titanic_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in c:\\python311\\lib\\site-packages (1.26.0)Note: you may need to restart the kernel to use updated packages.\n",
      "\n",
      "Requirement already satisfied: matplotlib in c:\\users\\danil\\appdata\\roaming\\python\\python311\\site-packages (3.8.0)\n",
      "Requirement already satisfied: pandas in c:\\python311\\lib\\site-packages (2.1.1)\n",
      "Requirement already satisfied: scikit-learn in c:\\python311\\lib\\site-packages (1.3.2)\n",
      "Requirement already satisfied: seaborn in c:\\python311\\lib\\site-packages (0.13.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\danil\\appdata\\roaming\\python\\python311\\site-packages (from matplotlib) (1.1.1)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\danil\\appdata\\roaming\\python\\python311\\site-packages (from matplotlib) (0.12.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\python311\\lib\\site-packages (from matplotlib) (4.43.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\python311\\lib\\site-packages (from matplotlib) (1.4.5)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\danil\\appdata\\roaming\\python\\python311\\site-packages (from matplotlib) (23.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\python311\\lib\\site-packages (from matplotlib) (10.0.1)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\python311\\lib\\site-packages (from matplotlib) (3.1.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\danil\\appdata\\roaming\\python\\python311\\site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\python311\\lib\\site-packages (from pandas) (2023.3.post1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in c:\\python311\\lib\\site-packages (from pandas) (2023.3)\n",
      "Requirement already satisfied: scipy>=1.5.0 in c:\\python311\\lib\\site-packages (from scikit-learn) (1.11.3)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\danil\\appdata\\roaming\\python\\python311\\site-packages (from scikit-learn) (1.3.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\python311\\lib\\site-packages (from scikit-learn) (3.2.0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\danil\\appdata\\roaming\\python\\python311\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 23.3 -> 23.3.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "%pip install numpy matplotlib pandas scikit-learn seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data, train_data = train_test_split(pd.read_csv(\"train.csv\"), test_size=0.1)\n",
    "train_data = train_data.dropna()\n",
    "test_data = test_data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>137</th>\n",
       "      <td>138</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mr. Jacques Heath</td>\n",
       "      <td>male</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
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       "      <th>558</th>\n",
       "      <td>559</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Taussig, Mrs. Emil (Tillie Mandelbaum)</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>110413</td>\n",
       "      <td>79.6500</td>\n",
       "      <td>E67</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>431</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bjornstrom-Steffansson, Mr. Mauritz Hakan</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>110564</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C52</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>446</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Dodge, Master. Washington</td>\n",
       "      <td>male</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>33638</td>\n",
       "      <td>81.8583</td>\n",
       "      <td>A34</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>453</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Foreman, Mr. Benjamin Laventall</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113051</td>\n",
       "      <td>27.7500</td>\n",
       "      <td>C111</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>642</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sagesser, Mlle. Emma</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17477</td>\n",
       "      <td>69.3000</td>\n",
       "      <td>B35</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>672</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Davidson, Mr. Thornton</td>\n",
       "      <td>male</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>F.C. 12750</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>B71</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>873</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Carlsson, Mr. Frans Olof</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>695</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>660</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Newell, Mr. Arthur Webster</td>\n",
       "      <td>male</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>35273</td>\n",
       "      <td>113.2750</td>\n",
       "      <td>D48</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                       Name  \\\n",
       "137          138         0       1                Futrelle, Mr. Jacques Heath   \n",
       "558          559         1       1     Taussig, Mrs. Emil (Tillie Mandelbaum)   \n",
       "430          431         1       1  Bjornstrom-Steffansson, Mr. Mauritz Hakan   \n",
       "445          446         1       1                  Dodge, Master. Washington   \n",
       "452          453         0       1            Foreman, Mr. Benjamin Laventall   \n",
       "297          298         0       1               Allison, Miss. Helen Loraine   \n",
       "641          642         1       1                       Sagesser, Mlle. Emma   \n",
       "671          672         0       1                     Davidson, Mr. Thornton   \n",
       "872          873         0       1                   Carlsson, Mr. Frans Olof   \n",
       "659          660         0       1                 Newell, Mr. Arthur Webster   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket      Fare        Cabin Embarked  \n",
       "137    male  37.0      1      0      113803   53.1000         C123        S  \n",
       "558  female  39.0      1      1      110413   79.6500          E67        S  \n",
       "430    male  28.0      0      0      110564   26.5500          C52        S  \n",
       "445    male   4.0      0      2       33638   81.8583          A34        S  \n",
       "452    male  30.0      0      0      113051   27.7500         C111        C  \n",
       "297  female   2.0      1      2      113781  151.5500      C22 C26        S  \n",
       "641  female  24.0      0      0    PC 17477   69.3000          B35        C  \n",
       "671    male  31.0      1      0  F.C. 12750   52.0000          B71        S  \n",
       "872    male  33.0      0      0         695    5.0000  B51 B53 B55        S  \n",
       "659    male  58.0      0      2       35273  113.2750          D48        C  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'male'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_11576\\3887879258.py\u001b[0m in \u001b[0;36m?\u001b[1;34m()\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0my_test\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtest_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Survived\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;31m# x_test = x_test.to_numpy().reshape(-1, 1)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m knn_model = knn.fit(\n\u001b[0m\u001b[0;32m     13\u001b[0m     \u001b[0mX\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m     \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m )\n",
      "\u001b[1;32mc:\\Python311\\Lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1148\u001b[0m                 skip_parameter_validation=(\n\u001b[0;32m   1149\u001b[0m                     \u001b[0mprefer_skip_nested_validation\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mglobal_skip_validation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1150\u001b[0m                 )\n\u001b[0;32m   1151\u001b[0m             ):\n\u001b[1;32m-> 1152\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mfit_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\Python311\\Lib\\site-packages\\sklearn\\neighbors\\_classification.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    229\u001b[0m         \u001b[1;33m-\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    230\u001b[0m         \u001b[0mself\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0mKNeighborsClassifier\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    231\u001b[0m             \u001b[0mThe\u001b[0m \u001b[0mfitted\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mnearest\u001b[0m \u001b[0mneighbors\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    232\u001b[0m         \"\"\"\n\u001b[1;32m--> 233\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\Python311\\Lib\\site-packages\\sklearn\\neighbors\\_base.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    453\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    454\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tags\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"requires_y\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    455\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mKDTree\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mBallTree\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNeighborsBase\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 456\u001b[1;33m                 X, y = self._validate_data(\n\u001b[0m\u001b[0;32m    457\u001b[0m                     \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"csr\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"C\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    458\u001b[0m                 )\n\u001b[0;32m    459\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python311\\Lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[0;32m    618\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;34m\"estimator\"\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcheck_y_params\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    619\u001b[0m                     \u001b[0mcheck_y_params\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mdefault_check_params\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    620\u001b[0m                 \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_name\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"y\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    621\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 622\u001b[1;33m                 \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    623\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    624\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    625\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mcheck_params\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"ensure_2d\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m   1142\u001b[0m         raise ValueError(\n\u001b[0;32m   1143\u001b[0m             \u001b[1;34mf\"{estimator_name} requires y to be passed, but the target y is None\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1144\u001b[0m         )\n\u001b[0;32m   1145\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1146\u001b[1;33m     X = check_array(\n\u001b[0m\u001b[0;32m   1147\u001b[0m         \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1148\u001b[0m         \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maccept_sparse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1149\u001b[0m         \u001b[0maccept_large_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maccept_large_sparse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[0;32m    912\u001b[0m                         )\n\u001b[0;32m    913\u001b[0m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    914\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    915\u001b[0m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_asarray_with_order\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 916\u001b[1;33m             \u001b[1;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mcomplex_warning\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    917\u001b[0m                 raise ValueError(\n\u001b[0;32m    918\u001b[0m                     \u001b[1;34m\"Complex data not supported\\n{}\\n\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    919\u001b[0m                 ) from complex_warning\n",
      "\u001b[1;32mc:\\Python311\\Lib\\site-packages\\sklearn\\utils\\_array_api.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(array, dtype, order, copy, xp)\u001b[0m\n\u001b[0;32m    376\u001b[0m         \u001b[1;31m# Use NumPy API to support order\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    377\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    378\u001b[0m             \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    379\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 380\u001b[1;33m             \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    381\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    382\u001b[0m         \u001b[1;31m# At this point array is a NumPy ndarray. We convert it to an array\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    383\u001b[0m         \u001b[1;31m# container that is consistent with the input's namespace.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python311\\Lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m   2082\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnpt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDTypeLike\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2083\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2084\u001b[1;33m         \u001b[0marr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2085\u001b[0m         if (\n\u001b[0;32m   2086\u001b[0m             \u001b[0mastype_is_view\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2087\u001b[0m             \u001b[1;32mand\u001b[0m \u001b[0musing_copy_on_write\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'male'"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=10)\n",
    "x_train = train_data[[\"Age\", \"Sex\"]]\n",
    "y_train = train_data[\"Survived\"]\n",
    "\n",
    "# x_train = x_train.to_numpy().reshape(-1, 1)\n",
    "\n",
    "x_test = test_data[[\"Age\", \"Sex\"]]\n",
    "y_test = test_data[\"Survived\"]\n",
    "\n",
    "# x_test = x_test.to_numpy().reshape(-1, 1)\n",
    "\n",
    "knn_model = knn.fit(\n",
    "    X=x_train,\n",
    "    y=y_train\n",
    ")\n",
    "\n",
    "knn_predictions = knn.predict(x_test)\n",
    "accuracy = accuracy_score(y_test, knn_predictions)\n",
    "display(f\"Accuracy score: {accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test.to_numpy().reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['B58 B60', 'C91', 'B73', 'C22 C26', 'B35', 'D48', 'A7', 'E8',\n",
       "       'D50', 'A34', 'C123', 'C52', 'E67', 'C30', 'B71', 'D36', 'C49',\n",
       "       'C111', 'E24', 'B51 B53 B55', 'C148', 'D20', 'B22', 'D', 'B79'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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