NBA_logistic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=read.csv('C:/Users/MANI/Documents/R sem 2/logistic_regression/exercises-logistic-regression-exercise-1/exercises-logistic-regression-exercise-1/nba_logreg.csv')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem_statement: To predict the player will be playing after 5 years or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>GP</th><th scope=col>MIN</th><th scope=col>PTS</th><th scope=col>FGM</th><th scope=col>FGA</th><th scope=col>FG.</th><th scope=col>X3P.Made</th><th scope=col>X3PA</th><th scope=col>X3P.</th><th scope=col>FTM</th><th scope=col>FTA</th><th scope=col>FT.</th><th scope=col>OREB</th><th scope=col>DREB</th><th scope=col>REB</th><th scope=col>AST</th><th scope=col>STL</th><th scope=col>BLK</th><th scope=col>TOV</th><th scope=col>TARGET_5Yrs</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>36  </td><td>27.4</td><td>7.4 </td><td>2.6 </td><td>7.6 </td><td>34.7</td><td>0.5 </td><td>2.1 </td><td>25  </td><td>1.6 </td><td>2.3 </td><td>69.9</td><td>0.7 </td><td>3.4 </td><td>4.1 </td><td>1.9 </td><td>0.4 </td><td>0.4 </td><td>1.3 </td><td>0   </td></tr>\n",
       "\t<tr><td>35  </td><td>26.9</td><td>7.2 </td><td>2.0 </td><td>6.7 </td><td>29.6</td><td>0.7 </td><td>2.8 </td><td>23.5</td><td>2.6 </td><td>3.4 </td><td>76.5</td><td>0.5 </td><td>2.0 </td><td>2.4 </td><td>3.7 </td><td>1.1 </td><td>0.5 </td><td>1.6 </td><td>0   </td></tr>\n",
       "\t<tr><td>74  </td><td>15.3</td><td>5.2 </td><td>2.0 </td><td>4.7 </td><td>42.2</td><td>0.4 </td><td>1.7 </td><td>24.4</td><td>0.9 </td><td>1.3 </td><td>67.0</td><td>0.5 </td><td>1.7 </td><td>2.2 </td><td>1.0 </td><td>0.5 </td><td>0.3 </td><td>1.0 </td><td>0   </td></tr>\n",
       "\t<tr><td>58  </td><td>11.6</td><td>5.7 </td><td>2.3 </td><td>5.5 </td><td>42.6</td><td>0.1 </td><td>0.5 </td><td>22.6</td><td>0.9 </td><td>1.3 </td><td>68.9</td><td>1.0 </td><td>0.9 </td><td>1.9 </td><td>0.8 </td><td>0.6 </td><td>0.1 </td><td>1.0 </td><td>1   </td></tr>\n",
       "\t<tr><td>48  </td><td>11.5</td><td>4.5 </td><td>1.6 </td><td>3.0 </td><td>52.4</td><td>0   </td><td>0.1 </td><td>0   </td><td>1.3 </td><td>1.9 </td><td>67.4</td><td>1.0 </td><td>1.5 </td><td>2.5 </td><td>0.3 </td><td>0.3 </td><td>0.4 </td><td>0.8 </td><td>1   </td></tr>\n",
       "\t<tr><td>75  </td><td>11.4</td><td>3.7 </td><td>1.5 </td><td>3.5 </td><td>42.3</td><td>0.3 </td><td>1.1 </td><td>32.5</td><td>0.4 </td><td>0.5 </td><td>73.2</td><td>0.2 </td><td>0.7 </td><td>0.8 </td><td>1.8 </td><td>0.4 </td><td>0.0 </td><td>0.7 </td><td>0   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllllll}\n",
       " GP & MIN & PTS & FGM & FGA & FG. & X3P.Made & X3PA & X3P. & FTM & FTA & FT. & OREB & DREB & REB & AST & STL & BLK & TOV & TARGET\\_5Yrs\\\\\n",
       "\\hline\n",
       "\t 36   & 27.4 & 7.4  & 2.6  & 7.6  & 34.7 & 0.5  & 2.1  & 25   & 1.6  & 2.3  & 69.9 & 0.7  & 3.4  & 4.1  & 1.9  & 0.4  & 0.4  & 1.3  & 0   \\\\\n",
       "\t 35   & 26.9 & 7.2  & 2.0  & 6.7  & 29.6 & 0.7  & 2.8  & 23.5 & 2.6  & 3.4  & 76.5 & 0.5  & 2.0  & 2.4  & 3.7  & 1.1  & 0.5  & 1.6  & 0   \\\\\n",
       "\t 74   & 15.3 & 5.2  & 2.0  & 4.7  & 42.2 & 0.4  & 1.7  & 24.4 & 0.9  & 1.3  & 67.0 & 0.5  & 1.7  & 2.2  & 1.0  & 0.5  & 0.3  & 1.0  & 0   \\\\\n",
       "\t 58   & 11.6 & 5.7  & 2.3  & 5.5  & 42.6 & 0.1  & 0.5  & 22.6 & 0.9  & 1.3  & 68.9 & 1.0  & 0.9  & 1.9  & 0.8  & 0.6  & 0.1  & 1.0  & 1   \\\\\n",
       "\t 48   & 11.5 & 4.5  & 1.6  & 3.0  & 52.4 & 0    & 0.1  & 0    & 1.3  & 1.9  & 67.4 & 1.0  & 1.5  & 2.5  & 0.3  & 0.3  & 0.4  & 0.8  & 1   \\\\\n",
       "\t 75   & 11.4 & 3.7  & 1.5  & 3.5  & 42.3 & 0.3  & 1.1  & 32.5 & 0.4  & 0.5  & 73.2 & 0.2  & 0.7  & 0.8  & 1.8  & 0.4  & 0.0  & 0.7  & 0   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| GP | MIN | PTS | FGM | FGA | FG. | X3P.Made | X3PA | X3P. | FTM | FTA | FT. | OREB | DREB | REB | AST | STL | BLK | TOV | TARGET_5Yrs |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 36   | 27.4 | 7.4  | 2.6  | 7.6  | 34.7 | 0.5  | 2.1  | 25   | 1.6  | 2.3  | 69.9 | 0.7  | 3.4  | 4.1  | 1.9  | 0.4  | 0.4  | 1.3  | 0    |\n",
       "| 35   | 26.9 | 7.2  | 2.0  | 6.7  | 29.6 | 0.7  | 2.8  | 23.5 | 2.6  | 3.4  | 76.5 | 0.5  | 2.0  | 2.4  | 3.7  | 1.1  | 0.5  | 1.6  | 0    |\n",
       "| 74   | 15.3 | 5.2  | 2.0  | 4.7  | 42.2 | 0.4  | 1.7  | 24.4 | 0.9  | 1.3  | 67.0 | 0.5  | 1.7  | 2.2  | 1.0  | 0.5  | 0.3  | 1.0  | 0    |\n",
       "| 58   | 11.6 | 5.7  | 2.3  | 5.5  | 42.6 | 0.1  | 0.5  | 22.6 | 0.9  | 1.3  | 68.9 | 1.0  | 0.9  | 1.9  | 0.8  | 0.6  | 0.1  | 1.0  | 1    |\n",
       "| 48   | 11.5 | 4.5  | 1.6  | 3.0  | 52.4 | 0    | 0.1  | 0    | 1.3  | 1.9  | 67.4 | 1.0  | 1.5  | 2.5  | 0.3  | 0.3  | 0.4  | 0.8  | 1    |\n",
       "| 75   | 11.4 | 3.7  | 1.5  | 3.5  | 42.3 | 0.3  | 1.1  | 32.5 | 0.4  | 0.5  | 73.2 | 0.2  | 0.7  | 0.8  | 1.8  | 0.4  | 0.0  | 0.7  | 0    |\n",
       "\n"
      ],
      "text/plain": [
       "  GP MIN  PTS FGM FGA FG.  X3P.Made X3PA X3P. FTM FTA FT.  OREB DREB REB AST\n",
       "1 36 27.4 7.4 2.6 7.6 34.7 0.5      2.1  25   1.6 2.3 69.9 0.7  3.4  4.1 1.9\n",
       "2 35 26.9 7.2 2.0 6.7 29.6 0.7      2.8  23.5 2.6 3.4 76.5 0.5  2.0  2.4 3.7\n",
       "3 74 15.3 5.2 2.0 4.7 42.2 0.4      1.7  24.4 0.9 1.3 67.0 0.5  1.7  2.2 1.0\n",
       "4 58 11.6 5.7 2.3 5.5 42.6 0.1      0.5  22.6 0.9 1.3 68.9 1.0  0.9  1.9 0.8\n",
       "5 48 11.5 4.5 1.6 3.0 52.4 0        0.1  0    1.3 1.9 67.4 1.0  1.5  2.5 0.3\n",
       "6 75 11.4 3.7 1.5 3.5 42.3 0.3      1.1  32.5 0.4 0.5 73.2 0.2  0.7  0.8 1.8\n",
       "  STL BLK TOV TARGET_5Yrs\n",
       "1 0.4 0.4 1.3 0          \n",
       "2 1.1 0.5 1.6 0          \n",
       "3 0.5 0.3 1.0 0          \n",
       "4 0.6 0.1 1.0 1          \n",
       "5 0.3 0.4 0.8 1          \n",
       "6 0.4 0.0 0.7 0          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df$GP <- factor(df$GP)\n",
    "#df$X3P.Made <- factor(df$X3P.Made)\n",
    "#df$X3PA <- factor(df$X3PA)\n",
    "#df$X3P. <- factor(df$X3P.)\n",
    "#df$TARGET_5Yrs <- factor(df$TARGET_5Yrs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Converting the significant columns into factors for better classification for the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Printing the head of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t1340 obs. of  20 variables:\n",
      " $ GP         : Factor w/ 70 levels \"11\",\"12\",\"14\",..: 24 23 62 46 36 63 50 36 53 30 ...\n",
      " $ MIN        : num  27.4 26.9 15.3 11.6 11.5 11.4 10.9 10.3 9.9 8.5 ...\n",
      " $ PTS        : num  7.4 7.2 5.2 5.7 4.5 3.7 6.6 5.7 2.4 3.7 ...\n",
      " $ FGM        : num  2.6 2 2 2.3 1.6 1.5 2.5 2.3 1 1.4 ...\n",
      " $ FGA        : num  7.6 6.7 4.7 5.5 3 3.5 5.8 5.4 2.4 3.5 ...\n",
      " $ FG.        : num  34.7 29.6 42.2 42.6 52.4 42.3 43.5 41.5 39.2 38.3 ...\n",
      " $ X3P.Made   : Factor w/ 23 levels \"0\",\"0.1\",\"0.2\",..: 6 8 5 2 1 4 1 5 2 2 ...\n",
      " $ X3PA       : Factor w/ 54 levels \"0\",\"0.1\",\"0.2\",..: 22 29 18 6 2 12 2 16 6 4 ...\n",
      " $ X3P.       : Factor w/ 254 levels \"0\",\"4.3\",\"4.5\",..: 82 72 78 66 1 149 251 125 70 58 ...\n",
      " $ FTM        : num  1.6 2.6 0.9 0.9 1.3 0.4 1.5 0.7 0.4 1 ...\n",
      " $ FTA        : num  2.3 3.4 1.3 1.3 1.9 0.5 1.8 0.8 0.5 1.4 ...\n",
      " $ FT.        : num  69.9 76.5 67 68.9 67.4 73.2 81.1 87.5 71.4 67.8 ...\n",
      " $ OREB       : num  0.7 0.5 0.5 1 1 0.2 0.5 0.8 0.2 0.4 ...\n",
      " $ DREB       : num  3.4 2 1.7 0.9 1.5 0.7 1.4 0.9 0.6 0.7 ...\n",
      " $ REB        : num  4.1 2.4 2.2 1.9 2.5 0.8 2 1.7 0.8 1.1 ...\n",
      " $ AST        : num  1.9 3.7 1 0.8 0.3 1.8 0.6 0.2 2.3 0.3 ...\n",
      " $ STL        : num  0.4 1.1 0.5 0.6 0.3 0.4 0.2 0.2 0.3 0.2 ...\n",
      " $ BLK        : num  0.4 0.5 0.3 0.1 0.4 0 0.1 0.1 0 0 ...\n",
      " $ TOV        : num  1.3 1.6 1 1 0.8 0.7 0.7 0.7 1.1 0.7 ...\n",
      " $ TARGET_5Yrs: Factor w/ 2 levels \"0\",\"1\": 1 1 1 2 2 1 2 2 1 1 ...\n"
     ]
    }
   ],
   "source": [
    "str(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking the structure of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(Amelia)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using library amelia for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SRekEV5IUXiLUKoTkuzV43WO3qrACtp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\nRK5CYII=",
      "text/plain": [
       "Plot with title \"NBA - Missings Map\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "missmap(df, main=\"NBA - Missings Map\", \n",
    "        col=c(\"yellow\", \"black\"), legend=FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Registered S3 methods overwritten by 'ggplot2':\n",
      "  method         from \n",
      "  [.quosures     rlang\n",
      "  c.quosures     rlang\n",
      "  print.quosures rlang\n"
     ]
    }
   ],
   "source": [
    "library(ggplot2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(df,aes(TARGET_5Yrs)) + geom_bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# There are more guys who can play after 5 years also"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZawaYAESDceD0gVKuMHJOIllrBpgARINx4PSBUq4wck4iWWsGmABEg3Hg9IFSrj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\nAAkhgQAJIYEACSGBAAkhgQAJIYH+G6K3CccvzMIKAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(df,aes(X3P.Made)) + geom_bar(aes(fill=factor(X3P.Made)),alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The above graph is showing the players how many 3 points made by the basket ball players."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HFeX+sYRMqCSO5DEEkAkQhxXl/rGETKgkjuQxBJAJEIcV5f6xizSNok+kaRxhtO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\nAEQCKAAiARQAkQAKgEgABfj/CWNkc1aUlhkAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(df,aes(X3PA)) + geom_bar(aes(fill=factor(X3PA)),alpha=0.5) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The above graph shows that the points attempt made by players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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b0JqbmXkEJS1puQmnsJKSRlvQmpuZeQ\nQlLWm5CaewkpJGW9Cam5l5BCUtabkJp7CSkkZb0JqbmXkEJS1puQmnsJKSRlvQmpuZeQQlLW\nm5CaewkpJGW9Cam5l5BCUtabkJp7CSkkZb0JqbmXkEJS1puQmnsJKSRlvQmpuZeQQlLWm5Ca\newkpJGW9Cam5l5BCUtabkJp7CSkkZb0JqbmXkEJS1puQmnsJKSRlvQmpuZeQQlLWm5Caewkp\nJGW9Cam5l5BCUtabkJp7CSkkZb0JqbmXkEJS1puQmnsJKSRlvQmpuZeQQlLWm5CaewkpJGW9\nCam5l5BCUtabkJp7CSkkZb0JqbmXkEJS1puQmnsJKSRlvQmpuZeQQlLWm5CaewkpJGW9Cam5\nl5BCUtabkJp7CSkkZb0JqbmXkEJS1puQmnsJKSRlvQmpuZeQQlLWm5CaewkpJGW9Cam5l5BC\nUtabkJp7CSkkZb0JqbmXkEJS1puQmnsJKSRlvQmpuZeQQlLWm5CaewkpJGW9Cam5l5BCUtab\nkJp7CSkkZb0JqbmXkEJS1puQmntPHtL1+8cXfv+RkN5gfuAUCGm170hIr/28fvj1CSHdZ37g\nFAhpte9ASNevhHSY+YFTIKTVviOvSIR0nPmBUyCk1b6MkP74xgMPT+FDLVTHAmeBV6QlzA+c\nAq9Iq32EtJb5gVMgpNU+QlrL/MApENJqHyGtZX7gFAhptY+Q1jI/cAqEtNp3PCS+s+EI8wOn\nQEirfYdCeosh+8BjUtabkJp7CSkkZb0JqbmXkEJS1puQmnsJKSRlvQmpuZeQQlLWm5Caewkp\nJGW9Cam5l5BCUtabkJp7CSkkZb0JqbmXkEJS1puQmnsJKSRlvQmpuZeQQlLWm5CaewkpJGW9\nCam5l5BCUtabkJp7CSkkZb0JqbmXkEJS1puQmnsJKSRlvQmpuZeQQlLWm5CaewkpJGW9Cam5\nl5BCUtabkJp7CSkkZb0JqbmXkEJS1puQmnsJKSRlvQmpuZeQQlLWm5CaewkpJGW9Cam5l5BC\nUtabkJp7CSkkZb0JqbmXkEJS1puQmnsJKSRlvQmpuZeQQlLWm5CaewkpJGW9Cam5l5BCUtab\nkJp7CSkkZb0JqbmXkEJS1puQmnsJKSRlvQmpuZeQQlLWm5CaewkpJGW9Cam5l5BCUtabkJp7\nCSkkZb0JqbmXkEJS1puQmnsJKSRlvQmpuZeQQlLWm5CaewkpJGW9Cam5l5BCUtabkJp7CSkk\nZb0JqbmXkEJS1puQmnsJKSRlvQmpuZeQQlLWm5CaewkpJGW9Cam5l5BCUtabkJp7CSkkZb0J\nqbmXkEJS1puQmnsJKSRlvQmpuZeQQlLWm5CaewkpJGW9Cam5l5BCUtabkJp7CSkkZb0JqbmX\nkEJS1puQmnsJKSRlvQmpuZeQQlLWm5CaewkpJGW9Cam5l5BCUtabkJp77UMSrTchNfcSkgZC\nau4lJA2E1NxLSBp0IT3gnb7EoZPWQEiEJPNOX+LQSWsgJEKSeacvceikNRASIcm805c4dNIa\nCImQZN7pSxw6aQ2EREgy7/QlDp20BkIiJJl3+hKHTloDIRGSzDt9iUMnrYGQCEnmnb7EoZPW\nQEiEJPNOX+LQSWsgJEKSeacvceikNRASIcm805c4dNIaCImQZN7pSxw6aQ2EREgy7/QlDp20\nBkIiJJl3+hKHTloDIRGSzDt9iUMnrYGQCEnmnb7EoZPWQEiEJPNOX+LQSWsgJEKSeacvceik\nNRASIcm805c4dNIaCImQZN7pSxw6aQ2EREgy7/QlDp20BkIiJJl3+hKHTloDIRGSzDt9iUMn\nrYGQCEnmnb7EoZPWQEiEJPNOX+LQSWsgJEKSeacvceikNRASIcm805c4dNIaCImQZN7pSxw6\naQ2EREgy7/QlDp20BkIiJJl3+hKHTloDIRGSzDt9iUMnrYGQCEnmnb7EoZPWQEiEJPNOX+LQ\nSWsgJEKSeacvceikNRASIcm805c4dNIaCImQZN7pSxw6aQ2EREgy7/QlDp20BkIiJJl3+hKH\nTloDIRGSzDt9iUMnrYGQCEnmnb7EoZPWQEiEJPNOX+LQSWsgJEKSeacvceikNRASIcm805c4\ndNIaCImQZN7pSxw6aQ2EREgy7/QlDp20BkIiJJl3+hKHTloDIRGSzDt9iUMnrYGQCEnmnb7E\noZPWQEiEJPNOX+LQSWsgJEKSeacvceikNRASIcm805c4dNIaCImQZN7pSxw6aQ2EREgy7/Ql\nDp20BkIiJJl3+hKHTloDIRGSzDt9iUMnrYGQCEnmnb7EoZPWQEiEJPNOX+LQSWsgJEKSeacv\nceikNRASIcm805c4dNIaCImQZN7pSxw6aQ2EREgy7/QlDp20BkIipDN55+95+hlqeQlJAyGJ\nIKRNfIBb7L4WeBxekU7snf8X5vQz1PIWfkUasv/j76Su7X0ISQQhEdKZvPP3PP0MtbyEpIGQ\nRBASIZ3JO3/P089Qy0tIGghJBCER0pm88/c8/Qy1vISkgZBEEBIhnck7f8/Tz1DLS0gaCEkE\nIRHSmbzz9zz9DLW8hKSBkEQQEiGdyTt/z9PPUMtLSBoISQQhEdKZvPP3PP0MtbyEpIGQRBAS\nIZ3JO3/P089Qy0tIGghJBCER0pm88/c8/Qy1vISkgZBEEBIhnck7f8/Tz1DLS0gaCEkEIRHS\nmbzz9zz9DLW8hKSBkEQQEiGdyTt/z9PPUMtLSBoISQQhEdKZvPP3PP0MtbyEpIGQRBASIZ3J\nO3/P089Qy0tIGghJBCER0pm88/c8/Qy1vISkgZBEEBIhFfMePWkNhERIxbxHT1oDIRFSMe/R\nk9ZASITUyfuXeHpZBiAkQurkJaTlPkJSQkhdxYQkhZC6iglJCiF1FROSFELqKiYkKYTUVUxI\nUgipq5iQpBBSVzEhSSGkrmJCkkJIXcWEJIWQuooJSQohdRUTkhRC6iomJCmE1FVMSFIIqauY\nkKQQUlcxIUkhpK5iQpJCSF3FhCSFkLqKCUkKIXUVE5IUQuoqJiQphNRVTEhSCKmrmJCkEFJX\nMSFJIaSuYkKSQkhdxYQkhZC6iglJCiF1FROSlK0hPcD0PsU3LIKQJBDSbab3Kb5hEYQkgZBu\nM71P8Q2LICQJhHSb6X2Kb1gEIUkgpNtM71N8wyIISQIh3WZ6n+IbFkFIEgjpNtP7FN+wCEKS\nQEi3md6n+IZFEJIEQrrN9D7FNyyCkCQQ0m2m9ym+YRGEJIGQbjO9T/ENiyAkCYR0m+l9im9Y\nBCFJIKTbTO9TfMMiCEkCId1mep/iGxZBSBII6TbT+xTfsAhCkkBIt5nep/iGRRCSBEK6zfQ+\nxTcsgpAkENJtpvcpvmERhCSBkG4zvU/xDYsgJAmEdJvpfYpvWAQhSSCk20zvU3zDIghJAiHd\nZnqf4hsWQUgSCOk20/sU37AIQpJASLeZ3qf4hkUQkgRCus30PsU3LIKQJBDSbab3Kb5hEYQk\ngZBuM71P8Q2LICQJhHSbB+7s2LMQEiF18hLSch8hKSEkFYQkgZBW8uYNiyAkCYS0kjdvWAQh\nSSCklbx5wyIISQIhreTNGxZBSBIIaSVv3rAIQpJASCt584ZFEJIEQlrJmzcsgpAkENJK3rxh\nEYQkgZBW8uYNiyAkCYS0kjdvWAQhSSCklbx5wyIISQIhreTNGxZBSBIIaSVv3rAIQpJASCt5\n84ZFEJIEQlrJmzcsgpAkENJK3rxhEYQkgZA2e6cX9/hqLfYRkhJCemV6cY+v1mIfISkhpFem\nF/f4ai32EZISQnqcycWee/hxHyEpIaTHmVzsuYcf9xGSEkJ6nMnFnnv4cR8hKSGkx5lc7LmH\nH/cRkhJCepzJxZ57+HEfISkhpMeZXOy5hx/3EZISQnqcycWee/hxHyEpIaTHmVzsuYcf9xGS\nEkJK5dBqrYWQpBBSKodWay2EJIWQUjm0WmshJCmElMqh1VoLIUkhpFQOrdZaCEkKIaVyaLXW\nQkhSCCmVQ6u1FkKSQkipHFqttRCSFEJK5dBqrYWQpBBSKodWay0zIV1fIKQS3qYDH1qttUyE\ndP354dGQxMf8Bj336oTi7QOPrOU/IKR72O6Vi5eQNNjulYu3Zkh/fOPowwFaInhF+mfGIw/K\nYJeYgbuKCcnCy8DLfYTk4GXg5T5CcvAy8HIfITl4GXi5bzykJd/ZIIK9au4tFdLfGbJnj3N2\nMQN3FROShZeBl/sIycHLwMt9hOTgZeDlPkJy8DLwch8hOXgZeLmPkBy8DLzcR0gOXgZe7iMk\nBy8DL/cRkoOXgZf7CMnBy8DLfYTk4GXg5T5CcvAy8HIfITl4GXi5j5AcvAy83EdIDl4GXu4j\nJAcvAy/3EZKDl4GX+wjJwcvAy32E5OBl4OU+QnLwMvByHyE5eBl4uY+QHLwMvNxHSA5eBl7u\nIyQHLwMv9xGSg5eBl/sIycHLwMt9hOTgZeDlvrSQRrD7/8tk4P4QkgAG7g8hCWDg/hCSAAbu\nz46QANpBSAAJEBJAAoQEkAAhASRASAAJ6EO6viCX7uP7rDZD/xjUZuAfyEO6/vxgwfXXvA5D\n/xjUZuCfENJSrl8JyQNCWoxXSH9BSArcjpiQPCCkxfiFdP1qNvBfENJiCMkDQlqMXUh2A3+H\nkBbjtlfXXx89Bn6FkBZjFtL1tx8sBv4B39mwGK/vbLheXyd1GfgnfK8dQAKEBJAAIQEkQEgA\nCRASQAKEBJAAIQEkQEgACRASQAKEVIrLdz59/vXXL7x88vznx+vl/Z+7vz5fCKkUP9v5/PeQ\nPl+//9X1efdX6AohleLy/b6eLu//9vnXd5dPLwl9eX952vSF2UNIpfgRzp0fny/c5yY4+FLc\nC+nj5X/3fEHwCiGV4ns4z58un37//OUnddfLu6d/f9n1ZQEh1eLn+wtffnz++g+e//Xu5e++\n+8+2r8wdQirF63tzf739/f3zX//s89On95d/b/mygJBq8d9vJvzjc68/lnoiCKkU90K6XJ5v\n/nNQwcGX4l5IT5f3L788en66fNR/TfANQirF3Z/avXv91RNv3G2CkEpx/9dIf75/yeiJ7xDa\nBSEBJEBIAAkQEkAChASQACEBJEBIAAkQEkAChASQACEBJEBIAAkQEkAChASQwP8DKjFmyx6/\n03cAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(df,aes(PTS)) + geom_histogram(fill='blue',bins=20,alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The mean of the points is between 0 and 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in is_character(x): object 'Name' not found\n",
     "output_type": "error",
     "traceback": [
      "Error in is_character(x): object 'Name' not found\nTraceback:\n",
      "1. select(df, -Name)",
      "2. select.data.frame(df, -Name)",
      "3. tidyselect::vars_select(sel_vars(.data), !!!quos(...))",
      "4. vars_select_eval(.vars, quos)",
      "5. map_if(quos, !is_helper, eval_tidy, mask)",
      "6. map(.x[sel], .f, ...)",
      "7. .f(.x[[i]], ...)",
      "8. -Name",
      "9. is_character(x)"
     ]
    }
   ],
   "source": [
    "df <- select(df, -Name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Drop the name column because it is not required for the model building "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(caTools)\n",
    "set.seed(101)\n",
    "\n",
    "split = sample.split(df$TARGET_5Yrs, SplitRatio = 0.70)\n",
    "\n",
    "final.train = subset(df, split == TRUE)\n",
    "final.test = subset(df, split == FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# splitting the data into final.train and final.test using sample.split function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "final.log.model <- glm(formula= TARGET_5Yrs ~ GP + X3P.Made , family = binomial(link='logit'),data = final.train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training the model using final.train data and the dependent variable is target_5 years and independent variable is games played (GP) and 3 points made."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "glm(formula = TARGET_5Yrs ~ GP + X3P.Made, family = binomial(link = \"logit\"), \n",
       "    data = final.train)\n",
       "\n",
       "Deviance Residuals: \n",
       "    Min       1Q   Median       3Q      Max  \n",
       "-1.8881  -1.0624   0.6341   0.8684   1.8775  \n",
       "\n",
       "Coefficients:\n",
       "              Estimate Std. Error z value Pr(>|z|)    \n",
       "(Intercept) -2.3433023  0.2661287  -8.805   <2e-16 ***\n",
       "GP           0.0480641  0.0044478  10.806   <2e-16 ***\n",
       "X3P.Made     0.0002999  0.1973492   0.002    0.999    \n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "(Dispersion parameter for binomial family taken to be 1)\n",
       "\n",
       "    Null deviance: 1245.4  on 937  degrees of freedom\n",
       "Residual deviance: 1107.4  on 935  degrees of freedom\n",
       "AIC: 1113.4\n",
       "\n",
       "Number of Fisher Scoring iterations: 4\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(final.log.model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Games played and 3 points made are the significant variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "fitted.probabilities <- predict(final.log.model,newdata=final.test,type='response')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using predict function and the data is final.test and storing the results in fitted.probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "fitted.results <- ifelse(fitted.probabilities > 0.5,1,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using ifelse condition setting the center value 0.5 and storing the values 0 and 1 in fitted.results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Accuracy 0.696517412935323\"\n"
     ]
    }
   ],
   "source": [
    "misClasificError <- mean(fitted.results != final.test$TARGET_5Yrs)\n",
    "print(paste('Accuracy',1-misClasificError))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   \n",
       "    FALSE TRUE\n",
       "  0    69   84\n",
       "  1    38  211"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(final.test$TARGET_5Yrs, fitted.probabilities > 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# confusion matrix\n",
    "69 - predicted true negative\n",
    "211 - predicted true positive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "TP = 211\n",
    "TN = 69\n",
    "FP = 38\n",
    "FN = 84"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Error Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.303482587064677"
      ],
      "text/latex": [
       "0.303482587064677"
      ],
      "text/markdown": [
       "0.303482587064677"
      ],
      "text/plain": [
       "[1] 0.3034826"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "error.rate = (FP+FN) / (TP+TN+FN+FP)\n",
    "error.rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0.3 is the error rate that the number of all incorrect predictions made by the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.696517412935323"
      ],
      "text/latex": [
       "0.696517412935323"
      ],
      "text/markdown": [
       "0.696517412935323"
      ],
      "text/plain": [
       "[1] 0.6965174"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy = (TP + TN) / (TP+TN+FN+FP)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 69% Total accuracy that the model predicted correct values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# sensitivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.715254237288136"
      ],
      "text/latex": [
       "0.715254237288136"
      ],
      "text/markdown": [
       "0.715254237288136"
      ],
      "text/plain": [
       "[1] 0.7152542"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SN = (TP) / (TP + FN)\n",
    "SN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 71% sensitivity is the number of correct positive predictions made by the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Specificity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.644859813084112"
      ],
      "text/latex": [
       "0.644859813084112"
      ],
      "text/markdown": [
       "0.644859813084112"
      ],
      "text/plain": [
       "[1] 0.6448598"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SP = (TN) / (TN + FP)\n",
    "SP\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 64% is the percentage of true negative predictions made by the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.847389558232932"
      ],
      "text/latex": [
       "0.847389558232932"
      ],
      "text/markdown": [
       "0.847389558232932"
      ],
      "text/plain": [
       "[1] 0.8473896"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prec = (TP) / (TP + FP)\n",
    "prec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# when the model predicts yes.We calculate how often is it correct.So the model is 84% correct."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# False Positive Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.355140186915888"
      ],
      "text/latex": [
       "0.355140186915888"
      ],
      "text/markdown": [
       "0.355140186915888"
      ],
      "text/plain": [
       "[1] 0.3551402"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FPR = (FP) / (TN +  FP)\n",
    "FPR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# false positive rate is when its actually no how often does it predict yes.so the model has  35% predicted yes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Type 'citation(\"pROC\")' for a citation.\n",
      "\n",
      "Attaching package: 'pROC'\n",
      "\n",
      "The following objects are masked from 'package:stats':\n",
      "\n",
      "    cov, smooth, var\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(pROC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gQQEiCAkAABhAQIICRAACEBAggJEEBIgABCAgQQEiCAkAABhAQIICRAACEBAhKE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\nSUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_roc = roc(final.test$TARGET_5Yrs ~ fitted.probabilities, plot =TRUE, print.auc = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# When Area under curve (AUC) is 0.76, it means there is 76% chance that model will be able to distinguish between positive class that is 1 and negative class that is 0 accurately.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}