ML in Python - Regression with scikit-learn.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:2.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "Regresssion with scikit-learn<br><br> using Soccer Dataset\n",
    "\n",
    "<br><br></p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "Import Libraries<br><br></p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import pandas as pd \n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "Read data from the database into Pandas\n",
    "<br><br></p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create your connection.\n",
    "cnx = sqlite3.connect('database.sqlite')\n",
    "df = pd.read_sql_query(\"SELECT * FROM Player_Attributes\", cnx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>id</th>\n",
       "      <th>player_fifa_api_id</th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>date</th>\n",
       "      <th>overall_rating</th>\n",
       "      <th>potential</th>\n",
       "      <th>preferred_foot</th>\n",
       "      <th>attacking_work_rate</th>\n",
       "      <th>defensive_work_rate</th>\n",
       "      <th>crossing</th>\n",
       "      <th>...</th>\n",
       "      <th>vision</th>\n",
       "      <th>penalties</th>\n",
       "      <th>marking</th>\n",
       "      <th>standing_tackle</th>\n",
       "      <th>sliding_tackle</th>\n",
       "      <th>gk_diving</th>\n",
       "      <th>gk_handling</th>\n",
       "      <th>gk_kicking</th>\n",
       "      <th>gk_positioning</th>\n",
       "      <th>gk_reflexes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>218353</td>\n",
       "      <td>505942</td>\n",
       "      <td>2016-02-18 00:00:00</td>\n",
       "      <td>67.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>right</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>49.0</td>\n",
       "      <td>...</td>\n",
       "      <td>54.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>218353</td>\n",
       "      <td>505942</td>\n",
       "      <td>2015-11-19 00:00:00</td>\n",
       "      <td>67.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>right</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>49.0</td>\n",
       "      <td>...</td>\n",
       "      <td>54.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>218353</td>\n",
       "      <td>505942</td>\n",
       "      <td>2015-09-21 00:00:00</td>\n",
       "      <td>62.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>right</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>49.0</td>\n",
       "      <td>...</td>\n",
       "      <td>54.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>218353</td>\n",
       "      <td>505942</td>\n",
       "      <td>2015-03-20 00:00:00</td>\n",
       "      <td>61.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>right</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>48.0</td>\n",
       "      <td>...</td>\n",
       "      <td>53.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>218353</td>\n",
       "      <td>505942</td>\n",
       "      <td>2007-02-22 00:00:00</td>\n",
       "      <td>61.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>right</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>48.0</td>\n",
       "      <td>...</td>\n",
       "      <td>53.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  player_fifa_api_id  player_api_id                 date  overall_rating  \\\n",
       "0   1              218353         505942  2016-02-18 00:00:00            67.0   \n",
       "1   2              218353         505942  2015-11-19 00:00:00            67.0   \n",
       "2   3              218353         505942  2015-09-21 00:00:00            62.0   \n",
       "3   4              218353         505942  2015-03-20 00:00:00            61.0   \n",
       "4   5              218353         505942  2007-02-22 00:00:00            61.0   \n",
       "\n",
       "   potential preferred_foot attacking_work_rate defensive_work_rate  crossing  \\\n",
       "0       71.0          right              medium              medium      49.0   \n",
       "1       71.0          right              medium              medium      49.0   \n",
       "2       66.0          right              medium              medium      49.0   \n",
       "3       65.0          right              medium              medium      48.0   \n",
       "4       65.0          right              medium              medium      48.0   \n",
       "\n",
       "      ...       vision  penalties  marking  standing_tackle  sliding_tackle  \\\n",
       "0     ...         54.0       48.0     65.0             69.0            69.0   \n",
       "1     ...         54.0       48.0     65.0             69.0            69.0   \n",
       "2     ...         54.0       48.0     65.0             66.0            69.0   \n",
       "3     ...         53.0       47.0     62.0             63.0            66.0   \n",
       "4     ...         53.0       47.0     62.0             63.0            66.0   \n",
       "\n",
       "   gk_diving  gk_handling  gk_kicking  gk_positioning  gk_reflexes  \n",
       "0        6.0         11.0        10.0             8.0          8.0  \n",
       "1        6.0         11.0        10.0             8.0          8.0  \n",
       "2        6.0         11.0        10.0             8.0          8.0  \n",
       "3        5.0         10.0         9.0             7.0          7.0  \n",
       "4        5.0         10.0         9.0             7.0          7.0  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "Declare the columns you want to use as features\n",
    "<br><br></p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features = [\n",
    "       'potential', 'crossing', 'finishing', 'heading_accuracy',\n",
    "       'short_passing', 'volleys', 'dribbling', 'curve', 'free_kick_accuracy',\n",
    "       'long_passing', 'ball_control', 'acceleration', 'sprint_speed',\n",
    "       'agility', 'reactions', 'balance', 'shot_power', 'jumping', 'stamina',\n",
    "       'strength', 'long_shots', 'aggression', 'interceptions', 'positioning',\n",
    "       'vision', 'penalties', 'marking', 'standing_tackle', 'sliding_tackle',\n",
    "       'gk_diving', 'gk_handling', 'gk_kicking', 'gk_positioning',\n",
    "       'gk_reflexes']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "What is the target for prediction\n",
    "<br><br></p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "target = ['overall_rating']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "Clean the data<br><br></p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "Extract features and target ('overall_rating') into separate dataframes\n",
    "<br><br></p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = df[features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = df[target]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "Let us look at a typical row from our features: <br><br></p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "potential             66.0\n",
       "crossing              49.0\n",
       "finishing             44.0\n",
       "heading_accuracy      71.0\n",
       "short_passing         61.0\n",
       "volleys               44.0\n",
       "dribbling             51.0\n",
       "curve                 45.0\n",
       "free_kick_accuracy    39.0\n",
       "long_passing          64.0\n",
       "ball_control          49.0\n",
       "acceleration          60.0\n",
       "sprint_speed          64.0\n",
       "agility               59.0\n",
       "reactions             47.0\n",
       "balance               65.0\n",
       "shot_power            55.0\n",
       "jumping               58.0\n",
       "stamina               54.0\n",
       "strength              76.0\n",
       "long_shots            35.0\n",
       "aggression            63.0\n",
       "interceptions         41.0\n",
       "positioning           45.0\n",
       "vision                54.0\n",
       "penalties             48.0\n",
       "marking               65.0\n",
       "standing_tackle       66.0\n",
       "sliding_tackle        69.0\n",
       "gk_diving              6.0\n",
       "gk_handling           11.0\n",
       "gk_kicking            10.0\n",
       "gk_positioning         8.0\n",
       "gk_reflexes            8.0\n",
       "Name: 2, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.iloc[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "Split the data set into Training and Test set\n",
    "<br><br></p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=324)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "Fit the model to the training set\n",
    "<br><br></p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=5, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_split=1e-07,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "           splitter='best')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = DecisionTreeRegressor(max_depth=5)\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "Perform Prediction\n",
    "<br><br></p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_prediction = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "What is the mean of the expected target value in test set ?\n",
    "<br><br></p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "overall_rating    68.635818\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "Evaluate Regression Accuracy using Root Mean Square Error\n",
    "\n",
    "<br><br></p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "RMSE = sqrt(mean_squared_error(y_true = y_test, y_pred = y_prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.3549465659987265\n"
     ]
    }
   ],
   "source": [
    "print(RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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