demo_eda/testML.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-06-20T09:46:02.486093400Z",
     "start_time": "2023-06-20T09:46:02.161449400Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "       City    Year     Sport Discipline           Event  \\\n0  Montreal  1976.0  Aquatics     Diving  3m springboard   \n1  Montreal  1976.0  Aquatics     Diving  3m springboard   \n2  Montreal  1976.0  Aquatics     Diving  3m springboard   \n3  Montreal  1976.0  Aquatics     Diving  3m springboard   \n4  Montreal  1976.0  Aquatics     Diving    10m platform   \n\n                    Athlete Gender Country_Code        Country Event_gender  \\\n0           KÖHLER, Christa  Women          GDR   East Germany            W   \n1       KOSENKOV, Aleksandr    Men          URS   Soviet Union            M   \n2      BOGGS, Philip George    Men          USA  United States            M   \n3  CAGNOTTO, Giorgio Franco    Men          ITA          Italy            M   \n4    WILSON, Deborah Keplar  Women          USA  United States            W   \n\n    Medal  \n0  Silver  \n1  Bronze  \n2    Gold  \n3  Silver  \n4  Bronze  ",
      "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>City</th>\n      <th>Year</th>\n      <th>Sport</th>\n      <th>Discipline</th>\n      <th>Event</th>\n      <th>Athlete</th>\n      <th>Gender</th>\n      <th>Country_Code</th>\n      <th>Country</th>\n      <th>Event_gender</th>\n      <th>Medal</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Montreal</td>\n      <td>1976.0</td>\n      <td>Aquatics</td>\n      <td>Diving</td>\n      <td>3m springboard</td>\n      <td>KÖHLER, Christa</td>\n      <td>Women</td>\n      <td>GDR</td>\n      <td>East Germany</td>\n      <td>W</td>\n      <td>Silver</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Montreal</td>\n      <td>1976.0</td>\n      <td>Aquatics</td>\n      <td>Diving</td>\n      <td>3m springboard</td>\n      <td>KOSENKOV, Aleksandr</td>\n      <td>Men</td>\n      <td>URS</td>\n      <td>Soviet Union</td>\n      <td>M</td>\n      <td>Bronze</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Montreal</td>\n      <td>1976.0</td>\n      <td>Aquatics</td>\n      <td>Diving</td>\n      <td>3m springboard</td>\n      <td>BOGGS, Philip George</td>\n      <td>Men</td>\n      <td>USA</td>\n      <td>United States</td>\n      <td>M</td>\n      <td>Gold</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Montreal</td>\n      <td>1976.0</td>\n      <td>Aquatics</td>\n      <td>Diving</td>\n      <td>3m springboard</td>\n      <td>CAGNOTTO, Giorgio Franco</td>\n      <td>Men</td>\n      <td>ITA</td>\n      <td>Italy</td>\n      <td>M</td>\n      <td>Silver</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Montreal</td>\n      <td>1976.0</td>\n      <td>Aquatics</td>\n      <td>Diving</td>\n      <td>10m platform</td>\n      <td>WILSON, Deborah Keplar</td>\n      <td>Women</td>\n      <td>USA</td>\n      <td>United States</td>\n      <td>W</td>\n      <td>Bronze</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/Summer-Olympic-medals.csv', encoding=\"ISO-8859-1\")\n",
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-20T09:46:02.533394500Z",
     "start_time": "2023-06-20T09:46:02.486093400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "(15433, 11)"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-20T09:46:02.564875300Z",
     "start_time": "2023-06-20T09:46:02.533394500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15433 entries, 0 to 15432\n",
      "Data columns (total 11 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   City          15316 non-null  object \n",
      " 1   Year          15316 non-null  float64\n",
      " 2   Sport         15316 non-null  object \n",
      " 3   Discipline    15316 non-null  object \n",
      " 4   Event         15316 non-null  object \n",
      " 5   Athlete       15316 non-null  object \n",
      " 6   Gender        15316 non-null  object \n",
      " 7   Country_Code  15316 non-null  object \n",
      " 8   Country       15316 non-null  object \n",
      " 9   Event_gender  15316 non-null  object \n",
      " 10  Medal         15316 non-null  object \n",
      "dtypes: float64(1), object(10)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-20T09:46:02.627858600Z",
     "start_time": "2023-06-20T09:46:02.549048800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "               Year\ncount  15316.000000\nmean    1993.620789\nstd       10.159851\nmin     1976.000000\n25%     1984.000000\n50%     1996.000000\n75%     2004.000000\nmax     2008.000000",
      "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>Year</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>15316.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>1993.620789</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>10.159851</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1976.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>1984.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1996.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>2004.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>2008.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-20T09:46:02.643454Z",
     "start_time": "2023-06-20T09:46:02.580624Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "City                9\nYear                9\nSport              28\nDiscipline         41\nEvent             293\nAthlete         11337\nGender              2\nCountry_Code      128\nCountry           127\nEvent_gender        3\nMedal               3\ndtype: int64"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-20T09:46:02.690327500Z",
     "start_time": "2023-06-20T09:46:02.612190200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "Empty DataFrame\nColumns: []\nIndex: [0, 1, 2, 3, 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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n    </tr>\n    <tr>\n      <th>1</th>\n    </tr>\n    <tr>\n      <th>2</th>\n    </tr>\n    <tr>\n      <th>3</th>\n    </tr>\n    <tr>\n      <th>4</th>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get if there are unique only data in columns\n",
    "df_unique = df.loc[:, df.nunique() == 1]\n",
    "df_unique.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-20T09:46:02.690327500Z",
     "start_time": "2023-06-20T09:46:02.612190200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: missingno in c:\\users\\stefa\\miniconda3\\envs\\mchinelearning\\lib\\site-packages (0.5.2)\n",
      "Requirement already satisfied: numpy in c:\\users\\stefa\\miniconda3\\envs\\mchinelearning\\lib\\site-packages (from missingno) (1.23.5)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\stefa\\miniconda3\\envs\\mchinelearning\\lib\\site-packages (from missingno) (3.5.3)\n",
      "Requirement already satisfied: scipy in c:\\users\\stefa\\miniconda3\\envs\\mchinelearning\\lib\\site-packages (from missingno) (1.9.3)\n",
      "Requirement already satisfied: seaborn in c:\\users\\stefa\\miniconda3\\envs\\mchinelearning\\lib\\site-packages (from missingno) (0.12.2)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\stefa\\miniconda3\\envs\\mchinelearning\\lib\\site-packages (from matplotlib->missingno) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\stefa\\miniconda3\\envs\\mchinelearning\\lib\\site-packages (from matplotlib->missingno) (4.39.4)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\stefa\\miniconda3\\envs\\mchinelearning\\lib\\site-packages (from matplotlib->missingno) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\stefa\\miniconda3\\envs\\mchinelearning\\lib\\site-packages (from matplotlib->missingno) (23.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\stefa\\miniconda3\\envs\\mchinelearning\\lib\\site-packages (from matplotlib->missingno) (9.4.0)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\stefa\\miniconda3\\envs\\mchinelearning\\lib\\site-packages (from matplotlib->missingno) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\stefa\\miniconda3\\envs\\mchinelearning\\lib\\site-packages (from matplotlib->missingno) (2.8.2)\n",
      "Requirement already satisfied: pandas>=0.25 in c:\\users\\stefa\\miniconda3\\envs\\mchinelearning\\lib\\site-packages (from seaborn->missingno) (1.5.3)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\stefa\\miniconda3\\envs\\mchinelearning\\lib\\site-packages (from pandas>=0.25->seaborn->missingno) (2023.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\stefa\\miniconda3\\envs\\mchinelearning\\lib\\site-packages (from python-dateutil>=2.7->matplotlib->missingno) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "# if uncommented, install missingno if not already installed\n",
    "!pip install missingno\n",
    "# note: to works it need matplotlib=3.5.0\n",
    "import missingno as msno"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-20T09:46:04.395738300Z",
     "start_time": "2023-06-20T09:46:02.627858600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2500x1000 with 2 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "warnings.filterwarnings('ignore')\n",
    "msno.matrix(df)\n",
    "warnings.filterwarnings('default')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-20T09:46:04.681718300Z",
     "start_time": "2023-06-20T09:46:04.408258500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2000x1200 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "warnings.filterwarnings('ignore')\n",
    "msno.heatmap(df)\n",
    "warnings.filterwarnings('default')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-20T09:46:05.067628400Z",
     "start_time": "2023-06-20T09:46:04.681718300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "    City  Year Sport Discipline Event Athlete Gender Country_Code Country  \\\n770  NaN   NaN   NaN        NaN   NaN     NaN    NaN          NaN     NaN   \n771  NaN   NaN   NaN        NaN   NaN     NaN    NaN          NaN     NaN   \n772  NaN   NaN   NaN        NaN   NaN     NaN    NaN          NaN     NaN   \n773  NaN   NaN   NaN        NaN   NaN     NaN    NaN          NaN     NaN   \n774  NaN   NaN   NaN        NaN   NaN     NaN    NaN          NaN     NaN   \n..   ...   ...   ...        ...   ...     ...    ...          ...     ...   \n882  NaN   NaN   NaN        NaN   NaN     NaN    NaN          NaN     NaN   \n883  NaN   NaN   NaN        NaN   NaN     NaN    NaN          NaN     NaN   \n884  NaN   NaN   NaN        NaN   NaN     NaN    NaN          NaN     NaN   \n885  NaN   NaN   NaN        NaN   NaN     NaN    NaN          NaN     NaN   \n886  NaN   NaN   NaN        NaN   NaN     NaN    NaN          NaN     NaN   \n\n    Event_gender Medal  \n770          NaN   NaN  \n771          NaN   NaN  \n772          NaN   NaN  \n773          NaN   NaN  \n774          NaN   NaN  \n..           ...   ...  \n882          NaN   NaN  \n883          NaN   NaN  \n884          NaN   NaN  \n885          NaN   NaN  \n886          NaN   NaN  \n\n[117 rows x 11 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>City</th>\n      <th>Year</th>\n      <th>Sport</th>\n      <th>Discipline</th>\n      <th>Event</th>\n      <th>Athlete</th>\n      <th>Gender</th>\n      <th>Country_Code</th>\n      <th>Country</th>\n      <th>Event_gender</th>\n      <th>Medal</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>770</th>\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      <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>771</th>\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      <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>772</th>\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      <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>773</th>\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      <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>774</th>\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      <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>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\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>882</th>\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      <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>883</th>\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      <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>884</th>\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      <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>885</th>\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      <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>886</th>\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      <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<p>117 rows × 11 columns</p>\n</div>"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# return only the cols with at least 5 missing values\n",
    "df[df.isna().sum(axis=1) > 2]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-20T09:46:05.225119700Z",
     "start_time": "2023-06-20T09:46:05.067628400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "# drop the empty columns"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-20T09:46:05.225119700Z",
     "start_time": "2023-06-20T09:46:05.098890Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "Empty DataFrame\nColumns: [City, Year, Sport, Discipline, Event, Athlete, Gender, Country_Code, Country, Event_gender, Medal]\nIndex: []",
      "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>City</th>\n      <th>Year</th>\n      <th>Sport</th>\n      <th>Discipline</th>\n      <th>Event</th>\n      <th>Athlete</th>\n      <th>Gender</th>\n      <th>Country_Code</th>\n      <th>Country</th>\n      <th>Event_gender</th>\n      <th>Medal</th>\n    </tr>\n  </thead>\n  <tbody>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_vals = df.isna().sum(axis=1)\n",
    "\n",
    "drop_rows = missing_vals[missing_vals > 2].index\n",
    "\n",
    "df.drop(drop_rows, inplace=True)\n",
    "df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "df[df.isna().sum(axis=1) > 2]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-20T09:46:05.225119700Z",
     "start_time": "2023-06-20T09:46:05.114909Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "Empty DataFrame\nColumns: [City, Year, Sport, Discipline, Event, Athlete, Gender, Country_Code, Country, Event_gender, Medal, feature]\nIndex: []",
      "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>City</th>\n      <th>Year</th>\n      <th>Sport</th>\n      <th>Discipline</th>\n      <th>Event</th>\n      <th>Athlete</th>\n      <th>Gender</th>\n      <th>Country_Code</th>\n      <th>Country</th>\n      <th>Event_gender</th>\n      <th>Medal</th>\n      <th>feature</th>\n    </tr>\n  </thead>\n  <tbody>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verify if there are outliners defined as out of 3 standard deviation\n",
    "numerical_cols = df.select_dtypes(include=['float64', 'int64'])\n",
    "\n",
    "means = numerical_cols.mean()\n",
    "stds = numerical_cols.std()\n",
    "\n",
    "# set the threshold to 3 standard deviations\n",
    "threshold = 3\n",
    "\n",
    "outliers = pd.DataFrame()\n",
    "\n",
    "for col in numerical_cols.columns:\n",
    "    col_outliers = df[(df[col] < means[col] - threshold * stds[col]) |\n",
    "                                            (df[col] > means[col] + threshold * stds[col])]\n",
    "    col_outliers['feature'] = col\n",
    "    outliers = pd.concat([outliers, col_outliers])\n",
    "\n",
    "outliers.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-20T09:46:05.225119700Z",
     "start_time": "2023-06-20T09:46:05.146554900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[<AxesSubplot:title={'center':'Year'}>]], dtype=object)"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check distribution\n",
    "df_numeric_ = df.select_dtypes(include=[np.number])\n",
    "df_numeric_.hist()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-20T09:47:41.224385500Z",
     "start_time": "2023-06-20T09:47:41.114550700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "No variables found for grid columns.",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mValueError\u001B[0m                                Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[16], line 2\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[38;5;66;03m# check relation with df pairplot\u001B[39;00m\n\u001B[1;32m----> 2\u001B[0m \u001B[43msns\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpairplot\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdf\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mvars\u001B[39;49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mnumerical_cols\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcolumns\u001B[49m\u001B[43m[\u001B[49m\u001B[43m:\u001B[49m\u001B[38;5;241;43m-\u001B[39;49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mhue\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mGender\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\miniconda3\\envs\\MchineLearning\\lib\\site-packages\\seaborn\\axisgrid.py:2114\u001B[0m, in \u001B[0;36mpairplot\u001B[1;34m(data, hue, hue_order, palette, vars, x_vars, y_vars, kind, diag_kind, markers, height, aspect, corner, dropna, plot_kws, diag_kws, grid_kws, size)\u001B[0m\n\u001B[0;32m   2112\u001B[0m \u001B[38;5;66;03m# Set up the PairGrid\u001B[39;00m\n\u001B[0;32m   2113\u001B[0m grid_kws\u001B[38;5;241m.\u001B[39msetdefault(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdiag_sharey\u001B[39m\u001B[38;5;124m\"\u001B[39m, diag_kind \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mhist\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m-> 2114\u001B[0m grid \u001B[38;5;241m=\u001B[39m \u001B[43mPairGrid\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdata\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mvars\u001B[39;49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mvars\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mx_vars\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mx_vars\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my_vars\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43my_vars\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mhue\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mhue\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   2115\u001B[0m \u001B[43m                \u001B[49m\u001B[43mhue_order\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mhue_order\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpalette\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mpalette\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcorner\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcorner\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   2116\u001B[0m \u001B[43m                \u001B[49m\u001B[43mheight\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mheight\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maspect\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43maspect\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdropna\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdropna\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mgrid_kws\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   2118\u001B[0m \u001B[38;5;66;03m# Add the markers here as PairGrid has figured out how many levels of the\u001B[39;00m\n\u001B[0;32m   2119\u001B[0m \u001B[38;5;66;03m# hue variable are needed and we don't want to duplicate that process\u001B[39;00m\n\u001B[0;32m   2120\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m markers \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
      "File \u001B[1;32m~\\miniconda3\\envs\\MchineLearning\\lib\\site-packages\\seaborn\\axisgrid.py:1266\u001B[0m, in \u001B[0;36mPairGrid.__init__\u001B[1;34m(self, data, hue, vars, x_vars, y_vars, hue_order, palette, hue_kws, corner, diag_sharey, height, aspect, layout_pad, despine, dropna)\u001B[0m\n\u001B[0;32m   1263\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msquare_grid \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mx_vars \u001B[38;5;241m==\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39my_vars\n\u001B[0;32m   1265\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m x_vars:\n\u001B[1;32m-> 1266\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mNo variables found for grid columns.\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m   1267\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m y_vars:\n\u001B[0;32m   1268\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mNo variables found for grid rows.\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n",
      "\u001B[1;31mValueError\u001B[0m: No variables found for grid columns."
     ]
    }
   ],
   "source": [
    "# check relation with df pairplot\n",
    "sns.pairplot(df, vars=numerical_cols.columns[:-1], hue=\"Gender\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-20T09:46:05.364587500Z",
     "start_time": "2023-06-20T09:46:05.162190600Z"
    }
   }
  },
  {
   "cell_type": "raw",
   "source": [
    "The best data would be to have already an information on how 2 persons are vewing each other and try to infere the missings. But in reality it would be harder have that and we could try another approach.\n",
    "\n",
    "We could propose how similar one person is to another and then propose a % of match.\n",
    "\n",
    "We only have the biological gender and not the sexual orientation, we would greatly increase the precision of the outcome having that  information to do a better match.\n",
    "\n",
    "The idea could be to propose how similar these peoples are. One Idea is to try apply a dimension reduction an see the distance between 2 persons. That would be how similar 2 persons are based on the most important features.\n",
    "\n",
    "Another way would be to use all most important dimensions and find the eucledian distance.\n",
    "\n",
    "A third way is would be applyying a clustering model, and find groups of people that are in the same cluster and suppose they would be a couple.\n",
    "\n",
    "I dont have the time to do all so i will try the third option. Which is definetly possible with out data. Because the clustering is not a superviosiond learning alghoritm.\n",
    "\n",
    "In all cases would be good to apply a scaling of the data and having ranges between 0 and 1. Also since we miss the sexual preference we should pair with male/female sex only."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, OrdinalEncoder, OneHotEncoder\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-06-20T09:46:05.333335800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'Montreal'",
     "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_21052\\2525431524.py\u001B[0m in \u001B[0;36m?\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 3\u001B[1;33m \u001B[1;32mfrom\u001B[0m \u001B[0msklearn\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mcluster\u001B[0m \u001B[1;32mimport\u001B[0m \u001B[0mKMeans\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m      4\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      5\u001B[0m \u001B[0mkmeans\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mKMeans\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mn_clusters\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;36m2\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mrandom_state\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mn_init\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;34m\"auto\"\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mfit\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mdf\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32m~\\miniconda3\\envs\\MchineLearning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py\u001B[0m in \u001B[0;36m?\u001B[1;34m(self, X, y, sample_weight)\u001B[0m\n\u001B[0;32m   1413\u001B[0m             \u001B[0mFitted\u001B[0m \u001B[0mestimator\u001B[0m\u001B[1;33m.\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1414\u001B[0m         \"\"\"\n\u001B[0;32m   1415\u001B[0m         \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_validate_params\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   1416\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m-> 1417\u001B[1;33m         X = self._validate_data(\n\u001B[0m\u001B[0;32m   1418\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   1419\u001B[0m             \u001B[0maccept_sparse\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;34m\"csr\"\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1420\u001B[0m             \u001B[0mdtype\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mnp\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mfloat64\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mnp\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mfloat32\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~\\miniconda3\\envs\\MchineLearning\\lib\\site-packages\\sklearn\\base.py\u001B[0m in \u001B[0;36m?\u001B[1;34m(self, X, y, reset, validate_separately, **check_params)\u001B[0m\n\u001B[0;32m    531\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    532\u001B[0m         \u001B[1;32mif\u001B[0m \u001B[0mno_val_X\u001B[0m \u001B[1;32mand\u001B[0m \u001B[0mno_val_y\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    533\u001B[0m             \u001B[1;32mraise\u001B[0m \u001B[0mValueError\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m\"Validation should be done on X, y or both.\"\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    534\u001B[0m         \u001B[1;32melif\u001B[0m \u001B[1;32mnot\u001B[0m \u001B[0mno_val_X\u001B[0m \u001B[1;32mand\u001B[0m \u001B[0mno_val_y\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 535\u001B[1;33m             \u001B[0mX\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mcheck_array\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mX\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0minput_name\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;34m\"X\"\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    536\u001B[0m             \u001B[0mout\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mX\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    537\u001B[0m         \u001B[1;32melif\u001B[0m \u001B[0mno_val_X\u001B[0m \u001B[1;32mand\u001B[0m \u001B[1;32mnot\u001B[0m \u001B[0mno_val_y\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    538\u001B[0m             \u001B[0my\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0m_check_y\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[1;32m~\\miniconda3\\envs\\MchineLearning\\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    875\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    876\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    877\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[0;32m    878\u001B[0m             \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[1;32m--> 879\u001B[1;33m                 raise ValueError(\n\u001B[0m\u001B[0;32m    880\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    881\u001B[0m                 ) from complex_warning\n\u001B[0;32m    882\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32m~\\miniconda3\\envs\\MchineLearning\\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    181\u001B[0m     \u001B[1;32mif\u001B[0m \u001B[0mxp\u001B[0m \u001B[1;32mis\u001B[0m \u001B[1;32mNone\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    182\u001B[0m         \u001B[0mxp\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0m_\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mget_namespace\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    183\u001B[0m     \u001B[1;32mif\u001B[0m \u001B[0mxp\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m__name__\u001B[0m \u001B[1;32min\u001B[0m \u001B[1;33m{\u001B[0m\u001B[1;34m\"numpy\"\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;34m\"numpy.array_api\"\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    184\u001B[0m         \u001B[1;31m# Use NumPy API to support order\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 185\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    186\u001B[0m         \u001B[1;32mreturn\u001B[0m \u001B[0mxp\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[0mcopy\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mcopy\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    187\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    188\u001B[0m         \u001B[1;32mreturn\u001B[0m \u001B[0mxp\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[0mdtype\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[0mcopy\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32m~\\miniconda3\\envs\\MchineLearning\\lib\\site-packages\\pandas\\core\\generic.py\u001B[0m in \u001B[0;36m?\u001B[1;34m(self, dtype)\u001B[0m\n\u001B[0;32m   2069\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[1;32m-> 2070\u001B[1;33m         \u001B[1;32mreturn\u001B[0m \u001B[0mnp\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0masarray\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[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[1;31mValueError\u001B[0m: could not convert string to float: 'Montreal'"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "kmeans = KMeans(n_clusters=5, random_state=0, n_init=\"auto\").fit(df)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-20T09:48:49.160378200Z",
     "start_time": "2023-06-20T09:48:49.066666900Z"
    }
   }
  }
 ],
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