Coursework-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c92e394a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set()\n",
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "path = \"C:\\\\Users\\\\andre\\\\OneDrive\\\\Desktop\\\\University\\\\CITY - 3 YEAR\\\\Intro to AI\\\\Coursework\"\n",
    "\n",
    "#load from file\n",
    "filename_read = os.path.join(path, \"Dataset_Kmeans_Clustering_csv.csv\")\n",
    "df = pd.read_csv(filename_read)\n",
    "df = pd.read_csv(filename_read, na_values=['NA', '?'])\n",
    "\n",
    "#divide the dataset into individual columns\n",
    "column_t1_summoner1_role = df[\"t1_summoner1_role\"]\n",
    "column_t1_summoner2_role = df[\"t1_summoner2_role\"]\n",
    "column_t1_summoner3_role = df[\"t1_summoner3_role\"]\n",
    "column_t1_summoner4_role = df[\"t1_summoner4_role\"]\n",
    "column_t1_summoner5_role = df[\"t1_summoner5_role\"]\n",
    "\n",
    "column_t1_summoner1_mastery_points = df[\"t1_mast1\"]\n",
    "column_t1_summoner2_mastery_points = df[\"t1_mast2\"]\n",
    "column_t1_summoner3_mastery_points = df[\"t1_mast3\"]\n",
    "column_t1_summoner4_mastery_points = df[\"t1_mast4\"]\n",
    "column_t1_summoner5_mastery_points = df[\"t1_mast5\"]\n",
    "\n",
    "column_t2_summoner1_role = df[\"t2_summoner1_role\"]\n",
    "column_t2_summoner2_role = df[\"t2_summoner2_role\"]\n",
    "column_t2_summoner3_role = df[\"t2_summoner3_role\"]\n",
    "column_t2_summoner4_role = df[\"t2_summoner4_role\"]\n",
    "column_t2_summoner5_role = df[\"t2_summoner5_role\"]\n",
    "\n",
    "column_t2_summoner1_mastery_points = df[\"t2_mast1\"]\n",
    "column_t2_summoner2_mastery_points = df[\"t2_mast2\"]\n",
    "column_t2_summoner3_mastery_points = df[\"t2_mast3\"]\n",
    "column_t2_summoner4_mastery_points = df[\"t2_mast4\"]\n",
    "column_t2_summoner5_mastery_points = df[\"t2_mast5\"]\n",
    "\n",
    "column_winner = df[\"winner\"]\n",
    "\n",
    "#merge each team 1 player's column with his mastery points\n",
    "summoner1_frame = [column_t1_summoner1_role, column_t1_summoner1_mastery_points]\n",
    "summoner2_frame = [column_t1_summoner2_role, column_t1_summoner2_mastery_points]\n",
    "summoner3_frame = [column_t1_summoner3_role, column_t1_summoner3_mastery_points]\n",
    "summoner4_frame = [column_t1_summoner4_role, column_t1_summoner4_mastery_points]\n",
    "summoner5_frame = [column_t1_summoner5_role, column_t1_summoner5_mastery_points]\n",
    "\n",
    "\n",
    "#displaying the relationship between player position and mastery points\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax1.scatter(column_t1_summoner1_role, column_t1_summoner1_mastery_points, c='b')\n",
    "ax1.scatter(column_t1_summoner2_role, column_t1_summoner2_mastery_points, c='b')\n",
    "ax1.scatter(column_t1_summoner3_role, column_t1_summoner3_mastery_points, c='b')\n",
    "ax1.scatter(column_t1_summoner4_role, column_t1_summoner4_mastery_points, c='b')\n",
    "ax1.scatter(column_t1_summoner5_role, column_t1_summoner5_mastery_points, c='b')\n",
    "\n",
    "#plt.legend(loc='upper left');\n",
    "#plt.show()\n",
    "\n",
    "\n",
    "#merge each team 2 player's column with his mastery points\n",
    "summoner1_frame = [column_t2_summoner1_role, column_t2_summoner1_mastery_points]\n",
    "summoner2_frame = [column_t2_summoner2_role, column_t2_summoner2_mastery_points]\n",
    "summoner3_frame = [column_t2_summoner3_role, column_t2_summoner3_mastery_points]\n",
    "summoner4_frame = [column_t2_summoner4_role, column_t2_summoner4_mastery_points]\n",
    "summoner5_frame = [column_t2_summoner5_role, column_t2_summoner5_mastery_points]\n",
    "\n",
    "\n",
    "#displaying the relationship between player position and mastery points\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax1.scatter(column_t2_summoner1_role, column_t2_summoner1_mastery_points, c='b')\n",
    "ax1.scatter(column_t2_summoner2_role, column_t2_summoner2_mastery_points, c='b')\n",
    "ax1.scatter(column_t2_summoner3_role, column_t2_summoner3_mastery_points, c='b')\n",
    "ax1.scatter(column_t2_summoner4_role, column_t2_summoner4_mastery_points, c='b')\n",
    "ax1.scatter(column_t2_summoner5_role, column_t2_summoner5_mastery_points, c='b')\n",
    "\n",
    "#plt.legend(loc='upper left');\n",
    "#plt.show()\n",
    "\n",
    "\n",
    "\n",
    "#merging each summoner 1 from both teams's position column with his mastery point column\n",
    "t2_s1_merge = pd.merge(column_t2_summoner1_role , column_t2_summoner1_mastery_points, right_index=True, left_index=True)\n",
    "t1_s1_merge = pd.merge(column_t1_summoner1_role , column_t1_summoner1_mastery_points, right_index=True, left_index=True)\n",
    "s1_result = pd.merge(t2_s1_merge, t1_s1_merge, right_index=True, left_index=True) \n",
    "\n",
    "#locating where t2 summoner 1 player has greater amount of mastery points\n",
    "t2_greater_mp = s1_result.loc[s1_result['t2_mast1'] > s1_result['t1_mast1']]\n",
    "\n",
    "#comparing with original data(winner)\n",
    "t2_greater_mp_winner = pd.merge(t2_greater_mp, column_winner, right_index=True, left_index=True)\n",
    "\n",
    "#Only when Team 2 are winners(no matter mastery points difference)\n",
    "only_t2_winner = t2_greater_mp_winner.loc[t2_greater_mp_winner['winner'] == \"t2\"]\n",
    "\n",
    "#Only when Team 1 are winners(no matter mastery points difference)\n",
    "only_t1_winner = t2_greater_mp_winner.loc[t2_greater_mp_winner['winner'] == \"t1\"]\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "#Team 2 wins\n",
    "ax1.scatter(only_t2_winner['t2_summoner1_role'],only_t2_winner['t2_mast1'], c='r')\n",
    "#Team 1 wins\n",
    "ax1.scatter(only_t1_winner['t1_summoner1_role'],only_t1_winner['t1_mast1'], c='b')\n",
    "plt.legend(loc='upper left');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5113b3a1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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