
      "596          1              1      0\n",
      "597          0              1      0\n",
      "598          1              2      1\n",
      "599          0              1      1\n",
      "\n",
      "[600 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "# for convert categorical to int\n",
    "\n",
    "categorical = [col for col in fraud_data.columns if fraud_data[col].dtype==object]\n",
    "\n",
    "for col in categorical:\n",
    "    temp = {}\n",
    "    count = 0\n",
    "    for val in fraud_data[col].values:\n",
    "        try:\n",
    "            temp[val]\n",
    "        except:\n",
    "            temp[val] = count\n",
    "            count += 1\n",
    "    fraud_data[col] = [temp[x] for x in fraud_data[col].values]\n",
    "print(fraud_data[categorical])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Undergrad</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>TaxableIncome</th>\n",
       "      <th>CityPopulation</th>\n",
       "      <th>WorkExperience</th>\n",
       "      <th>Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>Good</td>\n",
       "      <td>50047</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Good</td>\n",
       "      <td>134075</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Good</td>\n",
       "      <td>160205</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Good</td>\n",
       "      <td>193264</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Good</td>\n",
       "      <td>27533</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Good</td>\n",
       "      <td>39492</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Good</td>\n",
       "      <td>55369</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Good</td>\n",
       "      <td>154058</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Good</td>\n",
       "      <td>180083</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Good</td>\n",
       "      <td>158137</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>600 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Undergrad  MaritalStatus TaxableIncome  CityPopulation  WorkExperience  \\\n",
       "0            0              0          Good           50047              10   \n",
       "1            1              1          Good          134075              18   \n",
       "2            0              2          Good          160205              30   \n",
       "3            1              0          Good          193264              15   \n",
       "4            0              2          Good           27533              28   \n",
       "..         ...            ...           ...             ...             ...   \n",
       "595          1              1          Good           39492               7   \n",
       "596          1              1          Good           55369               2   \n",
       "597          0              1          Good          154058               0   \n",
       "598          1              2          Good          180083              17   \n",
       "599          0              1          Good          158137              16   \n",
       "\n",
       "     Urban  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        0  \n",
       "4        1  \n",
       "..     ...  \n",
       "595      0  \n",
       "596      0  \n",
       "597      0  \n",
       "598      1  \n",
       "599      1  \n",
       "\n",
       "[600 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#feture selection\n",
    "y = fraud_data.TaxableIncome\n",
    "fraud_data.drop(['TaxableIncome'], axis=1, inplace=True)\n",
    "X = fraud_data\n",
    "del fraud_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split dataset into training set and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) # 70% training and 30% test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Decision Tree classifer object\n",
    "clf = DecisionTreeClassifier()\n",
    "\n",
    "# Train Decision Tree Classifer\n",
    "clf = clf.fit(X_train,y_train)\n",
    "\n",
    "#Predict the response for test dataset\n",
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:  [[111  33]\n",
      " [ 27   9]]\n",
      "Accuracy :  66.66666666666666\n",
      "Report :                precision    recall  f1-score   support\n",
      "\n",
      "        Good       0.80      0.77      0.79       144\n",
      "        Risk       0.21      0.25      0.23        36\n",
      "\n",
      "    accuracy                           0.67       180\n",
      "   macro avg       0.51      0.51      0.51       180\n",
      "weighted avg       0.69      0.67      0.68       180\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# model Confusion Accuracy\n",
    "print(\"Confusion Matrix: \", \n",
    "confusion_matrix(y_test, y_pred)) \n",
    "      \n",
    "print (\"Accuracy : \", \n",
    "accuracy_score(y_test,y_pred)*100) \n",
    "      \n",
    "print(\"Report : \", \n",
    "classification_report(y_test, y_pred)) "
   ]
  }
 ],
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