Titanic_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "94cb4a06",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "992729f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv('train.csv')\n",
    "test_data = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7357d7a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   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  \n",
      "   PassengerId  Pclass                                          Name     Sex  \\\n",
      "0          892       3                              Kelly, Mr. James    male   \n",
      "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
      "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
      "3          895       3                              Wirz, Mr. Albert    male   \n",
      "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
      "\n",
      "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
      "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
      "1  47.0      1      0   363272   7.0000   NaN        S  \n",
      "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
      "3  27.0      0      0   315154   8.6625   NaN        S  \n",
      "4  22.0      1      1  3101298  12.2875   NaN        S  \n"
     ]
    }
   ],
   "source": [
    "print(train_data.head())\n",
    "print(test_data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b3391959",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 12)\n",
      "(418, 11)\n"
     ]
    }
   ],
   "source": [
    "print(train_data.shape)\n",
    "print(test_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "59e60ada",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PassengerId      0\n",
      "Survived         0\n",
      "Pclass           0\n",
      "Name             0\n",
      "Sex              0\n",
      "Age            177\n",
      "SibSp            0\n",
      "Parch            0\n",
      "Ticket           0\n",
      "Fare             0\n",
      "Cabin          687\n",
      "Embarked         2\n",
      "dtype: int64\n",
      "PassengerId      0\n",
      "Pclass           0\n",
      "Name             0\n",
      "Sex              0\n",
      "Age             86\n",
      "SibSp            0\n",
      "Parch            0\n",
      "Ticket           0\n",
      "Fare             1\n",
      "Cabin          327\n",
      "Embarked         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train_data.isnull().sum())\n",
    "print(test_data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0f0b46b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survived\n",
      "0    0.616162\n",
      "1    0.383838\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(train_data['Survived'].value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "62e97462",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Sex', y='Survived', data=train_data)\n",
    "plt.title('Survival Rate by Gender')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "84b4a5be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Pclass', y='Survived', data=train_data)\n",
    "plt.title('Sınıfa Göre Hayatta Kalma Oranı')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7c906369",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Age'] = train_data['Age'].fillna(train_data['Age'].median())\n",
    "test_data['Age'] = test_data['Age'].fillna(test_data['Age'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b1da5bb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Embarked'] = train_data['Embarked'].fillna(train_data['Embarked'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "14bdfa5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Fare'] = test_data['Fare'].fillna(test_data['Fare'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2b5e8113",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Sex'] = train_data['Sex'].map({'male': 0, 'female': 1})\n",
    "test_data['Sex'] = test_data['Sex'].map({'male': 0, 'female': 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "53697eea",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.get_dummies(train_data, columns=['Embarked'], drop_first=True)\n",
    "test_data = pd.get_dummies(test_data, columns=['Embarked'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "39d96da7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train_data[['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked_Q', 'Embarked_S']]\n",
    "y_train = train_data['Survived']\n",
    "\n",
    "X_test = test_data[['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked_Q', 'Embarked_S']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "36a41f28",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "055c8458",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_split, X_val, y_train_split, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "865ae185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=5, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=5, random_state=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=5, random_state=1)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1)\n",
    "rf_model.fit(X_train_split, y_train_split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "022b9303",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Doğrulama seti doğruluk skoru: 0.8156424581005587\n"
     ]
    }
   ],
   "source": [
    "y_val_pred = rf_model.predict(X_val)\n",
    "\n",
    "accuracy = accuracy_score(y_val, y_val_pred)\n",
    "print(\"Validation set accuracy score:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9879da1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = rf_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c0a1a6e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions were saved in the 'predictions.csv' file.\n"
     ]
    }
   ],
   "source": [
    "output = pd.DataFrame({'PassengerId': test_data['PassengerId'], 'Survived': predictions})\n",
    "\n",
    "output.to_csv('predictions.csv', index=False)\n",
    "print(\"Predictions were saved in the 'predictions.csv' file.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f67f8385",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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