7.Decision tree facewash buy yes or no.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "67eab247",
   "metadata": {},
   "source": [
    "# A person who buy or not facewash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6dd92e06",
   "metadata": {},
   "outputs": [],
   "source": [
    "#classification problem\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1480cb20",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>age</th>\n",
       "      <th>income</th>\n",
       "      <th>gender</th>\n",
       "      <th>m_status</th>\n",
       "      <th>buys</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>&lt;25</td>\n",
       "      <td>high</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>&lt;25</td>\n",
       "      <td>high</td>\n",
       "      <td>male</td>\n",
       "      <td>married</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25-35</td>\n",
       "      <td>high</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>&gt;35</td>\n",
       "      <td>medium</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>&gt;35</td>\n",
       "      <td>low</td>\n",
       "      <td>female</td>\n",
       "      <td>single</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>&gt;35</td>\n",
       "      <td>low</td>\n",
       "      <td>female</td>\n",
       "      <td>single</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>25-35</td>\n",
       "      <td>low</td>\n",
       "      <td>female</td>\n",
       "      <td>married</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>&lt;25</td>\n",
       "      <td>medium</td>\n",
       "      <td>male</td>\n",
       "      <td>married</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>&lt;25</td>\n",
       "      <td>low</td>\n",
       "      <td>female</td>\n",
       "      <td>single</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>&gt;35</td>\n",
       "      <td>medium</td>\n",
       "      <td>female</td>\n",
       "      <td>married</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>&lt;25</td>\n",
       "      <td>medium</td>\n",
       "      <td>female</td>\n",
       "      <td>single</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>25-35</td>\n",
       "      <td>medium</td>\n",
       "      <td>male</td>\n",
       "      <td>married</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>25-35</td>\n",
       "      <td>high</td>\n",
       "      <td>female</td>\n",
       "      <td>single</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>&gt;35</td>\n",
       "      <td>medium</td>\n",
       "      <td>male</td>\n",
       "      <td>married</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>&lt;25</td>\n",
       "      <td>high</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  income  gender m_status buys\n",
       "0     <25    high    male   single   no\n",
       "1     <25    high    male  married   no\n",
       "2   25-35    high    male   single  yes\n",
       "3     >35  medium    male   single  yes\n",
       "4     >35     low  female   single  yes\n",
       "5     >35     low  female   single   no\n",
       "6   25-35     low  female  married  yes\n",
       "7     <25  medium    male  married   no\n",
       "8     <25     low  female   single  yes\n",
       "9     >35  medium  female  married  yes\n",
       "10    <25  medium  female   single  yes\n",
       "11  25-35  medium    male  married  yes\n",
       "12  25-35    high  female   single  yes\n",
       "13    >35  medium    male  married   no\n",
       "14    <25    high    male   single   no"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df =pd.read_csv('7.Decision tree facewash buy yes or no.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1793e7cb",
   "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>age</th>\n",
       "      <th>income</th>\n",
       "      <th>gender</th>\n",
       "      <th>m_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>&lt;25</td>\n",
       "      <td>high</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>&lt;25</td>\n",
       "      <td>high</td>\n",
       "      <td>male</td>\n",
       "      <td>married</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25-35</td>\n",
       "      <td>high</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>&gt;35</td>\n",
       "      <td>medium</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>&gt;35</td>\n",
       "      <td>low</td>\n",
       "      <td>female</td>\n",
       "      <td>single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>&gt;35</td>\n",
       "      <td>low</td>\n",
       "      <td>female</td>\n",
       "      <td>single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>25-35</td>\n",
       "      <td>low</td>\n",
       "      <td>female</td>\n",
       "      <td>married</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>&lt;25</td>\n",
       "      <td>medium</td>\n",
       "      <td>male</td>\n",
       "      <td>married</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>&lt;25</td>\n",
       "      <td>low</td>\n",
       "      <td>female</td>\n",
       "      <td>single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>&gt;35</td>\n",
       "      <td>medium</td>\n",
       "      <td>female</td>\n",
       "      <td>married</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>&lt;25</td>\n",
       "      <td>medium</td>\n",
       "      <td>female</td>\n",
       "      <td>single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>25-35</td>\n",
       "      <td>medium</td>\n",
       "      <td>male</td>\n",
       "      <td>married</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>25-35</td>\n",
       "      <td>high</td>\n",
       "      <td>female</td>\n",
       "      <td>single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>&gt;35</td>\n",
       "      <td>medium</td>\n",
       "      <td>male</td>\n",
       "      <td>married</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>&lt;25</td>\n",
       "      <td>high</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  income  gender m_status\n",
       "0     <25    high    male   single\n",
       "1     <25    high    male  married\n",
       "2   25-35    high    male   single\n",
       "3     >35  medium    male   single\n",
       "4     >35     low  female   single\n",
       "5     >35     low  female   single\n",
       "6   25-35     low  female  married\n",
       "7     <25  medium    male  married\n",
       "8     <25     low  female   single\n",
       "9     >35  medium  female  married\n",
       "10    <25  medium  female   single\n",
       "11  25-35  medium    male  married\n",
       "12  25-35    high  female   single\n",
       "13    >35  medium    male  married\n",
       "14    <25    high    male   single"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "x = df.iloc[:,:-1]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1a4b2d95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      no\n",
       "1      no\n",
       "2     yes\n",
       "3     yes\n",
       "4     yes\n",
       "5      no\n",
       "6     yes\n",
       "7      no\n",
       "8     yes\n",
       "9     yes\n",
       "10    yes\n",
       "11    yes\n",
       "12    yes\n",
       "13     no\n",
       "14     no\n",
       "Name: buys, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df.iloc[:,4] # ignor 4 attribute \n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "593f13c0",
   "metadata": {},
   "source": [
    "# then preprocessing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f93e84d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "199df2b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "Le_x = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d193e2fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = x.apply(Le_x.fit_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dcf2a855",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>income</th>\n",
       "      <th>gender</th>\n",
       "      <th>m_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  income  gender  m_status\n",
       "0     1       0       1         1\n",
       "1     1       0       1         0\n",
       "2     0       0       1         1\n",
       "3     2       2       1         1\n",
       "4     2       1       0         1\n",
       "5     2       1       0         1\n",
       "6     0       1       0         0\n",
       "7     1       2       1         0\n",
       "8     1       1       0         1\n",
       "9     2       2       0         0\n",
       "10    1       2       0         1\n",
       "11    0       2       1         0\n",
       "12    0       0       0         1\n",
       "13    2       2       1         0\n",
       "14    1       0       1         1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2da83b49",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "393dadd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtf = tree.DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c07a272f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtf.fit(x.iloc[:,0:4],y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "aece2828",
   "metadata": {},
   "outputs": [],
   "source": [
    "#[age 25 below,income low4,gender female 4,married]\n",
    "xinput =np.array([1,1,0,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "10b338c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['yes'], dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict =dtf.predict([xinput])\n",
    "y_predict"
   ]
  },
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