Random Forest Classifier - Tobgui .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "\n",
    "pubg_df = pd.DataFrame()\n",
    "test_df = pd.DataFrame()\n",
    "train_df=pd.DataFrame()\n",
    "\n",
    "test_df = pd.read_csv('test_V2.csv')\n",
    "train_df = pd.read_csv('train_V2.csv')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <th>Id</th>\n",
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       "      <th>killPlace</th>\n",
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       "      <th>revives</th>\n",
       "      <th>rideDistance</th>\n",
       "      <th>roadKills</th>\n",
       "      <th>swimDistance</th>\n",
       "      <th>teamKills</th>\n",
       "      <th>vehicleDestroys</th>\n",
       "      <th>walkDistance</th>\n",
       "      <th>weaponsAcquired</th>\n",
       "      <th>winPoints</th>\n",
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       "      <td>49</td>\n",
       "      <td>...</td>\n",
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       "      <td>787.8</td>\n",
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       "      <th>3</th>\n",
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       "      <td>54</td>\n",
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       "      <td>2963.0</td>\n",
       "      <td>4</td>\n",
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       "<p>5 rows × 28 columns</p>\n",
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      "text/plain": [
       "               Id         groupId         matchId  assists  boosts  \\\n",
       "0  9329eb41e215eb  676b23c24e70d6  45b576ab7daa7f        0       0   \n",
       "1  639bd0dcd7bda8  430933124148dd  42a9a0b906c928        0       4   \n",
       "2  63d5c8ef8dfe91  0b45f5db20ba99  87e7e4477a048e        1       0   \n",
       "3  cf5b81422591d1  b7497dbdc77f4a  1b9a94f1af67f1        0       0   \n",
       "4  ee6a295187ba21  6604ce20a1d230  40754a93016066        0       4   \n",
       "\n",
       "   damageDealt  DBNOs  headshotKills  heals  killPlace  ...  rankPoints  \\\n",
       "0        51.46      0              0      0         73  ...        1500   \n",
       "1       179.10      0              0      2         11  ...        1503   \n",
       "2        23.40      0              0      4         49  ...        1565   \n",
       "3        65.52      0              0      0         54  ...        1465   \n",
       "4       330.20      1              2      1          7  ...        1480   \n",
       "\n",
       "   revives  rideDistance  roadKills  swimDistance teamKills  vehicleDestroys  \\\n",
       "0        0           0.0          0           0.0         0                0   \n",
       "1        2        4669.0          0           0.0         0                0   \n",
       "2        0           0.0          0           0.0         0                0   \n",
       "3        0           0.0          0           0.0         0                0   \n",
       "4        1           0.0          0           0.0         0                0   \n",
       "\n",
       "   walkDistance  weaponsAcquired  winPoints  \n",
       "0         588.0                1          0  \n",
       "1        2017.0                6          0  \n",
       "2         787.8                4          0  \n",
       "3        1812.0                3          0  \n",
       "4        2963.0                4          0  \n",
       "\n",
       "[5 rows x 28 columns]"
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [
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       "               Id         groupId         matchId  assists  boosts  \\\n",
       "0  7f96b2f878858a  4d4b580de459be  a10357fd1a4a91        0       0   \n",
       "1  eef90569b9d03c  684d5656442f9e  aeb375fc57110c        0       0   \n",
       "2  1eaf90ac73de72  6a4a42c3245a74  110163d8bb94ae        1       0   \n",
       "3  4616d365dd2853  a930a9c79cd721  f1f1f4ef412d7e        0       0   \n",
       "4  315c96c26c9aac  de04010b3458dd  6dc8ff871e21e6        0       0   \n",
       "\n",
       "   damageDealt  DBNOs  headshotKills  heals  killPlace  ...  revives  \\\n",
       "0         0.00      0              0      0         60  ...        0   \n",
       "1        91.47      0              0      0         57  ...        0   \n",
       "2        68.00      0              0      0         47  ...        0   \n",
       "3        32.90      0              0      0         75  ...        0   \n",
       "4       100.00      0              0      0         45  ...        0   \n",
       "\n",
       "   rideDistance  roadKills  swimDistance  teamKills vehicleDestroys  \\\n",
       "0        0.0000          0          0.00          0               0   \n",
       "1        0.0045          0         11.04          0               0   \n",
       "2        0.0000          0          0.00          0               0   \n",
       "3        0.0000          0          0.00          0               0   \n",
       "4        0.0000          0          0.00          0               0   \n",
       "\n",
       "   walkDistance  weaponsAcquired  winPoints  winPlacePerc  \n",
       "0        244.80                1       1466        0.4444  \n",
       "1       1434.00                5          0        0.6400  \n",
       "2        161.80                2          0        0.7755  \n",
       "3        202.70                3          0        0.1667  \n",
       "4         49.75                2          0        0.1875  \n",
       "\n",
       "[5 rows x 29 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>boosts</th>\n",
       "      <th>damageDealt</th>\n",
       "      <th>headshotKills</th>\n",
       "      <th>killPlace</th>\n",
       "      <th>rankPoints</th>\n",
       "      <th>matchDuration</th>\n",
       "      <th>revives</th>\n",
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       "      <td>0</td>\n",
       "      <td>91.47</td>\n",
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       "      <td>1484</td>\n",
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       "    <tr>\n",
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       "      <td>68.00</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>1491</td>\n",
       "      <td>1318</td>\n",
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       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>32.90</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>1408</td>\n",
       "      <td>1436</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1560</td>\n",
       "      <td>1424</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   boosts  damageDealt  headshotKills  killPlace  rankPoints  matchDuration  \\\n",
       "0       0         0.00              0         60          -1           1306   \n",
       "1       0        91.47              0         57        1484           1777   \n",
       "2       0        68.00              0         47        1491           1318   \n",
       "3       0        32.90              0         75        1408           1436   \n",
       "4       0       100.00              0         45        1560           1424   \n",
       "\n",
       "   revives  numGroups  winPoints  \n",
       "0        0         26       1466  \n",
       "1        0         25          0  \n",
       "2        0         47          0  \n",
       "3        0         30          0  \n",
       "4        0         95          0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_col = ['boosts','damageDealt','headshotKills','killPlace','rankPoints','matchDuration','revives','numGroups','winPoints']\n",
    "X = train_df[feature_col]\n",
    "y= train_df['winPlacePerc'].apply(ctn_win)\n",
    "\n",
    "X.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.40, random_state=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ctn_win(x):\n",
    "    if x>=1:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9703663226682003\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "\n",
    "my_RandomForest = RandomForestClassifier(n_estimators = 10, bootstrap = True, random_state=3)\n",
    "\n",
    "my_RandomForest.fit(X_train,y_train)\n",
    "randomForest_predict = my_RandomForest.predict(X_test)\n",
    "\n",
    "accuracy = accuracy_score(y_test,randomForest_predict)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.9 0.1]\n",
      " [1.  0. ]\n",
      " [1.  0. ]\n",
      " ...\n",
      " [1.  0. ]\n",
      " [0.9 0.1]\n",
      " [1.  0. ]]\n"
     ]
    }
   ],
   "source": [
    "y_predict_prob_lr = my_RandomForest.predict_proba(X_test)\n",
    "\n",
    "print(y_predict_prob_lr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00000000e+00 2.19935790e-05 2.19935790e-05 1.41221929e-04\n",
      " 1.41800707e-04 1.42958264e-04 1.52797496e-04 1.53376275e-04\n",
      " 5.40579021e-04 5.41157800e-04 5.42315357e-04 5.42894135e-04\n",
      " 1.51292673e-03 1.51350550e-03 1.51350550e-03 1.51408428e-03\n",
      " 1.51582062e-03 1.51582062e-03 1.51755695e-03 1.51755695e-03\n",
      " 3.66598236e-03 3.66656114e-03 3.67755793e-03 3.67987304e-03\n",
      " 7.83029291e-03 7.83029291e-03 7.83145047e-03 7.83434436e-03\n",
      " 7.83608070e-03 7.83608070e-03 1.58973062e-02 1.58978850e-02\n",
      " 3.08980846e-02 3.08998210e-02 3.09015573e-02 3.09044512e-02\n",
      " 3.09177631e-02 6.03550111e-02 6.03584838e-02 6.03596413e-02\n",
      " 6.03607989e-02 6.03619564e-02 6.03625352e-02 6.03636928e-02\n",
      " 6.03717957e-02 1.28262501e-01 1.28264817e-01 1.28268868e-01\n",
      " 1.28270604e-01 1.28296071e-01 1.28297228e-01 1.28297807e-01\n",
      " 1.28298965e-01 1.28300701e-01 1.28302437e-01 1.00000000e+00]\n",
      "[0.         0.00378357 0.00382278 0.00888061 0.00891982 0.00891982\n",
      " 0.00946873 0.00946873 0.02236816 0.02236816 0.02236816 0.02236816\n",
      " 0.04752009 0.04752009 0.0475397  0.0475397  0.04775534 0.04777495\n",
      " 0.04779455 0.04781415 0.09072731 0.09072731 0.09127622 0.09127622\n",
      " 0.15751813 0.15753774 0.15753774 0.15763576 0.15763576 0.15765536\n",
      " 0.25381298 0.25381298 0.37835718 0.37835718 0.37835718 0.37835718\n",
      " 0.37853362 0.54354048 0.54357969 0.54357969 0.54357969 0.54357969\n",
      " 0.54357969 0.54357969 0.5436189  0.74904921 0.74904921 0.74904921\n",
      " 0.74904921 0.74908841 0.74908841 0.74910802 0.74910802 0.74910802\n",
      " 0.74910802 1.        ]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "fpr, tpr, thresholds = metrics.roc_curve(y_test, y_predict_prob_lr[:,1], pos_label=1)\n",
    "\n",
    "print(fpr)\n",
    "print(tpr)\n",
    "\n",
    "AUC = metrics.auc(fpr,tpr)\n",
    "\n",
    "#print(AUC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(fpr,tpr,color='red',lw=2,label='ROC Curve (area = %0.2f)' % AUC)\n",
    "\n",
    "\n",
    "# Random Guess line:\n",
    "plt.plot([0, 1], [0, 1], color='blue', lw=1, linestyle='--')\n",
    "\n",
    "# Defining The Range of X-Axis and Y-Axis:\n",
    "plt.xlim([-0.005, 1.005])\n",
    "plt.ylim([0.0, 1.01])\n",
    "\n",
    "# Labels, Title, Legend:\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}