pokemon_solution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "#data processing\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "#data visualization\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "%config IPCompleter.greedy=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pokemon = pd.read_csv('pokemon.csv')\n",
    "combats = pd.read_csv('combats.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>#</th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "      <th>Legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Mega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>123</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   #           Name Type 1  Type 2  HP  Attack  Defense  Sp. Atk  Sp. Def  \\\n",
       "0  1      Bulbasaur  Grass  Poison  45      49       49       65       65   \n",
       "1  2        Ivysaur  Grass  Poison  60      62       63       80       80   \n",
       "2  3       Venusaur  Grass  Poison  80      82       83      100      100   \n",
       "3  4  Mega Venusaur  Grass  Poison  80     100      123      122      120   \n",
       "4  5     Charmander   Fire     NaN  39      52       43       60       50   \n",
       "\n",
       "   Speed  Generation  Legendary  \n",
       "0     45           1      False  \n",
       "1     60           1      False  \n",
       "2     80           1      False  \n",
       "3     80           1      False  \n",
       "4     65           1      False  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "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>#</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>800.0000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>400.5000</td>\n",
       "      <td>69.258750</td>\n",
       "      <td>79.001250</td>\n",
       "      <td>73.842500</td>\n",
       "      <td>72.820000</td>\n",
       "      <td>71.902500</td>\n",
       "      <td>68.277500</td>\n",
       "      <td>3.32375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>231.0844</td>\n",
       "      <td>25.534669</td>\n",
       "      <td>32.457366</td>\n",
       "      <td>31.183501</td>\n",
       "      <td>32.722294</td>\n",
       "      <td>27.828916</td>\n",
       "      <td>29.060474</td>\n",
       "      <td>1.66129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>200.7500</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>49.750000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>2.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>400.5000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>3.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>600.2500</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>5.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>800.0000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>194.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>6.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              #          HP      Attack     Defense     Sp. Atk     Sp. Def  \\\n",
       "count  800.0000  800.000000  800.000000  800.000000  800.000000  800.000000   \n",
       "mean   400.5000   69.258750   79.001250   73.842500   72.820000   71.902500   \n",
       "std    231.0844   25.534669   32.457366   31.183501   32.722294   27.828916   \n",
       "min      1.0000    1.000000    5.000000    5.000000   10.000000   20.000000   \n",
       "25%    200.7500   50.000000   55.000000   50.000000   49.750000   50.000000   \n",
       "50%    400.5000   65.000000   75.000000   70.000000   65.000000   70.000000   \n",
       "75%    600.2500   80.000000  100.000000   90.000000   95.000000   90.000000   \n",
       "max    800.0000  255.000000  190.000000  230.000000  194.000000  230.000000   \n",
       "\n",
       "            Speed  Generation  \n",
       "count  800.000000   800.00000  \n",
       "mean    68.277500     3.32375  \n",
       "std     29.060474     1.66129  \n",
       "min      5.000000     1.00000  \n",
       "25%     45.000000     2.00000  \n",
       "50%     65.000000     3.00000  \n",
       "75%     90.000000     5.00000  \n",
       "max    180.000000     6.00000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(800, 12)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 800 entries, 0 to 799\n",
      "Data columns (total 12 columns):\n",
      "#             800 non-null int64\n",
      "Name          799 non-null object\n",
      "Type 1        800 non-null object\n",
      "Type 2        414 non-null object\n",
      "HP            800 non-null int64\n",
      "Attack        800 non-null int64\n",
      "Defense       800 non-null int64\n",
      "Sp. Atk       800 non-null int64\n",
      "Sp. Def       800 non-null int64\n",
      "Speed         800 non-null int64\n",
      "Generation    800 non-null int64\n",
      "Legendary     800 non-null bool\n",
      "dtypes: bool(1), int64(8), object(3)\n",
      "memory usage: 69.6+ KB\n"
     ]
    }
   ],
   "source": [
    "display(pokemon.head())\n",
    "display(pokemon.describe())\n",
    "display(pokemon.shape)\n",
    "pokemon.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>First_pokemon</th>\n",
       "      <th>Second_pokemon</th>\n",
       "      <th>Winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>266</td>\n",
       "      <td>298</td>\n",
       "      <td>298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>702</td>\n",
       "      <td>701</td>\n",
       "      <td>701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>191</td>\n",
       "      <td>668</td>\n",
       "      <td>668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>237</td>\n",
       "      <td>683</td>\n",
       "      <td>683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151</td>\n",
       "      <td>231</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First_pokemon  Second_pokemon  Winner\n",
       "0            266             298     298\n",
       "1            702             701     701\n",
       "2            191             668     668\n",
       "3            237             683     683\n",
       "4            151             231     151"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "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>First_pokemon</th>\n",
       "      <th>Second_pokemon</th>\n",
       "      <th>Winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>400.495640</td>\n",
       "      <td>403.159660</td>\n",
       "      <td>408.890100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>229.549429</td>\n",
       "      <td>230.083644</td>\n",
       "      <td>231.159961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>203.000000</td>\n",
       "      <td>207.000000</td>\n",
       "      <td>206.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>399.000000</td>\n",
       "      <td>402.000000</td>\n",
       "      <td>422.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>597.250000</td>\n",
       "      <td>602.000000</td>\n",
       "      <td>606.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       First_pokemon  Second_pokemon        Winner\n",
       "count   50000.000000    50000.000000  50000.000000\n",
       "mean      400.495640      403.159660    408.890100\n",
       "std       229.549429      230.083644    231.159961\n",
       "min         1.000000        1.000000      1.000000\n",
       "25%       203.000000      207.000000    206.000000\n",
       "50%       399.000000      402.000000    422.000000\n",
       "75%       597.250000      602.000000    606.000000\n",
       "max       800.000000      800.000000    800.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 3 columns):\n",
      "First_pokemon     50000 non-null int64\n",
      "Second_pokemon    50000 non-null int64\n",
      "Winner            50000 non-null int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 1.1 MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(combats.head())\n",
    "display(combats.describe())\n",
    "display(combats.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>#</th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "      <th>Legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fighting</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65</td>\n",
       "      <td>105</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>70</td>\n",
       "      <td>95</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     # Name    Type 1 Type 2  HP  Attack  Defense  Sp. Atk  Sp. Def  Speed  \\\n",
       "62  63  NaN  Fighting    NaN  65     105       60       60       70     95   \n",
       "\n",
       "    Generation  Legendary  \n",
       "62           1      False  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon.loc[pokemon['Name'].isnull()==True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>#</th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "      <th>Legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>69</td>\n",
       "      <td>Abra</td>\n",
       "      <td>Psychic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>105</td>\n",
       "      <td>55</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     #  Name   Type 1 Type 2  HP  Attack  Defense  Sp. Atk  Sp. Def  Speed  \\\n",
       "68  69  Abra  Psychic    NaN  25      20       15      105       55     90   \n",
       "\n",
       "    Generation  Legendary  \n",
       "68           1      False  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon.loc[pokemon['Name']=='Abra']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pokemon.loc[62 , 'Name']=\"Primeape\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "#                   63\n",
       "Name          Primeape\n",
       "Type 1        Fighting\n",
       "Type 2             NaN\n",
       "HP                  65\n",
       "Attack             105\n",
       "Defense             60\n",
       "Sp. Atk             60\n",
       "Sp. Def             70\n",
       "Speed               95\n",
       "Generation           1\n",
       "Legendary        False\n",
       "Name: 62, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon.loc[62]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['First_pokemon', 'Second_pokemon', 'Winner'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon.columns\n",
    "combats.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pokemon['Total'] = pokemon['HP'] + pokemon['Attack'] + pokemon['Defense'] + pokemon['Sp. Atk'] + pokemon['Sp. Def'] + pokemon['Speed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['first' , '']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_total_dict = dict(zip(pokemon['#'],pokemon['Total']))\n",
    "cols = ['First_pokemon' , 'Second_pokemon' , 'Winner']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "combat_dif = combats[cols].replace(no_total_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>First_pokemon</th>\n",
       "      <th>Second_pokemon</th>\n",
       "      <th>Winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>300</td>\n",
       "      <td>340</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>580</td>\n",
       "      <td>580</td>\n",
       "      <td>580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>405</td>\n",
       "      <td>485</td>\n",
       "      <td>485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>250</td>\n",
       "      <td>485</td>\n",
       "      <td>485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>495</td>\n",
       "      <td>505</td>\n",
       "      <td>495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First_pokemon  Second_pokemon  Winner\n",
       "0            300             340     340\n",
       "1            580             580     580\n",
       "2            405             485     485\n",
       "3            250             485     485\n",
       "4            495             505     495"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(combat_dif.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "combats['Losers'] = combats.apply(lambda x: x['First_pokemon'] if x['First_pokemon'] !=x['Winner'] else x['Second_pokemon'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>First_pokemon</th>\n",
       "      <th>Second_pokemon</th>\n",
       "      <th>Winner</th>\n",
       "      <th>Losers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>266</td>\n",
       "      <td>298</td>\n",
       "      <td>298</td>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>702</td>\n",
       "      <td>701</td>\n",
       "      <td>701</td>\n",
       "      <td>702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>191</td>\n",
       "      <td>668</td>\n",
       "      <td>668</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>237</td>\n",
       "      <td>683</td>\n",
       "      <td>683</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151</td>\n",
       "      <td>231</td>\n",
       "      <td>151</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First_pokemon  Second_pokemon  Winner  Losers\n",
       "0            266             298     298     266\n",
       "1            702             701     701     702\n",
       "2            191             668     668     191\n",
       "3            237             683     683     237\n",
       "4            151             231     151     231"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "combats['is_first_win'] = combats['First_pokemon'] == combats['Winner']\n",
    "combats['diff_stat'] = combat_dif['First_pokemon']-combat_dif['Second_pokemon']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>First_pokemon</th>\n",
       "      <th>Second_pokemon</th>\n",
       "      <th>Winner</th>\n",
       "      <th>Losers</th>\n",
       "      <th>is_first_win</th>\n",
       "      <th>diff_stat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>266</td>\n",
       "      <td>298</td>\n",
       "      <td>298</td>\n",
       "      <td>266</td>\n",
       "      <td>False</td>\n",
       "      <td>-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>702</td>\n",
       "      <td>701</td>\n",
       "      <td>701</td>\n",
       "      <td>702</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>191</td>\n",
       "      <td>668</td>\n",
       "      <td>668</td>\n",
       "      <td>191</td>\n",
       "      <td>False</td>\n",
       "      <td>-80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>237</td>\n",
       "      <td>683</td>\n",
       "      <td>683</td>\n",
       "      <td>237</td>\n",
       "      <td>False</td>\n",
       "      <td>-235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151</td>\n",
       "      <td>231</td>\n",
       "      <td>151</td>\n",
       "      <td>231</td>\n",
       "      <td>True</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First_pokemon  Second_pokemon  Winner  Losers  is_first_win  diff_stat\n",
       "0            266             298     298     266         False        -40\n",
       "1            702             701     701     702         False          0\n",
       "2            191             668     668     191         False        -80\n",
       "3            237             683     683     237         False       -235\n",
       "4            151             231     151     231          True        -10"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(combats.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merging the DataBase\n",
    "nfirsts = combats['First_pokemon'].value_counts()\n",
    "nseconds = combats['Second_pokemon'].value_counts()\n",
    "nfights = nfirsts + nseconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "fight_df = pd.DataFrame({\n",
    "    'nfights':nfights,\n",
    "    'nwins':combats['Winner'].value_counts()\n",
    "},columns = ['nfights','nwins'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "fight_df['Win_ratio'] = fight_df['nwins']/fight_df['nfights']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>nfights</th>\n",
       "      <th>nwins</th>\n",
       "      <th>Win_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>119</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0.966387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>704</th>\n",
       "      <td>125</td>\n",
       "      <td>121.0</td>\n",
       "      <td>0.968000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>119</td>\n",
       "      <td>116.0</td>\n",
       "      <td>0.974790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>129</td>\n",
       "      <td>127.0</td>\n",
       "      <td>0.984496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     nfights  nwins  Win_ratio\n",
       "20       119  115.0   0.966387\n",
       "704      125  121.0   0.968000\n",
       "513      119  116.0   0.974790\n",
       "155      129  127.0   0.984496\n",
       "231      135    NaN        NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fight_df = fight_df.sort_values(by='Win_ratio')\n",
    "display(fight_df.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 784 entries, 290 to 231\n",
      "Data columns (total 3 columns):\n",
      "nfights      784 non-null int64\n",
      "nwins        783 non-null float64\n",
      "Win_ratio    783 non-null float64\n",
      "dtypes: float64(2), int64(1)\n",
      "memory usage: 24.5 KB\n"
     ]
    },
    {
     "data": {
      "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>nfights</th>\n",
       "      <th>nwins</th>\n",
       "      <th>Win_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     nfights  nwins  Win_ratio\n",
       "231      135    NaN        NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fight_df.info()\n",
    "display(fight_df.loc[fight_df['Win_ratio'].isnull()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "fight_df.loc[231,['nfights','nwins','Win_ratio']] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>#</th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "      <th>Legendary</th>\n",
       "      <th>Total</th>\n",
       "      <th>Win_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>318</td>\n",
       "      <td>0.278195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>405</td>\n",
       "      <td>0.380165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>525</td>\n",
       "      <td>0.674242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Mega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>123</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>625</td>\n",
       "      <td>0.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>309</td>\n",
       "      <td>0.491071</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   #           Name Type 1  Type 2  HP  Attack  Defense  Sp. Atk  Sp. Def  \\\n",
       "0  1      Bulbasaur  Grass  Poison  45      49       49       65       65   \n",
       "1  2        Ivysaur  Grass  Poison  60      62       63       80       80   \n",
       "2  3       Venusaur  Grass  Poison  80      82       83      100      100   \n",
       "3  4  Mega Venusaur  Grass  Poison  80     100      123      122      120   \n",
       "4  5     Charmander   Fire     NaN  39      52       43       60       50   \n",
       "\n",
       "   Speed  Generation  Legendary  Total  Win_ratio  \n",
       "0     45           1      False    318   0.278195  \n",
       "1     60           1      False    405   0.380165  \n",
       "2     80           1      False    525   0.674242  \n",
       "3     80           1      False    625   0.560000  \n",
       "4     65           1      False    309   0.491071  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fight_df['#']= fight_df.index\n",
    "pokemon_fight_df = pokemon.copy()\n",
    "win_ratio_dict = dict(zip(fight_df['#'],fight_df['Win_ratio']))\n",
    "pokemon_fight_df['Win_ratio'] = pokemon_fight_df['#'].replace(win_ratio_dict)\n",
    "display(pokemon_fight_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 800 entries, 0 to 799\n",
      "Data columns (total 14 columns):\n",
      "#             800 non-null int64\n",
      "Name          800 non-null object\n",
      "Type 1        800 non-null object\n",
      "Type 2        414 non-null object\n",
      "HP            800 non-null int64\n",
      "Attack        800 non-null int64\n",
      "Defense       800 non-null int64\n",
      "Sp. Atk       800 non-null int64\n",
      "Sp. Def       800 non-null int64\n",
      "Speed         800 non-null int64\n",
      "Generation    800 non-null int64\n",
      "Legendary     800 non-null bool\n",
      "Total         800 non-null int64\n",
      "Win_ratio     800 non-null float64\n",
      "dtypes: bool(1), float64(1), int64(9), object(3)\n",
      "memory usage: 82.1+ KB\n"
     ]
    }
   ],
   "source": [
    "pokemon_fight_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "non_fight_pokemon_df = pokemon_fight_df.loc[(pokemon_fight_df['Win_ratio']>1) | pokemon_fight_df['Win_ratio'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>#</th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "      <th>Legendary</th>\n",
       "      <th>Total</th>\n",
       "      <th>Win_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Blastoise</td>\n",
       "      <td>Water</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>85</td>\n",
       "      <td>105</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>530</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>Sandshrew</td>\n",
       "      <td>Ground</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>75</td>\n",
       "      <td>85</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>300</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>Wigglytuff</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Fairy</td>\n",
       "      <td>140</td>\n",
       "      <td>70</td>\n",
       "      <td>45</td>\n",
       "      <td>85</td>\n",
       "      <td>50</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>435</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>66</td>\n",
       "      <td>Poliwag</td>\n",
       "      <td>Water</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>300</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>78</td>\n",
       "      <td>Victreebel</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>80</td>\n",
       "      <td>105</td>\n",
       "      <td>65</td>\n",
       "      <td>100</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>490</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>90</td>\n",
       "      <td>Magneton</td>\n",
       "      <td>Electric</td>\n",
       "      <td>Steel</td>\n",
       "      <td>50</td>\n",
       "      <td>60</td>\n",
       "      <td>95</td>\n",
       "      <td>120</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>465</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>144</td>\n",
       "      <td>Ditto</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>288</td>\n",
       "      <td>144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>183</td>\n",
       "      <td>Ariados</td>\n",
       "      <td>Bug</td>\n",
       "      <td>Poison</td>\n",
       "      <td>70</td>\n",
       "      <td>90</td>\n",
       "      <td>70</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>390</td>\n",
       "      <td>183.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>236</td>\n",
       "      <td>Ursaring</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "      <td>130</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>500</td>\n",
       "      <td>236.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>322</td>\n",
       "      <td>Hariyama</td>\n",
       "      <td>Fighting</td>\n",
       "      <td>NaN</td>\n",
       "      <td>144</td>\n",
       "      <td>120</td>\n",
       "      <td>60</td>\n",
       "      <td>40</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>474</td>\n",
       "      <td>322.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>419</td>\n",
       "      <td>Mega Latias</td>\n",
       "      <td>Dragon</td>\n",
       "      <td>Psychic</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>120</td>\n",
       "      <td>140</td>\n",
       "      <td>150</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>700</td>\n",
       "      <td>419.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>479</td>\n",
       "      <td>Honchkrow</td>\n",
       "      <td>Dark</td>\n",
       "      <td>Flying</td>\n",
       "      <td>100</td>\n",
       "      <td>125</td>\n",
       "      <td>52</td>\n",
       "      <td>105</td>\n",
       "      <td>52</td>\n",
       "      <td>71</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>505</td>\n",
       "      <td>479.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>556</td>\n",
       "      <td>Servine</td>\n",
       "      <td>Grass</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>75</td>\n",
       "      <td>60</td>\n",
       "      <td>75</td>\n",
       "      <td>83</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>413</td>\n",
       "      <td>556.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>618</td>\n",
       "      <td>Maractus</td>\n",
       "      <td>Grass</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75</td>\n",
       "      <td>86</td>\n",
       "      <td>67</td>\n",
       "      <td>106</td>\n",
       "      <td>67</td>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>461</td>\n",
       "      <td>618.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>655</td>\n",
       "      <td>Jellicent</td>\n",
       "      <td>Water</td>\n",
       "      <td>Ghost</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>70</td>\n",
       "      <td>85</td>\n",
       "      <td>105</td>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>480</td>\n",
       "      <td>655.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>782</td>\n",
       "      <td>Pumpkaboo Small Size</td>\n",
       "      <td>Ghost</td>\n",
       "      <td>Grass</td>\n",
       "      <td>44</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "      <td>44</td>\n",
       "      <td>55</td>\n",
       "      <td>56</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>335</td>\n",
       "      <td>782.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       #                  Name    Type 1   Type 2   HP  Attack  Defense  \\\n",
       "11    12             Blastoise     Water      NaN   79      83      100   \n",
       "32    33             Sandshrew    Ground      NaN   50      75       85   \n",
       "45    46            Wigglytuff    Normal    Fairy  140      70       45   \n",
       "65    66               Poliwag     Water      NaN   40      50       40   \n",
       "77    78            Victreebel     Grass   Poison   80     105       65   \n",
       "89    90              Magneton  Electric    Steel   50      60       95   \n",
       "143  144                 Ditto    Normal      NaN   48      48       48   \n",
       "182  183               Ariados       Bug   Poison   70      90       70   \n",
       "235  236              Ursaring    Normal      NaN   90     130       75   \n",
       "321  322              Hariyama  Fighting      NaN  144     120       60   \n",
       "418  419           Mega Latias    Dragon  Psychic   80     100      120   \n",
       "478  479             Honchkrow      Dark   Flying  100     125       52   \n",
       "555  556               Servine     Grass      NaN   60      60       75   \n",
       "617  618              Maractus     Grass      NaN   75      86       67   \n",
       "654  655             Jellicent     Water    Ghost  100      60       70   \n",
       "781  782  Pumpkaboo Small Size     Ghost    Grass   44      66       70   \n",
       "\n",
       "     Sp. Atk  Sp. Def  Speed  Generation  Legendary  Total  Win_ratio  \n",
       "11        85      105     78           1      False    530       12.0  \n",
       "32        20       30     40           1      False    300       33.0  \n",
       "45        85       50     45           1      False    435       46.0  \n",
       "65        40       40     90           1      False    300       66.0  \n",
       "77       100       70     70           1      False    490       78.0  \n",
       "89       120       70     70           1      False    465       90.0  \n",
       "143       48       48     48           1      False    288      144.0  \n",
       "182       60       60     40           2      False    390      183.0  \n",
       "235       75       75     55           2      False    500      236.0  \n",
       "321       40       60     50           3      False    474      322.0  \n",
       "418      140      150    110           3       True    700      419.0  \n",
       "478      105       52     71           4      False    505      479.0  \n",
       "555       60       75     83           5      False    413      556.0  \n",
       "617      106       67     60           5      False    461      618.0  \n",
       "654       85      105     60           5      False    480      655.0  \n",
       "781       44       55     56           6      False    335      782.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(non_fight_pokemon_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fedf45e3a58>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data of pokemon that have fight and those haveing fight_ratio <1\n",
    "have_fight_df = pokemon_fight_df.loc[pokemon_fight_df['Win_ratio']<=1]\n",
    "#Visualising th data \n",
    "sns.lmplot(x = 'Total' , y= 'Win_ratio' , data = have_fight_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# as the data is in straight line so it can be predicted linearly using logistic regression\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(have_fight_df['Total'].values.reshape(-1,1),have_fight_df['Win_ratio'].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/linuxbrew/.linuxbrew/opt/python/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "non_fight_pokemon_df['Win_ratio'] = lr.predict(non_fight_pokemon_df['Total'].values.reshape(-1,1)) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>#</th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "      <th>Legendary</th>\n",
       "      <th>Total</th>\n",
       "      <th>Win_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Blastoise</td>\n",
       "      <td>Water</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>85</td>\n",
       "      <td>105</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>530</td>\n",
       "      <td>0.632079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>Sandshrew</td>\n",
       "      <td>Ground</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>75</td>\n",
       "      <td>85</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>300</td>\n",
       "      <td>0.314586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>Wigglytuff</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Fairy</td>\n",
       "      <td>140</td>\n",
       "      <td>70</td>\n",
       "      <td>45</td>\n",
       "      <td>85</td>\n",
       "      <td>50</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>435</td>\n",
       "      <td>0.500941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>66</td>\n",
       "      <td>Poliwag</td>\n",
       "      <td>Water</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>300</td>\n",
       "      <td>0.314586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>78</td>\n",
       "      <td>Victreebel</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>80</td>\n",
       "      <td>105</td>\n",
       "      <td>65</td>\n",
       "      <td>100</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>490</td>\n",
       "      <td>0.576863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>90</td>\n",
       "      <td>Magneton</td>\n",
       "      <td>Electric</td>\n",
       "      <td>Steel</td>\n",
       "      <td>50</td>\n",
       "      <td>60</td>\n",
       "      <td>95</td>\n",
       "      <td>120</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>465</td>\n",
       "      <td>0.542353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>144</td>\n",
       "      <td>Ditto</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>288</td>\n",
       "      <td>0.298021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>183</td>\n",
       "      <td>Ariados</td>\n",
       "      <td>Bug</td>\n",
       "      <td>Poison</td>\n",
       "      <td>70</td>\n",
       "      <td>90</td>\n",
       "      <td>70</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>390</td>\n",
       "      <td>0.438823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>236</td>\n",
       "      <td>Ursaring</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "      <td>130</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>500</td>\n",
       "      <td>0.590667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>322</td>\n",
       "      <td>Hariyama</td>\n",
       "      <td>Fighting</td>\n",
       "      <td>NaN</td>\n",
       "      <td>144</td>\n",
       "      <td>120</td>\n",
       "      <td>60</td>\n",
       "      <td>40</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>474</td>\n",
       "      <td>0.554777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>419</td>\n",
       "      <td>Mega Latias</td>\n",
       "      <td>Dragon</td>\n",
       "      <td>Psychic</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>120</td>\n",
       "      <td>140</td>\n",
       "      <td>150</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>700</td>\n",
       "      <td>0.866748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>479</td>\n",
       "      <td>Honchkrow</td>\n",
       "      <td>Dark</td>\n",
       "      <td>Flying</td>\n",
       "      <td>100</td>\n",
       "      <td>125</td>\n",
       "      <td>52</td>\n",
       "      <td>105</td>\n",
       "      <td>52</td>\n",
       "      <td>71</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>505</td>\n",
       "      <td>0.597569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>556</td>\n",
       "      <td>Servine</td>\n",
       "      <td>Grass</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>75</td>\n",
       "      <td>60</td>\n",
       "      <td>75</td>\n",
       "      <td>83</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>413</td>\n",
       "      <td>0.470572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>618</td>\n",
       "      <td>Maractus</td>\n",
       "      <td>Grass</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75</td>\n",
       "      <td>86</td>\n",
       "      <td>67</td>\n",
       "      <td>106</td>\n",
       "      <td>67</td>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>461</td>\n",
       "      <td>0.536831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>655</td>\n",
       "      <td>Jellicent</td>\n",
       "      <td>Water</td>\n",
       "      <td>Ghost</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>70</td>\n",
       "      <td>85</td>\n",
       "      <td>105</td>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>480</td>\n",
       "      <td>0.563059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>782</td>\n",
       "      <td>Pumpkaboo Small Size</td>\n",
       "      <td>Ghost</td>\n",
       "      <td>Grass</td>\n",
       "      <td>44</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "      <td>44</td>\n",
       "      <td>55</td>\n",
       "      <td>56</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>335</td>\n",
       "      <td>0.362900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       #                  Name    Type 1   Type 2   HP  Attack  Defense  \\\n",
       "11    12             Blastoise     Water      NaN   79      83      100   \n",
       "32    33             Sandshrew    Ground      NaN   50      75       85   \n",
       "45    46            Wigglytuff    Normal    Fairy  140      70       45   \n",
       "65    66               Poliwag     Water      NaN   40      50       40   \n",
       "77    78            Victreebel     Grass   Poison   80     105       65   \n",
       "89    90              Magneton  Electric    Steel   50      60       95   \n",
       "143  144                 Ditto    Normal      NaN   48      48       48   \n",
       "182  183               Ariados       Bug   Poison   70      90       70   \n",
       "235  236              Ursaring    Normal      NaN   90     130       75   \n",
       "321  322              Hariyama  Fighting      NaN  144     120       60   \n",
       "418  419           Mega Latias    Dragon  Psychic   80     100      120   \n",
       "478  479             Honchkrow      Dark   Flying  100     125       52   \n",
       "555  556               Servine     Grass      NaN   60      60       75   \n",
       "617  618              Maractus     Grass      NaN   75      86       67   \n",
       "654  655             Jellicent     Water    Ghost  100      60       70   \n",
       "781  782  Pumpkaboo Small Size     Ghost    Grass   44      66       70   \n",
       "\n",
       "     Sp. Atk  Sp. Def  Speed  Generation  Legendary  Total  Win_ratio  \n",
       "11        85      105     78           1      False    530   0.632079  \n",
       "32        20       30     40           1      False    300   0.314586  \n",
       "45        85       50     45           1      False    435   0.500941  \n",
       "65        40       40     90           1      False    300   0.314586  \n",
       "77       100       70     70           1      False    490   0.576863  \n",
       "89       120       70     70           1      False    465   0.542353  \n",
       "143       48       48     48           1      False    288   0.298021  \n",
       "182       60       60     40           2      False    390   0.438823  \n",
       "235       75       75     55           2      False    500   0.590667  \n",
       "321       40       60     50           3      False    474   0.554777  \n",
       "418      140      150    110           3       True    700   0.866748  \n",
       "478      105       52     71           4      False    505   0.597569  \n",
       "555       60       75     83           5      False    413   0.470572  \n",
       "617      106       67     60           5      False    461   0.536831  \n",
       "654       85      105     60           5      False    480   0.563059  \n",
       "781       44       55     56           6      False    335   0.362900  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(non_fight_pokemon_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 800 entries, 0 to 781\n",
      "Data columns (total 14 columns):\n",
      "#             800 non-null int64\n",
      "Name          800 non-null object\n",
      "Type 1        800 non-null object\n",
      "Type 2        414 non-null object\n",
      "HP            800 non-null int64\n",
      "Attack        800 non-null int64\n",
      "Defense       800 non-null int64\n",
      "Sp. Atk       800 non-null int64\n",
      "Sp. Def       800 non-null int64\n",
      "Speed         800 non-null int64\n",
      "Generation    800 non-null int64\n",
      "Legendary     800 non-null bool\n",
      "Total         800 non-null int64\n",
      "Win_ratio     800 non-null float64\n",
      "dtypes: bool(1), float64(1), int64(9), object(3)\n",
      "memory usage: 88.3+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['#', 'Name', 'Type 1', 'Type 2', 'HP', 'Attack', 'Defense', 'Sp. Atk',\n",
       "       'Sp. Def', 'Speed', 'Generation', 'Legendary', 'Total', 'Win_ratio'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = [have_fight_df , non_fight_pokemon_df]\n",
    "database_pokemon = pd.concat(frames)\n",
    "database_pokemon.info()\n",
    "database_pokemon.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = combats.copy()\n",
    "hp_dict = dict(zip(database_pokemon['#'],database_pokemon['HP']))\n",
    "attack_dict = dict(zip(database_pokemon['#'],database_pokemon['Attack']))\n",
    "defense_dict = dict(zip(database_pokemon['#'],database_pokemon['Defense']))\n",
    "sp_atk_dict = dict(zip(database_pokemon['#'],database_pokemon['Sp. Atk']))\n",
    "sp_def_dict = dict(zip(database_pokemon['#'],database_pokemon['Sp. Def']))\n",
    "speed_dict = dict(zip(database_pokemon['#'],database_pokemon['Speed']))\n",
    "total_dict = dict(zip(database_pokemon['#'],database_pokemon['Total']))\n",
    "win_ratio_dict = dict(zip(database_pokemon['#'],database_pokemon['Win_ratio']))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 22 columns):\n",
      "First_pokemon       50000 non-null int64\n",
      "Second_pokemon      50000 non-null int64\n",
      "Winner              50000 non-null int64\n",
      "Losers              50000 non-null int64\n",
      "is_first_win        50000 non-null bool\n",
      "diff_stat           50000 non-null int64\n",
      "first_hp            50000 non-null int64\n",
      "first_attack        50000 non-null int64\n",
      "first_defense       50000 non-null int64\n",
      "first_sp_atk        50000 non-null int64\n",
      "first_sp_def        50000 non-null int64\n",
      "first_speed         50000 non-null int64\n",
      "first_total         50000 non-null int64\n",
      "first_win_ratio     50000 non-null float64\n",
      "second_hp           50000 non-null int64\n",
      "second_attack       50000 non-null int64\n",
      "second_defense      50000 non-null int64\n",
      "second_sp_atk       50000 non-null int64\n",
      "second_sp_def       50000 non-null int64\n",
      "second_speed        50000 non-null int64\n",
      "second_total        50000 non-null int64\n",
      "second_win_ratio    50000 non-null float64\n",
      "dtypes: bool(1), float64(2), int64(19)\n",
      "memory usage: 8.1 MB\n"
     ]
    }
   ],
   "source": [
    "train_df['first_hp'] =  train_df['First_pokemon'].replace(hp_dict)\n",
    "train_df['first_attack'] =  train_df['First_pokemon'].replace(attack_dict)\n",
    "train_df['first_defense'] =  train_df['First_pokemon'].replace(defense_dict)\n",
    "train_df['first_sp_atk'] =  train_df['First_pokemon'].replace(sp_atk_dict)\n",
    "train_df['first_sp_def'] =  train_df['First_pokemon'].replace(sp_def_dict)\n",
    "train_df['first_speed'] =  train_df['First_pokemon'].replace(speed_dict)\n",
    "train_df['first_total'] =  train_df['First_pokemon'].replace(total_dict)\n",
    "train_df['first_win_ratio'] =  train_df['First_pokemon'].replace(win_ratio_dict)\n",
    "train_df['second_hp'] =  train_df['Second_pokemon'].replace(hp_dict)\n",
    "train_df['second_attack'] =  train_df['Second_pokemon'].replace(attack_dict)\n",
    "train_df['second_defense'] =  train_df['Second_pokemon'].replace(defense_dict)\n",
    "train_df['second_sp_atk'] =  train_df['Second_pokemon'].replace(sp_atk_dict)\n",
    "train_df['second_sp_def'] =  train_df['Second_pokemon'].replace(sp_def_dict)\n",
    "train_df['second_speed'] =  train_df['Second_pokemon'].replace(speed_dict)\n",
    "train_df['second_total'] =  train_df['Second_pokemon'].replace(total_dict)\n",
    "train_df['second_win_ratio'] =  train_df['Second_pokemon'].replace(win_ratio_dict)\n",
    "train_df.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['is_first_win'] = train_df.apply(lambda x: 1 if x['is_first_win']==True else 0, axis=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>First_pokemon</th>\n",
       "      <th>Second_pokemon</th>\n",
       "      <th>Winner</th>\n",
       "      <th>Losers</th>\n",
       "      <th>is_first_win</th>\n",
       "      <th>diff_stat</th>\n",
       "      <th>first_hp</th>\n",
       "      <th>first_attack</th>\n",
       "      <th>first_defense</th>\n",
       "      <th>first_sp_atk</th>\n",
       "      <th>...</th>\n",
       "      <th>first_total</th>\n",
       "      <th>first_win_ratio</th>\n",
       "      <th>second_hp</th>\n",
       "      <th>second_attack</th>\n",
       "      <th>second_defense</th>\n",
       "      <th>second_sp_atk</th>\n",
       "      <th>second_sp_def</th>\n",
       "      <th>second_speed</th>\n",
       "      <th>second_total</th>\n",
       "      <th>second_win_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>266</td>\n",
       "      <td>298</td>\n",
       "      <td>298</td>\n",
       "      <td>266</td>\n",
       "      <td>0</td>\n",
       "      <td>-40</td>\n",
       "      <td>50</td>\n",
       "      <td>64</td>\n",
       "      <td>50</td>\n",
       "      <td>45</td>\n",
       "      <td>...</td>\n",
       "      <td>300</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>40</td>\n",
       "      <td>60</td>\n",
       "      <td>40</td>\n",
       "      <td>60</td>\n",
       "      <td>340</td>\n",
       "      <td>0.424779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>702</td>\n",
       "      <td>701</td>\n",
       "      <td>701</td>\n",
       "      <td>702</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>90</td>\n",
       "      <td>72</td>\n",
       "      <td>90</td>\n",
       "      <td>...</td>\n",
       "      <td>580</td>\n",
       "      <td>0.896000</td>\n",
       "      <td>91</td>\n",
       "      <td>129</td>\n",
       "      <td>90</td>\n",
       "      <td>72</td>\n",
       "      <td>90</td>\n",
       "      <td>108</td>\n",
       "      <td>580</td>\n",
       "      <td>0.853659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>191</td>\n",
       "      <td>668</td>\n",
       "      <td>668</td>\n",
       "      <td>191</td>\n",
       "      <td>0</td>\n",
       "      <td>-80</td>\n",
       "      <td>55</td>\n",
       "      <td>40</td>\n",
       "      <td>85</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>405</td>\n",
       "      <td>0.236220</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>125</td>\n",
       "      <td>95</td>\n",
       "      <td>40</td>\n",
       "      <td>485</td>\n",
       "      <td>0.156716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>237</td>\n",
       "      <td>683</td>\n",
       "      <td>683</td>\n",
       "      <td>237</td>\n",
       "      <td>0</td>\n",
       "      <td>-235</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>250</td>\n",
       "      <td>0.032520</td>\n",
       "      <td>77</td>\n",
       "      <td>120</td>\n",
       "      <td>90</td>\n",
       "      <td>60</td>\n",
       "      <td>90</td>\n",
       "      <td>48</td>\n",
       "      <td>485</td>\n",
       "      <td>0.278571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151</td>\n",
       "      <td>231</td>\n",
       "      <td>151</td>\n",
       "      <td>231</td>\n",
       "      <td>1</td>\n",
       "      <td>-10</td>\n",
       "      <td>70</td>\n",
       "      <td>60</td>\n",
       "      <td>125</td>\n",
       "      <td>115</td>\n",
       "      <td>...</td>\n",
       "      <td>495</td>\n",
       "      <td>0.355072</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>230</td>\n",
       "      <td>10</td>\n",
       "      <td>230</td>\n",
       "      <td>5</td>\n",
       "      <td>505</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   First_pokemon  Second_pokemon  Winner  Losers  is_first_win  diff_stat  \\\n",
       "0            266             298     298     266             0        -40   \n",
       "1            702             701     701     702             0          0   \n",
       "2            191             668     668     191             0        -80   \n",
       "3            237             683     683     237             0       -235   \n",
       "4            151             231     151     231             1        -10   \n",
       "\n",
       "   first_hp  first_attack  first_defense  first_sp_atk  ...  first_total  \\\n",
       "0        50            64             50            45  ...          300   \n",
       "1        91            90             72            90  ...          580   \n",
       "2        55            40             85            80  ...          405   \n",
       "3        40            40             40            70  ...          250   \n",
       "4        70            60            125           115  ...          495   \n",
       "\n",
       "   first_win_ratio  second_hp  second_attack  second_defense  second_sp_atk  \\\n",
       "0         0.227273         70             70              40             60   \n",
       "1         0.896000         91            129              90             72   \n",
       "2         0.236220         75             75              75            125   \n",
       "3         0.032520         77            120              90             60   \n",
       "4         0.355072         20             10             230             10   \n",
       "\n",
       "   second_sp_def  second_speed  second_total  second_win_ratio  \n",
       "0             40            60           340          0.424779  \n",
       "1             90           108           580          0.853659  \n",
       "2             95            40           485          0.156716  \n",
       "3             90            48           485          0.278571  \n",
       "4            230             5           505          0.000000  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(train_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_need_columns = ['First_pokemon', 'Second_pokemon', 'Winner', 'Losers']\n",
    "train_df = train_df.drop(no_need_columns, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = train_df['is_first_win']\n",
    "no_need_columns = ['first_win_ratio','second_win_ratio','diff_stat','is_first_win']\n",
    "# no_need_columns = ['diff_stat','is_first_win']\n",
    "x =  train_df.drop(no_need_columns , axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/linuxbrew/.linuxbrew/opt/python/lib/python3.7/site-packages/sklearn/model_selection/_split.py:2179: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "xtrain , xtest , ytrain , ytest = train_test_split(x , y , train_size = 0.8 , random_state= 15) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 40000 entries, 45570 to 7624\n",
      "Data columns (total 14 columns):\n",
      "first_hp          40000 non-null int64\n",
      "first_attack      40000 non-null int64\n",
      "first_defense     40000 non-null int64\n",
      "first_sp_atk      40000 non-null int64\n",
      "first_sp_def      40000 non-null int64\n",
      "first_speed       40000 non-null int64\n",
      "first_total       40000 non-null int64\n",
      "second_hp         40000 non-null int64\n",
      "second_attack     40000 non-null int64\n",
      "second_defense    40000 non-null int64\n",
      "second_sp_atk     40000 non-null int64\n",
      "second_sp_def     40000 non-null int64\n",
      "second_speed      40000 non-null int64\n",
      "second_total      40000 non-null int64\n",
      "dtypes: int64(14)\n",
      "memory usage: 4.6 MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(xtrain.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/linuxbrew/.linuxbrew/opt/python/lib/python3.7/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "89.03"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda = LinearDiscriminantAnalysis()\n",
    "lda.fit(xtrain,ytrain)\n",
    "round(lda.score(xtest,ytest)*100,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47.46"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge = Ridge()\n",
    "ridge.fit(xtrain,ytrain)\n",
    "round(ridge.score(xtest,ytest)*100 , 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45.661"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso = Lasso()\n",
    "lasso.fit(xtrain,ytrain)\n",
    "round(lasso.score(xtest,ytest)*100,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/linuxbrew/.linuxbrew/opt/python/lib/python3.7/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "93.69"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier()\n",
    "rfc.fit(xtrain,ytrain)\n",
    "round(rfc.score(xtest , ytest)*100 , 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92.02"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc = DecisionTreeClassifier()\n",
    "dtc.fit(xtrain,ytrain)\n",
    "round(dtc.score(xtest , ytest)*100 , 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/linuxbrew/.linuxbrew/opt/python/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "89.15"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "lr.fit(xtrain,ytrain)\n",
    "round(lr.score(xtest , ytest)*100 , 2)\n",
    "# acc['Logistic Regression']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/linuxbrew/.linuxbrew/opt/python/lib/python3.7/site-packages/sklearn/linear_model/stochastic_gradient.py:166: FutureWarning: max_iter and tol parameters have been added in Perceptron in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "85.89"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per = Perceptron()\n",
    "per.fit(xtrain,ytrain)\n",
    "round(per.score(xtest , ytest)*100 , 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85.24"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(xtrain,ytrain)\n",
    "round(knn.score(xtest , ytest)*100 , 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.42"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb = GaussianNB()\n",
    "gnb.fit(xtrain,ytrain)\n",
    "round(gnb.score(xtest,ytest)*100,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 2 columns):\n",
      "First_pokemon     10000 non-null int64\n",
      "Second_pokemon    10000 non-null int64\n",
      "dtypes: int64(2)\n",
      "memory usage: 156.3 KB\n"
     ]
    }
   ],
   "source": [
    "test = pd.read_csv('tests.csv')\n",
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['first_hp'] =  test['First_pokemon'].replace(hp_dict)\n",
    "test['first_attack'] =  test['First_pokemon'].replace(attack_dict)\n",
    "test['first_defense'] =  test['First_pokemon'].replace(defense_dict)\n",
    "test['first_sp_atk'] =  test['First_pokemon'].replace(sp_atk_dict)\n",
    "test['first_sp_def'] =  test['First_pokemon'].replace(sp_def_dict)\n",
    "test['first_speed'] =  test['First_pokemon'].replace(speed_dict)\n",
    "test['first_total'] =  test['First_pokemon'].replace(total_dict)\n",
    "test['second_hp'] =  test['Second_pokemon'].replace(hp_dict)\n",
    "test['second_attack'] =  test['Second_pokemon'].replace(attack_dict)\n",
    "test['second_defense'] =  test['Second_pokemon'].replace(defense_dict)\n",
    "test['second_sp_atk'] =  test['Second_pokemon'].replace(sp_atk_dict)\n",
    "test['second_sp_def'] =  test['Second_pokemon'].replace(sp_def_dict)\n",
    "test['second_speed'] =  test['Second_pokemon'].replace(speed_dict)\n",
    "test['second_total'] =  test['Second_pokemon'].replace(total_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 14 columns):\n",
      "first_hp          10000 non-null int64\n",
      "first_attack      10000 non-null int64\n",
      "first_defense     10000 non-null int64\n",
      "first_sp_atk      10000 non-null int64\n",
      "first_sp_def      10000 non-null int64\n",
      "first_speed       10000 non-null int64\n",
      "first_total       10000 non-null int64\n",
      "second_hp         10000 non-null int64\n",
      "second_attack     10000 non-null int64\n",
      "second_defense    10000 non-null int64\n",
      "second_sp_atk     10000 non-null int64\n",
      "second_sp_def     10000 non-null int64\n",
      "second_speed      10000 non-null int64\n",
      "second_total      10000 non-null int64\n",
      "dtypes: int64(14)\n",
      "memory usage: 1.1 MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(test.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_need_columns = ['First_pokemon', 'Second_pokemon',]\n",
    "test = test.drop(no_need_columns, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    " y = rfc.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = pd.DataFrame({'Winner': y})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "output.to_csv('Result.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}