Decision Tree Excersise.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('titanic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     Survived  Pclass     Sex   Age     Fare\n",
       "0           0       3    male  22.0   7.2500\n",
       "1           1       1  female  38.0  71.2833\n",
       "2           1       3  female  26.0   7.9250\n",
       "3           1       1  female  35.0  53.1000\n",
       "4           0       3    male  35.0   8.0500\n",
       "..        ...     ...     ...   ...      ...\n",
       "886         0       2    male  27.0  13.0000\n",
       "887         1       1  female  19.0  30.0000\n",
       "888         0       3  female   NaN  23.4500\n",
       "889         1       1    male  26.0  30.0000\n",
       "890         0       3    male  32.0   7.7500\n",
       "\n",
       "[891 rows x 5 columns]"
      ],
      "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>Survived</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>0</td>\n      <td>3</td>\n      <td>male</td>\n      <td>22.0</td>\n      <td>7.2500</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>female</td>\n      <td>38.0</td>\n      <td>71.2833</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>3</td>\n      <td>female</td>\n      <td>26.0</td>\n      <td>7.9250</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>female</td>\n      <td>35.0</td>\n      <td>53.1000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>3</td>\n      <td>male</td>\n      <td>35.0</td>\n      <td>8.0500</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>886</th>\n      <td>0</td>\n      <td>2</td>\n      <td>male</td>\n      <td>27.0</td>\n      <td>13.0000</td>\n    </tr>\n    <tr>\n      <th>887</th>\n      <td>1</td>\n      <td>1</td>\n      <td>female</td>\n      <td>19.0</td>\n      <td>30.0000</td>\n    </tr>\n    <tr>\n      <th>888</th>\n      <td>0</td>\n      <td>3</td>\n      <td>female</td>\n      <td>NaN</td>\n      <td>23.4500</td>\n    </tr>\n    <tr>\n      <th>889</th>\n      <td>1</td>\n      <td>1</td>\n      <td>male</td>\n      <td>26.0</td>\n      <td>30.0000</td>\n    </tr>\n    <tr>\n      <th>890</th>\n      <td>0</td>\n      <td>3</td>\n      <td>male</td>\n      <td>32.0</td>\n      <td>7.7500</td>\n    </tr>\n  </tbody>\n</table>\n<p>891 rows × 5 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 86
    }
   ],
   "source": [
    "df = df.drop(['PassengerId', 'Name', 'SibSp', 'Parch', 'Ticket',  'Cabin', 'Embarked'], axis='columns')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   Pclass     Sex   Age     Fare\n",
       "0       3    male  22.0   7.2500\n",
       "1       1  female  38.0  71.2833\n",
       "2       3  female  26.0   7.9250\n",
       "3       1  female  35.0  53.1000\n",
       "4       3    male  35.0   8.0500"
      ],
      "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>0</th>\n      <td>3</td>\n      <td>male</td>\n      <td>22.0</td>\n      <td>7.2500</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>female</td>\n      <td>38.0</td>\n      <td>71.2833</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>female</td>\n      <td>26.0</td>\n      <td>7.9250</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>female</td>\n      <td>35.0</td>\n      <td>53.1000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3</td>\n      <td>male</td>\n      <td>35.0</td>\n      <td>8.0500</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 87
    }
   ],
   "source": [
    "inputs = df.drop(['Survived'], axis='columns')\n",
    "prdict = df['Survived']\n",
    "inputs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# inputs.Sex = inputs.Sex.map({'male': 1, 'female': 2})\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "le_sex = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs['sex_n'] = le_sex.fit_transform(inputs['Sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = inputs.drop(['Sex'], axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train , y_test  = train_test_split(inputs,prdict, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "<ipython-input-99-e8f9ec99137d>:1: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  x_train['Fare'] = x_train['Fare'].astype(int)\n<ipython-input-99-e8f9ec99137d>:2: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  x_test['Fare'] = x_test['Fare'].astype(int)\n"
     ]
    }
   ],
   "source": [
    "x_train['Fare'] = x_train['Fare'].astype(int)\n",
    "x_test['Fare'] = x_test['Fare'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "28.0"
      ]
     },
     "metadata": {},
     "execution_count": 102
    }
   ],
   "source": [
    "mediaage=x_test['Age'].median()\n",
    "mediaage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Python38\\lib\\site-packages\\pandas\\core\\series.py:4463: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  return super().fillna(\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     Pclass   Age  Fare  sex_n\n",
       "345       2  24.0    13      0\n",
       "404       3  20.0     8      0\n",
       "573       3   0.0     7      0\n",
       "605       3  36.0    15      1\n",
       "122       2  32.5    30      1\n",
       "..      ...   ...   ...    ...\n",
       "659       1  58.0   113      1\n",
       "529       2  23.0    11      1\n",
       "440       2  45.0    26      0\n",
       "257       1  30.0    86      0\n",
       "578       3   0.0    14      0\n",
       "\n",
       "[623 rows x 4 columns]"
      ],
      "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>Age</th>\n      <th>Fare</th>\n      <th>sex_n</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>345</th>\n      <td>2</td>\n      <td>24.0</td>\n      <td>13</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>404</th>\n      <td>3</td>\n      <td>20.0</td>\n      <td>8</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>573</th>\n      <td>3</td>\n      <td>0.0</td>\n      <td>7</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>605</th>\n      <td>3</td>\n      <td>36.0</td>\n      <td>15</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>122</th>\n      <td>2</td>\n      <td>32.5</td>\n      <td>30</td>\n      <td>1</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>659</th>\n      <td>1</td>\n      <td>58.0</td>\n      <td>113</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>529</th>\n      <td>2</td>\n      <td>23.0</td>\n      <td>11</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>440</th>\n      <td>2</td>\n      <td>45.0</td>\n      <td>26</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>257</th>\n      <td>1</td>\n      <td>30.0</td>\n      <td>86</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>578</th>\n      <td>3</td>\n      <td>0.0</td>\n      <td>14</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>623 rows × 4 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 122
    }
   ],
   "source": [
    "x_test['Age'].fillna(value=mediaage, inplace=True)xx_train'AAge].fillna(mvalue=mediaage, inplace=True\n",
    "x_x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "metadata": {},
     "execution_count": 120
    }
   ],
   "source": [
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.7985074626865671"
      ]
     },
     "metadata": {},
     "execution_count": 123
    }
   ],
   "source": [
    "model.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}