Titanic dataset - predicting the survival of humans on decision tree algorithm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "71de79b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bd48f06b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Desktop/train.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e53ab54",
   "metadata": {},
   "source": [
    "# data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d3508067",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>PassengerId</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>1</td>\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>2</td>\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>3</td>\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>4</td>\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>5</td>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass     Sex   Age     Fare\n",
       "0            1         0       3    male  22.0   7.2500\n",
       "1            2         1       1  female  38.0  71.2833\n",
       "2            3         1       3  female  26.0   7.9250\n",
       "3            4         1       1  female  35.0  53.1000\n",
       "4            5         0       3    male  35.0   8.0500"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf = df.drop(['Name','Ticket','Cabin','SibSp','Parch','Embarked','Cabin'],axis='columns')\n",
    "newdf.head()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "86177456",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ae03a2e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "le_sex = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "5b284ee4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = newdf.Survived\n",
    "target.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0a4088e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>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>1</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>2</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>3</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>4</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>5</td>\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>"
      ],
      "text/plain": [
       "   PassengerId  Pclass     Sex   Age     Fare\n",
       "0            1       3    male  22.0   7.2500\n",
       "1            2       1  female  38.0  71.2833\n",
       "2            3       3  female  26.0   7.9250\n",
       "3            4       1  female  35.0  53.1000\n",
       "4            5       3    male  35.0   8.0500"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs= newdf.drop(['Survived'], axis ='columns')\n",
    "inputs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "76ec9611",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>sex_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass     Sex   Age     Fare  sex_n\n",
       "0            1       3    male  22.0   7.2500      1\n",
       "1            2       1  female  38.0  71.2833      0\n",
       "2            3       3  female  26.0   7.9250      0\n",
       "3            4       1  female  35.0  53.1000      0\n",
       "4            5       3    male  35.0   8.0500      1"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs['sex_n'] = le_sex.fit_transform(inputs['Sex'])\n",
    "inputs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "fa6f2601",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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>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>0</th>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass   Age     Fare  sex_n\n",
       "0       3  22.0   7.2500      1\n",
       "1       1  38.0  71.2833      0\n",
       "2       3  26.0   7.9250      0\n",
       "3       1  35.0  53.1000      0\n",
       "4       3  35.0   8.0500      1"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_inputs = inputs.drop(['Sex','PassengerId'], axis = 'columns')\n",
    "inputs.Age = inputs.Age.fillna(inputs.Age.mean())\n",
    "inputs.Pclass = inputs.Pclass.fillna(inputs.Pclass.mean())\n",
    "inputs.Fare = inputs.Fare.fillna(inputs.Fare.mean())\n",
    "final_inputs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2d1f05b",
   "metadata": {},
   "source": [
    "# training and testing split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "99def107",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(final_inputs,target,train_size=0.80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "934fc8e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "712"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "4c42a4ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ec96918",
   "metadata": {},
   "source": [
    "# fitting the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "ba5f2706",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn import tree\n",
    "model = tree.DecisionTreeClassifier()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "f6bfd890",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12c19c49",
   "metadata": {},
   "source": [
    "# Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "b190b422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7821229050279329"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "d0ea6ed7",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "b76d2970",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[92, 18],\n",
       "       [21, 48]], dtype=int64)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_predicted)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "30b5910b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(33.0, 0.5, 'truth')"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure\n",
    "sn.heatmap(cm, annot=True)\n",
    "plt.xlabel('predicted')\n",
    "plt.ylabel('truth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34c1b996",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}