2022.11.30.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df = sns.load_dataset('titanic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
      "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
      "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
      "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
      "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
      "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
      "\n",
      "     who  adult_male deck  embark_town alive  alone  \n",
      "0    man        True  NaN  Southampton    no  False  \n",
      "1  woman       False    C    Cherbourg   yes  False  \n",
      "2  woman       False  NaN  Southampton   yes   True  \n",
      "3  woman       False    C  Southampton   yes  False  \n",
      "4    man        True  NaN  Southampton    no   True  \n"
     ]
    }
   ],
   "source": [
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 15 columns):\n",
      " #   Column       Non-Null Count  Dtype   \n",
      "---  ------       --------------  -----   \n",
      " 0   survived     891 non-null    int64   \n",
      " 1   pclass       891 non-null    int64   \n",
      " 2   sex          891 non-null    object  \n",
      " 3   age          714 non-null    float64 \n",
      " 4   sibsp        891 non-null    int64   \n",
      " 5   parch        891 non-null    int64   \n",
      " 6   fare         891 non-null    float64 \n",
      " 7   embarked     889 non-null    object  \n",
      " 8   class        891 non-null    category\n",
      " 9   who          891 non-null    object  \n",
      " 10  adult_male   891 non-null    bool    \n",
      " 11  deck         203 non-null    category\n",
      " 12  embark_town  889 non-null    object  \n",
      " 13  alive        891 non-null    object  \n",
      " 14  alone        891 non-null    bool    \n",
      "dtypes: bool(2), category(2), float64(2), int64(4), object(5)\n",
      "memory usage: 80.7+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['survived' 'pclass' 'sex' 'age' 'sibsp' 'parch' 'fare' 'embarked' 'class'\n",
      " 'who' 'adult_male' 'alive' 'alone']\n"
     ]
    }
   ],
   "source": [
    "rdf = df.drop(['deck', 'embark_town'], axis = 1)\n",
    "print(rdf.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "rdf = rdf.dropna(subset = ['age'], how = 'any', axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "714"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(rdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "most_freq = rdf['embarked'].value_counts(dropna=True).idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "'S'"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "          survived      pclass   sex         age       sibsp       parch  \\\ncount   714.000000  714.000000   714  714.000000  714.000000  714.000000   \nunique         NaN         NaN     2         NaN         NaN         NaN   \ntop            NaN         NaN  male         NaN         NaN         NaN   \nfreq           NaN         NaN   453         NaN         NaN         NaN   \nmean      0.406162    2.236695   NaN   29.699118    0.512605    0.431373   \nstd       0.491460    0.838250   NaN   14.526497    0.929783    0.853289   \nmin       0.000000    1.000000   NaN    0.420000    0.000000    0.000000   \n25%       0.000000    1.000000   NaN   20.125000    0.000000    0.000000   \n50%       0.000000    2.000000   NaN   28.000000    0.000000    0.000000   \n75%       1.000000    3.000000   NaN   38.000000    1.000000    1.000000   \nmax       1.000000    3.000000   NaN   80.000000    5.000000    6.000000   \n\n              fare embarked  class  who adult_male alive alone  \ncount   714.000000      712    714  714        714   714   714  \nunique         NaN        3      3    3          2     2     2  \ntop            NaN        S  Third  man       True    no  True  \nfreq           NaN      554    355  413        413   424   404  \nmean     34.694514      NaN    NaN  NaN        NaN   NaN   NaN  \nstd      52.918930      NaN    NaN  NaN        NaN   NaN   NaN  \nmin       0.000000      NaN    NaN  NaN        NaN   NaN   NaN  \n25%       8.050000      NaN    NaN  NaN        NaN   NaN   NaN  \n50%      15.741700      NaN    NaN  NaN        NaN   NaN   NaN  \n75%      33.375000      NaN    NaN  NaN        NaN   NaN   NaN  \nmax     512.329200      NaN    NaN  NaN        NaN   NaN   NaN  ",
      "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>sibsp</th>\n      <th>parch</th>\n      <th>fare</th>\n      <th>embarked</th>\n      <th>class</th>\n      <th>who</th>\n      <th>adult_male</th>\n      <th>alive</th>\n      <th>alone</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>714.000000</td>\n      <td>714.000000</td>\n      <td>714</td>\n      <td>714.000000</td>\n      <td>714.000000</td>\n      <td>714.000000</td>\n      <td>714.000000</td>\n      <td>712</td>\n      <td>714</td>\n      <td>714</td>\n      <td>714</td>\n      <td>714</td>\n      <td>714</td>\n    </tr>\n    <tr>\n      <th>unique</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>top</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>male</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>S</td>\n      <td>Third</td>\n      <td>man</td>\n      <td>True</td>\n      <td>no</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>freq</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>453</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>554</td>\n      <td>355</td>\n      <td>413</td>\n      <td>413</td>\n      <td>424</td>\n      <td>404</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.406162</td>\n      <td>2.236695</td>\n      <td>NaN</td>\n      <td>29.699118</td>\n      <td>0.512605</td>\n      <td>0.431373</td>\n      <td>34.694514</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.491460</td>\n      <td>0.838250</td>\n      <td>NaN</td>\n      <td>14.526497</td>\n      <td>0.929783</td>\n      <td>0.853289</td>\n      <td>52.918930</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>0.420000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>20.125000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>8.050000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.000000</td>\n      <td>2.000000</td>\n      <td>NaN</td>\n      <td>28.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>15.741700</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>NaN</td>\n      <td>38.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>33.375000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>NaN</td>\n      <td>80.000000</td>\n      <td>5.000000</td>\n      <td>6.000000</td>\n      <td>512.329200</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdf.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "rdf['embarked'].fillna(most_freq, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "0      S\n1      C\n2      S\n3      S\n4      S\n      ..\n885    Q\n886    S\n887    S\n889    C\n890    Q\nName: embarked, Length: 714, dtype: object"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdf['embarked']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "ndf = rdf[['survived', 'pclass', 'sex', 'age', 'sibsp', 'parch', 'embarked']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "     survived  pclass     sex   age  sibsp  parch embarked\n0           0       3    male  22.0      1      0        S\n1           1       1  female  38.0      1      0        C\n2           1       3  female  26.0      0      0        S\n3           1       1  female  35.0      1      0        S\n4           0       3    male  35.0      0      0        S\n..        ...     ...     ...   ...    ...    ...      ...\n885         0       3  female  39.0      0      5        Q\n886         0       2    male  27.0      0      0        S\n887         1       1  female  19.0      0      0        S\n889         1       1    male  26.0      0      0        C\n890         0       3    male  32.0      0      0        Q\n\n[714 rows x 7 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>sibsp</th>\n      <th>parch</th>\n      <th>embarked</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>1</td>\n      <td>0</td>\n      <td>S</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>1</td>\n      <td>0</td>\n      <td>C</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>0</td>\n      <td>0</td>\n      <td>S</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>1</td>\n      <td>0</td>\n      <td>S</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>0</td>\n      <td>0</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>885</th>\n      <td>0</td>\n      <td>3</td>\n      <td>female</td>\n      <td>39.0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>Q</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>0</td>\n      <td>0</td>\n      <td>S</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>0</td>\n      <td>0</td>\n      <td>S</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>0</td>\n      <td>0</td>\n      <td>C</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>0</td>\n      <td>0</td>\n      <td>Q</td>\n    </tr>\n  </tbody>\n</table>\n<p>714 rows × 7 columns</p>\n</div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 714 entries, 0 to 890\n",
      "Data columns (total 7 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   survived  714 non-null    int64  \n",
      " 1   pclass    714 non-null    int64  \n",
      " 2   sex       714 non-null    object \n",
      " 3   age       714 non-null    float64\n",
      " 4   sibsp     714 non-null    int64  \n",
      " 5   parch     714 non-null    int64  \n",
      " 6   embarked  714 non-null    object \n",
      "dtypes: float64(1), int64(4), object(2)\n",
      "memory usage: 44.6+ KB\n"
     ]
    }
   ],
   "source": [
    "ndf.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "onehot_sex = pd.get_dummies(ndf['sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "ndf = pd.concat([ndf,onehot_sex], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "onehot_embarked = pd.get_dummies(ndf['embarked'], prefix='town')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "ndf = pd.concat([ndf, onehot_embarked], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "   survived  pclass     sex   age  sibsp  parch embarked  female  male  \\\n0         0       3    male  22.0      1      0        S       0     1   \n1         1       1  female  38.0      1      0        C       1     0   \n2         1       3  female  26.0      0      0        S       1     0   \n3         1       1  female  35.0      1      0        S       1     0   \n4         0       3    male  35.0      0      0        S       0     1   \n\n   town_C  town_Q  town_S  \n0       0       0       1  \n1       1       0       0  \n2       0       0       1  \n3       0       0       1  \n4       0       0       1  ",
      "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>sibsp</th>\n      <th>parch</th>\n      <th>embarked</th>\n      <th>female</th>\n      <th>male</th>\n      <th>town_C</th>\n      <th>town_Q</th>\n      <th>town_S</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>1</td>\n      <td>0</td>\n      <td>S</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</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>1</td>\n      <td>0</td>\n      <td>C</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</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>0</td>\n      <td>0</td>\n      <td>S</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</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>1</td>\n      <td>0</td>\n      <td>S</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</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>0</td>\n      <td>0</td>\n      <td>S</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "ndf.drop(['sex', 'embarked'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "   survived  pclass   age  sibsp  parch  female  male  town_C  town_Q  town_S\n0         0       3  22.0      1      0       0     1       0       0       1\n1         1       1  38.0      1      0       1     0       1       0       0\n2         1       3  26.0      0      0       1     0       0       0       1\n3         1       1  35.0      1      0       1     0       0       0       1\n4         0       3  35.0      0      0       0     1       0       0       1",
      "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>age</th>\n      <th>sibsp</th>\n      <th>parch</th>\n      <th>female</th>\n      <th>male</th>\n      <th>town_C</th>\n      <th>town_Q</th>\n      <th>town_S</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>3</td>\n      <td>22.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>38.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>3</td>\n      <td>26.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>35.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>3</td>\n      <td>35.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "x = ndf[['pclass', 'age', 'sibsp', 'parch', 'female', 'male', 'town_C', 'town_Q', 'town_S']]\n",
    "y = ndf.survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "x = preprocessing.StandardScaler().fit(x).transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(499, 9)"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(215, 9)"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "KNeighborsClassifier()"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(x_train, y_train.values.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "y_hat = knn.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 1 0 0 1 1 1 0 0]\n",
      "[0 0 1 0 0 1 1 1 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(y_hat[0:10])\n",
    "print(y_test.values[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[111  14]\n",
      " [ 24  66]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.89      0.85       125\n",
      "           1       0.82      0.73      0.78        90\n",
      "\n",
      "    accuracy                           0.82       215\n",
      "   macro avg       0.82      0.81      0.82       215\n",
      "weighted avg       0.82      0.82      0.82       215\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "knn_matrix = metrics.confusion_matrix(y_test, y_hat)\n",
    "print(knn_matrix)\n",
    "\n",
    "knn_report = metrics.classification_report(y_test, y_hat)\n",
    "print(knn_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df.columns = ['id', 'clump', 'cell_size', 'cell_shape', 'adhesion', 'epithlial', 'bare_nuclei', 'chromatin', 'normal_nucleoli', 'mitoses', 'class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "        id  clump  cell_size  cell_shape  adhesion  epithlial bare_nuclei  \\\n0  1002945      5          4           4         5          7          10   \n1  1015425      3          1           1         1          2           2   \n2  1016277      6          8           8         1          3           4   \n3  1017023      4          1           1         3          2           1   \n4  1017122      8         10          10         8          7          10   \n\n   chromatin  normal_nucleoli  mitoses  class  \n0          3                2        1      2  \n1          3                1        1      2  \n2          3                7        1      2  \n3          3                1        1      2  \n4          9                7        1      4  ",
      "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>id</th>\n      <th>clump</th>\n      <th>cell_size</th>\n      <th>cell_shape</th>\n      <th>adhesion</th>\n      <th>epithlial</th>\n      <th>bare_nuclei</th>\n      <th>chromatin</th>\n      <th>normal_nucleoli</th>\n      <th>mitoses</th>\n      <th>class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1002945</td>\n      <td>5</td>\n      <td>4</td>\n      <td>4</td>\n      <td>5</td>\n      <td>7</td>\n      <td>10</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1015425</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1016277</td>\n      <td>6</td>\n      <td>8</td>\n      <td>8</td>\n      <td>1</td>\n      <td>3</td>\n      <td>4</td>\n      <td>3</td>\n      <td>7</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1017023</td>\n      <td>4</td>\n      <td>1</td>\n      <td>1</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1017122</td>\n      <td>8</td>\n      <td>10</td>\n      <td>10</td>\n      <td>8</td>\n      <td>7</td>\n      <td>10</td>\n      <td>9</td>\n      <td>7</td>\n      <td>1</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 698 entries, 0 to 697\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   id               698 non-null    int64 \n",
      " 1   clump            698 non-null    int64 \n",
      " 2   cell_size        698 non-null    int64 \n",
      " 3   cell_shape       698 non-null    int64 \n",
      " 4   adhesion         698 non-null    int64 \n",
      " 5   epithlial        698 non-null    int64 \n",
      " 6   bare_nuclei      698 non-null    object\n",
      " 7   chromatin        698 non-null    int64 \n",
      " 8   normal_nucleoli  698 non-null    int64 \n",
      " 9   mitoses          698 non-null    int64 \n",
      " 10  class            698 non-null    int64 \n",
      "dtypes: int64(10), object(1)\n",
      "memory usage: 60.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                 id       clump   cell_size  cell_shape    adhesion  \\\ncount  6.980000e+02  698.000000  698.000000  698.000000  698.000000   \nmean   1.071807e+06    4.416905    3.137536    3.210602    2.809456   \nstd    6.175323e+05    2.817673    3.052575    2.972867    2.856606   \nmin    6.163400e+04    1.000000    1.000000    1.000000    1.000000   \n25%    8.702582e+05    2.000000    1.000000    1.000000    1.000000   \n50%    1.171710e+06    4.000000    1.000000    1.000000    1.000000   \n75%    1.238354e+06    6.000000    5.000000    5.000000    4.000000   \nmax    1.345435e+07   10.000000   10.000000   10.000000   10.000000   \n\n        epithlial   chromatin  normal_nucleoli     mitoses       class  \ncount  698.000000  698.000000       698.000000  698.000000  698.000000  \nmean     3.217765    3.438395         2.869628    1.590258    2.690544  \nstd      2.215408    2.440056         3.055004    1.716162    0.951596  \nmin      1.000000    1.000000         1.000000    1.000000    2.000000  \n25%      2.000000    2.000000         1.000000    1.000000    2.000000  \n50%      2.000000    3.000000         1.000000    1.000000    2.000000  \n75%      4.000000    5.000000         4.000000    1.000000    4.000000  \nmax     10.000000   10.000000        10.000000   10.000000    4.000000  ",
      "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>id</th>\n      <th>clump</th>\n      <th>cell_size</th>\n      <th>cell_shape</th>\n      <th>adhesion</th>\n      <th>epithlial</th>\n      <th>chromatin</th>\n      <th>normal_nucleoli</th>\n      <th>mitoses</th>\n      <th>class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>6.980000e+02</td>\n      <td>698.000000</td>\n      <td>698.000000</td>\n      <td>698.000000</td>\n      <td>698.000000</td>\n      <td>698.000000</td>\n      <td>698.000000</td>\n      <td>698.000000</td>\n      <td>698.000000</td>\n      <td>698.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>1.071807e+06</td>\n      <td>4.416905</td>\n      <td>3.137536</td>\n      <td>3.210602</td>\n      <td>2.809456</td>\n      <td>3.217765</td>\n      <td>3.438395</td>\n      <td>2.869628</td>\n      <td>1.590258</td>\n      <td>2.690544</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>6.175323e+05</td>\n      <td>2.817673</td>\n      <td>3.052575</td>\n      <td>2.972867</td>\n      <td>2.856606</td>\n      <td>2.215408</td>\n      <td>2.440056</td>\n      <td>3.055004</td>\n      <td>1.716162</td>\n      <td>0.951596</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>6.163400e+04</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>8.702582e+05</td>\n      <td>2.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1.171710e+06</td>\n      <td>4.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1.238354e+06</td>\n      <td>6.000000</td>\n      <td>5.000000</td>\n      <td>5.000000</td>\n      <td>4.000000</td>\n      <td>4.000000</td>\n      <td>5.000000</td>\n      <td>4.000000</td>\n      <td>1.000000</td>\n      <td>4.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.345435e+07</td>\n      <td>10.000000</td>\n      <td>10.000000</td>\n      <td>10.000000</td>\n      <td>10.000000</td>\n      <td>10.000000</td>\n      <td>10.000000</td>\n      <td>10.000000</td>\n      <td>10.000000</td>\n      <td>4.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['10' '2' '4' '1' '3' '9' '7' '?' '5' '8' '6']\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(df['bare_nuclei'].unique())\n",
    "print('\\n')\n",
    "\n",
    "df['bare_nuclei'].replace('?', np.nan, inplace=True)\n",
    "df.dropna(subset = ['bare_nuclei'], axis = 0, inplace=True)\n",
    "df.bare_nuclei = df.bare_nuclei.astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 682 entries, 0 to 697\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype\n",
      "---  ------           --------------  -----\n",
      " 0   id               682 non-null    int64\n",
      " 1   clump            682 non-null    int64\n",
      " 2   cell_size        682 non-null    int64\n",
      " 3   cell_shape       682 non-null    int64\n",
      " 4   adhesion         682 non-null    int64\n",
      " 5   epithlial        682 non-null    int64\n",
      " 6   bare_nuclei      682 non-null    int64\n",
      " 7   chromatin        682 non-null    int64\n",
      " 8   normal_nucleoli  682 non-null    int64\n",
      " 9   mitoses          682 non-null    int64\n",
      " 10  class            682 non-null    int64\n",
      "dtypes: int64(11)\n",
      "memory usage: 63.9 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "X = df[['clump', 'cell_size', 'cell_shape', 'adhesion', 'epithlial', 'bare_nuclei', 'chromatin', 'normal_nucleoli', 'mitoses']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "y = df['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "X = preprocessing.StandardScaler().fit(X).transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [],
   "source": [
    "xTrain, xTest, yTrain, yTest = train_test_split(X, y, test_size=0.2, random_state=10)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "outputs": [],
   "source": [
    "treeModel = tree.DecisionTreeClassifier(criterion = 'entropy', max_depth = 5)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn import tree\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "outputs": [
    {
     "data": {
      "text/plain": "DecisionTreeClassifier(criterion='entropy', max_depth=5)"
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treeModel.fit(xTrain, yTrain)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "outputs": [],
   "source": [
    "y_hat = treeModel.predict(xTest)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4 2 2 4 2 2 2 2 2 2]\n",
      "[4 2 2 4 2 2 2 2 2 2]\n"
     ]
    }
   ],
   "source": [
    "print(y_hat[0:10])\n",
    "print(yTest.values[0:10])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import preprocessing"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
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