fpl_points_predictor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e922997f",
   "metadata": {},
   "source": [
    "# Machine Learning for FPL points prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "802aac92",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "80f2c5d9",
   "metadata": {},
   "source": [
    "Below I test a variety of approaches for building a point predictor for Fantasy Premier League. I arranged the data in a \"time-series fashion\", where I use information available from the previous 4 gameweeks (credit to https://github.com/vaastav/Fantasy-Premier-League for the raw data)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a6dab6a",
   "metadata": {},
   "source": [
    "The analysis showed that, among the ML algorithms tested, Random Forests produced the better results. This model can be used for captaincy and transfers choices, and can be easily adapted to predict points on a bigger time-window, becoming also a tool for long-term transfer planning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef967c44",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ae0a014b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\".\\complete_timeseries_dataset.csv\",index_col = 0)\n",
    "train = copy.deepcopy(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdabd06b",
   "metadata": {},
   "source": [
    "The dataset uses approximately 100 of the most popular FPL players last season.\n",
    "It then creates a row for each (player, gameweek) pair, and adds information for the previous four gameweeks as explanatory variables."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c77cdd1",
   "metadata": {},
   "source": [
    "Information can be roughly divided in 4 main categories:\n",
    "1) Information before kick-off (which team the player is facing, how many fantasy players transferred him in/out, ...)\n",
    "2) Player-specific data from previous 4 gameweeks (big chances created, points, influence, assists, ...)\n",
    "3) Team specific data from previous 4 gameweeks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "717d296c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1856, 222)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6aa4937d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['name', 'team', 'position', 'gameweek', 'total_points', 'opponent_team',\n",
      "       'transfers_in', 'transfers_out', 'value', 'was_home',\n",
      "       ...\n",
      "       '4_opp_npxGA', '4_opp_deep', '4_opp_deep_allowed', '4_opp_scored',\n",
      "       '4_opp_missed', '4_opp_xpts', '4_opp_wins', '4_opp_draws',\n",
      "       '4_opp_loses', '4_opp_npxGD'],\n",
      "      dtype='object', length=222)\n"
     ]
    }
   ],
   "source": [
    "print(train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3e7338ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>team</th>\n",
       "      <th>position</th>\n",
       "      <th>gameweek</th>\n",
       "      <th>total_points</th>\n",
       "      <th>opponent_team</th>\n",
       "      <th>transfers_in</th>\n",
       "      <th>transfers_out</th>\n",
       "      <th>value</th>\n",
       "      <th>was_home</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1068</th>\n",
       "      <td>Raphinha</td>\n",
       "      <td>Leeds</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>103223.0</td>\n",
       "      <td>46154.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1069</th>\n",
       "      <td>Raphinha</td>\n",
       "      <td>Leeds</td>\n",
       "      <td>2.0</td>\n",
       "      <td>29</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>414907.0</td>\n",
       "      <td>13297.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1070</th>\n",
       "      <td>Raphinha</td>\n",
       "      <td>Leeds</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>115574.0</td>\n",
       "      <td>43418.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1071</th>\n",
       "      <td>Raphinha</td>\n",
       "      <td>Leeds</td>\n",
       "      <td>2.0</td>\n",
       "      <td>31</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>33636.0</td>\n",
       "      <td>297503.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          name   team  position  gameweek  total_points  opponent_team  \\\n",
       "1068  Raphinha  Leeds       2.0        28           3.0            5.0   \n",
       "1069  Raphinha  Leeds       2.0        29           8.0            8.0   \n",
       "1070  Raphinha  Leeds       2.0        30           6.0           15.0   \n",
       "1071  Raphinha  Leeds       2.0        31           2.0           12.0   \n",
       "\n",
       "      transfers_in  transfers_out  value  was_home  \n",
       "1068      103223.0        46154.0   55.0      True  \n",
       "1069      414907.0        13297.0   56.0     False  \n",
       "1070      115574.0        43418.0   57.0      True  \n",
       "1071       33636.0       297503.0   56.0     False  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.iloc[1068:1072, 0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ab76260",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "60afdc53",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1_total_points</th>\n",
       "      <th>1_assists</th>\n",
       "      <th>1_bonus</th>\n",
       "      <th>1_bps</th>\n",
       "      <th>1_clean_sheets</th>\n",
       "      <th>1_creativity</th>\n",
       "      <th>1_goals_conceded</th>\n",
       "      <th>1_goals_scored</th>\n",
       "      <th>1_ict_index</th>\n",
       "      <th>1_influence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1068</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>15.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1069</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1070</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.3</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1071</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      1_total_points  1_assists  1_bonus  1_bps  1_clean_sheets  1_creativity  \\\n",
       "1068             2.0        0.0      0.0   13.0             0.0          17.4   \n",
       "1069             3.0        0.0      0.0    5.0             1.0          31.1   \n",
       "1070             8.0        0.0      1.0   28.0             0.0          53.6   \n",
       "1071             6.0        1.0      1.0   24.0             0.0          43.9   \n",
       "\n",
       "      1_goals_conceded  1_goals_scored  1_ict_index  1_influence  \n",
       "1068               2.0             0.0          8.1         15.4  \n",
       "1069               0.0             0.0          8.9         12.8  \n",
       "1070               1.0             1.0         12.3         45.0  \n",
       "1071               1.0             0.0         12.8         35.0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.iloc[1068:1072, 10:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cee832ab",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "766f477c",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>1_minutes</th>\n",
       "      <th>1_opponent_team</th>\n",
       "      <th>1_own_goals</th>\n",
       "      <th>1_penalties_missed</th>\n",
       "      <th>1_penalties_saved</th>\n",
       "      <th>1_red_cards</th>\n",
       "      <th>1_round</th>\n",
       "      <th>1_saves</th>\n",
       "      <th>1_selected</th>\n",
       "      <th>1_threat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1068</th>\n",
       "      <td>90.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>837804.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1069</th>\n",
       "      <td>90.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>897253.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1070</th>\n",
       "      <td>90.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1463983.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1071</th>\n",
       "      <td>90.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1377968.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      1_minutes  1_opponent_team  1_own_goals  1_penalties_missed  \\\n",
       "1068       90.0             19.0          0.0                 0.0   \n",
       "1069       90.0              5.0          0.0                 0.0   \n",
       "1070       90.0              8.0          0.0                 0.0   \n",
       "1071       90.0             15.0          0.0                 0.0   \n",
       "\n",
       "      1_penalties_saved  1_red_cards  1_round  1_saves  1_selected  1_threat  \n",
       "1068                0.0          0.0     27.0      0.0    837804.0      48.0  \n",
       "1069                0.0          0.0     28.0      0.0    897253.0      45.0  \n",
       "1070                0.0          0.0     29.0      0.0   1463983.0      24.0  \n",
       "1071                0.0          0.0     30.0      0.0   1377968.0      49.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.iloc[1068:1072, 20:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7964a88",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fc72a19e",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1_yellow_cards</th>\n",
       "      <th>1_goals_scored_team</th>\n",
       "      <th>1_goals_conceded_team</th>\n",
       "      <th>1_was_home</th>\n",
       "      <th>1_transfers_in</th>\n",
       "      <th>1_transfers_out</th>\n",
       "      <th>1_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1068</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>130876.0</td>\n",
       "      <td>39272.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1069</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>103223.0</td>\n",
       "      <td>46154.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1070</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>414907.0</td>\n",
       "      <td>13297.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1071</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>115574.0</td>\n",
       "      <td>43418.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      1_yellow_cards  1_goals_scored_team  1_goals_conceded_team  1_was_home  \\\n",
       "1068             0.0                  0.0                    2.0       False   \n",
       "1069             0.0                  0.0                    0.0        True   \n",
       "1070             0.0                  2.0                    1.0       False   \n",
       "1071             0.0                  2.0                    1.0        True   \n",
       "\n",
       "      1_transfers_in  1_transfers_out  1_value  \n",
       "1068        130876.0          39272.0     54.0  \n",
       "1069        103223.0          46154.0     55.0  \n",
       "1070        414907.0          13297.0     56.0  \n",
       "1071        115574.0          43418.0     57.0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.iloc[1068:1072, 30:37]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c86279ac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6210bc7b",
   "metadata": {},
   "source": [
    "Same thing repeats for the past 4 gameweeks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28ffa4e4",
   "metadata": {},
   "source": [
    "There are also team-specific variables, in this case we have Leeds related xG, XG conceded etc.\n",
    "For team related stats I am also using a \"4 gameweek window\", that is I am using all this information for the past 4 gameweeks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bc1a8dae",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1_xG</th>\n",
       "      <th>1_xGA</th>\n",
       "      <th>1_npxG</th>\n",
       "      <th>1_npxGA</th>\n",
       "      <th>1_deep</th>\n",
       "      <th>1_deep_allowed</th>\n",
       "      <th>1_scored</th>\n",
       "      <th>1_missed</th>\n",
       "      <th>1_xpts</th>\n",
       "      <th>1_wins</th>\n",
       "      <th>1_draws</th>\n",
       "      <th>1_loses</th>\n",
       "      <th>1_npxGD</th>\n",
       "      <th>2_xG</th>\n",
       "      <th>2_xGA</th>\n",
       "      <th>2_npxG</th>\n",
       "      <th>2_npxGA</th>\n",
       "      <th>2_deep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1068</th>\n",
       "      <td>0.77074</td>\n",
       "      <td>2.506140</td>\n",
       "      <td>0.77074</td>\n",
       "      <td>1.744970</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.2415</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.974230</td>\n",
       "      <td>2.64769</td>\n",
       "      <td>0.370278</td>\n",
       "      <td>2.64769</td>\n",
       "      <td>0.370278</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>1069</th>\n",
       "      <td>2.47930</td>\n",
       "      <td>0.592049</td>\n",
       "      <td>2.47930</td>\n",
       "      <td>0.592049</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.6173</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.887251</td>\n",
       "      <td>0.77074</td>\n",
       "      <td>2.506140</td>\n",
       "      <td>0.77074</td>\n",
       "      <td>1.744970</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1070</th>\n",
       "      <td>1.84753</td>\n",
       "      <td>1.479810</td>\n",
       "      <td>1.84753</td>\n",
       "      <td>1.479810</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.6148</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.367720</td>\n",
       "      <td>2.47930</td>\n",
       "      <td>0.592049</td>\n",
       "      <td>2.47930</td>\n",
       "      <td>0.592049</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1071</th>\n",
       "      <td>1.00106</td>\n",
       "      <td>0.876255</td>\n",
       "      <td>1.00106</td>\n",
       "      <td>0.876255</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.4385</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.124805</td>\n",
       "      <td>1.84753</td>\n",
       "      <td>1.479810</td>\n",
       "      <td>1.84753</td>\n",
       "      <td>1.479810</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         1_xG     1_xGA   1_npxG   1_npxGA  1_deep  1_deep_allowed  1_scored  \\\n",
       "1068  0.77074  2.506140  0.77074  1.744970     7.0            13.0       2.0   \n",
       "1069  2.47930  0.592049  2.47930  0.592049     6.0             1.0       0.0   \n",
       "1070  1.84753  1.479810  1.84753  1.479810    10.0             5.0       3.0   \n",
       "1071  1.00106  0.876255  1.00106  0.876255     6.0             1.0       0.0   \n",
       "\n",
       "      1_missed  1_xpts  1_wins  1_draws  1_loses   1_npxGD     2_xG     2_xGA  \\\n",
       "1068       4.0  0.2415     0.0      0.0      1.0 -0.974230  2.64769  0.370278   \n",
       "1069       1.0  2.6173     0.0      0.0      1.0  1.887251  0.77074  2.506140   \n",
       "1070       0.0  1.6148     1.0      0.0      0.0  0.367720  2.47930  0.592049   \n",
       "1071       1.0  1.4385     0.0      0.0      1.0  0.124805  1.84753  1.479810   \n",
       "\n",
       "       2_npxG   2_npxGA  2_deep  \n",
       "1068  2.64769  0.370278     7.0  \n",
       "1069  0.77074  1.744970     7.0  \n",
       "1070  2.47930  0.592049     6.0  \n",
       "1071  1.84753  1.479810    10.0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.iloc[1068:1072, 118:136]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e1326d3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b8a405d1",
   "metadata": {},
   "source": [
    "ofc important, who you're playing against, so I added stats of the opponent team for past 4 gwks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1446773f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1_opp_xG</th>\n",
       "      <th>1_opp_xGA</th>\n",
       "      <th>1_opp_npxG</th>\n",
       "      <th>1_opp_npxGA</th>\n",
       "      <th>1_opp_deep</th>\n",
       "      <th>1_opp_deep_allowed</th>\n",
       "      <th>1_opp_scored</th>\n",
       "      <th>1_opp_missed</th>\n",
       "      <th>1_opp_xpts</th>\n",
       "      <th>1_opp_wins</th>\n",
       "      <th>1_opp_draws</th>\n",
       "      <th>1_opp_loses</th>\n",
       "      <th>1_opp_npxGD</th>\n",
       "      <th>2_opp_xG</th>\n",
       "      <th>2_opp_xGA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1068</th>\n",
       "      <td>1.641440</td>\n",
       "      <td>0.623045</td>\n",
       "      <td>0.880268</td>\n",
       "      <td>0.623045</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2695</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.257223</td>\n",
       "      <td>2.393000</td>\n",
       "      <td>0.569446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1069</th>\n",
       "      <td>0.594471</td>\n",
       "      <td>1.206420</td>\n",
       "      <td>0.594471</td>\n",
       "      <td>1.206420</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7654</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.611949</td>\n",
       "      <td>2.550850</td>\n",
       "      <td>0.314075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1070</th>\n",
       "      <td>0.977747</td>\n",
       "      <td>1.210060</td>\n",
       "      <td>0.977747</td>\n",
       "      <td>1.210060</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1636</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.232313</td>\n",
       "      <td>0.926652</td>\n",
       "      <td>2.327350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1071</th>\n",
       "      <td>0.760574</td>\n",
       "      <td>1.928920</td>\n",
       "      <td>0.760574</td>\n",
       "      <td>1.928920</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3903</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.168346</td>\n",
       "      <td>2.088680</td>\n",
       "      <td>0.331188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      1_opp_xG  1_opp_xGA  1_opp_npxG  1_opp_npxGA  1_opp_deep  \\\n",
       "1068  1.641440   0.623045    0.880268     0.623045           9   \n",
       "1069  0.594471   1.206420    0.594471     1.206420           4   \n",
       "1070  0.977747   1.210060    0.977747     1.210060           2   \n",
       "1071  0.760574   1.928920    0.760574     1.928920           5   \n",
       "\n",
       "      1_opp_deep_allowed  1_opp_scored  1_opp_missed  1_opp_xpts  1_opp_wins  \\\n",
       "1068                   4             2             1      2.2695           1   \n",
       "1069                   5             1             1      0.7654           0   \n",
       "1070                   7             0             1      1.1636           0   \n",
       "1071                   2             2             1      0.3903           1   \n",
       "\n",
       "      1_opp_draws  1_opp_loses  1_opp_npxGD  2_opp_xG  2_opp_xGA  \n",
       "1068            0            0     0.257223  2.393000   0.569446  \n",
       "1069            1            0    -0.611949  2.550850   0.314075  \n",
       "1070            0            1    -0.232313  0.926652   2.327350  \n",
       "1071            0            0    -1.168346  2.088680   0.331188  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.iloc[1068:1072, 170:185]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05170fc4",
   "metadata": {},
   "source": [
    "#### Importing relevant libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "08d7e563",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "import random\n",
    "import copy\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import joblib\n",
    "import pickle\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "60bd1848",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "4a17ebfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.feature_selection import f_classif\n",
    "from sklearn import linear_model\n",
    "from sklearn.linear_model import LinearRegression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37f66928",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5f448cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import ensemble  \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor \n",
    "from sklearn.neural_network import MLPRegressor "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "000b1f13",
   "metadata": {},
   "source": [
    "### Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67572440",
   "metadata": {},
   "source": [
    "Splittting the dataset into train and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c814e6ae",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "523e12c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.concat([train.iloc[:,[2]], train.iloc[:,5:]], axis = 1)\n",
    "y = train.iloc[:,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "027dbf31",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68d9999e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "840115c0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c7504633",
   "metadata": {},
   "source": [
    "## Naive Bayes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1e370bf",
   "metadata": {},
   "source": [
    "Naive Bayes performs pretty badly due to its nature of classifier rather than regressor. Plots are given below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "999f1c83",
   "metadata": {},
   "outputs": [],
   "source": [
    "NaiveBayes = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "38af08df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NaiveBayes.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c489a62a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "c97e326c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(MSE):  78.71336206896552\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gle4D+PSvvPvf6s9dfQdH+d8MuuruG0D12Er0DQyNst8sK0wqcqu/zbEyfI986iQsbHrtHrlK2fY+Pu+NzZhdX4PKCsJhUydibuNEZZ8HTZttL7O5frTdAHzHSdiU7l7Xpq0xWcm0BkFEbwbQC+DHtgmil5m/plqOzkA5JzEIKAykXX0D2GZ74yyaWYcDg0NY8cDzowRUpze3m8jkJTAHGTReAqE5kY2tJM83oikImx/abS2TcazxoW219cXOXvzycef2qkakHjZ1Is49qhnX3POsa/Rofc04fOi4OVjxwAsj/D5t4vhRk5KKYOwUQR4mYtVJbFw0sw5jKoDOnkFcffe64XL+9+wlWDi9Fjv7BhzFTbtN9r4yv2TccN96fPjYudh/cGiU/V4RvX4fPna/uX0Rcor09YtUPmzqRHzguDnY0TMQyudB02Y7tcVut99Ckygf4qqLUuLMqqBKpicIACCiOQDuycoE4YWXiB1VQI1DuNIdLWoV1M0o7DDttdahEj3qdk4Ygd0tgtxep0pb3MbDYVMn4TO/XuNY/tLZ9Y7luNlkF7ovOn4eKivgei4QPKLXqy3WesL2wyUnzXe1WcXn9kUKqosavOz2W2gS5f2YJRHaj2IVqT9GRE8T0c1ENPodBYCILiaidiJq7+rqcjpFO14idlQBNQ7hSne0qPUce7vD1qESPRqkDhXB2G2RQdCIVbfx0DeQcyxn+173cvwWPliFeq9zw44jlQUaYfvBy2YVnwdNm60Soazqb5W2erU96LVZIqsTxLcBHArgCAAdAL7udBIzr2LmNmZua2xsTMQwPxE7ioAah3ClO1rUeo5Tu8PW4XS9PXpUl8DuFkHuVKdfW9zGw4SqMY7lTK9zL8dv4YO1Lq9zw44j1QUaYfshis+Dps1WiVAO4m+/ttopFhHaj0xOEMzcycxDzJwH8D0ARyVZv1cEqVO0pBl5bKb09Urt60XYFNJB7V3x3iPQXF/jmmJYJcp3TsOE4SjsMO1trq/BDe9ZgqqxFbj9ia24bPmCUZHbC6dNGrbDPEelDj8/Wm23nxM0YtVtPEyqqsSX3tk64vi1Zy5Ca1OdazluNlntNsfZ5JpxI85taajGqgvb0N03EDq63Gtsm8dWb+jCdYplW8u7/YmtaJgwzrefncq0RhyrjjV7W5wilO1tc/N3UD+6XasSTZ529LydrGoQTczcYfz/SQBvYubzvMqIcxWTirgEvCZij3VYxRR0FUmQ9OFB7bWvqHIT8NyifK11m6tuDg4NYZLiKiYnEfyEBVPw6u5+XP4b51U7puA+lIenTap+tNpujSAPE7HqNh627O5D594B7OwbwMy6arTOqBsW+d3KcbPJXteuvgFUGauYDg4Nobvv4PAiiSjR5SoLNFY88Lzr4gWv8qbXFr49W1d7OfWzW3p4v8UJfm0xVzE5tc1rnERJ6R4kmjytlU2ZFqmJ6BcATgQwBUAngC8Zz48AwAA2AfiIOWG4EWckdVbFJUCPCB1EeIzL5jhTjJc6QQXcqPVEKb/YIsp1kVQmhTBkWqRm5vOZuYmZxzLzLGb+ATNfyMyLmXkJM7/Lb3LQSbGJS3GmGI6rzarR6ln2e5ZIat/juCP946ozCySVSUE3qU8QWaPYxKU4UwzH1eYgYmhW/Z4lktr3OO5I/7jqzAJJZVLQjUwQCJ6GOW0bnfZGDpKaW0XAi7PNKmJoVvxeDFjF6zCLI9xw2185SvlBxdss7uMeBpV2JNXWIKSuQegirAYRNA1zGviJVyppMZzK9BPw4myzlxiadmRpMWIVr+37Huvc69svG0AQW4OIt1nZxz1qPX7tSCO6OtMitS7CThDFIILpTiUtCH5kLd17nGTBhjTJtEidNlkUhuzoTiUtCH5kLd17nGTBhqxS9hNEFoUhO7pTSQuCH1lL9x4nWbAhq5T9BJEVYShMNLSOiE+ddmYtClRwx6+voo4p1bGgM3uAzj20VdsatM4o75E03l9lr0EA6afd1ZEaOIk2eNkJhEsxLSSPqigbdkwFFX11ZA8Iukd8VBvCtjPO9OFhEZE64xSLSKZrv2shXeIeb2mUn0YkfpJR4XH6VETqjFMsIpmu/a6FdIm7r9IoP41I/CSjwtN6f8kEkQGKRSTTtd+1kC5x91Ua5acRiZ9kVHha7y+ZICzoFoHiFuritt/NTqeo7ayI/XGg6leV87Ig5MfdVyrlm354fFM31mzZHcgf1vKb6qpw5TsOx9HzJuPaM9XSkOdyeazZshv3re3Ami17kMvlR53jhlvWhaa6Kly6fD6+dvZSMMOxHaqp950Edx1R7GEQDcJAtwgUt1AXt/1ueEVtA6UXCR1E0FVZaJAVIT+J6GS38oPuL+1W/uZdfVj3ag8279qPGx9SS3Gey+Vx55ptI1LbX3vmIpy5dKZnKnar3XZx/NApE/HPLaNTmLvt0+2Vet9LcNcRxe6EiNQK6BaBkhaeyynyNUlU25vldM5ZQ1d6cpV9vO2s2bLbcf9rt73Cnex2ujbIPuZ+ZSYtuItIrYBuEShpUamcIl+TRLW9xZrOOQ10pSdX2cfbjtv+1257hTvZbb826D7mfmVmKfW9TBAGukWgpEWlcop8TRLV9hZrOuc00JWeXGUfbztu+1+77RXuZrf12qD7mPuVmaXU96lPEER0MxHtIKK1lmOTiegBInrR+Ov92y8CphjU3TcwvEcyEE4ESjNteFIicSmL0U7YBVG7EBlk/BSz73SK61HTk5u27D0wiMOnTXLcm7uzp9/Rztam2lGp7b32Cney23rtyguWobZqTKg9wN3KzFLq+9Q1CCJ6M4BeAD+27En9VQC7mPl6IvocgHpmvtyrnDAahF10ammoxjVnLMbYSgqcKjkLacOTighPO/I8aUxB1L6Xsl1MVBk/xei7OMT1sOnJ7QL3re2bcd4bmzG7vgbV4yrQ2z80al9zu525XB7rOvZi+95+TK+rQmuT917hTnbbU5WH3QPcXmYaqe8zL1IT0RwA91gmiOcBnMjMHUTUBOARZj7cq4wwE4RO0VAEyNImC2JiWmRpbHsJ3En3R5b8EoViFKmnmftQG3+nOp1ERBcTUTsRtXd1dQWuRKdoKAJkaZN1MTFOsjS2vQTupPsjS36Ji6xOEEow8ypmbmPmtsbGxsDX6xQNRYAsbbIuJsZJlsa2l8CddH9kyS9xkdUJotO4tQTj7444KtEpGhazAFmMJB2RnHUx0SQOvwSJAI4bL4E7bH8kkSY8CxH0YciqBvG/ALotIvVkZv6sVxlR9qTWJQYVowBZjKQVkZy2mKhiX1x+UYkATioq3EvgBoL1R1Sfqe4znZUIeicyLVIT0S8AnAhgCoBOAF8CcCeA2wA0A9gM4Bxm3uVVTjGn+xaCUSrioG6S9Eup9EES7ci6rzItUjPz+czcxMxjmXkWM/+AmbuZeTkzLzD+ek4OQnlRDuJgGJL0S6n0QRLtKGZfpT5BCEJQykEcDEOSfimVPkiiHcXsK5kgLBSrkKQTnamt40L3goC0+11X/UkulEgiRX0S/RK0HWFsKuYFLKlrELrIWrrvYkRnauskbNUhDqfdljjSzCclmseZoh5Ibo9z1XZE3VM6K4sZ7GRapNZF1tJ9FyM6U1sXC2m3Je3606TY9jgv1b7KtEidFYpZSNKFztTWxULabUm7/jQptj3Os2hT3IxJ24CsYApJ9m8HxSAk6ULVB2F8ZSZI69jbj6a6arQ21SonSIuTuPvdvLXQ2dPvmIxu6qTkxp2fLUHPi4qf78P6xcv+KG0rx8+I9N+hGaGYhSRdqPogqK/MbR7PXbUaH/3pkzh31d9x55ptgfYCjos4+928Z33aTX/G+d97DKfd9Gfct277sLCZzzNe7u4dka46rnHnZ0vQ83Tg5fuw/eJlf9S2leNnhGgQFrIsJCVFEMFO1VdRtnlMgrj63e+etfl6fc04nHXkLBABFQSctWwm5kxJJ7At6fvsfntXB+2XuHWNUvyM8NIg5BaThYoKwrzGiUUtOEVF1QdBfOW1zePS2ZHM1UJc/e51z3pe48Th1zv29uNbf3xp+JxjD23QPkH42RL0PF14+T5Mv3jZzx7ZXlXrKLfPCLnFJMROlG0eixm/AKksBrYVc1AX4G1/sbctDWSC8CGuYJ2kA9Ks5Wza2YsNO0aXGaSuIOdG2eZRN0kGxfndsw6bDdSt/4LasvKCZWDGiHLSus9u75dcLq89w2o5aghREQ3Cg7iCqJIOSLOWU18zDu8/pgU3PvTiiDJPWThNOTtnGLuibPOoizSC4vzuWQfNBurWf0GDtazbZToFqSV5n91p69+Pn7wAX7hzrfYMq6WoIURFAuVCEpdgl7RgaC3nkpPmj9qq0RSMnYRkp7qKNWCoFOx2678oYyJKOTqw26KrjYIaEigXkrgCY5IOSLOW47RVoymUqtZVrAFDpWC3W/9FGRNRytGB3RZdbRSiIxOEB3GJWkkLhvZynMp0E5Kd6ipWsa9U7I5jTIQtRwduttifZ72fSpFMTxBEtImIniGip4gokd2ArGJZBSEWUSuugDSV+m5/YqtjYFZrU61yXVHsCiMSF2O2Uyd0bG1p77+WhmqsurANnT392sTcpPvIaktTXRVqx1fimjMWpdZPwmtkWoMgok0A2ph5p9+5ugLl7CLmyguWYW7DRHT16hW14ghIU61vem0VhvIY1aYgdYWxK4xIXMzZTu316tra0uy/3fsHsG1PPy6//WltYi4QPIuqjj7K5xmbd/Xhyc17cOUdz6C+ZhzOaZuFw6ZNwsLptZg7RcTkuIgsUhPRZQB+CGAfgO8DWAbgc8x8v05DHerdhAQniCwJd6VIGP+WSp/E0Y6slBnHYooo5QjB0CFSf5iZewCcAqARwIcAXK/JPi8YwP1E9AQRXWx/kYguJqJ2Imrv6uqKXFmWhLtSJIx/S6VP4mhHVsqMYzFFlHIEfahOEOZvu9MA/JCZ11iOxclxzHwkgHcAuISI3mx9kZlXMXMbM7c1NjZGrixLwl0pEsa/pdIncbQjK2XGtZgibDmCPlQniCeI6H4UJog/ENEkALGn4mTmV42/OwDcAeCoOOuLkkEybJRrEpG9Zh2Pb+rGmi27I9XlZq9KO1T967RQoKWhGpecNB+XLp+P713Yhub6mkj26jpfpYxcLo8KAr7y7sWukcxe48at/5rra3z9GbQ9QcRrM9q5u28AN7xnidbFFLraEydZsCVuG1Q1iAoARwDYyMx7iKgBwExmflqrNSPrnACggpn3Gf8/AODLzHyf0/m6AuWCiphRolyTiOw167jhvvU4t60ZNz0cPALXz96gUdh+kcX2Or574ZHYsz8XWIwN6l9dYqtbVLBVeG1tqsX67ft8xw0Az/47ZeE0bN693zVqOEx7VMRre7RzS0M1rjljMcZWUqQ9JPyioNPe6tZqZ9q26LJBh0j9EDMv9zumEyKah8KvBqCQdfbnzHyd2/lxRFKrECXKNQlRzqzjouPnRY5OdbM3SBR2mDouXT4fqx4NbntQ/+roD9WoYKvPvMYNgND9p3N8ZSHaOUsidhZs0WVDaJGaiKqIaDKAKURUT0STjcccADOULQgBM29k5qXGo9VrckiTKFGuSYhyZh06olPd7A0ShR2mjrxHmuYw9sbZH6pRwVafefVNlP7TOb6yEO2cJRE7C7YkYYOfBvERAE8AeJ3x13z8FsC3tFlRxESJck1ClLPWEbUuN3uDRGGHqaOSwtke1L86+kM1KtjuM7d6o/SfzvGVhWjnLInYWbAlCRs8JwhmvpGZ5wL4DDPPY+a5xmMpM6/UZkUG8RJ/8nnGpp29eGxjN7bt2T8s0LlFKbtFpyYR2WvWcfeabbj05GhbW7rZGyQKO0wdi2fVjYi0vXT5fHzt7KVghqcoZ4/Q9bsuiIhu9v/Dz3Viw47Xxoe9jLvXbBuV6tzuM69xo9p/TgsldInHQdqla+yqvF/CRpFHscGkub5GybdxbhfgtPBB9+eHciQ1ER0LYA4su9Ax84+1WRIRnRqEl/gDAA8/34kXO3uHRUWrQNdUNzpKGXCPTgXiT61sCn+7+gYwtrIC+weHQguJbiKizghlN5HUGmkbJMo3yHUqIrq9/+3l2ctorq9xFJJVotutNrn1n9dCCV3isZNv3NoVFb/3n+mLKFHkUW0wFw6cvmQmKiuAtpbJOHZew4gU9klsF6Aj4lyHSP0TAIcCeArAkHGYmfnSQJbEiM4Jwm9f2zuf2hZINM2CoFUKhPWjbrE2aP/HTRzpwNNEpb/ifk/p2Ns67e0CVNERSd2GQtDafzLzx41HZiYH3XiJP509/YFF0ywIWqVAWD/qFmvDiuZxEUc68DRR6a+431N+nwEqdae9XYAOVCeItQCma689o/jtaxtUNM2CoFUKhPWjbrE2rGgeF3GkA08Tlf6K+z2lY2/rtLcL0IHqBDEFwLNE9Aciust8aLcmI/jta7t4Vp2rqBi0vChkIZJThah2mteHFV2DitV+Zfn1f9L9opLOvZhSZau8X8Lu560a/e8liKtuAxDX+z7JtPWqGsRbnI4z85+0WxQS3YFyfhGdm3f1obNnAPsHc2iePMFXHNKdZjoLkZwq6EhzbY/gDSO6BhWrVcpy6v+0+kVV8C4WVN4vqueEjf63Lg6wC+Kq2wDElV5eZ7myJ3UJUizCd1Q744wGjlJWmnUI6uiI/i/1Po0SSf0X4+8+IuqxPPYRUU8cxgpqFIvwHdXOOKOBo5SVZh2COjqi/8u5T/0C5Y43/k5i5lrLYxIz1yZjouBEsQjfUe1MIho4rsj1uOoQ1NER/V/OfRokUG4pgBOMp4/Gmck1DGFuMZn38Tp7+pXuZ+dyeazr2IuOvf1oqqvGwmmTsHXvAXT3DWCcTwCavS4zyMit7lwuj+c6e9A7kENuiDGYy6OlYQJaJtdg29796N43iI3dfcPZNN2Ctbza5mTT1j370dkzgL7BHFoUtBU/nzbX1wzf6zWDehbNqMO0SeOx/6Czv6xlNNVV4dmOfa73ir3aqWLLYVMnYWFT7QgNYVN3H7r7BlA9thJ9A0OjfOFUJ4Dh615VCOCyt3EoD+zY95qd2/buR+feAezsG8DMQ2rQ2lQ7IggrCEHHnt/1qud7vS+C9FuUzLBuupNdgzC1repxFaNsBoJvw6oDPx+ZOljY96qJri1H/x3Ab4xD7wawipm/GdiamAg6QQQVE3O5PO5cs21EeuNLTlqAb/3xRd802l4poJ2uyeXy+P26DuzuG0Tf4NCIqNgrT1uIXX0HcfXd64Y/5A5tnIjWplrMa5yoLJQ62fSZUw7H1t0HlNOVq/r0lIXTsHXPfjy5eQ9ufOgFT385leEmCKpEu3rZ4vahccN96/HhY+di/8GhUb5wEjdXXrAMgzlWFtL9Ip+tfWzWce2Zi3Dm0pmBJ4mgYy/q+0Qlvbxb3/i9FvSLil+0sV2EXvHA8542J7mXedBsDlEmLR0TxNMAjmHmPuP5BAB/Z+YlgSyJkaATRFDhac2W3SNELTNiVSUNc9BUyWu27MZDz+0AgBERu5ecNB8Lp0/CZ369xlF0Wzq7XrltTjZVViBShLBK9Kmfv4L0S9hoV7fXTOHyouPnufrCSdwMmo7cK/JZtY9ViZqmO2zKdK9+BoL3TZz7W+tMia8L3dkcvNARSU14LcUGjP+La92cjaDCk13UMiNWVSJXg6ZK7thbiNa1R+wSAX0DOcdrt+8NFmXqZFPUCGGV6FO/tgfpl7DRrn7CpZcvnMTNKJH1dn+o9rEqUdN0h02ZrpLCPOhrQQgzjrIUha47m0NYVCeIHwJ4jIiuIqKrAawG8AOtliRMUOHJTdSy/nUrJ2iq5Ka6alSSc5rrCVVjHK+dXhcsylRnWm2VelXTVgfpl7DRrirCpZsvnMZB1Mj6MH2sStQ03VFSpuvsmzj3t9aZEl8XurM5hCWISH0kgOONp39m5n9qtWR0facCuBFAJYDvM/P1XufHoUFYRaIZh1Th8U27lTUIcyvIvQcGQSC81NU74tpR21BaxNJ8nh01iLaWOlz8lvno7h3Ed/700nAmyWWzD8Fx86Zg3LjKQG2z3qP90LEtmH5INbbZNIjvXngkDqkah46efsyqr0bVmEp09Q6MyiZqF2jNMhfPqiv8Gsozdu0/6HufN0gwWy6Xx71rO7Digedx+pKZqKuqxOJZh4AZmHHISHG7paEa15+1BBVEGBwawq6+g6MCnw6dMhH/3FLQSf7t+HnoHcjhl49vHvbzG1sm401zJuPB53eMEt0PHBxSzixq2m36yapBWPv46rvX4bCpE3Hxmw/F2DGEhgnjkWfG+DGjRVQ30XzvgUFs2rkfVxj+bGupw/lvmoPPW/xrajw9/YMYygM7ewfQVFeN1qZaVFSQoyZ06JSJ2LFvAIw8xlRUoqd/EJNrxgMANuzsw00eWhMwOhuq6dtXew649r9TO91E27kNE4a3dPXrE/O9cPNfNuDdR87GNfc867qIwevzRHXBh5v4bF+48FJXL57euhc14yox65AaEAGvm16Llsk1eOTFHdnRIIxCjkRhFVMewF+Z+clAVgSAiCoBvADgbQC2AngcwPnM/KzbNVFWManuf2t+WG7v6cf0uiosnFaLrXsPjErDbK6WufkvG/CeI5tx9T2jBeU5DRNcxdJTW6cjn+cRq5iYX/uAveTEQ1FZWTniTe42ufmlrd64oxcTqsYOvyk+dGwLXtdUi6E8Y+6UCWh/ZffwROa0b7LTapDrz1qCXX2D6O4dGCWyq6wUUUlhbH9Tr3p0w6gPJPODb/f+AezsHcTLO/tcU7Sbk4l1suzZP4jxY8fgi79dO6rN9r5TjfC2irjmh+Mx8xrQOHE89hwYHBZLLzpuLhbOmISXuw7gW48UvoTc2r7ZsY1Wgdx6zBSKb23fPDyBNkyqwk0PvTCi7l19B/HDv742Vs1yTGG8ooJGRGm/1NWLFzt78dD67XjPkc34zqMv4cPHzi3cHhscGp5UzQkbwCifWCdJ1y9Nlv63jg+nicNJtFWNdrbas+KB53HeG5tRW114T6h8+AZZ8OEWwe30Pvr4yQvwzYfdv4CaE5BqNgc3dIjU/w3gHAC3o6A9nAngV8x8bWBrFCCiYwBcxcxvN55fAQDM/D9u1+iOpI4SPWle+9Wzl+KzHmJjWEFWR0pnFWFuX/9B332T3URbE1UhLaoY6ieKeol6AByF3LBCa9jU4/Yybzp/GT776zWebXQSyM1j9vOd+tA812+sWu03fWleY4r6QPj+9hvTcYu2OveW91rw4RbBbT8eZBFMVHSI1OcDeCMzX8XMXwJwNID3abHOmZkAtliebzWOjYCILiaidiJq7+rq0mpAFLHMvPaAj9gYVpDVub+0V1kq+ya7ibZOIrtq+1TaZLffTxT1ssVNyA0rtHoRRKA1x4+XPU7tMo85CeBu5/qNVav99mtMUT9Kf0dZvKBDtNW5t3zQRQ5Ox4MsgokT1QliEwCr+jEewAbt1ryG0++kUT91mHkVM7cxc1tjY6NWA6KIZea1NeO9xcYogmxUgUpFmFPZN9lNtHUT2cMIzqr2hxX1vIRc3WJqEIHWOn7c7HFql/WYn0Btnus3Vq32O12jq7/dro1btNW9t3yQRQ5ex4PaohvVCWIAwDoi+hER/RCF/SF6iegmIropBru2AphteT4LwKsx1OOKW0rd5voa31TO5rW3/G0jvnR664gyrj1zEVqb6hzrMFMKd/cNYM2W3a778aqmdFZJZ+y113FrU+3wvsNuddr3o25pqMbR8xpw2LRJaJgwDle+43W4dPl8fOzk+bjyHYfj+x8YvYew2/66Ky9YBmYo2f/C9j245oxFaGmoxuWnHo6V5y/D/3vvEeA8o7m+xjNFt9t+y16+CZtuuoKAlRcsG/bJZcvnY+UFyxzLvOVvG/HlM16zw2qPmba8dUbdqBTo5h7eqzd04Yunv374tdUbunCdzcfmuXev2YKr39U6oh+//b4j0TcwNML31nTn5vhevaELzQ01aJgwDp9622Ejyv/6OSP3ZH98UzfWbNk9KnW73x7XQVPw2/erzuXynu9bs/yWhmpMnTgu0H7b9nTyteMrcejUiUrvF7fjXmPQ632hG1UN4gNerzPzLdosKtQ3BgWRejmAbSiI1Bcw8zq3a+LI5uq0B69fimD7tT0HBpEbXhlShdamulH71oaJ5vRL6RxklZbXXtVmepHte/sx01jFtLNvwHFvZWta5PqacbjkxHmoGT8WV97xjJLIbRUnW5tqfVehmPX2DRzEc9t7cevjr+D8N7VgR8+AYz1eop7bfstevgmTbtovktle5qy6aqzv7MGuvgFMqhoHBqN6bCVe6Oz1FMjzeR61wssuUJv7KAPAvWs78JO/v4z3HzsP4DxyebiuJLOuGBpTwXhlVz9ufOgFXHTcXBwyYRxe2tGLPAMVBCyZVYcTF0wdjlC3jm273X57XKum4D84NIRuyyo11ejxXC6PB5/rxI6ewu2bnX2DI9px8uHTlPc+ty/4sKeEV9nX3WkM+qWeCUPs6b6J6HZmfk/kgkaWeRqAb6CwzPVmZr7O6/wk0n3HmfZXdzRnGimKvYTHICK313En+80od68I6CzsFw0Ej2RWKdOpjCD1As4ifVCx2a+Pk4pUDutzU4AHwo2hYk0nr0Ok9mOepnKGYebfMfNhzHyo3+SQFHGm/dUdzZlGimIv4TGIyO113Ml+lQjoLOwXDehdYOBVRpB6o9ioIu5a+yeJvgnbHlPsDjuGSjGdvK4JojR2HfIhzrS/uqM500hR7Cc86hDtnOxXiYDOyn7Rpj3252EXGLiVEaTeKDaqiLvW/kmib8K2xxS7w46hUkwnr2uCKAv8hEm7EGeKSH4CmVWkDSJKBdlT109o8yKfZ2za2YvHNnbj4ec6sWGHtzjvJDyqitx+x53sN8X0u9dsw+SacaH2ZPbypfW1TTt7sWGH+n7TXj4JYp9XmU5lOAmn15zhXG9zfU0gwdjNFr8+9hL9dWL3j5M47/SeMsXuhgnBxpDTnul+e5/bx5vfZ4Rb2+LyoYkuDeKfzLxMgz2hSWrLUS+BySnNsYooaY8gXjSzDtMmFvZLcBOlguypGyVFsRlxrRrW7ya0eQnrqqKdXyTsuo692NU3gPqa8RjI5QPtF66SftpNaPcTCL18EqVfVARyu3DqFqFsjexua5mMo+dMxta9B5RsVNkPW2VBhE6cFn/Yo8c//Svn/t68qw/dvYPozw1hMJf3HENOixCuP2sJOvb2e4r8UVOw60w9noRIfQoz3x+5oAikvSe1m8gcJULU6/UgIm7UdulMLZxFVKObdQjMSRN2fGW5TUFwal/Q9Oy6y49j4UIUouxJ/QwRPe3weMbYIwIAkPbkkAXcROao6a117KkbhSRTC6eFanRz2lGtYQg7vrLcpiA4tU/neA5TfhwLF+JijM/rpydiRQlgF5nt3wbsz+0RokFfN4U/t+t0tssU7eKuKy38+sD6WrH5Iez4ynKbguDUPp3jOUz5bj7PYh94/oJg5le8HkkZGTeqIqwXbpHJdsHPHuHZXF/jKTpZIzwvOWk+Ll0+H9+7sA0Lp00KHekdtF2LZ9W5RsjGRdg+8RKb3XDzcXN9zagIWyehV8XnYezSgZeomc8zKitGR7DHKXr6EVa8dcMu1rtFn6uI0KqLQY5sOWRE+fb3/Ky66hFittcCgiC2xIFqJPXRAL4JYCGAcSgEr/Uxc22s1gUgrAYRVIT1K8tJiLNGRG5z2NDe3DvCTXSyp0Z2uy5IpHcQzAjTZzt6lCNLoxC2T4Lun2xvo5OPzdTrDz7XiZd39o3YH8IUc839IbwWC6Sx6b3VL3ZRE3gtRba9TcfOawi897UuO6OIt17l2vcZCZqeXXUxyPTawqISU/Q3I9g/b6nXTONtTSseJMW97nGkI913O4DzAPwKQBuA9wOYz8yfD22VZsJOEEmKsGEFQdXr4hIckxYyw/aJjhTtbkK1mz0qiwWyKARncfFBnOJt3O891TaoZBfQtT+4KloiqZn5JQCVzDzEzD8EcFJoizJEkiJsHCmidZTvR9JCZtg+0ZGi3elaL3tUFgtkUQjO4uKDOMXbuN97bud7LVqJe39wHahOEPuJaByAp4joq0T0SQDp3KTUTJL7u8aRIlpH+X4kHb0Ztk90pGh3utbLHpWI7zSi2v1IctwHscnJHvvzKGJy0LKipKG3nu/2XEfZcfaX6gRxoXHuxwD0oZCK+6y4jEoSp1TBcQl1YaMgVa+zR8TaxbGwglbS0Zt+feIm1DkJkm6RrEHa6GWPSsR30v5TIclxH8Qm3VHnbmXrfu8Bzmnr7W1QyS6gklY8qj9UUdUgLmPmG/2OpUmUQDlrquCo+7uq1BUmClJlj2lrRKxdHIsqaOmO3lSpz6lPAPe9ia2pqN32+g4S8eyWTto+RlSjmpP0nwpJjvsgNumOOncrW9d7zzzHbU/1lsk1oxaT+GUX0N0GL3SI1E8y85G2Y6mn17CSdiR12mQtOjMuwqS6djpHEHRSzGMuSiT1+UR0N4C5RHSX5fEIgO4YbBVCUkzRmVEIk+ra6RxB0Empjjm/SOq/AegAMAXA1y3H9wF42vEKIRWKKTozCiqRv6UeHSxkj1IdcyqR1I8w8zEAngMwyXhsZeZcXEYR0VVEtI2InjIep8VVV6mgU+ALEq2ZdGRn0FTXbucIyZFWFLkfOu3SMeac7Enbd6oaxDkAvgbgEQAE4AQA/8XMv47FKKKrAPQy89dUryl3DQLQI/AFidZMK0K4WEXhciTtKPIk7Yoy5pzsWXnBMgzmOHbf6RCp1wB4GzPvMJ43AniQmZdqs3JkfVdBJohUCCK2FbMwJyRDVsdI1uyKOy25FzoiqSvMycGgO8C1YfmYkVr8ZiKqdzqBiC4monYiau/q6orZnPIgiNhWqsKcoI+sjpGs2RV3WvKwqH7I/56I/kBEHySiDwK4F8DvolRMRA8S0VqHxxkAvg3gUABHoCCSf92pDGZexcxtzNzW2NgYxRzBIEi0ZhYjhIVskdUxkjW7nOzJQqS76gTBAL4LYAmApQBWRa2Ymd/KzIscHr9l5k4j51MewPcAHBW1viyRRTHKJIjYVgxisJ9fs+L3sISxP2ybw1xnj+7PyhiJOnZ1jxsnexbPqkv9/RUlUO5pZl4Si1FETczcYfz/SQBvYubzvK4pFg0iTTEqiI2qYluWxWA/ITKrAqoqYewP2+Yoddn3u04rnbiTfWEjq+MYN25p2eN+f4UWqYnoPwD8J4B5ADZYXpoE4K/M/K86DbXU+xMUbi8xgE0APmJOGG4UywSRphhVbpT6fsxh7E8q7XWUurJOqbUrikj9cwDvBHCX8dd8vCGuyQEAmPlCZl7MzEuY+V1+k0MxkVUxqhQp9f2Yw9ifVNrrKHVlnVJtlxOekdTMvBfAXgDnJ2NO6RP3HrnCa5T6fsxh7A/b5iTryjql2i4n0r8RmGHiEDDjEKOKXWiNC6+9pq2vZ01AVSWM/UmkvY5aV5Zwem8l3a40399KInUxoFuDiFPA1ClGFbvQGjdee02rpurOMmHsjzPtta66soDXewuIXzz2s0FXfZEjqYsB3RNEsQhRxWJnWoh/hLBkYewkYYOWPanLjWIRoorFzrQQ/whhycLYSdsGmSBcyFqkpRvFYmdaiH+EsGRh7KRtg0wQLhSLwOZnZ5ajtpOwI4x/hOIirj6M+zPAzW7r8QpCqp9DokF4UCwCm5udWY7aTlJcD+IfEfiLi7j7MK7PADe7T1k4Dfev7xz1np3bMFF53+qgiEhdpmQ5artcBEAhXoq1D93svvXio3HuqtWJtkdE6jIly1HbaYtvWbFBiEax9qGb3R17s9UemSBKmKymEAbSF9+yYoMQjWLtQze7m+qqM9UemSBSRIe45lVGVlMIu9mWlB2mz7r7BjKZijos5Si4F8tiEjtudrc21WaqPaJBpIQOcU2ljLRSCKvan7Qddp+1NFTjmjMWY2wlYVptdhci+FHOgnuxLCax47V4Isn2iEidQXSIa8Uq0KVJqfqsVNslxI+I1BlEh7hWrAJdmpSqz0q1XUK6pDZBENE5RLSOiPJE1GZ77QoieomInieit6dlo0kul8eaLbtx39oOrNmyB7lc3v8iH3SIa8Uq0KVJqfqsVNuVFV0lK3YkTZq/INYCOAvAo9aDRPR6AOcBaAVwKoD/I6LK5M0rkMvlceeabTh31Wp89KdP4txVf8eda7ZFniR0iGvFKtClSan6rBTbZeoqp930Z5z/vcdw2k1/xn3rtif+4ZwVO9IgdQ2CiB4B8BlmbjeeXwEAzPw/xvM/ALiKmf/uVU5cGsSaLbsdA1duvfhoLJ1dH6lsHWJUsQp0aVKqPiu1dmVFV8mKHXHhpUF47iiXEjMBrLY832ocGwURXQzgYgBobm6OxRi3wJXte/uxdHa0sisqCPMaJ0YaZDrKKDdK1Wel1i4vXSXJNmbFjjSI9RYTET1IRGsdHmd4XeZwzPFnDjOvYuY2Zm5rbGzUY7QNt8CV6XXFfW9XELJOVnSVrNiRBrH+gmDmt4a4bCsA63fzWQBe1WNRcFqbanHtmYvwhTvXDq8vv/bMRWhtqkvLpJLCvC3S2dNf1HEIUYnbD2HLT7N/TF3lhvvW4/QlM1FZAbyxZfLwlrEq6LBfhx3FShZvMd0F4OdEtALADAALAPwjLWPGjKnAmUtnYsHUidi+tx/T66rQ2lSHMWNkhXBUyjm4y0oSGUnDlJ92/1RUEE5ZOA0Hh/KuW8Z6ocv+qHYUM2kuc303EW0FcAyAew0xGsy8DsBtAJ4FcB+AS5h5KC07gcIksXR2Pd6+qAlLZ9fL5KCJTd19w29eoHBf91O3PYVN3X0pW5YscfshbPlZ6J/Nu/cPfygHtUGn/VHsKGZS+6Rj5juYeRYzj2fmacz8dstr1zHzocx8ODP/Pi0bhXiR4K4CcfshbPlZ6J8oNui0Pwu+SAP5KiykRjmLf1bi9kPY8rPQP1Fs0Gl/FnyRBjJBCNpRjTotxeCuMMTth7DlZ6F/otig0/4s+CINUg+U00WxJesrVYIKg6UW3BWWuP0Qtvws9E8UG3TanwVfxIFkcxUSo9SjTgWh1JBsrkJilKuYJwiliEwQglbKVcwThFJEJghBK+Uq5glCUiSZejyLkdRCEVNRQTi1dTped+kJJSfmCULaJB3dLr8gBO2YWUWPnjcF8xonyuQgCJpIOrpdJghBEIQiIelFIDJBCIIgFAlJLwKRCaKMKNd9dQWhVEh6EYiI1GVC2qmbBUGITtKLQOQXRJmQhdTNgiBEJ8lFIDJBlAkS4SwIQlBkgigTJMJZEISgpLmj3DlEtI6I8kTUZjk+h4gOENFTxuM7adlYSkiEczLIQgChlEhTpF4L4CwA33V4bQMzH5GsOaWNRDjHjywEEEqNNLccXc/Mz6dVfzkiEc7xIgsBhFIjqxrEXCL6JxH9iYhOcDuJiC4monYiau/q6krSPkEYhSwEEEqNWG8xEdGDAKY7vPR5Zv6ty2UdAJqZuZuI3gDgTiJqZeYe+4nMvArAKqCwYZAuuwUhDOZCAPtmSbIQQChWYp0gmPmtIa4ZADBg/P8EEW0AcBgA2S5OyDTmQgC7BiELAYRiJXOR1ETUCGAXMw8R0TwACwBsTNksQfBFFgIIpUZqEwQRvRvANwE0AriXiJ5i5rcDeDOALxNRDsAQgI8y86607BSEIJgLAWT/baEUSG2CYOY7ANzhcPx2ALcnb5EgCIJgJaurmARBEISUkQlCEARBcEQmCEEQBMERmSAEQRAER2SCEARBEByRCUIQBEFwJHOBcoIgCHbyecam7j509vRjWq0EICaFTBCCIGQaSaOeHnKLSRCETCNp1NNDJghBEDKNpFFPD5kgBEHINLKfenrIBCEIQqaR/dTT2+tcRGpBEDJNuadRT1Okl18QgiBknnLeTz1NkV4mCEEQhAyTpkgvE4QgCEKGSVOkT22CIKL/JaLniOhpIrqDiA6xvHYFEb1ERM8T0dvTslFInrTEOEHIKmmK9MSczhuQiE4B8DAz54joBgBg5suJ6PUAfgHgKAAzADwI4DBmHvIqr62tjdvb2+M2W4gRiZgVBGfMVCNxiPRE9AQztzm9ltovCGa+n5lzxtPVAGYZ/58B4JfMPMDMLwN4CYXJQihxJGJWEJxJS6TPigbxYQC/N/6fCWCL5bWtxrFRENHFRNRORO1dXV0xmyjEjUTMCkK2iHWCIKIHiWitw+MMyzmfB5AD8DPzkENRjvfBmHkVM7cxc1tjY6P+BgiJIhGzgpAtYg2UY+a3er1ORB8AcDqA5fyaGLIVwGzLabMAvBqPhUKWMMU4uwZRThGzgpAlUoukJqJTAVwO4C3MvN/y0l0Afk5EK1AQqRcA+EcKJgoJU+4Rs4KQNdJMtbESwHgADxARAKxm5o8y8zoiug3AsyjcerrEbwWTUDqYYty8xolpmyIIZU9qEwQzz/d47ToA1yVojiAIgmAjK6uYBEEQhIwhE4QgCILgiEwQgiAIgiMyQQiCIAiOpJaLSTdE1AXglZCXTwGwU6M5xYr4oYD4oYD4oTx80MLMjpHGJTNBRIGI2t2SVZUT4ocC4ocC4gfxgdxiEgRBEByRCUIQBEFwRCaIAqvSNiAjiB8KiB8KiB/K3AeiQQiCIAiOyC8IQRAEwRGZIARBEARHyn6CIKJTieh5InqJiD6Xtj1xQkQ3E9EOIlprOTaZiB4goheNv/WW164w/PI8Eb09Hav1QkSzieiPRLSeiNYR0WXG8XLzQxUR/YOI1hh+uNo4XlZ+AAAiqiSifxLRPcbzsvOBG2U9QRBRJYBvAXgHgNcDOJ+IXp+uVbHyIwCn2o59DsBDzLwAwEPGcxh+OA9Aq3HN/xn+KnZyAD7NzAsBHA3gEqOt5eaHAQAnM/NSAEcAOJWIjkb5+QEALgOw3vK8HH3gSFlPEACOAvASM29k5kEAvwRwhs81RQszPwpgl+3wGQBuMf6/BcCZluO/ZOYBZn4ZwEso+KuoYeYOZn7S+H8fCh8MM1F+fmBm7jWejjUejDLzAxHNAvAvAL5vOVxWPvCi3CeImQC2WJ5vNY6VE9OYuQMofHgCmGocL3nfENEcAMsAPIYy9INxa+UpADsAPMDM5eiHbwD4LIC85Vi5+cCVcp8gnPaylHW/BUraN0Q0EcDtAD7BzD1epzocKwk/MPMQMx+Bwr7vRxHRIo/TS84PRHQ6gB3M/ITqJQ7HitoHfpT7BLEVwGzL81kAXk3JlrToJKImADD+7jCOl6xviGgsCpPDz5j5N8bhsvODCTPvAfAICvfVy8kPxwF4FxFtQuH28slE9FOUlw88KfcJ4nEAC4hoLhGNQ0GAuitlm5LmLgAfMP7/AIDfWo6fR0TjiWgugAUA/pGCfVqhwgboPwCwnplXWF4qNz80EtEhxv/VAN4K4DmUkR+Y+QpmnsXMc1B47z/MzP+KMvKBH6ntSZ0FmDlHRB8D8AcAlQBuZuZ1KZsVG0T0CwAnAphCRFsBfAnA9QBuI6KLAGwGcA4AMPM6IroNwLMorPy5hJmHUjFcL8cBuBDAM8b9dwC4EuXnhyYAtxircCoA3MbM9xDR31FefnCi3MaCK5JqQxAEQXCk3G8xCYIgCC7IBCEIgiA4IhOEIAiC4IhMEIIgCIIjMkEIgiAIjsgEIQiCIDgiE4QgCILgyP8HcDZBloGEvfIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_train = NaiveBayes.predict(X_train)\n",
    "predict_test = NaiveBayes.predict(X_test)\n",
    "\n",
    "plt.figure()\n",
    "sns.scatterplot(np.arange(len(y_train)), predict_train)\n",
    "plt.title('Points prediction for train data set')\n",
    "\n",
    "plt.figure()\n",
    "sns.scatterplot(np.arange(len(y_test)), predict_test)\n",
    "plt.title('Points prediction for test data set')\n",
    "\n",
    "residuals = predict_train - y_train\n",
    "\n",
    "plt.figure()\n",
    "sns.scatterplot(np.arange(len(y_train)), residuals)\n",
    "plt.title('Residuals for train data set')\n",
    "\n",
    "residuals2 = predict_test - y_test\n",
    "plt.figure()\n",
    "sns.scatterplot(np.arange(len(y_test)), residuals2)\n",
    "plt.title('Residuals for test data set')\n",
    "\n",
    "print('sqrt(MSE): ', sum(residuals2**2)/len(residuals2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4adf9728",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5399302b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8abc587c",
   "metadata": {},
   "source": [
    "### Regression\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b51a13a6",
   "metadata": {},
   "source": [
    "Creating a normalized (more precisely we make a linear transformation of the data such that it has mean 0 and variance 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "121b4aa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train_normalized = scaler.fit_transform(X_train)\n",
    "X_test_normalized = scaler.transform(X_test)   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c45296d6",
   "metadata": {},
   "source": [
    "We are now fitting a simple regression, which we aim to use as our baseline model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "e6898dff",
   "metadata": {},
   "outputs": [],
   "source": [
    "Regression = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "a580af2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Regression.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "be4d4535",
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_train = Regression.predict(X_train)\n",
    "\n",
    "predict_test = Regression.predict(X_test)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e6f5b2f",
   "metadata": {},
   "source": [
    "The overall structure of the plots of predicted values seems alright, even though at first sight it looks like too many data points are around 3 (considering that most of the players in the data set scored 100+ points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "b7f2acd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Predicted values for test set')"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GlW8ntxkn4pRwrOHgKEE9HAo0FJw8g3By2uwi0nmUJh7j88MxdSRCvdjNcFgTcZSNOtF2+DnZXcvJabOLSOdRmnheWToPq2tn45Wl88IWqIlQL3YzHNZEgtKoiWg/gJMABgEMWIXiC5dIaCBOxskzCCenLRy0ZoPu/sGI59FuE0881YtTTDTDYU0kFNPHF4QQRyOWEthToLFoHEN951CnZNHI43CYLhoZDu5y8VIvTjPROGVNZKg4yvQBhLfDLxZuOOG8cyhTsmjlcThMF40MB3e5eKkXNtHYS1AnvBDRZwCOAxAA/iCEWGlyTS2AWgAoLy8/o76+3uakBmZfSwcWrXjTR9t4JYJeI+G+M9QTMqKZx+F2esc7e4/i6kff031WkuPGQ0tmxJW7XDzUi1lZA8Dq2tmYU1UQkXc6xdQyVOw44eVzQohDRFQE4HUi2iGE2Ki9QBHeKwHvUVxhpXiIxMJ+F+47Q52SRTOPdk8XY92RIuUuF23iYRofbRON00wtdhOU6UMIcUj5fQTA8wDOimSihkosvEai/c549Yxxwu6weDEbDAeiXdbD3dQSUFATUQYRZcm/AVwAYGukEzYUYtERo/3OeBU2TuhIkXCXY8yJdlkP90BfwZg+igE8T0Ty+qeEEOsimqohEgs3nGi/M15djZziVhZJs0GsTTtOI5ommnjxhhkqAQW1EGIfgOlRSIstxMJ+F+13xoON0kikO1K0hKTVe4a7jdTpDPc9GEF5fYRKTU2NqKurs/25w4VE1LysBNkFE4vRcLwrrLKIlpD09579rZ1R9zhKVPwNlk73hvGHHV4fjiKeBV2ial5mJpvy3HS8tr057LKIVjAvf+8JZNqJ5zZrB3blP1D/idVMM9L1G3eCOt4FXTSEilOFgrEj7WvpsKUsomX/9vcef6adeG+z4WJn/mX/yU1PwWWzykAE7DzcjkklWagsiM3MJRr167idiYFwgvdAOIcbRHp12glucMFiV1lEy2XR33v8eeM4oc3GEjvz39zeg9z0FFw3pwKr3tqHRzbswR827sOHDSdi1sajUb9xJ6hj7YYTriCMtFBxglAIdiCzqyyi5bLo7z3+3NFi3WZjTTj5N7aloiw3rqwpw4oNu3Vt/I7nP4nZwBeN+o0700es3XDCNV1EenU61m5woUwD7SqLaLksGt8zKtuNQQ/w3metqonJzEYa6zYba8IJPmZsS49cMxPjirIc4eopiUb9xp2gjrUbjh1bxiMpVGItFKwGsknL5sEj4GM3t6ssorWQJN9TmZ8R9QHJyRjXRcpz01VvnpIc95Dyb9aWbnrqIzzzf+Y6auCLRv3GnaCO9YYPOwShXULFbNEw2EYT6oJjsNebDWS56Sn4sOEE7nj+E0et1IdDKDOrWLfZSDMw4ME/tjbh1rUfo6ffg4r8NNw8vxp3/n2rThP+x83z0NIRfP6tlKK+gUFHDXzRqN+4E9RA9Dd8aIXUULUDs2dFyk0pUKMJdZU6lOvNBrIra8pUIQ3E7ixMO71hQp1ZxeuAFAiPR+Dtfa2qkAaAK88YrQpp4JQm/I+b54UUOc9KKcrLSMWs8jxHDHzGNnVWZX5E0hGXgjqaWNnJQtUOrJ4VrpuSlfDzJxRCtbOHcr2ZRh8tm6I/QRxK2Qcj0GNtYooEQxnI9rd2oq7+mK4cCjNTTeu74VgnTi8Kvr79zQ6dMPBF0+2SBXUArOxkrywNTTuwetZQNctwbOWh3hvK9WbTQCEQcaEWqNMEW/b+ngPAlplVOJp9pHzkhyp0mtt74DHUb3rqCNP6Tk8JTdw43WQUrY1WQBwJ6lht4rDLi0KmP5hnRUqjk891EYV0b6jvMmo7Ho+w1W5udl2gThNsPfpbDP206WTYM6twtLBIanBDFTrF2W68tOUgls6vVl3mmk50YdmCajy8freazmULqlGcnRpyupygOVsh21RJjlvdfAMAxzp7E1NQx3Jnlx1TXJn+nYfbAz4r2LyGutKsfW5ueopPR/J3b7ir2sFoRsHm2+q63PRkv4I42Hq0EujN7b22zKzC0cL8DSLetPeis28AFXkZGFMQmiIzVIWkMj8Dty6ciOXrtuOGc6qQ5AKmlo1ER+8Aas+tgkcALgKqizNRnhfddZxIU5ztRkV+GpbUlKuDlDvZheqiTMzyiMRYTNRWVnpK0pAatx0VbofrjXbbq1bzMHtWsB051Gmh9rlNbT34yzv1qD23CjNHj0SF4i1ida8dU9BAmlGw+ba6bk2tf5etYOvRSqB39g3YMrOy22SVm56CbYfa0XCsy2fgDUWRGapCoraNUVm6tgEApxdmhtxe4mm7fWV+Bu67ZCpqn6jTtcdb136MqaU5tmrVjhTUxspaumBsyI3brgq3Q0jJDtbU1oMn3q3HDedUgQiYN7YAZ1bm6Z4Vqj042Gmh9rlyquYRXntiMPnR+g/vb+3UbfKwowMFm2+r6/oH/btsBVuPVgK9Ii/DFjt7ODM0K4+anc0nsXLjvrBspUOZoRmVIOO7hmKyiKbdN1xcLkJyEkVlodyRgtpYWcbFCiBw47azwsO1k2k7WFNbD377xh64k124bGZp1LwJ5HNlnASp1T/25j5H2EiDzbfVdQWZqSjITMXj15+Frr4BlBum/8HOrqwEOgBbfHfDmaFZedTsaD4ZtrAIRSGJZDsId00o2maTaHn/BB2PmoiSANQBOCiEWOzv2nDjURtPMC7JceuESzANI5KnIA9ls0gormGR6ATyuTsOt+u0LyD4uMmRPAE9HBv1I9fMRN+AsLzXrjK1K95xOM8x3isE8OKWg/jDEOt0KNjdDvRmzhFYsvKdIT07FmYTO99pVzzqZQC2A8gO6e1DwDhKNbX1YE1dA9bUzkF3/2BQjTtSI91QKiYUbSVSLkkuF+GCicVhTdUiGUck2Hxbuf99+TenBIdx9hSKa56/ATjYmZVdz7EqJ6NHzdSynJAWh8PFznZg7E8V+Wn46Vem6HY1BpuXWJhNouVCGJSgJqIyAF8G8DMAt9iaAoVAu/9uXTgRU0tHBl0Akdp/P9TGYNY5rTp0JFySPB6B17Y3B+V5YkWkp3nB5tt43Tt7j/oVHMEIFju17mhqdS4XYf74YowtzMSs8lxTs48dGLVeu9qBsT/Vt3bjNxt2h6SUSWIVkCwaLoTBatQPAfgRgCyrC4ioFkAtAJSXl4eUCDt3/0nCHemshKidftXR7NCheJ5Y4dTgQoEGkGAGmM+O2qONxUqrqyzIjFjgfDu1Xu0z97d2YpeJfb2+tRvd/YMhmyhDUSTixQVQElBQE9FiAEeEEJuI6Dyr64QQKwGsBLw26lASYefuPy1DHen8CVG7tMpod+hQPE/8afqRnuYNpQMFGkACfe/xCGxvao+5+51TsVPrBfT969vzqmzTzv3Vs3HGbty8FM6aRTSEfTAa9ecAXExEiwC4AWQT0ZNCiGvtSkQ0GncohepPiNqlVUa7QwfreRJI04/kNG+os4xAA0ig7/e3dmL3kZMxd7+LNuFERByq1gvo+9faTY1DnuEZ8eexY3T31S6o56anYIdiEpQRKGPpAWVGQEEthLgdwO0AoGjUP7BTSAORb9yhFmogIWqHVhntDh3sABOqpm+nVhHOLCPQAOLv++b2HjxT5yswfn7p1Ki63xmJpMYWbkTEcNqqtn9pZ3jTSrNRXZwVVj7N6tl4NqdHQP17KB5lQPRnxI7wo4607TPUQg10UGk4nUfe39rZi+WXT1PDQ0ba3hus2SIUTd9urWIosww74moXZ7txvKtPFRhZ7iSUjUxHRmoS9rd2hlTHdpmHIq2xmfWJ5eu2o3SkG119g7qyDKV/DiVOTVNbD1a9tS9i7oRm7Uq+/7JZvsd6BSNwoz0jDklQCyH+F8D/2p0IY+M2O+IonMYZaqFaNczy3PSwOo/ZoszK62qQnERRWdAIxmwRivZkt1YRquZmV1ztCyYWq/X93IeN+PrcCvzg2S1DFpB2mIcirbEZ+0RJjhtLasqxZOW7pvkOZvAJtj7Kc9OjpqR4PAIDg0LXrtZualTdGYkwJIEb7RmxYw63lY37rMp8fNp0El/+jX2naA/lENVJJVn46w2z8ddvn4XfXTML44uzUH+sy7TzBHuoprbzleS4sXhaKerqjyE9Jbht3EbCOQ3dCjlIyfLy14nsPtQzlHcDoR/ka3V9w/Eu9WDah5bMUP2Rg3lmpIj0ganGPmGmWS5ftx2fHDyBd/YeVWcWc6oKUFWYadpWg6kP6Sb64Os7ccM5VV578XU1uGBiccS8ne584RMsnV+t5vd4Vx/GFGTgHzfPw7zqgqBkg7Gvleemh9RWw8URpg8tdrlJDfVUFqkVLF+33Scq1gNXTA9ruqMNi6i1i63cGPw2bmM67Z4aS+1p0rJ5umhsZtitVYRqNrA7rrY/n+tjnb3qMyJhLzYzx0RSYzPOGpNces0ykIZtRjD1oRXmv31jj5qvdRZnaoZLc3sP6lu7dZ5OQgAFmSk4vSgTYzyBzTr+ZmKvRClWtqME9VDcpMwaOYAh+2XLhnTDOVU+GkYgz4BA9jnZ+YZqFzNLp5mNsW/Qg5Qkl4+tMRTM3JcumFisHlgqDzANN5BPMG6BVs8IdeNFMMJPGxNFxhjOTk3CoRM9uHbV+7bbi62EwPnjiwKaB8JZLzEOiukpI3SeEFYatpkNO5Ty9Xem5sPrd2HxtFIkuYAzK/IwtyofI0aEN+mXaZKeTjJNl88qNS0HM9lgnAlfNqsMOw63o3Rkmu1R8qxwlKAO1U3KqpGPL84asl+2bEjSdqUNCp6WkoSfXzrV55BW6acZSMOVWsyOw+H77FrZGJeu/shnJhCqULEaBPoHPT6Cw0qrMDuV+rXtzUHH46jIT8N9l0zV2e8BBNx48cg1MyGEd7eiUZgEsyhWmZ+BR66Zid3NHaoJZOmCsfiR5kxAO+3F/spamgeSXEBNRR7Orjp1Hp8dMyppbqzMz8CGnc26beiBNGyz+gmmfK0iAD68fpdPu11++TRcNO00v4N1oAEqmDQFWlOwcyY8VBwlqEN1k7Kyif3ua7OCEoRG88igB+rpJwBMg4I/tGSGqWZudAEy68xy9C4dmWYaGCmUaW1Rlr7BSw3IbCYQqlAx03oWTyvVHWAqnytX6gMdZ7Xyuhq/5aM1eUmhIOP8mg3A0sZ/rKMXT94wG/2D3s+2HTqJbz3+vqlmFoz25HIRxuRn4qanPlK16tKctIDtaajabaCy1mqBWq+IYBYbg03T/tZONb/SPDCuOMu0ffmrn2AOVraKALh4WqlPuzWL6xzqAOVyeWPcrKmdg6a2HpTkpGFySXZIgtXOmfBQcZSgNrpJEXlPh5hVPtJUS7OyiWVYnNlmdZJKbnoKvj63Ag+v362efrL6gwbcunCiT2f4rzWbTTXzYO2lLhdhamlOWO6IHo/AZ60dphpQKKvYVh3ZTOsxalj+nmsUIrnpKWhq67a8vzI/Q2fysuoQcgC28n0tzEzFL//pu7bw0JIZqC7yBra3ip2sLYvu/kFdOFh/u+c8HoGGY534sOGEz0zLaCqS9astc+OAG6isK/MzLLdea6/Rpik3PQVX1pRhXFEWJpZk+8QB0e5alQNDSY5bN3tMMxHaWvPQzsPtmFSShcoC/wcrmw2WQgC7jgQO1erxCHxy8ETQboXyHn8zuWCwcyY8VBwlqLVuO3Ln3INfnYHyPHPTwqPX1Zh2oOKs1ICCUCtMLptVpgo8efrJlTVlIAQvnEL1vTY21vLc9JB2Tmo1oCx3EiaVZOtWoANp6/40E63WIzt5TUVu0CYp4yEF182pwKET3T73V+SnIS05CR/sP6YzeVkNNnIAthLkv7l6pqqZSSGS5U7Csc4+NbqeWUc1lsWyBWNxZc2pd1jtnpPumsbQsVamIrNwrI9cM9OnrZ5ZkWda1qOy3QG3XstrZJqM8cfN8q9tu9LUl+QCxhVnqrPHZJdLVz9mz63Iz0B5XvCHUGgFsFWetes/Ml/+TDLG/Nnh5mjnTHioOMY9L5DbjlmB3/nCJ1h++TQfF5nyvAzV3Wp17WysWzYP44uz8N5nraobm1aYGAVDU1sPVqzfg7zMlKDd+qxcy2RnXrRC724IeE/AmFNVgMr8DLy2vRmLVryJW57Zgr9vPoh/bW/Globjpq53Wg3ouQ8b4fEAd72wFUvnV6sHjVq5DUk3ow/2HzN3VTvWif2tnSjMSsGzN87FD780His37sOtaz/BsgXe55bkuLF0wVg8cMV0CAEft0CpJQKntC9p0tKalW6eX40lK9/Fm3uO+nwv3/OfXxiLm+aPxR0XjkeO27tGYKVxupOTkOQ6JURWvbUPA4MCP3n5Ux8hKt3O9rV0+HgaPVPXiPK8dPV/7e65P1w7C68snYeFk0eh4bjXXXOEy+WTHjNT0ceNbaZrJ5NKstS2+srSeZhdmWfargc98Nl6bXWN3H1nNag1HOtU3c1c5D0UoSI/TS23v9U14rVPm7G9qR2FmW54IHTv0w5k8rl3PP+JpRujmSup/Oy9z1pRnp+m5tmsfcn+Lw8RkVjlb39rp3qflbKl7w+t2HLAvL8B+plwtFzytDhGozZz26nIT8OKq2aiq28Q3f2DPgVe39qN0pFuSxeZyvwMuAg+09JHrpmJ1KQk3Qg+VM1cYuXWJn2vraaJ2rxLAbOmrgHuEUlYuvoj9PTrF25Kctw6bwdtQ33i3XpcNqsMI1xQ7bZa7dwYDEerQREBmalJurIyxkQAgBVXzUR7Tz/u/PtW0yk1AJ1ZRg6CxmBQn68uwHV/fF99ttbklZmahJ9dOgVH2ntVc9R3Pl+FN3YexeoPGvCDCyaYaoDZqSMwsSRbFSK56SkoznYH1MC0bpfyeZkG85nZ7rnm9h7kpqeguigzKPOFduuypKffg8PtPap/sqwjs4XED+qPmQ4ecut1eW46Nu5p0bVps9mJ9LIw9okVS2ZiyaPvmmrLj15XgzV1DWr9mJXrZbPKsKv55Km+52fhU84ulq/brq4nnF2Vj1duPgebG9t8zEjy8OKNO4/grsWTcJ8y+FoN2sc6e7Hj8Em/YX3NXHH9mYmiEZTMCscIan87pXLTU/DjL080LfC8jFRTm5jViSZSs3lhs1fzXFPXgIyUJF3lazXz8rwMTFg6D8c6e5GsuLz521Ys3dpkhVcXZQWcJsq8my0IahdupC199QcN6lRcTkO1x9X/6e16TBs9EnNP19vRjbMS42Lp0gVj1Q4CnBIs0nzx8Hpv2la9ZT2lHl+cpTPLjNcsSmmDQU0vy1HfozUtyO8f+0YNfvz8VrVcjnb2qfX481e2Y+n8amzYcRiXzhqt1tsLmw/ijkUTMaYgQ72v8XiXzt1uwqgs/FDZdSjbgzS7aPMUzEntxdluXFlThvvXbfcxjZxR7msqykxJshQaZnVkXEj0t/W6Mj9Dd9K9FGgnu/t93vn1uRWq7Vq2m08a2zBj9EhLLfzOFz7BLV8cr84Sbls4XjdYWplXAJjalXc3d2D1Bw269vfC5oP4xaXTdG1QashraueiIj8NC6eUYOXGveogNqcq39QckZzkUvuhldlKpkv2N62itHhaKXYdOenjJqj1lJFnh0pHBO0aiN3C2zGC2tgItQsW182pwK/+uSOkKFuywUvNUYtHeLXxdVubUHvu6bjv5U+Rm56C2nOrMK44CxNH6UfSyvwM7DgcOCyiUTOWC1FW08QZo0eiqjBTzbvUfrRa0GWzylRNRitkVG2qLAcZKUmqQKnIT8Pdiyehu28Q+1o6dI1GOxiu3dSIuxZPsgxWIzHahGXarKac/1dZ8JNCuSTHbVpv2e5knQB/4l3vqeiTS3JQXazfeJI6woW+QY9OQK3b2oSl54/DTU99qH5e39qNn7+yHb9WpqdEXjPG7QsnoKt/EA+v323aHp6pa8TPL52K/a2dajq1J7WfVZmLLHeyzyBdmZ+BcUVZphsqctJG4JFrZuLjxjakpyShqiATPQODlsLfX3zm3PQUHO/sAwD89CtTcefffd1DtW1P5vf5Dw/g6tkV6uL44mmlyHEnoTgnzXSg/f21Z/ho4doZV1lummqzHpXtRnl+Bm55ZrNlW5i0bB4+bTqpsyuX5Ljx9bkVGKV47ci2neVOQpY7Ge9+1mqa/4FBD/774in4zpObdIPYC5sPmvqbd/UN+szkstxJmDsmH4Megb9vOYiGY10+bXpNnX7wqMhPw70XT0HqCJepm6jWEcGfbAgXxwhqq51Sxqm9HEkXTCiyPPHFaJvymZaS97N544pUIS1P5d7dfBJTTss2FcDahmi20mzUjHv6vbbEW744znR6JhclZd6lNqRNc5Y7SW04WiEjBeEPLhiHR97Yo9O+v2cxoBhDnTa0dpkKZq0wX7agWmd2Skt26Tqz0XRCinuj9jszU0zDsU6d0Dre1Ye05CSMH3UqAL7UcquLMtUwlDId88YV4ePGEz7pr2/thkcItTyPd/XhZO+AWkba58p0JxFwRsVIjFSm15Kmth78ra4RlfkZ+PZfNplqi+X56XAnu3QD05U1ZegZGERHzyBe2HwQS2rKsfVQm7q4p/VomlTiPYvDapGwJMeN73y+Cu/vP6aagWrPrcKU0hwUZ6aiq39QXYSW9Srze8M5Vfjx81sxrihTVUhuOKcKHtGtMw/Jcth1uB2/vHyauvAb6CDk8rwMTFo2DzsOm3tsNLf3Yvm67aqpSj6vu38Q+492IkfTtm84pwoP/cvXw0YK9lv+thk3z682rW9p/tTOerXmQbmW873zx+LTwyfR1NaNlRv3qe+SbYIIusXor8+twMj0FNz4pL7utW6iWkcEme9IuOw5RlAb7T9pyd6dUtrRXes+dPbp+aYue2U5aXj108PY19IBd7LLdMV+almO6m6Tm56CG8+tQmtXH9JTvFHTdjZ3oK2rH139gzo3QCl45OgvzTLSplWS4/bRSAAg0+3rLig9HuTGjAsmFmNSSRYq8jPw8PpdaprL89JxyzNbdEJG+xytpmnUbIxxdo07Ccvy0kwFs1Z4VhdnojDTrQqZzJQRWLagGj39g6amk0c27MJdiydh5ca9phtvZo/JVzt5dXEmas+tgkd4hVZ1cSbK8/TB/nccbsf967bjW2eP8XFH9FicTp+XkYpZ5XlqeWoH7bWbvBp276AHD76+S03buOJsTBiV5fO8K2vKTKfiUltcrjF7aE1TE0Zl47bnPlan1XKQ1bZhADi3ugBt3f1qnRhtsFfW6M0+cvBIS07C0qc/MvXK6Rnw6DRFqZDIgT9tRBIKslJNterfXD0TlQUZ6gDtzw0P8Jr6rOzAfYODWFJTjgde886GewYG1bJYu6kRv7x8Gv5D8cWWaTX21ytryrB++2HUnns6Dhzrsqxv46zXuBnqypoypKck4wfPblHrQr5rTZ3XlOgioEvjmgkA9760zafuf3fNLLVMynMD+9jbgWMENaB32/F4hE7LNFZOSY4bWw+ewI7DJ9WFrevPrsDY4izcuvZjjCvKVBu8nFZLs0ZFXjoaT3ShIDMFmSlJ6Oo/pfn86rUdWFJTjmXKQp5cSNEKJTn6Gxv6HReOVyvcnezCuKJMLDmrHPe/qrdhaj0etELsgonFIAJ+dfl0tPf247nvzMXeli6dkDEOOrPHnHJrMk5ZzeyG2p2E/YMetaHKBZ1xxZl48oazMOARusDrUmj+Yt0OdXHvnosm47t/PWV6SE9JwvwJo7By4158/4IJ+J/XdqjaIwAsX7cdE0ZlqUF95o8vRlVBpuXCTMoIQunINNS3duP3G/fh63Mr8KsrpqOrdwBjCjPww2e3+JTH8sunnTJNFGSibGQ6tjSe0Am6fs8pIQ14y+z7f9uMV5fOM92MYdYRW072qtfJmd7EUVn4wbNbcMM5Vep0X1snZoP1wRM9OKBMwUty3D422Ekl2dh6qF2nKEgTmLb9/S09BXdcOAFHO/tQXZTloynK95aOTMcPn92Cm+ePNTXJfXLQu35z1Znl6vqKVGQ8AshISULDsS40tfUgPSXJxw6sVVzSkkfoZsP/teCURny8qw/bDrX79GutqSLJBZxRPhITRmX7tTdrTT+ynIybobr7B9HS3qurC/muy2aVoSgrGWkpyUhVbNErNuzGvRdNNjXDZKeNUM0ddp5Q4w9HCWotqheFohVJraYiPw0PXDkd7312HI3Hu3S+oh19g9h84ARy01N8GvzM8lzMO70ALhfp7Es/v3QKvvPXD1XNx2xn350vfIJ7L56iToHM7LQlOW6UjEzH42/vw9WzK3DHhRNQmOVWzRDaxndudSGuXfWe7h1/fGsvIATqj3Xh4fW7Ma4oE8vOH4c9hi31RMADV0xHarILYwszsffoKQ8LIHCcXe1Own0tHXj4X7vUabEs33svnoKM1CSc7OlXQ81eMLFYl46O3kF8auhoUgj09Htw6ESXj0a9dH412rv7sK+lQ+cvrl2YkdvNtzW14aanPlI7QlNbD5av26nmcd2yebh14UQsX7fd7zbr17Y367Rer1aZ7DNDKh2Zjh2HT2JSSZZu56kw0dor8tPQZAh+/9s39uCm+WPV9qF1I7Oa2d13yVTUPlGHX10x3WctQJpR7r5oEpJIv/ArNUJjHXf3e9T+IO3SS+dXo3dgUM3DZ0e9s4sn320wNcmlp3jNEVIIXX92Bbr6B9Xnfn1uBWqf2KTOoOTg98S79Vi2oBrZaclqW/rBBeN0ZXRAManIsujuH1DTpS2fprYevPzxQdx3yVS4R7iwqeGo7j1mx8iZbfPOTU9BR98gxhVlYXReOrr6Bn3qAgBy3EnIy3Tj3pe24dvnVKEy37sYLWfCUnsuzk5BWvIIfNzYps7s7Dyhxh+OFdRak8as8pH4x83zcLyrFwdP9KC5rRd3v7DVp8F+e14VAL2Pp4wv3NkzgPf3H0VyUpI68ja19eBoR59O+Jq5M/UNCPQNeHSfG+20182pwAOv7VB3M/7ggnHYrllEkXayy2aV4bBhQ8jX51agujgLH2s0v2+fezq2NJ7A3+pOTc9kB5INYuV1NaqHxbIF1agqzMRPLp6Mu1/cZrkg1NLRq1sMW7pgvM6jJDstGfe8uNVnQeW+S6YiI0W/4USrTZTkuNE7cMqWrRXagDcta+oaUJLjxkPrN+tcslo7+/H9v52ast48v1pd6NF2BK22NugBLphYjAmjsoIKpqMdKMtGpqmCT5brDy3iT8uZnVbLvu+SqdhUf8xy7QOA6s++pq4Bty+cgNauPmS6k7DyujPQ3j2Ayvx0HDnp1fAOnuhSTQPa5102qwz3v+o1+9y2cKI66Bvbn7z21//apbZruWlrWlk2RqanqCa1MQUZ6sB35GSPj71+Ukm2KojXbmrEzy6dos6aLptVhtUfNKiCUmru8p0dvYN6e+2Ax9K09sS79bj+7Arcd8kU3PXCVjS19WBNXQNWXleDtBQXDp3oQe0TdfjueWN1Ji6t59CVZ5Tik4Mn0NTWg8LMVF3blLNZrQJyx6KJuOeiybj3pW144t163HJ+NXIzUnHgeBc2HziBxdNK8bNXtuPBr05HRb7XLHjHhRPQ2Teo7lRevm47bvpCta5f23lCjRXBHG7rBrARQKpy/bNCiHtsTQV8426YRW8bX5yFa1e9r5uSuJNdSB1xqjG8tOWgWpAlOW7ceG4VuvoH8ee39+HyWeVoatdvZZaNVT5L/jYK0m2H2nxGf6mpaDWbPUc6kJuegpy0FJzoHtAJMuOWZO3iyseNJ3ReF929A/CIU/7Fty+aqJoSpAao9YwQAvj5K5/iqjPL8cAV05GbkYzH3jz1Dmne+PeeoxAegYLMVLR09GLQ41Gnt/mZqerU3co9ULu4qLXxLakpx9GTvT6am3aQGF+chQcU05J8flpykq5zL55Wijv/vlW30EMEPHzVTLR19eHuF7fpBg+rQxeMC8pa2/Da78xVBd9NXxhradevKsiAEEBpjht/vv5MdPQOYGRaCo529Opi0sgBZMboHPzi0ql4SAkwtKauAVedWY6U5CTVtFb7xCavme5zlfB4vAHtO3oH8dKWg7jtQr0LauoIl2r2+eGXxvuYwHoHvOsEi6eVYkxBuq5dy01bq2tnY0rpSEwozsYIF6n24hUbduPJdxtUQSTrQKsFA1C9JwDoFrZlHdyzeDLufXmbUpf6ReTM1CR1vUIOzDNHj8TLN52Do5296o7cMypydYPt/tZOXLfqfeSmp2BMQYYuzVIe/O5rM/HB/uOqDboiPw0/uWQKGo93edvzeWN1DgDSI+j3187CX64/S33/taveUxU86cDQcKwLty6ciJ+8/CnuXDQJN6/+CDd9YSx2HG7H4mmlqsunNq9JLmBUTlrE/KqD0ah7AcwXQnQQUTKAt4joVSHEu3YlwugQb9xoIaft0vUrXdmMIBeGipRFvI07j6D23NPR2tGrCtDWLu9CzC+vmI4fKQsJ2s4gp4B/fa9e9c01LuZIU4RslE1tPdiw4zBuXzQR5fkZqNcIhL5B7z2Nx7tUrWrFht0+niDGxRXglEYm86i9X5oStBqgzIvWrUh2uJ9cPEkVqlqXo9z0FJ1wXLZgrDq9bVXiLRg1NaPLmvRpl9qEHES0C359gx7TxUZj8J289BSdYNAuKmnd6qT/tnHwsNKCtT7FxplQfkYKDrf1+GyIMU6bv/N5b71IjUrm5eb5Y9UBVE73V27cC/eIJHVAHOEC7r9sGlxE+Maf3tcNfl+bXY4HX9+FZQuqVRPFt84eg5b2HtXO7BHAjNE56mCVlpyk0yqfeLce/3leFf7zC9X47Ru7cdtC830G0lZaf6wLP1r7MXLTU7zrIFdMR3ffAE4vysTXHtOY4RQtWA7wjZoFPOMsyTuI7MGTN8wGINDZ67vAXFORg6Xzx+HHikuhWcQ54z4IuZFIzlJlu9eauLLdI/Ddv36kS8tv39iNB66YDgCmcTnqW7vR1j2gxul5Z+9RnYIny7Cj1+vaWt/aje2HT6rtpP5YF5JcUAdpoytfJKPpBdxCLrx0KP8mKz/hHyWiwej+ZrWDS8Z6eHTjXtyzeLLqenX/q9tx+8IJuHCq1y4tANy1eJLqGdDT70F374BOSMoOcLyrD1WFGfjjN87C2afn4WuzK1Xb9tIFYzFxVDZ6+r0r57/f6HV5uu3C8bh6dgXe/+w4VqzfpVu8WbupEaNz0/FMXaOugZXnnVodlh1NG5XtpS0HkadorDKPN35+rHr/pJJsrNiwWyfoZF6SXNB9XpLjRlrKCPzlHe87tN8Z3YmeqWtEdXEWHl6/28euCkA3W5Fp/9k/tuN/rpyOpQvG4vIzytDTN4DF00rxi3U78Jd3Tu0uvGvxZJ1Q9gjfHXty0NUiB4GTvQNqWrVmptsXTbTcNqxtT2bb0uVGpgmjstUBVX6vHZTkJpujnX14eP1unduW9Hw53tWHjt5B3Pfyp+r3XoGxBz9/dSeu//MH6Owb8Bn8CjNT0dPvQWffIP7yTj0WTytFUbYbq/79mWpnXrupEce7+rB0frVuU4223ZblZeDuF7Zi8bRSn+/dyd5gVDLk676jHarwe3j9btz89Ee4+8VtaDyun2HKNiXNh4+/U4/vnT8O7mSXOkvSUt/ajf5BD/IyUnH3i1tx60J93cyuKlSFdEmOW11o/eRgm+WJRHIjkSzPJ96tV33Az60uRHIS4ZBJkK/61m70Dgzg9MJMn63mskzMYo+v3eTtq6ve2ou7Fk/CS1sO4vTCTMVzRa94TRyVrQ7S379ggt92aCdBxfogoiQi2gzgCIDXhRDvmVxTS0R1RFTX0tISUiKsDp80/t87MIil86ux60gHnn6/Hg9cMR3l+emob+1WO3V9azeWr9uJRzbswbjiLFVLlQJBa1NaumAsnrxhNuaPL8bpRZnITkvB7c9/ona2Fev3qNNgIqifn+wZRH1rl/o+uVNOPr+1o1etzMXTSkEEdZCRNLX14FBbt66h/PHtzwB4Fwu/c95YzB2TixVLZmJaabZqIze6Kz7xbj2qi7J0AlDuyDve1YcDJ7p135nFNZF2eukaJjX5ivw0dXu0lpQRhEGPwMqN+/DIhj04qHmHNDHc/+pOn8XGtZsaMXFUtu55B090qYMT4B2w7rlosleLM6wLyFgUe/xEWtO2J21d3zR/LB6//ixV2xlT4N2sohXm2rKRC4JyoNcuIGsHJOmeFSiQlLZNawen4119+O0be7CtyTut/vW/dqkCdV9LJ9bUNaA0J023qeam+WNxwzlV6FNs2rJtyu9vu3A8Hr5qJjp6B/Dl33jjxyQRmXp5SDdWbXtYU9eAqaU5ahn++e39uOGcKtW+beyX0oW1vrUbe450mM6Q5Gzl5Y8PYtADrN/RjH/vOYqBAX2ZAVA3Emnb+XMfNmLAA1y76j1c/eh7loLYnZyMphPdpjFvpEeQRAaBk3317LGFKMxKxYolMzEqJxW/uHQqXtpyUKd4SWF+vKvPbwRDuwlKUAshBoUQMwCUATiLiKaYXLNSCFEjhKgpLCwMKRHG89vkooNREzotJ13VMM8dX4Tth0/i0PFu004tNb9xxVlYtqAaj7+9D/csnqwK01Vv7UN5XjpmlI003bknkbvWtAtFshMbBaYM2rNwyig8+NUZON7Vh+c+bISLgJQkl0+gHenPLRvKJTNKMTovHRNLsvGlyaNQWZiF6eW5+NKUElQVZvp0ePnuB17boW5ZlumTAkhqAdp7jA28taNXtz1XTt2XXz7dVFO79+IpukD6z9Q1YmJJts9zBz0en7SuemsvfnHpVPXzp99vQFluGmrP9QqgS2aU4rSRqT7n2a3d1Khqa4G0JW17kgPHY2/uQ2FWqi5uw8SSbF2MEbndXZJE+gVCrTCXz5WeDGbl6k4+FS9GKzjk4PTu3hbctXiSep9xk5cUDnJAl+98ZMMerHprH4qz03Tv1gbp2naoTXVblYObNtCURLZv7XNuXTgRVQWZPmX4wGs7TINFaY8O6xv0mJaD1jy36q19WLF+D/7jiTr8Y2uTaQAkY3syeris/H971QFdvuOnX5mCvgEPHn+nXp3NLltQjUeu9kYonFKSrT7PGATO64ueh/njijC9PBczy/NwyYxS/OmbZ6EyP11tJ7OrCnGypx8PXDEdZ5+eF1BrtwsSIjQrBhHdA6BTCPGA1TU1NTWirq4u6GdaBW0Zk5+pC9APmB+x1TcgsPNwO/5gsud/3bJ5AIDm9l4AHiS5knC0oxclOW5MLsnRHfWzr6UDi1a8afkMGcjm2/OqkEQwfZ90fzOLU2x1asn+1s6AQV78neUoj2569dPDuHXtx/j2vCo89uY+H7eiu17Yarrl9ZFrZiIlKUm1+UqWLhiLFev36BYEhQCml2Wj9okPdembVpqNb35ujE+gH2PgnTMr8jC7Mg+Nbd26EK8Nx7t8/m/t7MWhEz3q9uAfXDAOD7y2y29sCbPTYozfW7U76XEiBZyZjbp3YFBX73Kx2Ri3QvtOWcdy51zfoAcne/rR1NarDow57iSMG5WNG5/chG/Pq8IjG/bonq91e5PPvmBisep+qPXxX/XWPvUZ//mFsWpslp9cPBk3r/7ItH17BHRt0KyvaWNsm53oY9Y+tV48gx6o6wxmfcafTJBt0djm7vyyV7sdpfTnhuNdWLTiTZ0Xk7HcFk4ehf2tnaZ9PZi0GMs/nFjXWohokxCixvS7QIKaiAoB9AshThBRGoDXACwXQrxsdU+ogho4tUofjMAyXgfAMni7WaFZBcwP1MHlfcc6e3G0ow+fHe30u8ffSvCbNYZQyki7VdaYfvn9QY2AMw58o7K9QWS0g+B7n7Xi6kf1Fq2b5o/FY2/6dqw1tXOxZOU7QXV4j0fgH1ubfGIxWDVmf0dypaeMUN+rXW03Cycw1PakHTRGZXv9qI919qFnYBBCCIxMS8GuIx0+A9KY/Ewc7zKvFzP2HulQY2RLaipy8LXZldjf2umjBMhIkt39g6ZHnsk20dTWgxuf/FCtO63Qn1aarXNZC6YugilD4/XG9un1i2/H+h3NPsIWAFbXzjY9Ik/7/rTkEaZtztiXtO1HuwBtvKe5vcenvQebFrPytyOaXriCehqAxwEkwWsqeUYI8RN/9wxFUNtBMIXmTxgDXoHf3N6Lrr4BlOdl+JyGoX1OoGvf2Xs0pMZgJ6E2ILNBpSI/TRcxzUqT8OcqF+pg5e96GSHOLg1mqAwMeLCtqU052sl3ZhYMVm3j2RvnoCAzNWilw4gsP2nyMJsBXFlThpmjR6JC2WwEwFRxsROPR+Dfe46qW8YlwSouoRzBpQ1wdeOTH/o8a3XtbBRluW1VouzAn6AO6J4nhPgYwEzbUxUBAh1SCVifNSdjNxgbgoyxbPauyoJTAYTMCOZU5kgRTFloMTvL7taFE3HBxGJMLc3xEfgyLovU3q1c5YI9okwS6PpYxQOW2HG0E2DdNvIyUlFZ4I15MmP0yJDzqa1H46aSnn5v/JYJo7Lx+XFFIZmJwsXlIsytyh/yyepWsaAB+Ox0lW1flqlZ/wvm0FsnEbKNOhiipVEP5UBRK01mTe0cfONP79s6wkarE9jFUKZxgTRmOzXqWGk6WuxKX6TahtlMryIv3dSubGd+QkmfVRsLtUy0dnGzw4yDNWXGatA3EpZG7VSG2tCtNBnp76rFn+YXDLE8EWIohKqFA4E14FA1F6drOqHOEKyIRNuw6hNjCjIs69Wu/ASLvzYW6vmG+1s7TRfXl18+DRdNOy1gGQ+lvceKuBXUQz200uzg1nFFWcjPSI2ImSKeGsNQCGTeCVUgOX1ws9OcZXfbGEqfiKV5zshQzGTGna49/d4DhaeW5qhRGodD/3PM4bah4q9S/SEFwbpl89SDW296+iMsXf0hfvqVKaY+oow1Vof6astNdhZ5LmCwp1QHe73ZwamRIpj8xoqh9Akn5ce4n0Kmx2rQKM52W56ZGIlNJ7EkbjXqcDQBl4vgEdAFhK9v7cZvNuzGmto5Pi5QjDWx1oCjvQ4Q6/z6Yyh9wkn5GYqZ7MyKPMfMCCJJ3C4mhttBY+k6x9iH0xcfo0m8LV6bEeoC38CAJyQ/fSczLBcTw9UEnGSbY4ZOtBfDnIyTtOOhEqpNecQIFy6adpqpC+lwIm4FNRDeYozTvQuYU/hzw+QBV89wWTwLhUTIc1wL6nAYDtpHIhBoOs8DLpMIxI2NeiibW4YTiZr/YGzQTtu4wDBDIe5t1E5eJImGAHVy/iNNMDboRJj6MolNXPhRWznyR+IkhVCQAnTRijdx9aPvYdGKN7Fu22Hb/Xidmv9oEKpvLRN5oum3zniJC0E91M0tkSZaAtSp+Y8GTtqQ4SRiJSyjpZwweuLC9OHUlf1ouYY5Nf/RgBd9fYmlKWyooRuY8IgLjdqpWlW0puVOzX+0CHVL+XAnlqawRJ7dxZKAGjURjQbwFwCjAHgArBRCPBzphGlxqlYVLdcwp+afiQ2x3OSTyLO7WBKM6WMAwPeFEB8SURaATUT0uhDi0winTYcTV/ajKUCdmH8mNsRSWLLfemwYyuG2LwB4RAjxutU1sTqKi2GcQKRdNmPtrsl+65HBNj9qIqqE91gu32hGcUKibhxhokM0hGisTWE8u4s+QWvURJQJ4P8B+JkQ4jmT72sB1AJAeXn5GfX19Xam0xZirYkwwx+O5scMFX8adVBeH0SUDGAtgL+aCWkAEEKsFELUCCFqCgsLh57aCJLIG0eY6MBeEfbBG2tOEYzXBwFYBWC7EOLByCcpciRiSMxomXrYpOSFvSLsgWe/eoLRqD8H4DoA84los/KzKMLpigih+j3H+4gerV1kvFvtFInu824XPPvVE1CjFkK8BWBYDGGhuBYNhxE9WrvIeLfaKWK90DdcSMTZrz/iYgu5XYTSiYaD8IlWY+dOpYe9IsKHTUh64mILeTAEa6YIdjvycFgUitYWd45wx9gNm5D0DAuNOhJmiuEwovsz9di5+OfU3Wq8wBm/sAlJT9yc8OKPSPiuDgcbNWC+iwyA7Xlz2m614VJ/TOLgz496WAjqd/YexdWP+m6WXF07G3OqCob8XKcJH7tIhE0ZiZBHZngR9oYXpxMpG+lwDa85HOzvgUiEPDKJw7AQ1LzwEBqJsPiXCHlkEodhYfoAhq+ZIhIkgv02EfLIDC+GvY2aCZ1EGNgSIY/M8MG2MKfM8CERNmUkQh6ZxGBY2KgZhmGGMyyoGYZhHA4LaoZhGIfDgpphGMbh8GKiA+CYFAzD+IMFdYxhf1+GYQIR0PRBRH8koiNEtDUaCUo0+CQLhmECEYyN+s8AFkY4HQkLx6RgGCYQAQW1EGIjgGNRSEtCwjEpGIYJhG1eH0RUS0R1RFTX0tJi12OHPRxQimGYQAQV64OIKgG8LISYEsxDOdZHaHBMCoZhONaHw+GYFAzD+IM3vDAMwzicYNzzngbwDoDxRNRIRDdEPlmMnQR7QjvDMM4koOlDCHF1NBLCRAbeUMMw8Y/jTR+sDYYHb6hhmPjH0YuJrA2Gj78NNbx4yTDxgaM1atYGw4c31DBM/ONoQc3bq8OHN9QwTPzjaNOH1Aa1wpq1wdBwuQgLJ4/ChKXzeEMNwyA+wwo7WlBLbdBoo2ZtMDR4Qw3DeInXda+gtpCHip1byHl7NcMwdrGvpQOLVrzpM0t/Zem8mCsycb2FnLVBhmHsIl69oBy9mMgwDGMn8eoFxYKaYZiEIV69oBxv+mAYhrGLePWCYkHNMExCEY/rXmz6YBiGcTgsqBmGYRwOC2qGYRiHw4KaYRjG4bCgZhiGcTgR2UJORC0A6od4ewGAozYmJ17hcuAykHA5eBnu5VAhhCg0+yIigjociKjOar97IsHlwGUg4XLwksjlwKYPhmEYh8OCmmEYxuE4UVCvjHUCHAKXA5eBhMvBS8KWg+Ns1AzDMIweJ2rUDMMwjAYW1AzDMA7HMYKaiBYS0U4i2kNEt8U6PZGEiP5IREeIaKvmszwiep2Idiu/czXf3a6Uy04i+lJsUm0vRDSaiN4gou1EtI2IlimfJ1o5uInofSLaopTDvcrnCVUOEiJKIqKPiOhl5f+ELAcfhBAx/wGQBGAvgCoAKQC2AJgU63RFML/nApgFYKvms18CuE35+zYAy5W/JynlkQpgjFJOSbHOgw1lUAJglvJ3FoBdSl4TrRwIQKbydzKA9wDMSbRy0JTHLQCeAvCy8n9CloPxxyka9VkA9ggh9gkh+gCsBnBJjNMUMYQQGwEcM3x8CYDHlb8fB/AVzeerhRC9QojPAOyBt7ziGiFEkxDiQ+XvkwC2AyhF4pWDEEJ0KP8mKz8CCVYOAEBEZQC+DOAxzccJVw5mOEVQlwI4oPm/UfkskSgWQjQBXiEGoEj5fNiXDRFVApgJrzaZcOWgTPc3AzgC4HUhREKWA4CHAPwIgPb02UQsBx+cIqjNzsFhv0Evw7psiCgTwFoA/yWEaPd3qclnw6IchBCDQogZAMoAnEVEU/xcPizLgYgWAzgihNgU7C0mn8V9OVjhFEHdCGC05v8yAIdilJZY0UxEJQCg/D6ifD5sy4aIkuEV0n8VQjynfJxw5SARQpwA8L8AFiLxyuFzAC4mov3wmj7nE9GTSLxyMMUpgvoDANVENIaIUgBcBeDFGKcp2rwI4BvK398A8ILm86uIKJWIxgCoBvB+DNJnK0REAFYB2C6EeFDzVaKVQyERjVT+TgNwPoAdSLByEELcLoQoE0JUwtv/NwghrkWClYMlsV7NlD8AFsG78r8XwI9jnZ4I5/VpAE0A+uHVDG4AkA9gPYDdyu88zfU/VsplJ4ALY51+m8rgHHinqh8D2Kz8LErAcpgG4COlHLYCuFv5PKHKwVAm5+GU10fCloP2h7eQMwzDOBynmD4YhmEYC1hQMwzDOBwW1AzDMA6HBTXDMIzDYUHNMAzjcFhQMwzDOBwW1AzDMA7n/wPgs67JDCLn4gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.scatterplot(np.arange(len(y_train)), predict_train)\n",
    "plt.title('Predicted values for train set')\n",
    "\n",
    "plt.figure()\n",
    "sns.scatterplot(np.arange(len(y_test)), predict_test)\n",
    "plt.title('Predicted values for test set')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caf37c9a",
   "metadata": {},
   "source": [
    "As expected there are a bunch of very negative residuals which correspond to extremely high isolated returns (13+ pts returns I'd say)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "61ec47ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.08582462203595886"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "residuals = predict_train - y_train\n",
    "plt.figure()\n",
    "sns.scatterplot(np.arange(len(y_train)), residuals)\n",
    "plt.ylabel('Residuals: y_hat - y')\n",
    "plt.title('Plot of residuals for  train set')\n",
    "residuals2 = predict_test - y_test\n",
    "plt.figure()\n",
    "sns.scatterplot(np.arange(len(y_test)), residuals2)\n",
    "plt.ylabel('Residuals: y_hat - y')\n",
    "plt.title('Plot of residuals for test set')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "226ab113",
   "metadata": {},
   "source": [
    "Let's try to use a regularization parameter (selecting it using cross-validation) to obtain better performance on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "e1d06f50",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=1.47411e-18): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, sym_pos=True,\n",
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=1.4648e-17): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, sym_pos=True,\n"
     ]
    }
   ],
   "source": [
    "alpha = [0.00, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000, 1000000,10000000, 100000000]\n",
    "mse_train = [math.nan for i in range(12)]\n",
    "mse_test = [math.nan for i in range(12)]\n",
    "abs_train = [math.nan for i in range(12)]\n",
    "abs_test = [math.nan for i in range(12)]\n",
    "for i in range(12):\n",
    "    Regression = Ridge(alpha = alpha[i],fit_intercept=True, normalize=False)\n",
    "    Regression.fit(X_train, y_train)\n",
    "    \n",
    "    predict_train = Regression.predict(X_train)\n",
    "\n",
    "    predict_test = Regression.predict(X_test)\n",
    "\n",
    "\n",
    "    residuals = predict_train - y_train\n",
    "\n",
    "    residuals2 = predict_test - y_test\n",
    "    \n",
    "    mse_train[i] = np.sqrt(sum(residuals**2)/len(residuals))\n",
    "    mse_test[i] = np.sqrt(sum(residuals2**2)/len(residuals2))\n",
    "    \n",
    "    abs_train[i] = sum(abs(residuals))/len(residuals)\n",
    "    abs_test[i] = sum(abs(residuals2))/len(residuals2)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f87c21cd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "7e9ae4bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Performance on train and test set for different penalties')"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.figure()\n",
    "plt.plot(np.arange(-3, -3 + len(mse_train)), mse_train)\n",
    "plt.plot(np.arange(-3, -3 +len(mse_test)), mse_test)\n",
    "plt.xlabel('Order of magnitude of penalty term')\n",
    "plt.ylabel('sqrt(MSE)')\n",
    "plt.title('Performance on train and test set for different penalties')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4c66596",
   "metadata": {},
   "source": [
    "\"Alphas\" in scikit-learn are unnormalized (i.e. they are not divided by the number of observations n in the loss function), the best alpha has order of magnitude of 10^6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "5fd2ad9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnormalized penalty alpha:  1000000\n",
      "Normalized penalty alpha:  718.3908045977012\n"
     ]
    }
   ],
   "source": [
    "print('Unnormalized penalty alpha: ', alpha[9])\n",
    "print('Normalized penalty alpha: ', alpha[9]/len(X_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d435fb64",
   "metadata": {},
   "source": [
    "Let's know fit a Ridge regression with the above penalty parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "2adf9ef0",
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 9\n",
    "Regression = Ridge(alpha = alpha[i],fit_intercept=True, normalize=False)\n",
    "Regression.fit(X_train, y_train)\n",
    "predict_train = Regression.predict(X_train)\n",
    "predict_test = Regression.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "574c659f",
   "metadata": {},
   "outputs": [],
   "source": [
    "residuals = predict_train - y_train\n",
    "residuals2 = predict_test - y_test\n",
    "mse_train = np.sqrt(sum(residuals**2)/len(residuals))\n",
    "mse_test = np.sqrt(sum(residuals2**2)/len(residuals2))\n",
    "abs_train = sum(abs(residuals))/len(residuals)\n",
    "abs_test = sum(abs(residuals2))/len(residuals2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0792e44",
   "metadata": {},
   "source": [
    "Its performance looks very solid. On average, on the test set, our predictions are off by only 2.7 points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "da72c902",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(MSE) on train set:  3.5690746673613045\n",
      "sqrt(MSE) on test set:  3.651772038360827\n",
      "Mean Absolute value of residuals on train set:  2.7859636833660213\n",
      "Mean Absolute value of residuals on test set:  2.735716135611483\n"
     ]
    }
   ],
   "source": [
    "print('sqrt(MSE) on train set: ', mse_train)\n",
    "print('sqrt(MSE) on test set: ', mse_test)\n",
    "print('Mean Absolute value of residuals on train set: ', abs_train)\n",
    "print('Mean Absolute value of residuals on test set: ', abs_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "0e382ba0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Predicted values for test set')"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "plt.figure()\n",
    "sns.scatterplot(np.arange(len(y_train)), predict_train)\n",
    "plt.title('Predicted values for train set')\n",
    "\n",
    "plt.figure()\n",
    "sns.scatterplot(np.arange(len(y_test)), predict_test)\n",
    "plt.title('Predicted values for test set')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7dd8b8a0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "febddbf2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Plot of residuals for test set')"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "\n",
    "plt.figure()\n",
    "sns.scatterplot(np.arange(len(y_train)), residuals)\n",
    "plt.ylabel('Residuals: y_hat - y')\n",
    "plt.title('Plot of residuals for  train set')\n",
    "\n",
    "plt.figure()\n",
    "sns.scatterplot(np.arange(len(y_test)), residuals2)\n",
    "plt.ylabel('Residuals: y_hat - y')\n",
    "plt.title('Plot of residuals for test set')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58c4b4ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3754b102",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a167d98f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f264cf8f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d958fd0c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c03a442c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7486fd38",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c81564bc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8faca5ba",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fe34c39",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fddebb6c",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb55cc30",
   "metadata": {},
   "source": [
    "This is the approach that gave better performance. I am including here on top the final model where I used 100000 decision trees (the rest of the the tuning turned out to be quite insignificant) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2741d350",
   "metadata": {},
   "source": [
    "Fitting the random forest on the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "327bb943",
   "metadata": {},
   "outputs": [],
   "source": [
    "regr = RandomForestRegressor(oob_score = True, n_estimators = 100000, max_features = 5)\n",
    "\n",
    "regr.fit(X_train, y_train)\n",
    "predict_train = regr.predict(X_train)\n",
    "\n",
    "predict_test = regr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "ed1d10c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "out_of_bag_predict = regr.oob_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ca11fd0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "1039d060",
   "metadata": {},
   "outputs": [],
   "source": [
    "residuals = predict_train - y_train\n",
    "residuals2 = predict_test - y_test\n",
    "residuals3 = out_of_bag_predict - y_train\n",
    "mse_train = np.sqrt(sum(residuals**2)/len(residuals))\n",
    "mse_test = np.sqrt(sum(residuals2**2)/len(residuals2))\n",
    "mse_out_of_bag = np.sqrt(sum(residuals3**2)/len(residuals3))\n",
    "abs_train = sum(abs(residuals))/len(residuals)\n",
    "abs_test = sum(abs(residuals2))/len(residuals2)\n",
    "abs_out_of_bag = sum(abs(residuals3))/len(residuals3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "cccb82ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(MSE) on train set:  1.3159610138659716\n",
      "sqrt(MSE) on test set:  3.628118310324374\n",
      "Mean Absolute value of residuals on train set:  1.022180926724139\n",
      "Mean Absolute value of residuals on test set:  2.748467780172415\n"
     ]
    }
   ],
   "source": [
    "print('sqrt(MSE) on train set: ', mse_train)\n",
    "print('sqrt(MSE) on test set: ', mse_test)\n",
    "print('Mean Absolute value of residuals on train set: ', abs_train)\n",
    "print('Mean Absolute value of residuals on test set: ', abs_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "2f9438c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "importances = regr.feature_importances_\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d8f56be",
   "metadata": {},
   "source": [
    "The plots below show that feature importance decreses as the time distance of the gameweek increses (very natural assumption we could have made a priori)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d23cefc",
   "metadata": {},
   "source": [
    "It also shows that the model takes into account human considerations (pretty cool, right?), as it gives particular importance\n",
    "to the transfers made during the week, which can be known before the gameweek deadline (which is the most significant variable of the model)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "e75e7d72",
   "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": [
    "\n",
    "plt.figure()\n",
    "plt.title(\"Feature importances\")\n",
    "ax = plt.barh(range(len(X_train.columns)),importances,align=\"center\");\n",
    "plt.xlabel('Average decrease in MSE');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4632e32",
   "metadata": {},
   "source": [
    "Below are the detailed plots, for significance of the expl variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "5124b42b",
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "for i in range(0,220,20):\n",
    "    plt.figure()\n",
    "    plt.title(\"Feature importances\")\n",
    "    ax = plt.barh(list(X_train.columns)[i:i+20],importances[i:i+20],align=\"center\");\n",
    "    plt.xlabel('Average decrease in MSE');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6649c0b7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "bba88acd",
   "metadata": {},
   "source": [
    "#### Model parameters choice"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf5e0c32",
   "metadata": {},
   "source": [
    "Using cross-validation to find the optimal number of randomly sampled variables from which to find the optimal split at each node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "7cdf8131",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [1,5,10,25,50,100]\n",
    "n = len(features)\n",
    "mse_train = [math.nan for i in range(n)]\n",
    "mse_test = [math.nan for i in range(n)]\n",
    "mse_out_of_bag = [math.nan for i in range(n)]\n",
    "abs_train = [math.nan for i in range(n)]\n",
    "abs_test = [math.nan for i in range(n)]\n",
    "abs_out_of_bag = [math.nan for i in range(n)]\n",
    "\n",
    "for i in range(n):\n",
    "    Forest = RandomForestRegressor(oob_score = True, n_estimators = 1000, max_features = features[i])\n",
    "    Forest.fit(X_train, y_train)\n",
    "    \n",
    "    predict_train = Forest.predict(X_train)\n",
    "\n",
    "    predict_test = Forest.predict(X_test)\n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    residuals = predict_train - y_train\n",
    "    residuals2 = predict_test - y_test\n",
    "    residuals3 = out_of_bag_predict - y_train\n",
    "    mse_train[i] = np.sqrt(sum(residuals**2)/len(residuals))\n",
    "    mse_test[i] = np.sqrt(sum(residuals2**2)/len(residuals2))\n",
    "    mse_out_of_bag[i] = np.sqrt(sum(residuals3**2)/len(residuals3))\n",
    "    abs_train[i] = sum(abs(residuals))/len(residuals)\n",
    "    abs_test[i] = sum(abs(residuals2))/len(residuals2)\n",
    "    abs_out_of_bag[i] = sum(abs(residuals3))/len(residuals3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "32337918",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'number of features = [1,5,10,25,50,100]')"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEICAYAAABPgw/pAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAZbElEQVR4nO3df5xddX3n8dd7JsMPCTRiRolJSFzFsoUHEDob0ViXpYgoWfXRsm1sxWrdjSDd4kO6utiW6qPa3W4fpYpsTVNhkYUVtSpFDLp0kYeklh+TmISGUMvDQpMmmhEkIYLoZD77xzk3OXPm3J9z71zuN+/nI+cx557v93zP58ydeZ9zzz2Tq4jAzMwG31C/CzAzs+5woJuZJcKBbmaWCAe6mVkiHOhmZolwoJuZJcKB/jwm6TFJ5/dp2y+R9E1JT0v604r2YyV9RdI+SV/oR43PJ5JC0o8kfazftVhjkt4t6UD+nL2i3/V0kwPd6lkL/AA4ISKurGi/GHgJ8KKI+A+z2ZCkD0u6eTZjPE+cGRG/W3sgab2kf5A0JemdjVaUdKOkn+RBU5uG6/RdJOl2SbvzUFpeaj9a0g2S9kv6nqT3N9juRZI2Snoq7/uXko7vsK5z830t9v2NDuua1T5KOkvSJknP5F/PqrVFxPURMb/etgeZA/0IIGleB6stAx6O+n95tgz4TkRMdl5Zd3S4f3NhK/BeYHOL/f9HRMwvTAfr9JsCvgb8cp32DwOnkD1H/w74gKQL6/T9GeCjwEuBfw0sAf6kw7oAdpf6fqbDujreR0lHAX8N3Ay8EPgM8Nf58rRFhKc2JuAx4HeAbcA+4HPAMXnbO4GNpf4BvCKfvxH4c+BO4ADwt8BJwMeBHwKPACtK27oKeDhv/1+1beXtq4EtwFPAt4AzSut+MK/zOWBexb68Bngw348HgdcU6vwp8JO8zvNL630kb/tp3v7ufPlvAjvyWr8OLCus8wlgJ7Af2AT8Qr78wtJYWwv1n19Y/8PAzfn88vz7+m7gn4FvNto+IODPgL35vm4DTu/yz8Wh57mibSPwzibr3wh8tM1tzsu3u7y0/F+ACwqP/xC4tcUxfwl4qJO6gHOBXQ3a266rk30ELsjbVWj/Z+DCVp+zQZ18ht6ZXyELopcBZ5AFeTvr/h6wkCxo/47sDG4h8FfANaX+vw68AXg58Mp8XSSdDdwAvAd4EfAXwO2Sji6s+zbgImBBlM6kJZ0IfBW4Nl//GuCrkl4UEe8EbuHwmdnfFNeNiD8A/gj4XN5+vaS3Ah8iC4RR4F7gs4XVHgTOAk4E/g/wBUnHRMTXSmOd2cL3sObfkp1VvqHJ9i8AXpd//xYAvwo8UTWgpD/PLz9UTdvaqK0T75X0ZH6JoN6ZaUOSXkh2tr21sHgrcFqLQ7wO2D6Lul4s6fuS/knSn0k6rkt1HdLCWKcB2yJP7dy2TrY1aBzonbk2InZHxJPAV8iCqlVfjohNEfFj4MvAjyPipshexn4OWFHqf11E7My39TGykAb4T8BfRMT9EXEwspe2zwHnlOrcGRHPVtRxEfCPEfG/I2IyIj5L9grh37exL0XvAf5bROzIDx5/BJwlaRlARNwcEU/k2/pT4GjgZzvcVs2HI+JH+f412v5PgeOBU8nO2nZExJ6qASPivRGxoM50xizrbeRasksILwZ+H7hR0qoOxqldG95XWLaPbP8bkvR64DeAqzus6xGy34VFwHnAz3P4BKXjuio0G2t+qW022xooDvTOfK8w/wyHf8Ba8f3C/LMVj8tj7SzMP052ZgLZtcMri2eQwNJCe3ndspfm4xU9DixuWH19y4BPFGp5kuxSx2IASVdK2pHfFfMU2bXbhR1uq6a4f3W3HxF3A9cB/xP4fv5m5Qmz3HZXRcTmwgFvA9krpF/qYKgD+dfi/p0APN1oJUnnkL1yujgivtNJXRHxvYh4OCKmIuKfgA+QvXnecV11NBvrQKltNtsaKA707voR8ILaA0kndWHMpYX5k4Hd+fxO4GOlM8gX5GfaNY3+K83dZCFYdDLZtcdO7ATeU6rn2Ij4lqRfILue/yvACyNiAdkZkxrUOe17SfZeQ1lxvbrbB4iIayPi58ledr8S+C9VOyFpXekujeJUvhTRS8Hh70/rK0X8ENgDFC9dncnMyyiHSFoB3A78ZkT8vy7WdahvJ3XVHbT5WNuBMyQV6zyjk20NGgd6d20FTstvmTqG7I282bpc0pL8mveHyC7LAPwlcKmkVylzXH4LWqsvKzcAr5T0a5LmSfpV4OeAOzqscx1wlaTTACT9jKTa7YzHA5PABDBP0tVMP4P6PrBcUvHncQuwRtKIpDEOn+m1vX1J/yb/Po2QHSh+DFTeqRERl8b0uzSKU1vXYCUdlf8cCBiRdExtH5Xd4heFvhdLmi9pSNIFwNvJQrbW/pgKtz7m49beLzk6f1xzE/B7kl4o6VSyy3M3Vo0l6XSyu0n+c0R8pWIfWq4r36eT85/HpcB/J7vbpO26ZrmP95A9v7+t7PbG38qX313ev+T0+13ZQZtocPdF/vh3ye7f3kn2w1++y+Wjhb7/Ebin8PgVwGRpW7W7XJ4iu/3qBYX2C8nebHyK7IzlC8DxVXXW2ZfXkt1xsi//+tpC27RaK9adtt/5skuAh8juZNkJ3JAvHwauz5fvIXspfqg+sjdlN5LdnbI5X/avgPvJXj7X3rwt3+Uyr8Xt/yLZm2IH8ufmFmB+l38uZtwxQRYsUZrOLdT6rULfe/PnYT/ZicGaQttRZJcLTi1tb9pUaDua7A3z/WQHy/fXG4vszqmp/HtTm7Z3UhfwfrJXeM/k3/9P1n4e261rNvuYt68g+5l+luymgxXF9nrP2aBPynfMzGZB0o/J3pS+NiJ+v4X+nwa+EBFfb6Hva4HLI+Jtzfp6rJa29S6y21iPAX4uIr7b623OFQe6mVkifA3dzCwRDnQzs0Q40M3MEtG3/9Ro4cKFsXz58n5t3sxsIG3atOkHETFa1da3QF++fDnj4+P92ryZ2UCSVP4L70N8ycXMLBEOdDOzRDjQzcwS4UA3M0uEA93MLBEOdDOzRLQc6JKGJX1b0oz/XjX/bzP3SdqST1dXjWFmZr3Tzn3oV5B9AG+9T3q5NyJWz76kxv74gT/mkScf6fVmzMx65tQTT+WDKz/Y9XFbOkOXtITsMyg/3fUKzMysK1o9Q/842YcSNPo0nFdL2kr20Wa/ExE9+binXhzVzMxS0PQMXdJqYG9EbGrQbTOwLCLOJPuUktvqjLVW0rik8YmJiU7qNTOzOlq55LIKeLOkx4BbgfMk3VzsEBH7I+JAPr+B7PMTZ3yie0Ssj4ixiBgbHa38v2XMzKxDTQM9Iq6KiCURsRxYA9wdEW8v9pF0Uu0TtiWtzMd9ogf1mplZHR3/b4uSLgWIiHVkn8h+maRJsg9lXRP+bDszsznVt88UHRsbC//3uWZm7ZG0KSLGqtr8l6JmZolwoJuZJcKBbmaWCAe6mVkiHOhmZolwoJuZJcKBbmaWCAe6mVkiHOhmZolwoJuZJcKBbmaWCAe6mVkiHOhmZolwoJuZJcKBbmaWCAe6mVkiHOhmZolwoJuZJcKBbmaWCAe6mVkiHOhmZolwoJuZJaLlQJc0LOnbku6oaJOkayU9KmmbpLO7W6aZmTXTzhn6FcCOOm1vBE7Jp7XAp2ZZl5mZtamlQJe0BLgI+HSdLm8BborMfcACSYu6VKOZmbWg1TP0jwMfAKbqtC8GdhYe78qXTSNpraRxSeMTExPt1GlmZk00DXRJq4G9EbGpUbeKZTFjQcT6iBiLiLHR0dE2yjQzs2ZaOUNfBbxZ0mPArcB5km4u9dkFLC08XgLs7kqFZmbWkqaBHhFXRcSSiFgOrAHujoi3l7rdDrwjv9vlHGBfROzpfrlmZlbPvE5XlHQpQESsAzYAbwIeBZ4B3tWV6szMrGVtBXpE3APck8+vKywP4PJuFmZmZu3xX4qamSXCgW5mlggHuplZIhzoZmaJcKCbmSXCgW5mlggHuplZIhzoZmaJcKCbmSXCgW5mlggHuplZIhzoZmaJcKCbmSXCgW5mloiO/z90M7Mj0tQUxEGYOlj62sby+S+GE17a9dIGL9B/+Dg8/regYRgaBg3lX0vzQ0MVy4qPhwrLmvSfsaz2teqjVC1ZEdlExddae1Vb1D5et2JZZZ+pwlR4XOtXuby8TlS01VsnmrTVltOktmbbr7M/MwLwYNZeflw3NMt9Gy1vJ4zr1NANq94Hr/9Id8YqGLxA/5dNcNtl/a7isBkhnwf9jGVDhw8a5YNCS2MMNQgPCvP12pnl+vXa6dGYpfa2AzQODdGVcazHKn5npv2+lOYb9i18nXdUxfJ6J2jNltc5Cexk7BNf3pPv4uAF+ivfAL+9pfUj+LR+UxXLio/LR+kG/ese/esc6av61z07CZiahMnnpo8hASq8MtDhZdBe+6EuHa5fbi++WunWmNPa622vvKyVPq3sS6vbos6+VHxt2idXCwDEoVeIKswfeg5LyyvXUeOxprWpYjtV22f6+g3Xqdr+0PTlfrXbNYMX6EcdBye+rN9VmJk97/guFzOzRDjQzcwS4UA3M0tE00CXdIykByRtlbRd0ox7bSSdK2mfpC35dHVvyjUzs3paeVP0OeC8iDggaQTYKOnOiLiv1O/eiFjd/RLNzKwVTQM9IgI4kD8cySffnGtm9jzT0jV0ScOStgB7gbsi4v6Kbq/OL8vcKem0OuOslTQuaXxiYqLzqs3MbIaWAj0iDkbEWcASYKWk00tdNgPLIuJM4JPAbXXGWR8RYxExNjo62nnVZmY2Q1t3uUTEU8A9wIWl5fsj4kA+vwEYkbSwSzWamVkLWrnLZVTSgnz+WOB84JFSn5Ok7G93Ja3Mx32i69WamVldrdzlsgj4jKRhsqD+fETcIelSgIhYB1wMXCZpEngWWJO/mWpmZnOklbtctgErKpavK8xfB1zX3dLMzKwd/ktRM7NEONDNzBLhQDczS4QD3cwsEQ50M7NEONDNzBLhQDczS4QD3cwsEQ50M7NEONDNzBLhQDczS4QD3cwsEQ50M7NEONDNzBLhQDczS4QD3cwsEQ50M7NEONDNzBLhQDczS4QD3cwsEQ50M7NEONDNzBLRNNAlHSPpAUlbJW2X9JGKPpJ0raRHJW2TdHZvyjUzs3rmtdDnOeC8iDggaQTYKOnOiLiv0OeNwCn59CrgU/lXMzObI03P0CNzIH84kk9R6vYW4Ka8733AAkmLuluqmZk10tI1dEnDkrYAe4G7IuL+UpfFwM7C4135svI4ayWNSxqfmJjosGQzM6vSUqBHxMGIOAtYAqyUdHqpi6pWqxhnfUSMRcTY6Oho28WamVl9bd3lEhFPAfcAF5aadgFLC4+XALtnU5iZmbWnlbtcRiUtyOePBc4HHil1ux14R363yznAvojY0+1izcysvlbuclkEfEbSMNkB4PMRcYekSwEiYh2wAXgT8CjwDPCuHtVrZmZ1NA30iNgGrKhYvq4wH8Dl3S3NzMza4b8UNTNLhAPdzCwRDnQzs0Q40M3MEuFANzNLhAPdzCwRDnQzs0Q40M3MEuFANzNLhAPdzCwRDnQzs0Q40M3MEuFANzNLhAPdzCwRDnQzs0Q40M3MEuFANzNLhAPdzCwRDnQzs0Q40M3MEuFANzNLhAPdzCwRTQNd0lJJ35C0Q9J2SVdU9DlX0j5JW/Lp6t6Ua2Zm9cxroc8kcGVEbJZ0PLBJ0l0R8XCp370Rsbr7JZqZWSuanqFHxJ6I2JzPPw3sABb3ujAzM2tPW9fQJS0HVgD3VzS/WtJWSXdKOq3O+msljUsan5iYaL9aMzOrq+VAlzQf+CLwvojYX2reDCyLiDOBTwK3VY0REesjYiwixkZHRzss2czMqrQU6JJGyML8loj4Urk9IvZHxIF8fgMwImlhVys1M7OGWrnLRcD1wI6IuKZOn5PyfkhamY/7RDcLNTOzxlq5y2UVcAnwkKQt+bIPAScDRMQ64GLgMkmTwLPAmoiI7pdrZmb1NA30iNgIqEmf64DrulWUmZm1z38pamaWCAe6mVkiHOhmZolwoJuZJcKBbmaWCAe6mVkiHOhmZolwoJuZJcKBbmaWCAe6mVkiHOhmZolwoJuZJcKBbmaWCAe6mVkiHOhmZolwoJuZJcKBbmaWCAe6mVkiHOhmZolwoJuZJcKBbmaWCAe6mVkimga6pKWSviFph6Ttkq6o6CNJ10p6VNI2SWf3plwzM6tnXgt9JoErI2KzpOOBTZLuioiHC33eCJyST68CPpV/NTOzOdL0DD0i9kTE5nz+aWAHsLjU7S3ATZG5D1ggaVHXqzUzs7rauoYuaTmwAri/1LQY2Fl4vIuZoY+ktZLGJY1PTEy0WaqZmTXScqBLmg98EXhfROwvN1esEjMWRKyPiLGIGBsdHW2vUjMza6ilQJc0Qhbmt0TElyq67AKWFh4vAXbPvjwzM2tVK3e5CLge2BER19Tpdjvwjvxul3OAfRGxp4t1mplZE63c5bIKuAR4SNKWfNmHgJMBImIdsAF4E/Ao8Azwrq5XamZmDTUN9IjYSPU18mKfAC7vVlFmZtY+/6WomVkiHOhmZolwoJuZJcKBbmaWCAe6mVkiHOhmZolwoJuZJcKBbmaWCAe6mVkiHOhmZolwoJuZJcKBbmaWCAe6mVkiHOhmZolwoJuZJcKBbmaWCAe6mVkiHOhmZolwoJuZJcKBbmaWCAe6mVkiHOhmZoloGuiSbpC0V9Lf12k/V9I+SVvy6erul2lmZs3Ma6HPjcB1wE0N+twbEau7UpGZmXWk6Rl6RHwTeHIOajEzs1no1jX0V0vaKulOSafV6yRpraRxSeMTExNd2rSZmUF3An0zsCwizgQ+CdxWr2NErI+IsYgYGx0d7cKmzcysZtaBHhH7I+JAPr8BGJG0cNaVmZlZW2Yd6JJOkqR8fmU+5hOzHdfMzNrT9C4XSZ8FzgUWStoF/AEwAhAR64CLgcskTQLPAmsiInpWsZmZVWoa6BHxtibt15Hd1mhmZn3kvxQ1M0uEA93MLBEOdDOzRLTyp//PK/d99wk+8Tf/yPCQGBoS84bEkMTwEAwPieGhIYbFobbhQ+35VJwvt5Xah/LH82rzQzAkMW9o6PD8cGGM0rrzGozfqH1oCOYNDTEkyG8gMhtIEUEERPEx5MvytoCDERycCqamgoNx+OvkwWCq1hbBwSkK88FkYb62TrnvweKYpfGn8jGK69TGK25nxji1vuVxCts5tG6Qt08xNZXt61tXLOaSc5Z1/fs9cIE+Fdk35rnJ4jdq+pNZfDKmtR2c/oRPTmU/UM9nQ8rCXYJytouZYV+V/1WHhPKBovKw0cJYVQecVmqoXK+qBFX3KC4vdpm+vJX+1QfMaf1nMWad8hvWUAs9mgQh+eNp65T6RKEP05ZFviTvX9jm4eXTt3l47Fpt05cV+6dCYvqJl2ond4UTSYnh4UKbpp8sZsvyk7QhGBkaYmSoNydqAxfor3n5Ql7z8u793VLE4SPvoQPBFExOTeVHW2YcDMpH7/JZwmTpqN30YJMfxQ9OTU07QyiuOzU1/bek6nem6m7Rql+u8qLqPq2N1VINXdhe1FlebJnWf9q6dfrU3VZ1p+n9o87y9vqXdzuI7KCR/UM6fAhReVneIFRoKy3LOx5eXhunvEyFbai0vel1oOnbpDSmagsabu/wNmvBWHy1OlR8Zazpr5jrvTqeHrTlV+OHx6+7ncIr5Nq6g/YKeeACvduU/2Ac8d8IMxt4flPUzCwRDnQzs0Q40M3MEuFANzNLhAPdzCwRDnQzs0Q40M3MEuFANzNLhPr1WRSSJoDHO1x9IfCDLpYzCLzPRwbv85FhNvu8LCIqP5S5b4E+G5LGI2Ks33XMJe/zkcH7fGTo1T77kouZWSIc6GZmiRjUQF/f7wL6wPt8ZPA+Hxl6ss8DeQ3dzMxmGtQzdDMzK3Ggm5klYuACXdKFkv5B0qOS/mu/6+k1STdI2ivp7/tdy1yRtFTSNyTtkLRd0hX9rqnXJB0j6QFJW/N9/ki/a5oLkoYlfVvSHf2uZS5IekzSQ5K2SBrv+viDdA1d0jDwHeD1wC7gQeBtEfFwXwvrIUmvAw4AN0XE6f2uZy5IWgQsiojNko4HNgFvTfx5FnBcRByQNAJsBK6IiPv6XFpPSXo/MAacEBGr+11Pr0l6DBiLiJ78IdWgnaGvBB6NiO9GxE+AW4G39LmmnoqIbwJP9ruOuRQReyJicz7/NLADWNzfqnorMgfyhyP5NDhnWx2QtAS4CPh0v2tJxaAF+mJgZ+HxLhL/RT/SSVoOrADu73MpPZdfftgC7AXuiojU9/njwAeAqT7XMZcC+L+SNkla2+3BBy3Qqz6CO+mzmCOZpPnAF4H3RcT+ftfTaxFxMCLOApYAKyUle4lN0mpgb0Rs6nctc2xVRJwNvBG4PL+k2jWDFui7gKWFx0uA3X2qxXoov478ReCWiPhSv+uZSxHxFHAPcGF/K+mpVcCb82vKtwLnSbq5vyX1XkTszr/uBb5Mdhm5awYt0B8ETpH0MklHAWuA2/tck3VZ/gbh9cCOiLim3/XMBUmjkhbk88cC5wOP9LWoHoqIqyJiSUQsJ/s9vjsi3t7nsnpK0nH5m/xIOg64AOjq3WsDFegRMQn8FvB1sjfKPh8R2/tbVW9J+izwd8DPStol6d39rmkOrAIuITtr25JPb+p3UT22CPiGpG1kJy53RcQRcSvfEeQlwEZJW4EHgK9GxNe6uYGBum3RzMzqG6gzdDMzq8+BbmaWCAe6mVkiHOhmZolwoJuZJcKBbmaWCAe6mVki/j+E8aNteAcZDgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(np.arange(6), mse_train)\n",
    "plt.plot(np.arange(6), mse_test)\n",
    "plt.plot(np.arange(6), mse_out_of_bag)\n",
    "plt.title('number of features = [1,5,10,25,50,100]')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "074392f0",
   "metadata": {},
   "source": [
    "We can see above that, changing the number of explanatory variables from which to sample the best split, doesn't change MSE values at all"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b754b45",
   "metadata": {},
   "source": [
    "Using cross-validation to find the optimal maximal depth of the trees on which to train the Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "4f888818",
   "metadata": {},
   "outputs": [],
   "source": [
    "depth = [1,3,5,8,10,12]\n",
    "n = len(depth)\n",
    "mse_train = [math.nan for i in range(n)]\n",
    "mse_test = [math.nan for i in range(n)]\n",
    "mse_out_of_bag = [math.nan for i in range(n)]\n",
    "abs_train = [math.nan for i in range(n)]\n",
    "abs_test = [math.nan for i in range(n)]\n",
    "abs_out_of_bag = [math.nan for i in range(n)]\n",
    "\n",
    "for i in range(n):\n",
    "    Forest = RandomForestRegressor(oob_score = True, n_estimators = 1000, max_depth = depth[i])\n",
    "    Forest.fit(X_train, y_train)\n",
    "    \n",
    "    predict_train = Forest.predict(X_train)\n",
    "\n",
    "    predict_test = Forest.predict(X_test)\n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    residuals = predict_train - y_train\n",
    "    residuals2 = predict_test - y_test\n",
    "    residuals3 = out_of_bag_predict - y_train\n",
    "    mse_train[i] = np.sqrt(sum(residuals**2)/len(residuals))\n",
    "    mse_test[i] = np.sqrt(sum(residuals2**2)/len(residuals2))\n",
    "    mse_out_of_bag[i] = np.sqrt(sum(residuals3**2)/len(residuals3))\n",
    "    abs_train[i] = sum(abs(residuals))/len(residuals)\n",
    "    abs_test[i] = sum(abs(residuals2))/len(residuals2)\n",
    "    abs_out_of_bag[i] = sum(abs(residuals3))/len(residuals3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "ab9895f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'sqrt(MSE) for oob, test and train')"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xBEDBICJJz9wb8s64Q+Xm5npeXl6dl7OleD/zVwWhMG/lVpZs3AUEwZCb04HhfTswvG9HTuypYBCRxs/MFrh7bqXjkjUIoikYRKQpS0gQmFkm8C6QQdAF9Zy7/yJqmtHAS8CqcNAL7n5ndcuNVxBEUzCISFNSXRDEcx/BfmCsuxebWTowx8xed/d5UdO95+7nxbGOI9KxVQbnnNidc07sDsDmg4JhC3f/4+t9DAoGEWnM4hYEHjQ1isOX6eGjcfVDRejUKoMJJ3ZnQg3B0PKrYOjI8L4dGKBgEJEGLq5HDZlZKrAA6Ac84O4fVjLZqWb2MbAOuN3dP4tnTbFSUzDc9Y8vAQWDiDR89bKz2MzaAX8DbnT3TyOGtwHKw+6jCcAUd+9fyfzXANcA9OrVa2h+fn7ca66rimD4YEUQDMs2BY2j6GA4sWdb0hQMIhJnDeKoITP7BbDb3X9XzTSrgVx331zVNPW1szjWinYd3GKIDIaT+3T46jyGAT3aKBhEJObqtLM4PPrnPGAU0APYC3wK/L26bhwz6wyUuPt2M2sOjAPuipqmG7DR3d3MhhHcOnNL7TarcencOoNzT+rOuScFXUnRwfDb17/uSlIwiEh9qjYIzOwO4JvAbOBDYBOQCRwN/DYMidvcfXEls3cHHg/3E6QA0939VTO7FsDdpwIXAteZWSlBwFzsje3EhiNUVTB8sHIz81ZuVTCISL2ptmvIzM51979XM74L0Mvd662vprF2DR2uol37+XDVlq/OY1gediW1ykgjN6e9gkFEDktc9hGYWZq7l9apsiOQLEEQrbpgODkMhm+c0I0+nVomuFIRaYiOOAjMbI67jwyf/8Xdvx8xbqG7D4l5tTVI1iCItmnXvoPOfK4IhmE5HbgoN4sJJ3anZUbSXFNQRGpQlyD4yN0Hh88P+uCPHFefFASVW79jLy9+tI5n8wpYuXk3LZqlct5J3ZmYm83Q3u2xRnBNdBGJn7ocNVRdv1FS7NRtLLq3bc51o4/i2jP6siB/G9PzCvj74vVMzyukb6eWXJibxXeHZNG1TWaiSxWRBqamFsFK4DaCo37+F7i9YhRwt7sfFfcKo6hFUHu795fy2ifreTavkPmrt5JiMPqYLlw0NIszj+tKszTtZBZJFnXpGnq0ugW7+5V1rO2wKQiOzKrNu3luQQHPLShk4879dGjZjAsG9WTiyVkc261NossTkThrEGcWx4qCoG7Kyp13lxXxXF4hb36+gZIy58SebZmYm8W3BvakbQvdzlCkKapLi+CbwGJ3zw9f/xz4LpAP3Ozuq6qcOU4UBLGzdfcBXlq0lul5hXyxfifN0lI4+4RuTMzNYsRRnUhJ0Q5mkaaiLkGwGBju7nvM7DzgHuASYDBwkbufHY+Cq6MgiI9P1+7g2bwCXly0jh17S+jZrjnfHZrFRUOzyO7QItHliUgd1SUIPnb3geHzR4Al7n5X+FrnETRB+0rKeOuLjUzPK+S9ZUW4w6l9OzLx5CzGn9Cd5s1SE12iiByBurYITgP2ENxO8rsVl5Mws8/d/fg41FstBUH9Wbd9L88vKOTZBYWs2bqH1hlpnDewBxNzsxiU3U7nJog0InU5j2AysAjYCXwREQKDgfUxrFEaoB7tmnPjmf25fkw/5q/eyvS8Al78aC3T5q+hf5dWXJSbxbcHZ9G5dUaiSxWROqjxqCEz6wl0AT529/JwWHcg3d3XxL/Eg6lFkFi79pWEJ6oVsHDNdtJSjDHHdmFibjajj+msu6+JNFB16Rqqdh+Auy+sY22HTUHQcCzftItnFxTy/IK1bC7eT6dWzfjOkGAHc/+urRNdnohEqEsQlAOfAUUVgyJGu7uPjVmVtaQgaHhKysp5Z0kRzy4o4O0vNlFa7gzu1Y6LhmZz3sDutMnUuQkiiVaXIPgxwXkDO4Cngb+5e3FcqqwlBUHDtrl4Py9+tJbpeQUs3VhMZnoKEwZ058LcLIb36ahzE0QSpM5nFptZH4LzB84nOJns1+6+KJZF1paCoHFwdxYX7mB6XgEvL1rHrv2lZHdozkVDs/nu0Cx6tmue6BJFkkpMLjFhZicAFwPfB37i7tNjV2LtKQgan30lZbzx2Qam5xUwd/kWzGBkv05clJvNN47vSma6zk0Qibe6dA31JfjwPx8oIOgeetXd98Wj0NpQEDRuBVv38NyCQp5bUMja7Xtpk5nG+YN6MjE3mwE92+jcBJE4qevO4sXASwTnEhw0sbvfE8M6a0VB0DSUlzsfrNzC9LwC/vHpBvaXlnNst9ZMzM3mgsE96dCyWaJLFGlS6hIEd1DNDWjc/b/rXN1hUhA0PTv2lvDKx8Hd1T4u3EF6qjHuuK5MzM1mVP9OpOncBJE602WopdFYsmEXz+YV8LeP1rJl9wG6tM7gu0OzuPjkbHp3bJno8kQarbq0CP4TeMDdt1UxfizQwt1fjUmltaAgSA4HSsuZ+eUmns0rYPbSItydCwb35Kax/cnppEAQOVx1udbQJ8CrZrYPWEhwYlkm0J/gUtQzgF/HsFYRAJqlpTB+QDfGD+jGxp37+PN7K/nLvHxeWrSOb4eB0KujLo8tEgu1PY+gPzAC6A7sBb4A3nX3vfEt71BqESSvTbv2MXX2Sp76MJ/ScufCIVncMLaf7pcgUguxOKHsInd/tqZh9UFBIJt27uMPs1fw1/lrKC93LsrN4vox/chqr0AQqUosguCQm9DoxjSSaBt27OPB2cuZNr8Ax7koN5vrx/TTWcsilajLzuJzgAnAROCZiFFtgOPdfVgsC60NBYFEW7d9L3+YvZxn/lkAwPdODgKhe1sFgkiF6oKgpgO01wF5wD5gQcTjZaDa+xWbWaaZzTezj83sMzM75JwDC9xnZsvNbHFNl70WqUyPds355QUnMvvfxnBRbjbP/LOAM+6ezS9e+pSNOxN2ErxIo1GbG9OkAk+4+2WHteDgWgEt3b3YzNKBOcDN7j4vYpoJwI0ErY5TgCnufkp1y1WLQGpSuG0PD8xazrN5haSkGJcO68WPRh9FlzaZiS5NJGHq0iLA3cuAjmZ2WOf8e6DiktXp4SM6dc4nCBkPA6JdePczkSOW1b4Fv/nOScy6fTQXDOrBX+blM+ruWdz5yuds2qUWgki0ms4jqJAPzDWzl4HdFQNrutZQ2JpYAPQjODHtw6hJehJczK5CYThM90OWOsvu0IK7LxzI9WP68fuZy3n8g9X8dX4+k07pzb+ecZTutSwSqu1FXNYBr4bTt454VMvdy9x9EJAFDDOzAVGTVHapyUP6qszsGjPLM7O8oqKiSmYRqVrvji353UUDefvWM5hwYncembuK0++exW9e+4ItxfsTXZ5IwtXbtYbM7BfAbnf/XcSwPwKz3X1a+HoJMNrdq2wRaB+B1NXKomJ+P3M5Ly1aS2Z6KpefmsM1p/fVFU+lSYvFeQSvUP1VSL9VyTydgRJ3325mzYE3gbsir0tkZucCN/D1zuL7ajokVUEgsbJ8UzH3vb2MVxavo0V6KleclsO/jOpLewWCNEF1udZQhZVAN+DJ8PUlwGrgjWrm6Q48Hu4nSAGmu/urZnYtgLtPBV4jCIHlwB7gylrWI1Jn/bq04r5LBnPj2H5MeXsZD76zgic+yOcHp+Xww1F9aNdCgSDJobYtgnfd/fSahtUHtQgkXpZs2MWUt5fy2icbaJ2RxpUj+3D1yD60bZ6e6NJE6qxOh4+GOoe3raxYYF+gcyyKE2kojunWmj9cNpTXbx7FiH6duO/tZYy8ayaT31rKzn0liS5PJG5q2yI4G/gTQReRA32Aa9z9zfiWdyi1CKS+fLZuB1PeWsabn2+kTWYaPxzVlytH5NA6Uy0EaXxi0SJoAwwAbgZmElyGenNsyhNpmE7o0ZaHLs/l1RtHMqxPR+6ZsZRRd8/igVnLKd5fmujyRGKmtkHwX+6+k+DcgbOAqcCDcatKpAEZ0LMtf74il5dvGMGQXu353zeWMOqumfxh9nJ2KxCkCahtEJSFP88Fprr7S4AOqZCkclJWOx75wcm8eP0IBma34+5/LGHU3bOY+s4K9hxQIEjjVdt9BK8Ca4FxwFCCu5TNd/eB8S3vUNpHIA3FwjXbmPzWMt5dWkSnVs3419OPYtLw3jRvlpro0kQOEYsTyloA44FP3H1ZeGG4E7WzWAQW5G9l8lvLeG/ZZjq1yuDaM/oyaXhvMtMVCNJw1DkIGhIFgTRU/1y9lXtnLOX9FVvo3DqD6844iktP6aVAkAZBQSBSjz5cuYV731rKvJVb6dI6g+vH9ON7J2crECShFAQiCfD+is1MnrGM+au30q1NJtePOYqJJ2eTkaZAkPqnIBBJEHfn/RVbuHfGUvLyt9GjbSY/GtOPibnZNEur7UF7InWnIBBJMHdnzvLN3DtjKQvXbKdnu+ZcP6YfFw7NUiBIvVAQiDQQ7s67y4JAWFSwnaz2zblxbD++MySL9FQFgsSPgkCkgXF3Zi8p4t63lrK4cAe9OrTghrH9+M7gnqQpECQOFAQiDZS7M/PLTdz71lI+XbuT3h1bcOPY/lwwqIcCQWJKQSDSwLk7b32xiXtnLOXz9Tvp06klN47tx/mDepKaUtmtvUUOTyyuPioicWRmnHV8V/5+00imThpKRloKt07/mLPueYeXFq2lrLxxfWGTxkVBINKAmBnjB3TjtZtG8eBlQ0hPTeHmpxdx9uR3efnjdQoEiQsFgUgDlJJinHNid16/eRQPXDqEFIObpn3E+Mnv8uridZQrECSGFAQiDVhKinHuSd35x82n8/tLBuPADX/9iHOmvMdrn6xXIEhMKAhEGoGUFOObA3vwxi2nM+XiQZSUl/OjpxYy4b73+MenG2hsB31Iw6KjhkQaobJy5+WP13Lf28tZtXk3x3dvwy3j+nPW8V0x01FGcigdPirSRJWWlfPSonXcN3MZ+Vv2MKBnG24582jOPK6LAkEOoiAQaeJKy8r520druW/mMgq27uWkrLbcMq4/Y45RIEhAQSCSJErKynlhYSG/n7mcwm17GZjdjh+P688ZR3dWICQ5BYFIkjlQWs7zCwu5f+Zy1m7fy+Be7fjxuKMZ1b+TAiFJKQhEktSB0nKeXVDA/TOXs37HPob2bs+tZx3NaUd1VCAkGQWBSJLbX1rG9H8W8MCsFWzYuY9hfTpw21lHc0rfjokuTepJQq41ZGbZZjbLzL4ws8/M7OZKphltZjvMbFH4+Hm86hFJZhlpqXz/1Bxm/9to7vjm8azavJvvPTSPSX/+kAX52xJdniRY3FoEZtYd6O7uC82sNbAAuMDdP4+YZjRwu7ufV9vlqkUgUnf7Ssp4cl4+D85ewZbdBxh9TGd+PO5oBma3S3RpEicJaRG4+3p3Xxg+3wV8AfSM1/pEpPYy01P54ai+vPuTMfx0/LEsKtjO+Q/M5YeP5/HZuh2JLk/qWb3sIzCzHOBdYIC774wYPhp4HigE1hG0Dj6rZP5rgGsAevXqNTQ/Pz/uNYskk137Snh07mr+9N5Kdu0r5ZwB3bhl3NEc0611okuTGEnozmIzawW8A/zK3V+IGtcGKHf3YjObAExx9/7VLU9dQyLxs2NvCQ+/t5JH5q5m94FSzjupB7eM689RnVslujSpo4QFgZmlA68Cb7j7PbWYfjWQ6+6bq5pGQSASf9t2H+Ch91by2NzV7C8t44LBPblpbH9yOrVMdGlyhBISBBYcpPw4sNXdb6limm7ARnd3MxsGPAf09mqKUhCI1J/Nxfv54zsreOKDfErLnQuHZHHD2H5kd2iR6NLkMCUqCEYC7wGfAOXh4P8AegG4+1QzuwG4DigF9gK3uvv71S1XQSBS/zbt3McfZq/grx+uwXEm5mZz/Zh+9GjXPNGlSS3phDIRiYn1O/bywKzlPPPPAgzj0lN68aPRR9GlTWaiS5MaKAhEJKYKtu7h/pnLeW5hIWkpxveH9+ba0UfRqVVGokuTKigIRCQu8rfsZsrby3jxo7VkpqdyxWk5XDOqL+1bNkt0aRJFQSAicbWiqJgpby3jlcXraNksjatG5HD1qL60bZ6e6NIkpCAQkXqxZMMupry9lNc+2UDrzDT+ZVRfrhyRQ+tMBUKiKQhEpF59tm4Hk99axozPN9KuRTrXnN6XK07NoWVGWqJLS1oKAhFJiMWF27l3xlJmLSmiY8tmXDf6KC47pTfNm6UmurSkoyAQkYRakL+Ne2csZc7yzXRuncH1o4/i4mG9yExXINQXBYGINAgfrtzCPTOW8uGqrXRvm8n1Y/oxMTebZmlxuxCyhBQEItJguDvvr9jC/725hIVrttOzXXNuOrMf3xmSRXqqAiFeFAQi0uC4O+8sLeLeGUv5uHAHvTu24Kax/blgcE9SU3Q/5VhLyI1pRESqY2aMPqYLL14/gj9fnkvLZmnc9uzHnHXvO7z88TrKyxvXl9TGTEEgIgllZow7viuv3jiSqZOGkJ6Swk3TPmL8lHd5/ZP1CoR6oCAQkQYhJcUYP6A7r988it9fMpiycue6pxZy7u/nMOPzjTS2buzGREEgIg1KSorxzYE9ePPHZ3DPxIHsOVDKvzyRx/kPzGXWkk0KhDjQzmIRadBKy8p5YeFa7pu5jMJtexnSqx23nnUMI/p1JLj/ldSGjhoSkUbvQGk5zy4o4P6Zy1m/Yx/D+nTgtrOO5pS+HRNdWqOgIBCRJmN/aRlPzy/ggVnL2bRrPyP6deTWs45haO/2iS6tQVMQiEiTs6+kjCfn5TP1nRVsLj7AGUd35tazjmZgdrtEl9YgKQhEpMnac6CUJz7I54/vrGDbnhKG9+3A1SP7MvbYLjoxLYKCQESavF37Svjrh2t44oN81m7fS++OLfjBaTlclJtNK13+WkEgIsmjtKycNz7byMNzVrJwzXZaZ6TxvZOzueK0HLI7tEh0eQmjIBCRpPTRmm08Mnc1r32yHnfn7BO6cdXIPuT2bp90h54qCEQkqa3fsZfH389n2vw17NhbwklZbblqRB8mnNg9aS6BrSAQESHYsfz8wrU8OncVK4t207VNBpefmsOlw3rRvmWzRJcXVwoCEZEI5eXOO8uKeGTOKt5btpnM9BS+PTiLq0fm0K9L60SXFxfVBYF2pYtI0klJMcYc04Uxx3RhyYZdPDp3Fc8vLGTa/DWcfnRnrh7Zh9P7d0qa/QhqEYiIAFuK9weHn87Lp2jXfvp1acWVI3L4zuAsmjdr/PdWVteQiEgtHSgt5++frOPhOav4dO1O2rVI59Jhvbj81By6tc1MdHlHLCFBYGbZwBNAN6AceMjdp0RNY8AUYAKwB/iBuy+sbrkKAhGpD+7OP1dv4+E5K3nz842kmnHuSd25akSfRnkZi0TtIygFbnP3hWbWGlhgZjPc/fOIac4B+oePU4AHw58iIgllZgzr04FhfTqwZsseHnt/NdPzCnhp0Tpye7fnqpF9+MbxXUlLbfyHn9Zb15CZvQTc7+4zIob9EZjt7tPC10uA0e6+vqrlqEUgIomya18J0/MKeez9VRRs3UvPds35wWk5fG9YNm0y0xNdXrUSfvN6M8sBBgMfRo3qCRREvC4Mh0XPf42Z5ZlZXlFRUdzqFBGpTuvMdK4e2YfZt49h6qSh9GzfnF+99gWn/vpt7nj5M1Zv3p3oEo9I3A8fNbNWwPPALe6+M3p0JbMc0kRx94eAhyBoEcS8SBGRw5CaYowf0I3xA7rx6dodPDJnFU99mM/jH6zmzGO7cvXIPgzv26HRHH4a1yAws3SCEHjK3V+oZJJCIDvidRawLp41iYjE0oCebbnne4P493OO5S/z8nnqwzW89aeNHNe9DVeNyOFbg3qQkdawDz+N51FDBjwObHX3W6qY5lzgBoKjhk4B7nP3YdUtV/sIRKQh21dSxkuL1vLwnFUs3VhMp1bNmDS8N5OG96ZTq4yE1ZWow0dHAu8BnxAcPgrwH0AvAHefGobF/cB4gsNHr3T3aj/lFQQi0hi4O3OXb+HhOSuZtaSIZqkpnD+oB1eN7MNx3dvUez06oUxEJIFWFBUHl7FYsJa9JWWcdlRHrhrRh7HHdiGlnu6ipiAQEWkAtu85wLT5BTzxwWrW79hHTscWXDmiDxcOzaJlnO+ipiAQEWlASsrKef3TDTw8ZxUfF2yndWYalwzrxRWn5dCzXfO4rFNBICLSQC1cs42H56ziH59uAGB8eBe1Ib3axfTwU12GWkSkgRrSqz1DLm3P2u17eeL91Uybv4a/f7KegdntuGpEDhNO7E56nC9joRaBiEgDsnt/Kc8vLOTRuatZtXk33dpkcvlpvbl0WC/atTjyu6ipa0hEpJEpL3dmLdnEI3NXMXf5FjLTU7j9G8fww1F9j2h56hoSEWlkUlKMM4/rypnHdeXLDTt5ZM4qesRpR7KCQESkgTu2WxvuvnBg3Jbf+C+kLSIidaIgEBFJcgoCEZEkpyAQEUlyCgIRkSSnIBARSXIKAhGRJKcgEBFJco3uEhNmVgTkJ7qOWuoEbE50EXHSlLcNmvb2adsar7psX29371zZiEYXBI2JmeVVdW2Pxq4pbxs07e3TtjVe8do+dQ2JiCQ5BYGISJJTEMTXQ4kuII6a8rZB094+bVvjFZft0z4CEZEkpxaBiEiSUxCIiCQ5BUEcmFm2mc0ysy/M7DMzuznRNcWamaWa2Udm9mqia4klM2tnZs+Z2Zfh7+/URNcUS2b24/Bv8lMzm2ZmmYmu6UiZ2SNmtsnMPo0Y1sHMZpjZsvBn+0TWWBdVbN//hn+bi83sb2bWLhbrUhDERylwm7sfBwwHrjez4xNcU6zdDHyR6CLiYArwD3c/FhhIE9pGM+sJ3ATkuvsAIBW4OLFV1cljwPioYf8OvO3u/YG3w9eN1WMcun0zgAHufhKwFPhZLFakIIgDd1/v7gvD57sIPkx6Jraq2DGzLOBc4M+JriWWzKwNcDrwMIC7H3D37QktKvbSgOZmlga0ANYluJ4j5u7vAlujBp8PPB4+fxy4oD5riqXKts/d33T30vDlPCArFutSEMSZmeUAg4EPE1xKLE0GfgKUJ7iOWOsLFAGPht1efzazlokuKlbcfS3wO2ANsB7Y4e5vJraqmOvq7ush+EIGdElwPfF0FfB6LBakIIgjM2sFPA/c4u47E11PLJjZecAmd1+Q6FriIA0YAjzo7oOB3TTuroWDhP3l5wN9gB5ASzOblNiq5EiY2f8j6IJ+KhbLUxDEiZmlE4TAU+7+QqLriaERwLfMbDXwNDDWzJ5MbEkxUwgUuntF6+05gmBoKsYBq9y9yN1LgBeA0xJcU6xtNLPuAOHPTQmuJ+bM7ArgPOAyj9GJYAqCODAzI+hn/sLd70l0PbHk7j9z9yx3zyHY0TjT3ZvEt0p33wAUmNkx4aAzgc8TWFKsrQGGm1mL8G/0TJrQzvDQy8AV4fMrgJcSWEvMmdl44KfAt9x9T6yWqyCIjxHA9wm+LS8KHxMSXZTUyo3AU2a2GBgE/Dqx5cRO2NJ5DlgIfELw/99oL8lgZtOAD4BjzKzQzK4GfgucZWbLgLPC141SFdt3P9AamBF+rkyNybp0iQkRkeSmFoGISJJTEIiIJDkFgYhIklMQiIgkOQWBiEiSUxBIo2RmbmZ/iXidZmZFdb0aqpmdHXHIb7GZLQmfP1H3qr9axx1mtjZc7jIze6EuFyU0s9FmdlrE68fM7MLYVCvJQEEgjdVuYICZNQ9fnwWsretC3f0Ndx/k7oOAPIKzNwe5++UV05hZal3XA9wbLrc/8Aww08w6H+GyRtP0zhCWeqQgkMbsdYKroAJcAkyrGGFmw8zs/fDice9XnC1sZrea2SPh8xPD6/K3qGlFZrbazH5uZnOAi8zsG2b2gZktNLNnw+tKYWZDzewdM1tgZm9UXO6gOu7+DPAmcGl1yzCz2WY2OdyeT8NtzAGuBX4ctjBGhYs9PZxupVoHUhMFgTRmTwMXhzdXOYmDr/D6JXB6ePG4n/P1GcKTgX5m9m3gUeBfD+NU/X3uPhJ4C/hPYJy7DyFoOdwaXl/q98CF7j4UeAT4VS2XvRA4thbLaOnupwE/Ah5x99XAVL5uYbwXTtcdGElwTZpGe3at1I+0RBcgcqTcfXH4jfgS4LWo0W2Bx82sP+BAejhPuZn9AFgM/NHd5x7GKp8Jfw4HjgfmBpfsoRnhpQCAAQSn/0Nw45f1tVy2hT9rWsa0cDveNbM21dyh6kV3Lwc+N7OutaxBkpSCQBq7lwmusT8a6Bgx/H+AWe7+7TAsZkeM6w8UE1yK+XDsDn8aMMPdL4kcaWYnAp+5+5Hc3nIwQcvCalhG9DVhqrpGzP7I0o6gHkki6hqSxu4R4E53/yRqeFu+3nn8g4qBZtaW4HaUpwMdj7D/fB4wwsz6hctsYWZHA0uAzhbe59jM0s3shJoWZmbfBb5B8G2/pmV8Lxw+kuDGMjuAXQQXIhM5IgoCadTcvdDdp1Qy6m7gN2Y2l6B7pcK9wB/cfSlwNfBbMzusu1i5exFBuEwLr1I6DzjW3Q8AFwJ3mdnHwCKqPpqnYufuMmASMDa8T0BNy9hmZu8T7Be4Ohz2CvDtqJ3FIrWmq4+KNBJmNhu43d3zEl2LNC1qEYiIJDm1CEREkpxaBCIiSU5BICKS5BQEIiJJTkEgIpLkFAQiIknu/wNkpwiTlnDhfgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(depth, mse_train)\n",
    "plt.plot(depth, mse_test)\n",
    "plt.plot(depth, mse_out_of_bag)\n",
    "plt.xlabel('Max Tree Depth')\n",
    "plt.ylabel('sqrt(MSE)')\n",
    "plt.title('sqrt(MSE) for oob, test and train')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efe228e8",
   "metadata": {},
   "source": [
    "We see that apart from the train MSE, which the model is overfitting, as max tree depth grows, the test set and out-of-bag predictions MSE do not improve as the depth of the trees increses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93a3d12b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34ed6e1e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4caa0dbc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2c7b8106",
   "metadata": {},
   "source": [
    "## Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bd53cc0",
   "metadata": {},
   "source": [
    "Overall NNets (which peformed very well on the train set due to their extreme flexibilty), did not quite manage to perfom as well as random forests."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "022339c9",
   "metadata": {},
   "source": [
    "In my opinion, that's partly because some of the explanatory variables were catergorical, and were casted to integers.\n",
    "This in not a particular problem for trees, but NNets do not capture this well."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de46f391",
   "metadata": {},
   "source": [
    "Here, we scale the dataset, so that it is between 0 and 1 (the range of values where NNets perform best). We adopt a 6 layer network with 100 neurons at each hidden layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "657be2fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "X_train2 = scaler.fit_transform(X_train)\n",
    "X_test2 = scaler.transform(X_test)\n",
    "\n",
    "neural = MLPRegressor( hidden_layer_sizes  = (100,100,100,100), max_iter = 2000, tol = 0.0001, n_iter_no_change = 10000,)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7e329c0",
   "metadata": {},
   "source": [
    "Fitting the neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "4d7343f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "neural_fit_train = neural.fit(X_train2, y_train)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "9119c813",
   "metadata": {},
   "outputs": [],
   "source": [
    "neural_predict_train = neural.predict(X_train2)\n",
    "neural_predict_test = neural.predict(X_test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "3e0a7347",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Points predicted by NNet on train set')"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.figure()\n",
    "sns.scatterplot(np.arange(len(neural_predict_train)),neural_predict_train)\n",
    "plt.title('Points predicted by NNet on train set')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f5400b5",
   "metadata": {},
   "source": [
    "Looks very good (it is indeed a perfect overfit, see below)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "ea1aa7b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Points predicted by NNet on train set')"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.figure()\n",
    "sns.scatterplot(np.arange(len(neural_predict_test)),neural_predict_test)\n",
    "plt.title('Points predicted by NNet on train set')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cf294fb",
   "metadata": {},
   "source": [
    "Test set predictions looks very reasonable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "2b9fff8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "residuals = neural_predict_train - y_train\n",
    "residuals2 = neural_predict_test - y_test\n",
    "mse_train = np.sqrt(sum(residuals**2)/len(residuals))\n",
    "mse_test = np.sqrt(sum(residuals2**2)/len(residuals2))\n",
    "abs_train = sum(abs(residuals))/len(residuals)\n",
    "abs_test = sum(abs(residuals2))/len(residuals2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "11634f4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(MSE) on train set:  0.026454880764487213\n",
      "sqrt(MSE) on test set:  4.252257959286345\n",
      "Mean Absolute value of residuals on train set:  0.01928905836992554\n",
      "Mean Absolute value of residuals on test set:  2.9892548307682256\n"
     ]
    }
   ],
   "source": [
    "print('sqrt(MSE) on train set: ', mse_train)\n",
    "print('sqrt(MSE) on test set: ', mse_test)\n",
    "print('Mean Absolute value of residuals on train set: ', abs_train)\n",
    "print('Mean Absolute value of residuals on test set: ', abs_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4330892d",
   "metadata": {},
   "source": [
    "Mad overfitting!! Performance on the test set is decent, but worse than Random Forest."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6d47320",
   "metadata": {},
   "source": [
    "Let's do some penalization, testing different \"alphas\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "c0770b0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "alpha = [0.00, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000, 1000000,10000000, 100000000]\n",
    "n = len(alpha)\n",
    "mse_train = [math.nan for i in range(n)]\n",
    "mse_test = [math.nan for i in range(n)]\n",
    "abs_train = [math.nan for i in range(n)]\n",
    "abs_test = [math.nan for i in range(n)]\n",
    "for i in range(n):\n",
    "    Regression = MLPRegressor(alpha = alpha[i], hidden_layer_sizes  = (100,100,100,100), \n",
    "                              max_iter = 2000, tol = 0.0001, n_iter_no_change = 10000)\n",
    "    Regression.fit(X_train2, y_train)\n",
    "    \n",
    "    predict_train = Regression.predict(X_train2)\n",
    "\n",
    "    predict_test = Regression.predict(X_test2)\n",
    "\n",
    "\n",
    "    residuals = predict_train - y_train\n",
    "\n",
    "    residuals2 = predict_test - y_test\n",
    "    \n",
    "    mse_train[i] = np.sqrt(sum(residuals**2)/len(residuals))\n",
    "    mse_test[i] = np.sqrt(sum(residuals2**2)/len(residuals2))\n",
    "    \n",
    "    abs_train[i] = sum(abs(residuals))/len(residuals)\n",
    "    abs_test[i] = sum(abs(residuals2))/len(residuals2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "3099a9b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.021646949856890408,\n",
       " 0.002456698962901149,\n",
       " 0.0656939738261973,\n",
       " 0.1703294948903424,\n",
       " 0.39742704287395686,\n",
       " 3.795724616698754,\n",
       " 3.7950105352303316,\n",
       " 3.7950135119142456,\n",
       " 3.794996680966512,\n",
       " 3.7949971613436846,\n",
       " 3.7949966659680174,\n",
       " 3.79499664329127]"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "d2d32af6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4.317145500429184,\n",
       " 4.245255872615433,\n",
       " 4.357304114833018,\n",
       " 4.40810062324287,\n",
       " 4.2742598003685695,\n",
       " 3.819129385288484,\n",
       " 3.815473836209525,\n",
       " 3.81622853552618,\n",
       " 3.815799042329807,\n",
       " 3.8157491163574275,\n",
       " 3.8158031884868477,\n",
       " 3.81581632166375]"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "16a1e21a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Performance on train and test set for different penalties')"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.figure()\n",
    "plt.plot(np.arange(-3, -3 + len(mse_train)), mse_train)\n",
    "plt.plot(np.arange(-3, -3 +len(mse_test)), mse_test)\n",
    "plt.xlabel('Order of magnitude of penalty term')\n",
    "plt.ylabel('sqrt(MSE)')\n",
    "plt.title('Performance on train and test set for different penalties')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfed2438",
   "metadata": {},
   "source": [
    "A penalty of 100 looks the most sensible. And MSE gets quite close to Random Forest performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bee93798",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a8ba577f",
   "metadata": {},
   "source": [
    "Let's try a slightly different normalization (transform dataset inearly s.t. mean = 0, variance = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "9addebcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler2 = StandardScaler()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8825dbfd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "ed716365",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train3 = scaler2.fit_transform(X_train)\n",
    "X_test3 = scaler2.transform(X_test)\n",
    "\n",
    "neural2 = MLPRegressor( hidden_layer_sizes  = (100,100,100,100), max_iter = 2000, tol = 0.0001, n_iter_no_change = 10000,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "f3c1f649",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MLPRegressor(hidden_layer_sizes=(100, 100, 100, 100), max_iter=2000,\n",
       "             n_iter_no_change=10000)"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neural2.fit(X_train3, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "5c512899",
   "metadata": {},
   "outputs": [],
   "source": [
    "neural2_predict_train = neural2.predict(X_train3)\n",
    "neural2_predict_test = neural2.predict(X_test3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "935f3ecf",
   "metadata": {},
   "outputs": [],
   "source": [
    "residuals = neural2_predict_train - y_train\n",
    "residuals2 = neural2_predict_test - y_test\n",
    "mse_train = np.sqrt(sum(residuals**2)/len(residuals))\n",
    "mse_test = np.sqrt(sum(residuals2**2)/len(residuals2))\n",
    "abs_train = sum(abs(residuals))/len(residuals)\n",
    "abs_test = sum(abs(residuals2))/len(residuals2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "039def2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(MSE) on train set:  0.02296912486763067\n",
      "sqrt(MSE) on test set:  4.067172926804932\n",
      "Mean Absolute value of residuals on train set:  0.015423636374447389\n",
      "Mean Absolute value of residuals on test set:  2.859867075428439\n"
     ]
    }
   ],
   "source": [
    "print('sqrt(MSE) on train set: ', mse_train)\n",
    "print('sqrt(MSE) on test set: ', mse_test)\n",
    "print('Mean Absolute value of residuals on train set: ', abs_train)\n",
    "print('Mean Absolute value of residuals on test set: ', abs_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5a3badc",
   "metadata": {},
   "source": [
    "Performance is roughly the same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4269cbb9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "5ad8eba4",
   "metadata": {},
   "outputs": [],
   "source": [
    "neural3 = MLPRegressor( hidden_layer_sizes  = (100,100), max_iter = 20000, tol = 0.0001, n_iter_no_change = 10000,)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "0b8d5959",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPRegressor(hidden_layer_sizes=(100, 100), max_iter=20000,\n",
       "             n_iter_no_change=10000)"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neural3.fit(X_train2, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "370a0566",
   "metadata": {},
   "source": [
    "Training a smaller NN leads to worst performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "7e40d670",
   "metadata": {},
   "outputs": [],
   "source": [
    "neural3_predict_train = neural3.predict(X_train2)\n",
    "neural3_predict_test = neural3.predict(X_test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "de027150",
   "metadata": {},
   "outputs": [],
   "source": [
    "residuals = neural3_predict_train - y_train\n",
    "residuals2 = neural3_predict_test - y_test\n",
    "mse_train = np.sqrt(sum(residuals**2)/len(residuals))\n",
    "mse_test = np.sqrt(sum(residuals2**2)/len(residuals2))\n",
    "abs_train = sum(abs(residuals))/len(residuals)\n",
    "abs_test = sum(abs(residuals2))/len(residuals2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "d4015e57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(MSE) on train set:  0.04128097245287082\n",
      "sqrt(MSE) on test set:  4.595880270553968\n",
      "Mean Absolute value of residuals on train set:  0.031561795680841234\n",
      "Mean Absolute value of residuals on test set:  3.3630578926234658\n"
     ]
    }
   ],
   "source": [
    "print('sqrt(MSE) on train set: ', mse_train)\n",
    "print('sqrt(MSE) on test set: ', mse_test)\n",
    "print('Mean Absolute value of residuals on train set: ', abs_train)\n",
    "print('Mean Absolute value of residuals on test set: ', abs_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efa220fb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c53cb17d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e96960ca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e13ab682",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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