Lab - Decision Tree Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn import tree\n",
    "training = pd.read_csv('Datasets/titanic-train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "training.info()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "   PassengerId  Survived  Pclass  \\\n0            1         0       3   \n1            2         1       1   \n2            3         1       3   \n3            4         1       1   \n4            5         0       3   \n\n                                                Name     Sex   Age  SibSp  \\\n0                            Braund, Mr. Owen Harris    male  22.0      1   \n1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n2                             Heikkinen, Miss. Laina  female  26.0      0   \n3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n4                           Allen, Mr. William Henry    male  35.0      0   \n\n   Parch            Ticket     Fare Cabin Embarked  \n0      0         A/5 21171   7.2500   NaN        S  \n1      0          PC 17599  71.2833   C85        C  \n2      0  STON/O2. 3101282   7.9250   NaN        S  \n3      0            113803  53.1000  C123        S  \n4      0            373450   8.0500   NaN        S  ",
      "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>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>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training.head(5)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "training['Sex'] = training['Sex'].apply(lambda x: 0 if x == 'male' else 1)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "training['Age'].fillna(training['Age'].mean(), inplace=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Series name: Age\n",
      "Non-Null Count  Dtype  \n",
      "--------------  -----  \n",
      "891 non-null    float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 7.1 KB\n"
     ]
    }
   ],
   "source": [
    "training['Age'].info()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "29.699117647058763"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training['Age'].mean()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "29.699118    177\n24.000000     30\n22.000000     27\n18.000000     26\n28.000000     25\n            ... \n36.500000      1\n55.500000      1\n0.920000       1\n23.500000      1\n74.000000      1\nName: Age, Length: 89, dtype: int64"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training['Age'].value_counts()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "y_target = training['Survived'].values"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "columns = ['Fare', 'Pclass', 'Sex', 'Age', 'SibSp']\n",
    "X_input = training[columns].values"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "clf_train = tree.DecisionTreeClassifier(criterion='entropy', max_depth=3)\n",
    "clf_train = clf_train.fit(X_input, y_target)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "0.8226711560044894"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_train.score(X_input, y_target)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [
    "from six import StringIO\n",
    "with open('Datasets/titanic.dot', 'w') as f:\n",
    "    f = tree.export_graphviz(clf_train, out_file=f, feature_names=columns)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "!dot -Tpng Datasets/titanic.dot -o titanic.png"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": "<IPython.core.display.Image object>"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image('titanic.png')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [],
   "source": [
    "testing = pd.read_csv('Datasets/titanic-test.csv')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          332 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Ticket       418 non-null    object \n",
      " 8   Fare         417 non-null    float64\n",
      " 9   Cabin        91 non-null     object \n",
      " 10  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "testing.info()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [],
   "source": [
    "testing['Sex'] = testing['Sex'].apply(lambda x: 0 if x == 'male' else 1)\n",
    "testing['Age'].fillna(testing['Age'].mean(), inplace=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Series name: Fare\n",
      "Non-Null Count  Dtype  \n",
      "--------------  -----  \n",
      "417 non-null    float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 3.4 KB\n"
     ]
    }
   ],
   "source": [
    "testing['Fare'].info()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [],
   "source": [
    "testing['Fare'].fillna(testing['Fare'].mean(), inplace=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [],
   "source": [
    "tx_input = testing[columns].values"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [],
   "source": [
    "target_labels = clf_train.predict(tx_input)\n",
    "target_labels = pd.DataFrame({'Est_Survival': target_labels, 'Name': testing['Name']})"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "all_data = pd.read_csv('Datasets/titanic-all.csv')\n",
    "testing_results = pd.merge(target_labels, all_data[['Name', 'Survived']], on=['Name'])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "outputs": [],
   "source": [
    "all_data.to_csv('Datasets/titanic-all.csv')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "outputs": [
    {
     "data": {
      "text/plain": "     Est_Survival                              Name  Survived\n0               0                  Kelly, Mr. James       0.0\n1               0                  Kelly, Mr. James       0.0\n2               1  Wilkes, Mrs. James (Ellen Needs)       1.0\n3               0         Myles, Mr. Thomas Francis       0.0\n4               0                  Wirz, Mr. Albert       0.0\n..            ...                               ...       ...\n393             0                Spector, Mr. Woolf       0.0\n394             1      Oliva y Ocana, Dona. Fermina       1.0\n395             0      Saether, Mr. Simon Sivertsen       0.0\n396             0               Ware, Mr. Frederick       0.0\n397             0          Peter, Master. Michael J       1.0\n\n[398 rows x 3 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>Est_Survival</th>\n      <th>Name</th>\n      <th>Survived</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>Kelly, Mr. James</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>Kelly, Mr. James</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>Myles, Mr. Thomas Francis</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>Wirz, Mr. Albert</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>393</th>\n      <td>0</td>\n      <td>Spector, Mr. Woolf</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>394</th>\n      <td>1</td>\n      <td>Oliva y Ocana, Dona. Fermina</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>395</th>\n      <td>0</td>\n      <td>Saether, Mr. Simon Sivertsen</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>396</th>\n      <td>0</td>\n      <td>Ware, Mr. Frederick</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>397</th>\n      <td>0</td>\n      <td>Peter, Master. Michael J</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>398 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_results"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "outputs": [],
   "source": [
    "acc = np.sum(testing_results['Est_Survival'] == testing_results['Survived']) / float(len(testing_results))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "outputs": [
    {
     "data": {
      "text/plain": "0.7688442211055276"
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "outputs": [],
   "source": [
    "all_data = pd.read_csv('Datasets/titanic-all.csv', usecols=['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Fare'])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "outputs": [],
   "source": [
    "all_data['Sex'] = all_data['Sex'].apply(lambda x: 0 if x == 'male' else 1)\n",
    "all_data['Age'].fillna(all_data['Age'].mean(), inplace=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "outputs": [],
   "source": [
    "all_data = all_data.dropna()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1308 entries, 0 to 1308\n",
      "Data columns (total 6 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Pclass    1308 non-null   float64\n",
      " 1   Survived  1308 non-null   float64\n",
      " 2   Sex       1308 non-null   int64  \n",
      " 3   Age       1308 non-null   float64\n",
      " 4   SibSp     1308 non-null   float64\n",
      " 5   Fare      1308 non-null   float64\n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 71.5 KB\n"
     ]
    }
   ],
   "source": [
    "all_data.info()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x = all_data[columns].values\n",
    "y = all_data['Survived'].values\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size=0.4, random_state=0)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "outputs": [],
   "source": [
    "clf_train = tree.DecisionTreeClassifier(criterion='entropy', max_depth=3)\n",
    "clf_train = clf_train.fit(X_train, Y_train)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score = 0.8201530612244898\n",
      "Testing score = 0.8053435114503816\n"
     ]
    }
   ],
   "source": [
    "train_score = str(clf_train.score(X_train, Y_train))\n",
    "test_score = str(clf_train.score(X_test, Y_test))\n",
    "print(f'Training score = {train_score}\\nTesting score = {test_score}')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
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