analiza_podataka.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "30cd1e58",
   "metadata": {},
   "source": [
    "# Olimpijske Igre"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91620b7f",
   "metadata": {},
   "source": [
    "Ovaj skup podataka možete naći na linku: https://www.kaggle.com/datasets/bhanupratapbiswas/olympic-data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ec76faf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "edd30ae7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('podaci/dataset_olympics.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36619b9b",
   "metadata": {},
   "source": [
    "## Upoznavanje sa podacima"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f77b3c99",
   "metadata": {},
   "source": [
    "Naši podaci se sastoje od 15 atributa za 70000 instanci - takmičara na Olimpijskim igrama tokom godina."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6fd3085b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000, 15)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "16e61328",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Team</th>\n",
       "      <th>NOC</th>\n",
       "      <th>Games</th>\n",
       "      <th>Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>City</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Event</th>\n",
       "      <th>Medal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A Dijiang</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>1992 Summer</td>\n",
       "      <td>1992</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>Basketball</td>\n",
       "      <td>Basketball Men's Basketball</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>A Lamusi</td>\n",
       "      <td>M</td>\n",
       "      <td>23.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>2012 Summer</td>\n",
       "      <td>2012</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Judo</td>\n",
       "      <td>Judo Men's Extra-Lightweight</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Gunnar Nielsen Aaby</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1920 Summer</td>\n",
       "      <td>1920</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>Football</td>\n",
       "      <td>Football Men's Football</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Edgar Lindenau Aabye</td>\n",
       "      <td>M</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark/Sweden</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1900 Summer</td>\n",
       "      <td>1900</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Paris</td>\n",
       "      <td>Tug-Of-War</td>\n",
       "      <td>Tug-Of-War Men's Tug-Of-War</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Christine Jacoba Aaftink</td>\n",
       "      <td>F</td>\n",
       "      <td>21.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NED</td>\n",
       "      <td>1988 Winter</td>\n",
       "      <td>1988</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Calgary</td>\n",
       "      <td>Speed Skating</td>\n",
       "      <td>Speed Skating Women's 500 metres</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID                      Name Sex   Age  Height  Weight            Team   \n",
       "0   1                 A Dijiang   M  24.0   180.0    80.0           China  \\\n",
       "1   2                  A Lamusi   M  23.0   170.0    60.0           China   \n",
       "2   3       Gunnar Nielsen Aaby   M  24.0     NaN     NaN         Denmark   \n",
       "3   4      Edgar Lindenau Aabye   M  34.0     NaN     NaN  Denmark/Sweden   \n",
       "4   5  Christine Jacoba Aaftink   F  21.0   185.0    82.0     Netherlands   \n",
       "\n",
       "   NOC        Games  Year  Season       City          Sport   \n",
       "0  CHN  1992 Summer  1992  Summer  Barcelona     Basketball  \\\n",
       "1  CHN  2012 Summer  2012  Summer     London           Judo   \n",
       "2  DEN  1920 Summer  1920  Summer  Antwerpen       Football   \n",
       "3  DEN  1900 Summer  1900  Summer      Paris     Tug-Of-War   \n",
       "4  NED  1988 Winter  1988  Winter    Calgary  Speed Skating   \n",
       "\n",
       "                              Event Medal  \n",
       "0       Basketball Men's Basketball   NaN  \n",
       "1      Judo Men's Extra-Lightweight   NaN  \n",
       "2           Football Men's Football   NaN  \n",
       "3       Tug-Of-War Men's Tug-Of-War  Gold  \n",
       "4  Speed Skating Women's 500 metres   NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6d6a80e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
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       "\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>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>70000.000000</td>\n",
       "      <td>67268.000000</td>\n",
       "      <td>53746.000000</td>\n",
       "      <td>52899.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18081.846986</td>\n",
       "      <td>25.644645</td>\n",
       "      <td>175.505303</td>\n",
       "      <td>70.900216</td>\n",
       "      <td>1977.766457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10235.613253</td>\n",
       "      <td>6.485239</td>\n",
       "      <td>10.384203</td>\n",
       "      <td>14.217489</td>\n",
       "      <td>30.103306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1896.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9325.750000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>1960.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18032.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1984.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>26978.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>183.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>35658.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID           Age        Height        Weight          Year\n",
       "count  70000.000000  67268.000000  53746.000000  52899.000000  70000.000000\n",
       "mean   18081.846986     25.644645    175.505303     70.900216   1977.766457\n",
       "std    10235.613253      6.485239     10.384203     14.217489     30.103306\n",
       "min        1.000000     11.000000    127.000000     25.000000   1896.000000\n",
       "25%     9325.750000     21.000000    168.000000     61.000000   1960.000000\n",
       "50%    18032.000000     25.000000    175.000000     70.000000   1984.000000\n",
       "75%    26978.000000     28.000000    183.000000     79.000000   2002.000000\n",
       "max    35658.000000     88.000000    223.000000    214.000000   2016.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fc7c53c",
   "metadata": {},
   "source": [
    "## Nedostajuce Vrednosti"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "726a6f00",
   "metadata": {},
   "source": [
    "Izbacujemo samo redove gde nedostaju vrednosti kolona Age, Height i Weight, a ne izbacujemo nedostajuće vrednosti kolone Medal zato što nam ona ukazuje na to koliko takmičara nije osvojilo medalju."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9a736636",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID            0\n",
       "Name          0\n",
       "Sex           0\n",
       "Age        2732\n",
       "Height    16254\n",
       "Weight    17101\n",
       "Team          0\n",
       "NOC           0\n",
       "Games         0\n",
       "Year          0\n",
       "Season        0\n",
       "City          0\n",
       "Sport         0\n",
       "Event         0\n",
       "Medal     60310\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b77f8d6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df.dropna(subset=['Age', 'Height', 'Weight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bf5ae27b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID            0\n",
       "Name          0\n",
       "Sex           0\n",
       "Age           0\n",
       "Height        0\n",
       "Weight        0\n",
       "Team          0\n",
       "NOC           0\n",
       "Games         0\n",
       "Year          0\n",
       "Season        0\n",
       "City          0\n",
       "Sport         0\n",
       "Event         0\n",
       "Medal     45165\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c3e92a6",
   "metadata": {},
   "source": [
    "## Medalje"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16048524",
   "metadata": {},
   "source": [
    "Vidimo da imamo sličan broj zlatnih, srebrnih i bronzanih medalja što nam ukazuje da je naš skup podataka u tom pogledu balansiran. Međutim broj takmičara bez medalja je mnogo puta veći pa ćemo morati da balansiramo podatke kako bi modeli bili precizniji. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "80fcb359",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Medal\n",
      "Gold      2429\n",
      "Bronze    2346\n",
      "Silver    2326\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "medalje = data.Medal.value_counts()\n",
    "print(medalje)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f075ebb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boje = ['#BAB6AA', '#CBA135', '#B67233', '#4E5B31']\n",
    "plt.bar(['Srebro', 'Zlato', 'Bronza', 'Bez medalje'], [medalje[1], medalje[0], medalje[2], data.Medal.isna().sum()], color = boje)\n",
    "plt.title('Broj takmičara sa medaljama')\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "47297c50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45165"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Medal.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e972ff87",
   "metadata": {},
   "source": [
    "## Odnos među polovima"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6d5fc54",
   "metadata": {},
   "source": [
    "Medju nasim podacima vidimo da je više muškaraca nego žena"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e1d82a80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pol = data.Sex.value_counts()\n",
    "\n",
    "labels = ['Muskarci', 'Zene']\n",
    "sizes = pol.values\n",
    "boje = ['#00AEEF', '#FFA6C9']\n",
    "\n",
    "plt.pie(sizes, labels=labels, colors = boje, autopct = '%.1f%%')\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "873521ba",
   "metadata": {},
   "source": [
    "## Dvadeset dršava sa najviše takmičara na olimpijadi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2998c8f6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "drzave = data.NOC.value_counts()\n",
    "plt.figure(figsize=(12, 5))\n",
    "plt.bar(drzave.keys()[:20], drzave.values[:20])\n",
    "plt.title('Dvadeset država sa najviše takmičara')\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "765e19ae",
   "metadata": {},
   "source": [
    "## Eksportovanje podataka"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "46ca6c51",
   "metadata": {},
   "outputs": [],
   "source": [
    "putanja = 'preprocesiranje/bez_nedostajucih.csv'\n",
    "data.to_csv(putanja, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a82b34e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}