notebooks/08_nba_knn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN exercise with NBA player data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "- NBA player statistics from 2014-2015 (partial season): [data](https://github.com/justmarkham/DAT4-students/blob/master/kerry/Final/NBA_players_2015.csv), [data dictionary](https://github.com/justmarkham/DAT-project-examples/blob/master/pdf/nba_paper.pdf)\n",
    "- **Goal:** Predict player position using assists, steals, blocks, turnovers, and personal fouls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Read the data into Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# read the data into a DataFrame\n",
    "import pandas as pd\n",
    "url = 'https://raw.githubusercontent.com/justmarkham/DAT4-students/master/kerry/Final/NBA_players_2015.csv'\n",
    "nba = pd.read_csv(url, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'season_end', u'player', u'pos', u'age', u'bref_team_id', u'g', u'gs',\n",
       "       u'mp', u'fg', u'fga', u'fg_', u'x3p', u'x3pa', u'x3p_', u'x2p', u'x2pa',\n",
       "       u'x2p_', u'ft', u'fta', u'ft_', u'orb', u'drb', u'trb', u'ast', u'stl',\n",
       "       u'blk', u'tov', u'pf', u'pts', u'G', u'MP', u'PER', u'TS%', u'3PAr',\n",
       "       u'FTr', u'TRB%', u'AST%', u'STL%', u'BLK%', u'TOV%', u'USG%', u'OWS',\n",
       "       u'DWS', u'WS', u'WS/48', u'OBPM', u'DBPM', u'BPM', u'VORP'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# examine the columns\n",
    "nba.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "G    200\n",
       "F    199\n",
       "C     79\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# examine the positions\n",
    "nba.pos.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Create X and y\n",
    "\n",
    "Use the following features: assists, steals, blocks, turnovers, personal fouls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# map positions to numbers\n",
    "nba['pos_num'] = nba.pos.map({'C':0, 'F':1, 'G':2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create feature matrix (X)\n",
    "feature_cols = ['ast', 'stl', 'blk', 'tov', 'pf']\n",
    "X = nba[feature_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# alternative way to create X\n",
    "X = nba.loc[:, 'ast':'pf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create response vector (y)\n",
    "y = nba.pos_num"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Train a KNN model (K=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import class\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# instantiate with K=5\n",
    "knn = KNeighborsClassifier(n_neighbors=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_neighbors=5, p=2, weights='uniform')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit with data\n",
    "knn.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Predict player position and calculate predicted probability of each position\n",
    "\n",
    "Predict for a player with these statistics: 1 assist, 1 steal, 0 blocks, 1 turnover, 2 personal fouls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a list to represent a player\n",
    "player = [1, 1, 0, 1, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make a prediction\n",
    "knn.predict(player)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0. ,  0.2,  0.8]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate predicted probabilities\n",
    "knn.predict_proba(player)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Repeat steps 3 and 4 using K=50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# repeat for K=50\n",
    "knn = KNeighborsClassifier(n_neighbors=50)\n",
    "knn.fit(X, y)\n",
    "knn.predict(player)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.06,  0.62,  0.32]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate predicted probabilities\n",
    "knn.predict_proba(player)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus: Explore the features to decide which ones are predictive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# allow plots to appear in the notebook\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# increase default figure and font sizes for easier viewing\n",
    "plt.rcParams['figure.figsize'] = (6, 4)\n",
    "plt.rcParams['font.size'] = 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pos</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>79</td>\n",
       "      <td>0.945570</td>\n",
       "      <td>0.858263</td>\n",
       "      <td>0</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.15</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>199</td>\n",
       "      <td>1.173367</td>\n",
       "      <td>1.086252</td>\n",
       "      <td>0</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.50</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>200</td>\n",
       "      <td>2.729000</td>\n",
       "      <td>2.128287</td>\n",
       "      <td>0</td>\n",
       "      <td>1.10</td>\n",
       "      <td>2.25</td>\n",
       "      <td>3.80</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     count      mean       std  min   25%   50%   75%   max\n",
       "pos                                                        \n",
       "C       79  0.945570  0.858263    0  0.40  0.80  1.15   4.4\n",
       "F      199  1.173367  1.086252    0  0.45  0.90  1.50   7.3\n",
       "G      200  2.729000  2.128287    0  1.10  2.25  3.80  10.2"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# description of assists grouped by position\n",
    "nba.groupby('pos').ast.describe().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1740b470>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZzeG2bA63pZO9mVkruIxjZtYQLuOYmbWck31mrg02h9uyOdyWTvZmZq3gmr2ZWUO4Zm9m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\nCzjZm5m1gJO9mVkL/H/3muqHhM41pQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1740b908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# box plot of assists grouped by position\n",
    "nba.boxplot(column='ast', by='pos')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000001740B780>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000182EE7F0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000000183102E8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000184730F0>]], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dl9zGzMxqV+tCdUuAvwO2AA4BrpA0dZj3uM3FzKxL1BQMIuJF4OHs5SJJk4AZ\nwKnZtvHAY7m3jCflORjArNzzPpqX6cx6mTOdmZWr0Q7km4FHI2K6pGXAN6o6kJ8idSBfVPU+dyBb\nU7gD2ayYYWsGkk4HriP98h8LfASYArw7O8TJbczMulwtzUTjgcuAbUlZz34NvDsibgIntzEz6wVO\nbpN7j5uJupebicyK8dpEZmbmYGBmZg4GZmaGg4GZmeFgYGZm1L4cxYgg1TcYxaOPzKxXOBhspJ6b\nu0cxmlnvqCXT2UmS7s6ynC2XdK2kiQMc52xnZmZdqpY+gynAN4F3AvsCLwE/l7RV5QBnOzMz6251\nz0CWNIa0LMWBEXG9UkN7TdnOOn0Gcr3NRO4z6ByegWxWTCOjicZl71uRvXa2MzOzLtdIMJgLLCJl\nPoO0gB0MnO1sW8zMrOPVNZpI0pmkX/uTo7Y2kgGOmZV73oeT21gjnNzGrFw19xlIOguYBkyNiAdz\n23cBfg9MioiFue3XA8sj4rDcNvcZWFO4z8CsmJqaiSTNBT4I7JsPBJlHSCku98sdPxqYDNxe0nWa\nmVkT1ZLp7Fzgo8BBwEpJlX6AVRHxXESEJGc7MzPrYrX0GRxFaj/5j6rts4BTwdnOzMy6nTOdNfwe\n9xl0EvcZmBXjVUvNzMwL1RVR7yqn4JVOzawzORgU0khTlJlZ53EzkZmZORiYmZmDgZmZUVtym32y\nhDaPSVovafoAxzixjZlZF6ulZjAGuBc4BlhNVa+pE9uYmXW/YYNBRMyLiJkR8RNgfX5fltjms8Cc\niLg6IhYD04GxwEeaccFmZla+on0GTmxjZtYDis4zGCqxzfYFz92TPFHNzDpRMyedDXIHm5V73sfI\nS27jiWplcHIbs3LVtVCdpFXAjIj4Xva65sQ22faeWqiuVdflmsHwvFCdWTFF+wyc2MbMrAfUktxm\nDLBr9nITYEdJewHPRMSjTmxjZtb9aukzmATcnD0PYHb2uAQ43IltzMy6n5PbNPwe9xl0EvcZmBXj\ntYnMzMz5DLqB5yaYWbM5GHQFz00ws+ZyMOhRrk2YWT0cDHqWaxNmVjsHA2tIIzUPcO3DrFOVNppI\n0qckPSJptaQFkiaXdW7rVFHnw8w6VSnBQNIHgbOB04C9SEtRzJP02jLOb2ZmzVVWzeA44OKI+E5E\nPBARnwGeAI4q6fx1mO8yOqqMVpZjZo0qHAwkbQa8mVyCm8yNtCXBzXyX0VFltLIcM2tUGR3IWwOj\nGDjBzbb/43HFAAAFY0lEQVT9D2+2pS6jwTIa7RSutxwz6zwtH000Zkzt3QgvvvgX1q6tt4Sl9b6h\nAb1aRr1rMzVajpl1mjKCwdPAOlIu5LzxpH6DjTz33GMNFNHIL9ZWvKfZZajqv80oR4M8L7OMDcc2\nr/ZhZkUUDgYRsVbSQlKCm5/kdr0L+FHVsb4TmJl1oLKaic4Evi/pLtKw0k+S+gvOL+n8ZmbWRKUE\ng4i4StKrSVnOtgN+A+wfEY+WcX4zM2uupiS3ySabHUUaWloZUfQEqdZwfjcHCUmviIgX230dZmZl\nKj25TbYMxf3AB4D7SLmQf5htOwRYXMZSFZK2kvReSXurqldS0hhJpxQtYxDPSZpQxokk7SNp99zr\nGZLul/QXSfdJKjxpT9IoSTMlXSvpk9m2wyX9TtLvJX0tmytSmKRXSfpXSZdIukHSPEkXSzoiy6Vd\nRhmjJE2UtP0A+0ZL+lgZ5ZiNNKXXDCQtAG7PZiEPtH8usHdETCpQxkTg58A2pIC2CDg4IpZm+7cF\nlkVEw8FO0jfoP9ZSwKeAy4EVQAz276yxjMXA0RFxi6QZwOnAOcAS4PXAp4GZEfHNAmV8Kbvm64Cp\npNzVM0j9POuBY4ELI2Jmo2Vk5UwAbgLGAr8gzTOBNKpsMrCKlBv7/gJlvBa4AXhDtula4LCIWJHt\nL/x3NxupmhEMVgN7RcQDg+x/A7AoIkYXKONa4CXgUGAcMJfUJLVvRDxYUjBYD9xLuulXkhcL2AdY\nADxPCgZTC5SxGtg9Iv4gaREwNyIuye0/BPhSROw+2DlqKONh4JiI+Gl2w74P+FhEXJbtfx9wRkT8\nbaNlZOe5hRQApkfEmqp9o0lBaHzB/18/AF4LHAlsAXyd9Pf/h4hY7mBg1rhmTDp7kvRLcMBgQLpp\n95t/UKd3kG78zwHPAdMknQncImlfYGXB8wOcDBxBupHeWtko6UXSr9HFJZSxilS7+QOwPamGk7cI\n2LFgGdsBvwaIiPslvVRVzqLsmKLeDkyqDgRZuWsknQbcXbCMqcCBEfFbAElTgcuA+dlzM2tQM35B\nfQ34lqQLJB0saXL2OFjSBcB5wFcLlrE5qYnjZRFxHHAVcAsbmhEaFhFzgI8A35E0R9Ko/O6i58/M\nA47Ons8HplXtn8bgQbVWTwB7AEjajfQDYGJu/wT6LyXSiBWkpq3B7JodU8Q44M+VFxGxlvQ3Wkz6\n/1dGUDMbkUqvGUTEeZKeIa1kejhp3SJIs5QXAodGxFUFi3kQmETqlM6XfaykTYBrKOGGHRF3SnoL\ncCFwp6R/KXrOKp8HbpP0n8BdwHGSppD+XbuTakAHFSzjCuB7WdPavqR+ibmSxpMC6ueBKwuWAXAR\ncGnWIX4jGwLMeNKExBOBswqW8RBpifTfVTZExEuSPkz6N/wUJ04wa0xENO0BbEZq/tge2KzE854E\n3DDE/nOB9SX/W44gtYmvByaUeN4tga+QAsAaYC1pMZ8fAG8t4fybAqeQbpSfyrZ9hnSzXk4KdGNK\n+recSKqJrK96LANOKOH8/we4cZB9ryD9CCj17+6HHyPl0ZR5Br0qa2Z5J3B1RJTRL9FzsmG+O7Nh\nfsmTwCNRwgdN0qakwDXg//usKW+HiPhD0bLMRhoHA2uJbFjo7Ig4vJvLMOtVDgbWEpL2Au6JJg77\nbEUZZr2q5fkMrDdJms7QnbdFh8i2pAyzkco1AytFNklvNVVDfnM2AUZHxKhB9ndEGWYjlavTVpZl\npJnNYwd6AH9PY1l6Wl2G2YjkYGBluQd4Uw+UYTYiuc/AynIGMNTKpL8jTXrr9DLMRiT3GZiZmZuJ\nzMzMwcDMzHAwMDMzHAzMzAwHAzMzA/4bnAEAtFd4HGcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17ff6c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram of assists grouped by position\n",
    "nba.hist(column='ast', by='pos', sharex=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}