Decision Tree.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree\n"
     ]
    }
   ],
   "source": [
    "print('Decision Tree') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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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>company</th>\n",
       "      <th>job</th>\n",
       "      <th>degree</th>\n",
       "      <th>salary100k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>google</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>google</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>masters</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>google</td>\n",
       "      <td>business manager</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>google</td>\n",
       "      <td>business manager</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>google</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>google</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>masters</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>business manager</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>business manager</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>facebook</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>facebook</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>facebook</td>\n",
       "      <td>business manager</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>facebook</td>\n",
       "      <td>business manager</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>facebook</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>facebook</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       company                  job     degree  salary100k\n",
       "0       google      sales executive  bachelors           0\n",
       "1       google      sales executive    masters           0\n",
       "2       google     business manager  bachelors           1\n",
       "3       google     business manager    masters           1\n",
       "4       google  computer programmer  bachelors           0\n",
       "5       google  computer programmer    masters           1\n",
       "6   abc pharma      sales executive    masters           0\n",
       "7   abc pharma  computer programmer  bachelors           0\n",
       "8   abc pharma     business manager  bachelors           0\n",
       "9   abc pharma     business manager    masters           1\n",
       "10    facebook      sales executive  bachelors           1\n",
       "11    facebook      sales executive    masters           1\n",
       "12    facebook     business manager  bachelors           1\n",
       "13    facebook     business manager    masters           1\n",
       "14    facebook  computer programmer  bachelors           1\n",
       "15    facebook  computer programmer    masters           1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv('decisionsalary.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>company</th>\n",
       "      <th>job</th>\n",
       "      <th>degree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>google</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>bachelors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>google</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>masters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>google</td>\n",
       "      <td>business manager</td>\n",
       "      <td>bachelors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>google</td>\n",
       "      <td>business manager</td>\n",
       "      <td>masters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>google</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>bachelors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>google</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>masters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>masters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>bachelors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>business manager</td>\n",
       "      <td>bachelors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>business manager</td>\n",
       "      <td>masters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>facebook</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>bachelors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>facebook</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>masters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>facebook</td>\n",
       "      <td>business manager</td>\n",
       "      <td>bachelors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>facebook</td>\n",
       "      <td>business manager</td>\n",
       "      <td>masters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>facebook</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>bachelors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>facebook</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>masters</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       company                  job     degree\n",
       "0       google      sales executive  bachelors\n",
       "1       google      sales executive    masters\n",
       "2       google     business manager  bachelors\n",
       "3       google     business manager    masters\n",
       "4       google  computer programmer  bachelors\n",
       "5       google  computer programmer    masters\n",
       "6   abc pharma      sales executive    masters\n",
       "7   abc pharma  computer programmer  bachelors\n",
       "8   abc pharma     business manager  bachelors\n",
       "9   abc pharma     business manager    masters\n",
       "10    facebook      sales executive  bachelors\n",
       "11    facebook      sales executive    masters\n",
       "12    facebook     business manager  bachelors\n",
       "13    facebook     business manager    masters\n",
       "14    facebook  computer programmer  bachelors\n",
       "15    facebook  computer programmer    masters"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs=df.drop(['salary100k'],axis='columns')\n",
    "inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "target=df.salary100k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0\n",
       "1     0\n",
       "2     1\n",
       "3     1\n",
       "4     0\n",
       "5     1\n",
       "6     0\n",
       "7     0\n",
       "8     0\n",
       "9     1\n",
       "10    1\n",
       "11    1\n",
       "12    1\n",
       "13    1\n",
       "14    1\n",
       "15    1\n",
       "Name: salary100k, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "le_company=LabelEncoder()\n",
    "le_job=LabelEncoder() \n",
    "le_degree=LabelEncoder()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>company</th>\n",
       "      <th>job</th>\n",
       "      <th>degree</th>\n",
       "      <th>company_n</th>\n",
       "      <th>job_n</th>\n",
       "      <th>degree_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>google</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>google</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>masters</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>google</td>\n",
       "      <td>business manager</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>google</td>\n",
       "      <td>business manager</td>\n",
       "      <td>masters</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>google</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>google</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>masters</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>masters</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>business manager</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>business manager</td>\n",
       "      <td>masters</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>facebook</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>facebook</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>facebook</td>\n",
       "      <td>business manager</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>facebook</td>\n",
       "      <td>business manager</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>facebook</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>facebook</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       company                  job     degree  company_n  job_n  degree_n\n",
       "0       google      sales executive  bachelors          2      2         0\n",
       "1       google      sales executive    masters          2      2         1\n",
       "2       google     business manager  bachelors          2      0         0\n",
       "3       google     business manager    masters          2      0         1\n",
       "4       google  computer programmer  bachelors          2      1         0\n",
       "5       google  computer programmer    masters          2      1         1\n",
       "6   abc pharma      sales executive    masters          0      2         1\n",
       "7   abc pharma  computer programmer  bachelors          0      1         0\n",
       "8   abc pharma     business manager  bachelors          0      0         0\n",
       "9   abc pharma     business manager    masters          0      0         1\n",
       "10    facebook      sales executive  bachelors          1      2         0\n",
       "11    facebook      sales executive    masters          1      2         1\n",
       "12    facebook     business manager  bachelors          1      0         0\n",
       "13    facebook     business manager    masters          1      0         1\n",
       "14    facebook  computer programmer  bachelors          1      1         0\n",
       "15    facebook  computer programmer    masters          1      1         1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs['company_n']=le_company.fit_transform(inputs['company'])\n",
    "inputs['job_n']=le_job.fit_transform(inputs['job'])\n",
    "inputs['degree_n']=le_degree.fit_transform(inputs['degree'])\n",
    "inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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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>company_n</th>\n",
       "      <th>job_n</th>\n",
       "      <th>degree_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    company_n  job_n  degree_n\n",
       "0           2      2         0\n",
       "1           2      2         1\n",
       "2           2      0         0\n",
       "3           2      0         1\n",
       "4           2      1         0\n",
       "5           2      1         1\n",
       "6           0      2         1\n",
       "7           0      1         0\n",
       "8           0      0         0\n",
       "9           0      0         1\n",
       "10          1      2         0\n",
       "11          1      2         1\n",
       "12          1      0         0\n",
       "13          1      0         1\n",
       "14          1      1         0\n",
       "15          1      1         1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs_n = inputs.drop(['company','job','degree'],axis='columns')\n",
    "inputs_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0\n",
       "1     0\n",
       "2     1\n",
       "3     1\n",
       "4     0\n",
       "5     1\n",
       "6     0\n",
       "7     0\n",
       "8     0\n",
       "9     1\n",
       "10    1\n",
       "11    1\n",
       "12    1\n",
       "13    1\n",
       "14    1\n",
       "15    1\n",
       "Name: salary100k, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', 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=False,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "model=tree.DecisionTreeClassifier()\n",
    "model.fit(inputs_n,target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(inputs_n,target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[2,1,0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Assignment Titanic\n"
     ]
    }
   ],
   "source": [
    "print('Assignment Titanic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>passengerid</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   passengerid  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('titanic_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Age.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Age=df.Age.fillna(df.Age.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      22.000000\n",
       "1      38.000000\n",
       "2      26.000000\n",
       "3      35.000000\n",
       "4      35.000000\n",
       "5      29.699118\n",
       "6      54.000000\n",
       "7       2.000000\n",
       "8      27.000000\n",
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       "26     29.699118\n",
       "27     19.000000\n",
       "28     29.699118\n",
       "29     29.699118\n",
       "         ...    \n",
       "861    21.000000\n",
       "862    48.000000\n",
       "863    29.699118\n",
       "864    24.000000\n",
       "865    42.000000\n",
       "866    27.000000\n",
       "867    31.000000\n",
       "868    29.699118\n",
       "869     4.000000\n",
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       "871    47.000000\n",
       "872    33.000000\n",
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       "875    15.000000\n",
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       "881    33.000000\n",
       "882    22.000000\n",
       "883    28.000000\n",
       "884    25.000000\n",
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       "887    19.000000\n",
       "888    29.699118\n",
       "889    26.000000\n",
       "890    32.000000\n",
       "Name: Age, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>passengerid</th>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   passengerid  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>8.4583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>51.8625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>21.0750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>11.1333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>30.0708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>16.7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>26.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>31.2750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>7.8542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>16.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>29.1250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>18.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>7.2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>26.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>8.0292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>35.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>21.0750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>31.3875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>7.2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>263.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>7.8792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>7.8958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>11.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>25.9292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>69.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>13.8583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>50.4958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>9.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>11.1333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>7.8958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>52.5542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>5.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>9.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>24.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>7.2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>9.8458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>7.8958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>7.8958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>83.1583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>26.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>7.8958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>10.5167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>10.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>7.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>29.1250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>23.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass     Sex        Age      Fare\n",
       "0         3    male  22.000000    7.2500\n",
       "1         1  female  38.000000   71.2833\n",
       "2         3  female  26.000000    7.9250\n",
       "3         1  female  35.000000   53.1000\n",
       "4         3    male  35.000000    8.0500\n",
       "5         3    male  29.699118    8.4583\n",
       "6         1    male  54.000000   51.8625\n",
       "7         3    male   2.000000   21.0750\n",
       "8         3  female  27.000000   11.1333\n",
       "9         2  female  14.000000   30.0708\n",
       "10        3  female   4.000000   16.7000\n",
       "11        1  female  58.000000   26.5500\n",
       "12        3    male  20.000000    8.0500\n",
       "13        3    male  39.000000   31.2750\n",
       "14        3  female  14.000000    7.8542\n",
       "15        2  female  55.000000   16.0000\n",
       "16        3    male   2.000000   29.1250\n",
       "17        2    male  29.699118   13.0000\n",
       "18        3  female  31.000000   18.0000\n",
       "19        3  female  29.699118    7.2250\n",
       "20        2    male  35.000000   26.0000\n",
       "21        2    male  34.000000   13.0000\n",
       "22        3  female  15.000000    8.0292\n",
       "23        1    male  28.000000   35.5000\n",
       "24        3  female   8.000000   21.0750\n",
       "25        3  female  38.000000   31.3875\n",
       "26        3    male  29.699118    7.2250\n",
       "27        1    male  19.000000  263.0000\n",
       "28        3  female  29.699118    7.8792\n",
       "29        3    male  29.699118    7.8958\n",
       "..      ...     ...        ...       ...\n",
       "861       2    male  21.000000   11.5000\n",
       "862       1  female  48.000000   25.9292\n",
       "863       3  female  29.699118   69.5500\n",
       "864       2    male  24.000000   13.0000\n",
       "865       2  female  42.000000   13.0000\n",
       "866       2  female  27.000000   13.8583\n",
       "867       1    male  31.000000   50.4958\n",
       "868       3    male  29.699118    9.5000\n",
       "869       3    male   4.000000   11.1333\n",
       "870       3    male  26.000000    7.8958\n",
       "871       1  female  47.000000   52.5542\n",
       "872       1    male  33.000000    5.0000\n",
       "873       3    male  47.000000    9.0000\n",
       "874       2  female  28.000000   24.0000\n",
       "875       3  female  15.000000    7.2250\n",
       "876       3    male  20.000000    9.8458\n",
       "877       3    male  19.000000    7.8958\n",
       "878       3    male  29.699118    7.8958\n",
       "879       1  female  56.000000   83.1583\n",
       "880       2  female  25.000000   26.0000\n",
       "881       3    male  33.000000    7.8958\n",
       "882       3  female  22.000000   10.5167\n",
       "883       2    male  28.000000   10.5000\n",
       "884       3    male  25.000000    7.0500\n",
       "885       3  female  39.000000   29.1250\n",
       "886       2    male  27.000000   13.0000\n",
       "887       1  female  19.000000   30.0000\n",
       "888       3  female  29.699118   23.4500\n",
       "889       1    male  26.000000   30.0000\n",
       "890       3    male  32.000000    7.7500\n",
       "\n",
       "[891 rows x 4 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs=df.drop(['passengerid','Survived','Name','SibSp','Parch','Ticket','Cabin','Embarked'],axis='columns')\n",
    "inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "target=df.Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      0\n",
       "5      0\n",
       "6      0\n",
       "7      0\n",
       "8      1\n",
       "9      1\n",
       "10     1\n",
       "11     1\n",
       "12     0\n",
       "13     0\n",
       "14     0\n",
       "15     1\n",
       "16     0\n",
       "17     1\n",
       "18     0\n",
       "19     1\n",
       "20     0\n",
       "21     1\n",
       "22     1\n",
       "23     1\n",
       "24     0\n",
       "25     1\n",
       "26     0\n",
       "27     0\n",
       "28     1\n",
       "29     0\n",
       "      ..\n",
       "861    0\n",
       "862    1\n",
       "863    0\n",
       "864    0\n",
       "865    1\n",
       "866    1\n",
       "867    0\n",
       "868    0\n",
       "869    1\n",
       "870    0\n",
       "871    1\n",
       "872    0\n",
       "873    0\n",
       "874    1\n",
       "875    1\n",
       "876    0\n",
       "877    0\n",
       "878    0\n",
       "879    1\n",
       "880    1\n",
       "881    0\n",
       "882    0\n",
       "883    0\n",
       "884    0\n",
       "885    0\n",
       "886    0\n",
       "887    1\n",
       "888    0\n",
       "889    1\n",
       "890    0\n",
       "Name: Survived, Length: 891, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "target\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
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       "      <th>14</th>\n",
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       "      <td>14.000000</td>\n",
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       "      <td>16.0000</td>\n",
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       "      <td>3</td>\n",
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       "      <td>29.1250</td>\n",
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       "      <td>2</td>\n",
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       "      <td>13.0000</td>\n",
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       "      <th>18</th>\n",
       "      <td>3</td>\n",
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       "      <td>31.000000</td>\n",
       "      <td>18.0000</td>\n",
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       "      <td>3</td>\n",
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       "      <td>29.699118</td>\n",
       "      <td>7.2250</td>\n",
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       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "      <td>7.8792</td>\n",
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       "      <th>873</th>\n",
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       "      <th>874</th>\n",
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       "      <th>875</th>\n",
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       "      <th>876</th>\n",
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       "      <td>9.8458</td>\n",
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       "      <th>877</th>\n",
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       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>3</td>\n",
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       "      <th>879</th>\n",
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       "      <th>880</th>\n",
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       "      <td>26.0000</td>\n",
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       "      <th>881</th>\n",
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       "      <th>882</th>\n",
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       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>2</td>\n",
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       "      <th>884</th>\n",
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       "      <th>885</th>\n",
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       "      <th>886</th>\n",
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       "      <th>887</th>\n",
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       "      <th>888</th>\n",
       "      <td>3</td>\n",
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       "     Pclass     Sex        Age      Fare\n",
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       "9         2  female  14.000000   30.0708\n",
       "10        3  female   4.000000   16.7000\n",
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       "12        3    male  20.000000    8.0500\n",
       "13        3    male  39.000000   31.2750\n",
       "14        3  female  14.000000    7.8542\n",
       "15        2  female  55.000000   16.0000\n",
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       "18        3  female  31.000000   18.0000\n",
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       "20        2    male  35.000000   26.0000\n",
       "21        2    male  34.000000   13.0000\n",
       "22        3  female  15.000000    8.0292\n",
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       "25        3  female  38.000000   31.3875\n",
       "26        3    male  29.699118    7.2250\n",
       "27        1    male  19.000000  263.0000\n",
       "28        3  female  29.699118    7.8792\n",
       "29        3    male  29.699118    7.8958\n",
       "..      ...     ...        ...       ...\n",
       "861       2    male  21.000000   11.5000\n",
       "862       1  female  48.000000   25.9292\n",
       "863       3  female  29.699118   69.5500\n",
       "864       2    male  24.000000   13.0000\n",
       "865       2  female  42.000000   13.0000\n",
       "866       2  female  27.000000   13.8583\n",
       "867       1    male  31.000000   50.4958\n",
       "868       3    male  29.699118    9.5000\n",
       "869       3    male   4.000000   11.1333\n",
       "870       3    male  26.000000    7.8958\n",
       "871       1  female  47.000000   52.5542\n",
       "872       1    male  33.000000    5.0000\n",
       "873       3    male  47.000000    9.0000\n",
       "874       2  female  28.000000   24.0000\n",
       "875       3  female  15.000000    7.2250\n",
       "876       3    male  20.000000    9.8458\n",
       "877       3    male  19.000000    7.8958\n",
       "878       3    male  29.699118    7.8958\n",
       "879       1  female  56.000000   83.1583\n",
       "880       2  female  25.000000   26.0000\n",
       "881       3    male  33.000000    7.8958\n",
       "882       3  female  22.000000   10.5167\n",
       "883       2    male  28.000000   10.5000\n",
       "884       3    male  25.000000    7.0500\n",
       "885       3  female  39.000000   29.1250\n",
       "886       2    male  27.000000   13.0000\n",
       "887       1  female  19.000000   30.0000\n",
       "888       3  female  29.699118   23.4500\n",
       "889       1    male  26.000000   30.0000\n",
       "890       3    male  32.000000    7.7500\n",
       "\n",
       "[891 rows x 4 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
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       "      <td>2</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>8.4583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>51.8625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>21.0750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>11.1333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>30.0708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>16.7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>26.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>31.2750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>7.8542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>16.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>29.1250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>18.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>7.2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>26.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>8.0292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>35.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>21.0750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>31.3875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>7.2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>263.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>7.8792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>7.8958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>11.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>25.9292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>69.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>13.8583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>50.4958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>9.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>11.1333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>7.8958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>52.5542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>5.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>9.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>24.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>7.2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>9.8458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>7.8958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>7.8958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>83.1583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>26.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>7.8958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>10.5167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>10.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>7.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>29.1250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>23.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  Sex        Age      Fare\n",
       "0         3    1  22.000000    7.2500\n",
       "1         1    2  38.000000   71.2833\n",
       "2         3    2  26.000000    7.9250\n",
       "3         1    2  35.000000   53.1000\n",
       "4         3    1  35.000000    8.0500\n",
       "5         3    1  29.699118    8.4583\n",
       "6         1    1  54.000000   51.8625\n",
       "7         3    1   2.000000   21.0750\n",
       "8         3    2  27.000000   11.1333\n",
       "9         2    2  14.000000   30.0708\n",
       "10        3    2   4.000000   16.7000\n",
       "11        1    2  58.000000   26.5500\n",
       "12        3    1  20.000000    8.0500\n",
       "13        3    1  39.000000   31.2750\n",
       "14        3    2  14.000000    7.8542\n",
       "15        2    2  55.000000   16.0000\n",
       "16        3    1   2.000000   29.1250\n",
       "17        2    1  29.699118   13.0000\n",
       "18        3    2  31.000000   18.0000\n",
       "19        3    2  29.699118    7.2250\n",
       "20        2    1  35.000000   26.0000\n",
       "21        2    1  34.000000   13.0000\n",
       "22        3    2  15.000000    8.0292\n",
       "23        1    1  28.000000   35.5000\n",
       "24        3    2   8.000000   21.0750\n",
       "25        3    2  38.000000   31.3875\n",
       "26        3    1  29.699118    7.2250\n",
       "27        1    1  19.000000  263.0000\n",
       "28        3    2  29.699118    7.8792\n",
       "29        3    1  29.699118    7.8958\n",
       "..      ...  ...        ...       ...\n",
       "861       2    1  21.000000   11.5000\n",
       "862       1    2  48.000000   25.9292\n",
       "863       3    2  29.699118   69.5500\n",
       "864       2    1  24.000000   13.0000\n",
       "865       2    2  42.000000   13.0000\n",
       "866       2    2  27.000000   13.8583\n",
       "867       1    1  31.000000   50.4958\n",
       "868       3    1  29.699118    9.5000\n",
       "869       3    1   4.000000   11.1333\n",
       "870       3    1  26.000000    7.8958\n",
       "871       1    2  47.000000   52.5542\n",
       "872       1    1  33.000000    5.0000\n",
       "873       3    1  47.000000    9.0000\n",
       "874       2    2  28.000000   24.0000\n",
       "875       3    2  15.000000    7.2250\n",
       "876       3    1  20.000000    9.8458\n",
       "877       3    1  19.000000    7.8958\n",
       "878       3    1  29.699118    7.8958\n",
       "879       1    2  56.000000   83.1583\n",
       "880       2    2  25.000000   26.0000\n",
       "881       3    1  33.000000    7.8958\n",
       "882       3    2  22.000000   10.5167\n",
       "883       2    1  28.000000   10.5000\n",
       "884       3    1  25.000000    7.0500\n",
       "885       3    2  39.000000   29.1250\n",
       "886       2    1  27.000000   13.0000\n",
       "887       1    2  19.000000   30.0000\n",
       "888       3    2  29.699118   23.4500\n",
       "889       1    1  26.000000   30.0000\n",
       "890       3    1  32.000000    7.7500\n",
       "\n",
       "[891 rows x 4 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs.Sex = inputs.Sex.map({'male': 1, 'female': 2})\n",
    "inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(inputs,target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>91.0792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>7.7750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>30.6958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>29.1250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>41.5792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>7.2292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>833</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>7.8542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>7.7750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>227.5250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>813</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>31.2750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>56.4958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>35.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>20.5250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>79.6500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>15.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>7.3125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>7.8542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>93.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>24.1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>769</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>8.3625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>21.0750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>27.7208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>15.8500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>30.0708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>10.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>6.9750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>26.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>10.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>7.8958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>23.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>211.3375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>45.500000</td>\n",
       "      <td>7.2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>8.6625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>13.8625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>12.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>19.2583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>9.2167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>7.5208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>7.4958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>113.2750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>15.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>8.5167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>70.500000</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>793</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>30.6958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>842</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>31.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>16.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>82.1708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>227.5250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>668 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  Sex        Age      Fare\n",
       "484       1    1  25.000000   91.0792\n",
       "603       3    1  44.000000    8.0500\n",
       "764       3    1  16.000000    7.7750\n",
       "174       1    1  56.000000   30.6958\n",
       "171       3    1   4.000000   29.1250\n",
       "685       2    1  25.000000   41.5792\n",
       "859       3    1  29.699118    7.2292\n",
       "833       3    1  23.000000    7.8542\n",
       "231       3    1  29.000000    7.7750\n",
       "700       1    2  18.000000  227.5250\n",
       "345       2    2  24.000000   13.0000\n",
       "597       3    1  49.000000    0.0000\n",
       "813       3    2   6.000000   31.2750\n",
       "826       3    1  29.699118   56.4958\n",
       "23        1    1  28.000000   35.5000\n",
       "191       2    1  19.000000   13.0000\n",
       "328       3    2  31.000000   20.5250\n",
       "795       2    1  39.000000   13.0000\n",
       "262       1    1  52.000000   79.6500\n",
       "186       3    2  29.699118   15.5000\n",
       "227       3    1  20.500000    7.2500\n",
       "614       3    1  35.000000    8.0500\n",
       "154       3    1  29.699118    7.3125\n",
       "623       3    1  21.000000    7.8542\n",
       "520       1    2  30.000000   93.5000\n",
       "811       3    1  39.000000   24.1500\n",
       "769       3    1  32.000000    8.3625\n",
       "374       3    2   3.000000   21.0750\n",
       "361       2    1  29.000000   27.7208\n",
       "403       3    1  28.000000   15.8500\n",
       "..      ...  ...        ...       ...\n",
       "9         2    2  14.000000   30.0708\n",
       "425       3    1  29.699118    7.2500\n",
       "570       2    1  62.000000   10.5000\n",
       "129       3    1  45.000000    6.9750\n",
       "11        1    2  58.000000   26.5500\n",
       "84        2    2  17.000000   10.5000\n",
       "881       3    1  33.000000    7.8958\n",
       "288       2    1  42.000000   13.0000\n",
       "783       3    1  29.699118   23.4500\n",
       "488       3    1  30.000000    8.0500\n",
       "730       1    2  29.000000  211.3375\n",
       "203       3    1  45.500000    7.2250\n",
       "534       3    2  30.000000    8.6625\n",
       "547       2    1  29.699118   13.8625\n",
       "389       2    2  17.000000   12.0000\n",
       "448       3    2   5.000000   19.2583\n",
       "138       3    1  16.000000    9.2167\n",
       "478       3    1  22.000000    7.5208\n",
       "514       3    1  24.000000    7.4958\n",
       "659       1    1  58.000000  113.2750\n",
       "729       3    2  25.000000    7.9250\n",
       "586       2    1  47.000000   15.0000\n",
       "803       3    1   0.420000    8.5167\n",
       "116       3    1  70.500000    7.7500\n",
       "406       3    1  51.000000    7.7500\n",
       "793       1    1  29.699118   30.6958\n",
       "842       1    2  30.000000   31.0000\n",
       "624       3    1  21.000000   16.1000\n",
       "375       1    2  29.699118   82.1708\n",
       "716       1    2  38.000000  227.5250\n",
       "\n",
       "[668 rows x 4 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "model=tree.DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', 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=False,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7982062780269058"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[1,1,25,100]])"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
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}