final_project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finalized Data Preprocessing and Model Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 23.3.1 -> 24.0\n",
      "[notice] To update, run: C:\\Users\\mboyk\\miniconda3\\python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "!pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\Users\\mboyk\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# All the imports that are needed for the models\n",
    "import keras\n",
    "import keras_tuner\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import yfinance as yf\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime, timedelta\n",
    "from keras.layers import Dense\n",
    "from keras.models import Sequential\n",
    "from scikeras.wrappers import KerasRegressor\n",
    "from keras.optimizers import SGD\n",
    "from sklearn import linear_model\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import ARDRegression, LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, confusion_matrix, precision_score, accuracy_score, recall_score\n",
    "from sklearn.model_selection import train_test_split, RepeatedKFold\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder, PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is our index of different timezone and major cities within each timezone in the form of a dictionary\n",
    "cities_by_timezone = {\n",
    "    'Pacific Time': ['Los Angeles', 'San Francisco Bay Area', 'Seattle', 'Portland', 'San Luis Obispo', 'Sacramento', 'San Diego', 'Santa Barbara', 'Camarillo', 'Fremont', 'Bend', 'Reno', 'Spokane', 'Las Vegas', 'Fresno'],\n",
    "    'Mountain Time': ['Denver', 'Boulder', 'Salt Lake City', 'Missoula', 'Santa Fe', 'Phoenix', 'Lehi', 'Logan', 'Boise'],\n",
    "    'Central Time': ['Austin', 'Chicago', 'Minneapolis', 'Milwaukee', 'Nashville', 'Dallas', 'Cincinnati', 'Columbus', 'Indianapolis', 'Nebraska City', 'Kansas City'],\n",
    "    'Eastern Time': ['New York City', 'Boston', 'Atlanta', 'Ann Arbor', 'Miami', 'Detroit', 'Stamford', 'Washington DC', 'Philadelphia', 'Raleigh', 'Walnut', 'Madison', 'Dover', 'Burlington', 'Wilmington', 'Nashua', 'Baltimore', 'Tampa Bay']\n",
    "}\n",
    "\n",
    "def date_to_index(stock_data, date):\n",
    "    date_col_stock_data = stock_data.index.to_pydatetime()\n",
    "    for i in range(len(date_col_stock_data)):\n",
    "        if date_col_stock_data[i] == date:\n",
    "            return i\n",
    "    return -1\n",
    "\n",
    "def get_closest_available_date(stock_data, date):\n",
    "    '''\n",
    "    Get the closest available date \n",
    "    '''\n",
    "    date_col_stock_data = stock_data.index.to_pydatetime()\n",
    "    # date_col_stock_data = stock_data.reset_index()['Date']\n",
    "    while date not in date_col_stock_data:\n",
    "        date = date - timedelta(days = 1)\n",
    "    return date\n",
    "\n",
    "\n",
    "def weekly_price_diff(stock_data, week_end, period: int = 7):\n",
    "    '''\n",
    "    Finds the weekly price difference in NDXT index price in percents during last week before layoff date.\n",
    "\n",
    "    @type stock_data: pandas DataFrame\n",
    "    @param stock_data: original stock_data df\n",
    "    @type week_end: datetime object\n",
    "    @param week_end: layoff date in format datetime(YYYY,MM,DD)\n",
    "    @type period: int\n",
    "    @param period: number of days to look back, defaults to 7\n",
    "\n",
    "    @returns price_diff: % of change in NDXT index price during last week before layoff date \n",
    "    '''\n",
    "    if (week_end < datetime(2020, 1, 15)):\n",
    "        return 0\n",
    "    date_col_stock_data = stock_data.reset_index() \n",
    "    date_end_closest = get_closest_available_date(stock_data, week_end)\n",
    "    date_start_closest = get_closest_available_date(stock_data, week_end - timedelta(days = period))\n",
    "    price_start, price_end = stock_data['Open'][date_start_closest], stock_data['Open'][date_end_closest]\n",
    "    price_diff = (price_end - price_start) / price_start * 100\n",
    "    return price_diff\n",
    "\n",
    "def str_to_datetime(dt_string):\n",
    "    '''\n",
    "    Converts a string to a formatted datetime representation of the date.\n",
    "\n",
    "    @type dt_string: string\n",
    "    @param dt_string: string to convert into a datetime object\n",
    "\n",
    "    @returns datetime.strptime(dt_string, \"%Y-%m-%d %H:%M:%S UTC\"): datetime object that formats a date in a specific way\n",
    "    '''\n",
    "    return datetime.strptime(dt_string, \"%Y-%m-%d %H:%M:%S UTC\")\n",
    "\n",
    "def get_timezone(city):\n",
    "    '''\n",
    "    Retrieve the timezone associated with a city.\n",
    "\n",
    "    @type city: string\n",
    "    @param city: name of city to retrieve timezone for\n",
    "    @rtype: string, None\n",
    "    @returns timezone: timezone of a city if in an index; otherwise, None\n",
    "    '''\n",
    "    for timezone, cities in cities_by_timezone.items():\n",
    "        if city in cities:\n",
    "            return timezone\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\mboyk\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\yfinance\\utils.py:775: FutureWarning: The 'unit' keyword in TimedeltaIndex construction is deprecated and will be removed in a future version. Use pd.to_timedelta instead.\n",
      "  df.index += _pd.TimedeltaIndex(dst_error_hours, 'h')\n",
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read in our stock data from August 1st, 2018 to December 1st, 2024\n",
    "stock_data = yf.download('^NDXT', start=datetime(2019, 8, 1),\n",
    "                         end=datetime(2024, 12, 1))\n",
    "stock_data.to_csv('datasets/stock_data.csv', index = True, encoding='utf-8')\n",
    "\n",
    "# Visualize our preliminary stock data of opening prices\n",
    "plt.figure(figsize = (20,10))\n",
    "plt.title('Opening Prices from {} to {}'.format(datetime(2019, 8, 1),\n",
    "                                                datetime(2024, 12, 1)))\n",
    "plt.plot(stock_data['Open'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>price_diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-01</th>\n",
       "      <td>4821.149902</td>\n",
       "      <td>4924.919922</td>\n",
       "      <td>4726.339844</td>\n",
       "      <td>4762.810059</td>\n",
       "      <td>4762.810059</td>\n",
       "      <td>425800</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-02</th>\n",
       "      <td>4695.549805</td>\n",
       "      <td>4712.990234</td>\n",
       "      <td>4629.250000</td>\n",
       "      <td>4655.560059</td>\n",
       "      <td>4655.560059</td>\n",
       "      <td>356000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-05</th>\n",
       "      <td>4525.939941</td>\n",
       "      <td>4534.649902</td>\n",
       "      <td>4434.459961</td>\n",
       "      <td>4469.970215</td>\n",
       "      <td>4469.970215</td>\n",
       "      <td>427900</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-06</th>\n",
       "      <td>4530.689941</td>\n",
       "      <td>4562.890137</td>\n",
       "      <td>4481.229980</td>\n",
       "      <td>4531.810059</td>\n",
       "      <td>4531.810059</td>\n",
       "      <td>317500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-07</th>\n",
       "      <td>4480.180176</td>\n",
       "      <td>4563.459961</td>\n",
       "      <td>4470.759766</td>\n",
       "      <td>4550.560059</td>\n",
       "      <td>4550.560059</td>\n",
       "      <td>321600</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-08</th>\n",
       "      <td>10672.330078</td>\n",
       "      <td>10724.919922</td>\n",
       "      <td>10408.530273</td>\n",
       "      <td>10415.330078</td>\n",
       "      <td>10415.330078</td>\n",
       "      <td>0</td>\n",
       "      <td>2.104794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-11</th>\n",
       "      <td>10335.139648</td>\n",
       "      <td>10387.099609</td>\n",
       "      <td>10254.669922</td>\n",
       "      <td>10359.639648</td>\n",
       "      <td>10359.639648</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.848064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-12</th>\n",
       "      <td>10449.839844</td>\n",
       "      <td>10532.879883</td>\n",
       "      <td>10337.089844</td>\n",
       "      <td>10530.349609</td>\n",
       "      <td>10530.349609</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.291597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-13</th>\n",
       "      <td>10466.629883</td>\n",
       "      <td>10475.089844</td>\n",
       "      <td>10361.190430</td>\n",
       "      <td>10391.860352</td>\n",
       "      <td>10391.860352</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.715043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-14</th>\n",
       "      <td>10401.429688</td>\n",
       "      <td>10418.419922</td>\n",
       "      <td>10226.459961</td>\n",
       "      <td>10298.419922</td>\n",
       "      <td>10298.419922</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.581488</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1163 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Open          High           Low         Close  \\\n",
       "Date                                                                 \n",
       "2019-08-01   4821.149902   4924.919922   4726.339844   4762.810059   \n",
       "2019-08-02   4695.549805   4712.990234   4629.250000   4655.560059   \n",
       "2019-08-05   4525.939941   4534.649902   4434.459961   4469.970215   \n",
       "2019-08-06   4530.689941   4562.890137   4481.229980   4531.810059   \n",
       "2019-08-07   4480.180176   4563.459961   4470.759766   4550.560059   \n",
       "...                  ...           ...           ...           ...   \n",
       "2024-03-08  10672.330078  10724.919922  10408.530273  10415.330078   \n",
       "2024-03-11  10335.139648  10387.099609  10254.669922  10359.639648   \n",
       "2024-03-12  10449.839844  10532.879883  10337.089844  10530.349609   \n",
       "2024-03-13  10466.629883  10475.089844  10361.190430  10391.860352   \n",
       "2024-03-14  10401.429688  10418.419922  10226.459961  10298.419922   \n",
       "\n",
       "               Adj Close  Volume price_diff  \n",
       "Date                                         \n",
       "2019-08-01   4762.810059  425800          0  \n",
       "2019-08-02   4655.560059  356000          0  \n",
       "2019-08-05   4469.970215  427900          0  \n",
       "2019-08-06   4531.810059  317500          0  \n",
       "2019-08-07   4550.560059  321600          0  \n",
       "...                  ...     ...        ...  \n",
       "2024-03-08  10415.330078       0   2.104794  \n",
       "2024-03-11  10359.639648       0  -2.848064  \n",
       "2024-03-12  10530.349609       0  -0.291597  \n",
       "2024-03-13  10391.860352       0  -0.715043  \n",
       "2024-03-14  10298.419922       0  -1.581488  \n",
       "\n",
       "[1163 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week_price_diff_df = pd.DataFrame(index = stock_data.index, columns = ['price_diff'])\n",
    "for date in week_price_diff_df.index:\n",
    "    week_price_diff_df.loc[date, 'price_diff'] = weekly_price_diff(stock_data, date)\n",
    "\n",
    "stock_data['price_diff'] = week_price_diff_df['price_diff']\n",
    "stock_data.to_csv('datasets/stock_data.csv', index = True, encoding='utf-8')\n",
    "stock_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date_layoffs</th>\n",
       "      <th>stock_delta</th>\n",
       "      <th>Company_Size_before_Layoffs</th>\n",
       "      <th>Money_Raised_in_$_mil</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1583996400</td>\n",
       "      <td>-17.938421</td>\n",
       "      <td>50</td>\n",
       "      <td>90000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1584082800</td>\n",
       "      <td>-15.003683</td>\n",
       "      <td>80</td>\n",
       "      <td>45000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1584342000</td>\n",
       "      <td>-19.758224</td>\n",
       "      <td>591</td>\n",
       "      <td>79000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1584342000</td>\n",
       "      <td>-19.758224</td>\n",
       "      <td>16</td>\n",
       "      <td>6000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1584514800</td>\n",
       "      <td>-24.824380</td>\n",
       "      <td>20</td>\n",
       "      <td>4000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>1704873600</td>\n",
       "      <td>14.204551</td>\n",
       "      <td>188000</td>\n",
       "      <td>26000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>1704873600</td>\n",
       "      <td>14.204551</td>\n",
       "      <td>450</td>\n",
       "      <td>57000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>1704960000</td>\n",
       "      <td>15.684460</td>\n",
       "      <td>1000</td>\n",
       "      <td>995000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>1704960000</td>\n",
       "      <td>15.684460</td>\n",
       "      <td>2000</td>\n",
       "      <td>14000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>901</th>\n",
       "      <td>1704960000</td>\n",
       "      <td>15.684460</td>\n",
       "      <td>462</td>\n",
       "      <td>274000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>902 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Date_layoffs  stock_delta  Company_Size_before_Layoffs  \\\n",
       "0      1583996400   -17.938421                           50   \n",
       "1      1584082800   -15.003683                           80   \n",
       "2      1584342000   -19.758224                          591   \n",
       "3      1584342000   -19.758224                           16   \n",
       "4      1584514800   -24.824380                           20   \n",
       "..            ...          ...                          ...   \n",
       "897    1704873600    14.204551                       188000   \n",
       "898    1704873600    14.204551                          450   \n",
       "899    1704960000    15.684460                         1000   \n",
       "900    1704960000    15.684460                         2000   \n",
       "901    1704960000    15.684460                          462   \n",
       "\n",
       "     Money_Raised_in_$_mil  \n",
       "0               90000000.0  \n",
       "1               45000000.0  \n",
       "2               79000000.0  \n",
       "3                6000000.0  \n",
       "4                4000000.0  \n",
       "..                     ...  \n",
       "897             26000000.0  \n",
       "898             57000000.0  \n",
       "899            995000000.0  \n",
       "900             14000000.0  \n",
       "901            274000000.0  \n",
       "\n",
       "[902 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layoff_data = pd.read_csv('datasets/tech_layoffs.csv')\n",
    "layoff_data['Date_layoffs'] = layoff_data['Date_layoffs'].apply(str_to_datetime)\n",
    "layoff_data['Company'] = layoff_data['Company'].replace({'SaleSan Franciscoorce': 'Salesforce'})\n",
    "layoff_data.set_index('Date_layoffs', inplace = True)\n",
    "layoff_data = layoff_data.sort_index()\n",
    "layoff_data = layoff_data[(layoff_data['Continent'] == 'North America') & \n",
    "                          (layoff_data['Country'] == 'USA') & \n",
    "                          (layoff_data['Company_Size_before_Layoffs'] >= 10)].drop(columns=[\"Company\", \"lat\", \"lng\", \"Continent\", \"Country\", \"#\"])\n",
    "\n",
    "# Assign new attribute 'Timezone' based on city\n",
    "layoff_data['Region'] = layoff_data['Location_HQ'].map(get_timezone)\n",
    "layoff_data = layoff_data.drop(columns=[\"Location_HQ\", \"Company_Size_after_layoffs\", \"Laid_Off\"]) \n",
    "\n",
    "#Incorporate stock price\n",
    "layoff_data = layoff_data.reset_index()\n",
    "\n",
    "#Create a new column for stock price change, and add it to the layoff_data_with_date_col dataframe\n",
    "layoff_data.insert(1, \"stock_delta\", layoff_data[\"Date_layoffs\"].apply(lambda x: weekly_price_diff(stock_data, x, period=90)))\n",
    "# bug: several stock_delta values are 0, which is highly improbable.\n",
    "\n",
    "\n",
    "industry_encoder = LabelEncoder()\n",
    "layoff_data['industry_encoded'] = industry_encoder.fit_transform(layoff_data['Industry'])\n",
    "\n",
    "time_encoder = LabelEncoder()\n",
    "layoff_data['Region_encoded'] = time_encoder.fit_transform(layoff_data['Region'])\n",
    "\n",
    "# Create LabelEncoder instance for stage column\n",
    "stage_encoder = LabelEncoder()\n",
    "layoff_data['stage_encoded'] = stage_encoder.fit_transform(layoff_data['Stage'])\n",
    "layoff_data = layoff_data.drop(columns=[\"Industry\", \"Stage\",\"Year\",\"Region\"])\n",
    "layoff_data['Money_Raised_in_$_mil'] = layoff_data['Money_Raised_in_$_mil'].str.replace('$', '').astype(float) * 1000000\n",
    "\n",
    "#convert date layoffs to unix\n",
    "layoff_data['Date_layoffs'] = layoff_data['Date_layoffs'].apply(lambda x: int(datetime.timestamp(x)))\n",
    "\n",
    "# Set our target to be the percentage of a company expected to be laid off at a specific point in time\n",
    "Y = layoff_data['Percentage']\n",
    "\n",
    "# Extract and encode our string data such that it makes our data preprocessing more organized\n",
    "industry = layoff_data['industry_encoded'].values.reshape(-1, 1)\n",
    "stage = layoff_data['stage_encoded'] .values.reshape(-1, 1)\n",
    "region = layoff_data['Region_encoded'].values.reshape(-1, 1)\n",
    "\n",
    "# One-hot encode the string data columns\n",
    "encoder = OneHotEncoder(sparse_output=False, drop='first')\n",
    "industry_onehot, stage_onehot, region_onehot = encoder.fit_transform(industry), encoder.fit_transform(stage), encoder.fit_transform(region)\n",
    "\n",
    "# Drop the original categorical columns from layoff_data\n",
    "layoff_data = layoff_data.drop(columns=[\"industry_encoded\", \"stage_encoded\", \"Region_encoded\", \"Percentage\"])\n",
    "display(layoff_data)\n",
    "\n",
    "# Concatenate numerical features and one-hot encoded features, and set it as X\n",
    "X = np.hstack((layoff_data.values, industry_onehot, stage_onehot, region_onehot))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LAYOFF DATA VALS: [ 1.5839964e+09 -1.7938421e+01  5.0000000e+01  9.0000000e+07]\n",
      "INDUSTRY ONEHOT: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.\n",
      " 0. 0. 0. 0. 0.]\n",
      "STAGE ONEHOT: [0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "REGION ONEHOT: [0. 0. 1. 0.]\n",
      "X: [ 1.5839964e+09 -1.7938421e+01  5.0000000e+01  9.0000000e+07\n",
      "  0.0000000e+00  0.0000000e+00  0.0000000e+00  0.0000000e+00\n",
      "  0.0000000e+00  0.0000000e+00  0.0000000e+00  0.0000000e+00\n",
      "  0.0000000e+00  0.0000000e+00  0.0000000e+00  0.0000000e+00\n",
      "  0.0000000e+00  0.0000000e+00  0.0000000e+00  0.0000000e+00\n",
      "  0.0000000e+00  0.0000000e+00  0.0000000e+00  0.0000000e+00\n",
      "  0.0000000e+00  0.0000000e+00  0.0000000e+00  1.0000000e+00\n",
      "  0.0000000e+00  0.0000000e+00  0.0000000e+00  0.0000000e+00\n",
      "  0.0000000e+00  0.0000000e+00  0.0000000e+00  0.0000000e+00\n",
      "  0.0000000e+00  0.0000000e+00  1.0000000e+00  0.0000000e+00\n",
      "  0.0000000e+00  0.0000000e+00  0.0000000e+00  0.0000000e+00\n",
      "  0.0000000e+00  0.0000000e+00  0.0000000e+00  0.0000000e+00\n",
      "  0.0000000e+00  1.0000000e+00  0.0000000e+00]\n"
     ]
    }
   ],
   "source": [
    "# layoff_data.values, industry_onehot, stage_onehot, region_onehot\n",
    "print(\"LAYOFF DATA VALS:\", str(layoff_data.values[0]))\n",
    "print(\"INDUSTRY ONEHOT:\", str(industry_onehot[0]))\n",
    "print(\"STAGE ONEHOT:\", str(stage_onehot[0]))\n",
    "print(\"REGION ONEHOT:\", str(region_onehot[0]))\n",
    "\n",
    "print(\"X:\", str(X[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((902, 51), (902,))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's take a look at the shape of our features and our target\n",
    "X.shape, Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#</th>\n",
       "      <th>Company</th>\n",
       "      <th>Location_HQ</th>\n",
       "      <th>Country</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Laid_Off</th>\n",
       "      <th>Date_layoffs</th>\n",
       "      <th>Percentage</th>\n",
       "      <th>Company_Size_before_Layoffs</th>\n",
       "      <th>Company_Size_after_layoffs</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Stage</th>\n",
       "      <th>Money_Raised_in_$_mil</th>\n",
       "      <th>Year</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>ShareChat</td>\n",
       "      <td>Bengaluru</td>\n",
       "      <td>India</td>\n",
       "      <td>Asia</td>\n",
       "      <td>200</td>\n",
       "      <td>2023-12-20 00:00:00 UTC</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1333</td>\n",
       "      <td>1133</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Series H</td>\n",
       "      <td>$1700</td>\n",
       "      <td>2023</td>\n",
       "      <td>12.97194</td>\n",
       "      <td>77.59369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>InSightec</td>\n",
       "      <td>Haifa</td>\n",
       "      <td>Israel</td>\n",
       "      <td>Asia</td>\n",
       "      <td>100</td>\n",
       "      <td>2023-12-19 00:00:00 UTC</td>\n",
       "      <td>20.0</td>\n",
       "      <td>500</td>\n",
       "      <td>400</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>$733</td>\n",
       "      <td>2023</td>\n",
       "      <td>32.81841</td>\n",
       "      <td>34.98850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>Enphase Energy</td>\n",
       "      <td>San Francisco Bay Area</td>\n",
       "      <td>USA</td>\n",
       "      <td>North America</td>\n",
       "      <td>350</td>\n",
       "      <td>2023-12-18 00:00:00 UTC</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3500</td>\n",
       "      <td>3150</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Post-IPO</td>\n",
       "      <td>$116</td>\n",
       "      <td>2023</td>\n",
       "      <td>37.54827</td>\n",
       "      <td>-121.98857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>Udaan</td>\n",
       "      <td>Bengaluru</td>\n",
       "      <td>India</td>\n",
       "      <td>Asia</td>\n",
       "      <td>100</td>\n",
       "      <td>2023-12-18 00:00:00 UTC</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>900</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1500</td>\n",
       "      <td>2023</td>\n",
       "      <td>12.97194</td>\n",
       "      <td>77.59369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>Cruise</td>\n",
       "      <td>San Francisco Bay Area</td>\n",
       "      <td>USA</td>\n",
       "      <td>North America</td>\n",
       "      <td>900</td>\n",
       "      <td>2023-12-14 00:00:00 UTC</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3750</td>\n",
       "      <td>2850</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>Acquired</td>\n",
       "      <td>$15000</td>\n",
       "      <td>2023</td>\n",
       "      <td>37.77493</td>\n",
       "      <td>-122.41942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1413</th>\n",
       "      <td>3286</td>\n",
       "      <td>Pitch</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Europe</td>\n",
       "      <td>80</td>\n",
       "      <td>2024-01-08 00:00:00 UTC</td>\n",
       "      <td>67.0</td>\n",
       "      <td>119</td>\n",
       "      <td>39</td>\n",
       "      <td>Other</td>\n",
       "      <td>Series B</td>\n",
       "      <td>$138</td>\n",
       "      <td>2024</td>\n",
       "      <td>52.52437</td>\n",
       "      <td>13.41053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1414</th>\n",
       "      <td>3287</td>\n",
       "      <td>BenchSci</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>70</td>\n",
       "      <td>2024-01-08 00:00:00 UTC</td>\n",
       "      <td>17.0</td>\n",
       "      <td>412</td>\n",
       "      <td>342</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Series D</td>\n",
       "      <td>$164</td>\n",
       "      <td>2024</td>\n",
       "      <td>43.70643</td>\n",
       "      <td>-79.39864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1415</th>\n",
       "      <td>3288</td>\n",
       "      <td>NanoString Technology</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>USA</td>\n",
       "      <td>North America</td>\n",
       "      <td>50</td>\n",
       "      <td>2024-01-08 00:00:00 UTC</td>\n",
       "      <td>9.0</td>\n",
       "      <td>556</td>\n",
       "      <td>506</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Post-IPO</td>\n",
       "      <td>$826</td>\n",
       "      <td>2024</td>\n",
       "      <td>47.60621</td>\n",
       "      <td>-122.33207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1416</th>\n",
       "      <td>3289</td>\n",
       "      <td>Trigo</td>\n",
       "      <td>Tel Aviv</td>\n",
       "      <td>Israel</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30</td>\n",
       "      <td>2024-01-04 00:00:00 UTC</td>\n",
       "      <td>15.0</td>\n",
       "      <td>200</td>\n",
       "      <td>170</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Series C</td>\n",
       "      <td>$199</td>\n",
       "      <td>2024</td>\n",
       "      <td>32.08088</td>\n",
       "      <td>34.78057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1417</th>\n",
       "      <td>3290</td>\n",
       "      <td>Frontdesk</td>\n",
       "      <td>Milwaukee</td>\n",
       "      <td>USA</td>\n",
       "      <td>North America</td>\n",
       "      <td>200</td>\n",
       "      <td>2024-01-02 00:00:00 UTC</td>\n",
       "      <td>100.0</td>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>$26</td>\n",
       "      <td>2024</td>\n",
       "      <td>43.03890</td>\n",
       "      <td>-87.90647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1418 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         #                Company             Location_HQ  Country  \\\n",
       "0        3              ShareChat               Bengaluru    India   \n",
       "1        4              InSightec                   Haifa   Israel   \n",
       "2        6         Enphase Energy  San Francisco Bay Area      USA   \n",
       "3        7                  Udaan               Bengaluru    India   \n",
       "4       14                 Cruise  San Francisco Bay Area      USA   \n",
       "...    ...                    ...                     ...      ...   \n",
       "1413  3286                  Pitch                  Berlin  Germany   \n",
       "1414  3287               BenchSci                 Toronto   Canada   \n",
       "1415  3288  NanoString Technology                 Seattle      USA   \n",
       "1416  3289                  Trigo                Tel Aviv   Israel   \n",
       "1417  3290              Frontdesk               Milwaukee      USA   \n",
       "\n",
       "          Continent  Laid_Off             Date_layoffs  Percentage  \\\n",
       "0              Asia       200  2023-12-20 00:00:00 UTC        15.0   \n",
       "1              Asia       100  2023-12-19 00:00:00 UTC        20.0   \n",
       "2     North America       350  2023-12-18 00:00:00 UTC        10.0   \n",
       "3              Asia       100  2023-12-18 00:00:00 UTC        10.0   \n",
       "4     North America       900  2023-12-14 00:00:00 UTC        24.0   \n",
       "...             ...       ...                      ...         ...   \n",
       "1413         Europe        80  2024-01-08 00:00:00 UTC        67.0   \n",
       "1414  North America        70  2024-01-08 00:00:00 UTC        17.0   \n",
       "1415  North America        50  2024-01-08 00:00:00 UTC         9.0   \n",
       "1416           Asia        30  2024-01-04 00:00:00 UTC        15.0   \n",
       "1417  North America       200  2024-01-02 00:00:00 UTC       100.0   \n",
       "\n",
       "      Company_Size_before_Layoffs  Company_Size_after_layoffs        Industry  \\\n",
       "0                            1333                        1133        Consumer   \n",
       "1                             500                         400      Healthcare   \n",
       "2                            3500                        3150          Energy   \n",
       "3                            1000                         900          Retail   \n",
       "4                            3750                        2850  Transportation   \n",
       "...                           ...                         ...             ...   \n",
       "1413                          119                          39           Other   \n",
       "1414                          412                         342      Healthcare   \n",
       "1415                          556                         506      Healthcare   \n",
       "1416                          200                         170          Retail   \n",
       "1417                          200                           0          Travel   \n",
       "\n",
       "         Stage Money_Raised_in_$_mil  Year       lat        lng  \n",
       "0     Series H                 $1700  2023  12.97194   77.59369  \n",
       "1      Unknown                  $733  2023  32.81841   34.98850  \n",
       "2     Post-IPO                  $116  2023  37.54827 -121.98857  \n",
       "3      Unknown                  1500  2023  12.97194   77.59369  \n",
       "4     Acquired                $15000  2023  37.77493 -122.41942  \n",
       "...        ...                   ...   ...       ...        ...  \n",
       "1413  Series B                  $138  2024  52.52437   13.41053  \n",
       "1414  Series D                  $164  2024  43.70643  -79.39864  \n",
       "1415  Post-IPO                  $826  2024  47.60621 -122.33207  \n",
       "1416  Series C                  $199  2024  32.08088   34.78057  \n",
       "1417   Unknown                   $26  2024  43.03890  -87.90647  \n",
       "\n",
       "[1418 rows x 16 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Additionally, we want to use a tech layoff dataset for our predictive task of finding tech layoffs. Let's look at how it is formatted\n",
    "layoff_data = pd.read_csv(\"datasets/tech_layoffs.csv\")\n",
    "layoff_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set shapes: X_train = (721, 51) , Y_train = (721,)\n",
      "Testing set shapes: X_test = (181, 51) , Y_test = (181,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Print the shapes of the resulting sets\n",
    "print(\"Training set shapes: X_train =\", X_train.shape, \", Y_train =\", y_train.shape)\n",
    "print(\"Testing set shapes: X_test =\", X_test.shape, \", Y_test =\", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       40.000000\n",
       "1       10.000000\n",
       "2       22.000000\n",
       "3      100.000000\n",
       "4       20.000000\n",
       "          ...    \n",
       "897      0.531915\n",
       "898     13.333333\n",
       "899     17.000000\n",
       "900      5.000000\n",
       "901     13.000000\n",
       "Name: Percentage, Length: 902, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finally, let's take a look at our target!\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model 1: Basic Regression Models\n",
    "\n",
    "For our first model, we want to determine whether or not our prediction model performs well with basic regression models. If it does, then it is a matter of choosing the correct regression model to analyze the data! If not, this means that we may need to look into more complex models, such as neural networks or decision trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE_train: 484.8927144143591\n",
      "MSE_test: 409.126023109986\n"
     ]
    }
   ],
   "source": [
    "mse_arr = []\n",
    "linreg = LinearRegression()\n",
    "\n",
    "X_train_df, y_train_df = pd.DataFrame(np.array(X_train)), pd.DataFrame(np.array(y_train))\n",
    "\n",
    "linreg.fit(X_train_df, y_train_df)\n",
    "yhat_train, yhat_test = linreg.predict(X_train), linreg.predict(X_test)\n",
    "\n",
    "MSE_train, MSE_test = np.mean((y_train.values - yhat_train)**2), np.mean((y_test.values - yhat_test)**2)\n",
    "mse_arr.append((MSE_train, MSE_test))\n",
    "print(f\"MSE_train: {MSE_train}\\nMSE_test: {MSE_test}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, while our Linear Regression model is not awful, it can certainly be a whole lot better. Looking into another form of regression models, Polynomial Regression, we can see how our error change as we add more degrees to our polynomial model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degree: 2\n",
      "    MSE_train: 518.1026127221376\n",
      "    MSE_test: 742.9174604635629\n",
      "Degree: 3\n",
      "    MSE_train: 531.5243308390676\n",
      "    MSE_test: 630.7097215553198\n",
      "Degree: 4\n",
      "    MSE_train: 455.36963657579736\n",
      "    MSE_test: 879.6145750647544\n"
     ]
    }
   ],
   "source": [
    "for k in range(2,5):\n",
    "    # Create kth degree polynomial\n",
    "    polyreg = PolynomialFeatures(k)\n",
    "\n",
    "    # Transform features to fit polynomial model\n",
    "    train_features = polyreg.fit_transform(X_train_df)\n",
    "    test_features = polyreg.fit_transform(X_test)\n",
    "\n",
    "    # Create polynomial regression\n",
    "    polyreg = LinearRegression()\n",
    "    polyreg.fit(train_features, y_train_df)\n",
    "\n",
    "    # Get training and testing predictions\n",
    "    yhat_train = polyreg.predict(train_features)\n",
    "    yhat_test = polyreg.predict(test_features)\n",
    "\n",
    "    # Display training and testing MSEs\n",
    "    MSE_train = np.mean((y_train.values - yhat_train) ** 2)\n",
    "    MSE_test = np.mean((y_test.values - yhat_test) ** 2)\n",
    "\n",
    "    mse_arr.append((MSE_train, MSE_test))\n",
    "    print(f'Degree: {k}\\n    MSE_train: {MSE_train}\\n    MSE_test: {MSE_test}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we graph our data, we can then see how jagged our error measures are for our training and testing data, and how our model may be underfitting due to the difference in complexity between the model and our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mse_train = [pair[0] for pair in mse_arr]\n",
    "mse_test = [pair[1] for pair in mse_arr]\n",
    "\n",
    "# Plot a graph of the training and testing MSE of the model over time\n",
    "degrees = range(1, len(mse_arr) + 1)\n",
    "plt.plot(degrees, mse_train, label='Training MSE')\n",
    "plt.plot(degrees, mse_test, label='Testing MSE')\n",
    "plt.xlabel('Polynomial Degree')\n",
    "plt.ylabel('MSE')\n",
    "plt.title('Polynomial Regression Training and Testing MSE')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can look at a special regression model called Automatic Relevance Determination Regression. The main premise of this method is that it, similar to Linear Regression, fits a line to the data and determines feature relevance by shrinking irrelevant features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Mean Squared Error: 386.4592934222615\n",
      "Testing Mean Squared Error: 290.17133207240596\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ryand\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\sklearn\\utils\\validation.py:1300: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "ard = ARDRegression().fit(X_train, y_train_df)\n",
    "yhat_train, yhat_test = ard.predict(X_train), ard.predict(X_test)\n",
    "\n",
    "MSE_train, MSE_test = np.mean((y_train.values - yhat_train)**2), np.mean((y_test.values - yhat_test)**2)\n",
    "\n",
    "print(f'Training Mean Squared Error: {MSE_train}\\nTesting Mean Squared Error: {MSE_test}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model 1: Analysis/Conclusion\n",
    "\n",
    "From these models, it can be seen that while they are able to fit the data to some degree, they simply do not fit the data well enough to call it a day. Each of these models have their shortcomings when it comes to predicting training or testing data, or even both.\n",
    "\n",
    "Thus, it would make sense that we would need a model that either illustrates the level of complexity of the model effectively or is specialized in analyzing time series data. For that, we turn to our next two models: a Neural Network and a RandomForestRegressor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model 2: Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will be looking at the neural network architecture and if it may perform effectively in predicting the layoff percentages of companies. We will first try to evaluate the data with a manually created ANN, then move on towards an ANN in which the hyperparameters are modified according to the data to minimize the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_nn():\n",
    "    '''\n",
    "    Build an Artificial Neural Network model composed of an input layer of 12 nodes with input_dim=51, an output layer of 1\n",
    "    node, and 3 hidden layers of 12 nodes each, with each layer having a sigmoid activation function applied to it.\n",
    "\n",
    "    Additionally, compile the model, setting the optimizer to be SGD with an LR of 0.1, measuring the loss with MSE and calculating\n",
    "    the MSE metric.\n",
    "\n",
    "    @type model: keras_Sequential() object\n",
    "    @returns model: a built and compiled Artificial Neural Network model with the given configuration\n",
    "    '''\n",
    "    model = Sequential([\n",
    "        Dense(12, activation='sigmoid', input_dim=51),\n",
    "        Dense(12, activation='sigmoid'),\n",
    "        Dense(12, activation='sigmoid'),\n",
    "        Dense(12, activation='sigmoid'),\n",
    "        Dense(1, activation='sigmoid'),\n",
    "    ])\n",
    "    model.compile(optimizer=SGD(learning_rate=0.1), loss='mse', metrics=['mse'])\n",
    "    return model\n",
    "\n",
    "def buildHPmodel(hp):\n",
    "  '''\n",
    "  Setup a hyperparameter model that enables us to choose the best to predict our target percentage of employees laid off at a company.\n",
    "\n",
    "  @type hp: keras_tuner_HyperParameters object\n",
    "  @param hp: the HyperParameters object that enables us to change our model's hyperparameters\n",
    "\n",
    "  @type model: keras_Sequential() object\n",
    "  @returns model: a fully configured Hyperparameter model that enables a user to modify an ANN's hyperparameters to their data\n",
    "  '''\n",
    "  model= Sequential([\n",
    "      Dense(12, activation = 'sigmoid', input_dim = 51),\n",
    "      Dense(units=hp.Int(\"units1\", min_value=3, max_value=24, step=5),activation=hp.Choice(\"acttype\", [\"sigmoid\", \"relu\", \"softmax\"])),\n",
    "      Dense(units=hp.Int(\"units1\", min_value=3, max_value=24, step=5),activation=hp.Choice(\"acttype\", [\"sigmoid\", \"relu\", \"softmax\"])),\n",
    "      Dense(units=hp.Int(\"units1\", min_value=3, max_value=24, step=5),activation=hp.Choice(\"acttype\", [\"sigmoid\", \"relu\", \"softmax\"])),\n",
    "      Dense(units=1,activation=hp.Choice(\"acttype\", [\"sigmoid\", \"relu\", \"softmax\"])),\n",
    "])\n",
    "  learning_rate = hp.Float(\"lr\", min_value=0.05, max_value=0.3, sampling=\"log\")\n",
    "  model.compile(optimizer='adam', loss='mean_squared_error', metrics=['mse'])\n",
    "  return model\n",
    "\n",
    "# This is a tuner that we will be using to perform hyperparameter tuning on the second NN model that we use!\n",
    "tuner = keras_tuner.GridSearch(\n",
    "    hypermodel=buildHPmodel,\n",
    "    objective=\"val_loss\",\n",
    "    directory=\"tunerStore\",\n",
    "    project_name=\"tunerInstance5\",\n",
    "    max_trials=50,\n",
    "    seed=12,\n",
    "    executions_per_trial=1,\n",
    "    tune_new_entries=True,\n",
    "    allow_new_entries=True,\n",
    "    max_consecutive_failed_trials=3\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our ANN and tuner definitions set, we can perform a similar process to that of Model 1 in where we will fit the training data to the model, and see its error:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Mean Squared Error: 801.5509074038033\n",
      "Testing Mean Squared Error: 682.485143246114\n"
     ]
    }
   ],
   "source": [
    "# Let us try out our first neural network to see how well in performs in our predictive task!\n",
    "nn_regressor = KerasRegressor(model=build_nn, epochs=100, batch_size=5, verbose=0)\n",
    "hist = nn_regressor.fit(X_train, y_train)\n",
    "predictions_train, predictions_test = nn_regressor.predict(X_train), nn_regressor.predict(X_test)\n",
    "\n",
    "print('Training Mean Squared Error:', mean_squared_error(predictions_train, y_train))\n",
    "print('Testing Mean Squared Error:', mean_squared_error(predictions_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, we can improve the model a step further through hyperparameter tuning, which is the practice of changing model hyperparameters, such as the activation functions, number of units per layer, the learning rate, and other factors associated with the neural network architecture, in order to converge to the optimal setup for the problem at hand.\n",
    "\n",
    "We will be using the GridSearch technique taught to us within the course to search for a specific model configuration that gives us the lowest error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 50 Complete [00h 00m 06s]\n",
      "val_loss: 235.16546630859375\n",
      "\n",
      "Best val_loss So Far: 220.14390563964844\n",
      "Total elapsed time: 00h 04m 54s\n"
     ]
    }
   ],
   "source": [
    "X_train, X_temp, y_train, y_temp = train_test_split(X, Y, test_size=0.3, random_state=42)\n",
    "X_test, X_val, y_test, y_val = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n",
    "\n",
    "X_train_np = np.array(X_train)\n",
    "y_train_np = np.array(y_train)\n",
    "X_test_np = np.array(X_test)\n",
    "y_test_np = np.array(y_test)\n",
    "X_val_np = np.array(X_val)\n",
    "y_val_np = np.array(y_val)\n",
    "\n",
    "#load from tunerStore trial 48\n",
    "# tuner = keras.models.load_model('tunerStore/tunerInstance3')\n",
    "#why isnt this working? model = keras.models.load_model('tunerStore/tunerInstance3')\n",
    "\n",
    "\"\"\"TO RUN A NEW GRID SEARCH UNCOMMENT THE LINE BELOW\"\"\"\n",
    "tuner.search(X_train_np, y_train_np, epochs=10, validation_data=(X_val_np, y_val_np), batch_size=15)\n",
    "# tuner.save('tunerStore/tunerInstance3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results summary\n",
      "Results in tunerStore\\tunerInstance5\n",
      "Showing 10 best trials\n",
      "Objective(name=\"val_loss\", direction=\"min\")\n",
      "\n",
      "Trial 13 summary\n",
      "Hyperparameters:\n",
      "units1: 3\n",
      "acttype: relu\n",
      "lr: 0.0936101501762388\n",
      "Score: 220.14390563964844\n",
      "\n",
      "Trial 47 summary\n",
      "Hyperparameters:\n",
      "units1: 8\n",
      "acttype: relu\n",
      "lr: 0.07825422900366437\n",
      "Score: 226.53050231933594\n",
      "\n",
      "Trial 44 summary\n",
      "Hyperparameters:\n",
      "units1: 8\n",
      "acttype: relu\n",
      "lr: 0.05\n",
      "Score: 228.0859375\n",
      "\n",
      "Trial 20 summary\n",
      "Hyperparameters:\n",
      "units1: 3\n",
      "acttype: relu\n",
      "lr: 0.2742923480285484\n",
      "Score: 228.7980499267578\n",
      "\n",
      "Trial 11 summary\n",
      "Hyperparameters:\n",
      "units1: 3\n",
      "acttype: relu\n",
      "lr: 0.05\n",
      "Score: 231.3817138671875\n",
      "\n",
      "Trial 49 summary\n",
      "Hyperparameters:\n",
      "units1: 8\n",
      "acttype: relu\n",
      "lr: 0.13395323057981742\n",
      "Score: 235.16546630859375\n",
      "\n",
      "Trial 17 summary\n",
      "Hyperparameters:\n",
      "units1: 3\n",
      "acttype: relu\n",
      "lr: 0.1602390336065017\n",
      "Score: 235.59889221191406\n",
      "\n",
      "Trial 48 summary\n",
      "Hyperparameters:\n",
      "units1: 8\n",
      "acttype: relu\n",
      "lr: 0.06541731153543581\n",
      "Score: 240.52723693847656\n",
      "\n",
      "Trial 12 summary\n",
      "Hyperparameters:\n",
      "units1: 3\n",
      "acttype: relu\n",
      "lr: 0.11197938216997383\n",
      "Score: 245.84762573242188\n",
      "\n",
      "Trial 15 summary\n",
      "Hyperparameters:\n",
      "units1: 3\n",
      "acttype: relu\n",
      "lr: 0.06541731153543581\n",
      "Score: 252.85423278808594\n"
     ]
    }
   ],
   "source": [
    "# Now, let's see how many units, types of activations, and learning rate performs best!\n",
    "tuner.results_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 12)                624       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 3)                 39        \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 3)                 12        \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 3)                 12        \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 1)                 4         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 691 (2.70 KB)\n",
      "Trainable params: 691 (2.70 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Now, let us look at the configuration of the best model that we have!\n",
    "models = tuner.get_best_models(num_models=3)\n",
    "models[0].summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For our best model (Trial 13): \n",
      "\n",
      "20/20 [==============================] - 0s 3ms/step\n",
      "The MSE for the training data:  402.8087933564796\n",
      "5/5 [==============================] - 0s 2ms/step\n",
      "The MSE for the testing data:  335.09143881909677\n",
      "5/5 [==============================] - 0s 4ms/step\n",
      "The MSE for the validation data:  220.1438920984369\n",
      "\n",
      "\n",
      "For our second best model (Trial 47): \n",
      "\n",
      "20/20 [==============================] - 0s 4ms/step\n",
      "The MSE for the training data:  404.70366308311804\n",
      "5/5 [==============================] - 0s 5ms/step\n",
      "The MSE for the testing data:  339.1884078101372\n",
      "5/5 [==============================] - 0s 4ms/step\n",
      "The MSE for the validation data:  226.5304958750832\n",
      "\n",
      "\n",
      "For our third best model (Trial 44): \n",
      "\n",
      "20/20 [==============================] - 0s 3ms/step\n",
      "The MSE for the training data:  397.4348058435213\n",
      "5/5 [==============================] - 0s 4ms/step\n",
      "The MSE for the testing data:  338.8793873186497\n",
      "5/5 [==============================] - 0s 3ms/step\n",
      "The MSE for the validation data:  228.08594676033468\n"
     ]
    }
   ],
   "source": [
    "print(\"For our best model (Trial 13): \\n\")\n",
    "print(\"The MSE for the training data: \", mean_squared_error(y_train, models[0].predict(X_train)))\n",
    "print(\"The MSE for the testing data: \", mean_squared_error(y_test, models[0].predict(X_test)))\n",
    "print(\"The MSE for the validation data: \", mean_squared_error(y_val, models[0].predict(X_val_np)))\n",
    "print(\"\\n\")\n",
    "print(\"For our second best model (Trial 47): \\n\")\n",
    "print(\"The MSE for the training data: \", mean_squared_error(y_train, models[1].predict(X_train)))\n",
    "print(\"The MSE for the testing data: \", mean_squared_error(y_test, models[1].predict(X_test)))\n",
    "print(\"The MSE for the validation data: \", mean_squared_error(y_val, models[1].predict(X_val_np)))\n",
    "print(\"\\n\")\n",
    "print(\"For our third best model (Trial 44): \\n\")\n",
    "print(\"The MSE for the training data: \", mean_squared_error(y_train, models[2].predict(X_train)))\n",
    "print(\"The MSE for the testing data: \", mean_squared_error(y_test, models[2].predict(X_test)))\n",
    "print(\"The MSE for the validation data: \", mean_squared_error(y_val, models[2].predict(X_val_np)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter Optimization Results\n",
    "Our best three trials can be seen below.\n",
    "Trial 13 rendered the best predictions, with an MSE of 220.144. We can see that it found that having 3 units within each layer, using the ReLU activation function, and having a learning rate of 0.0936 was most optimal. Trial 47 and 44 coming in second and third respectively, with different model configurations with a higher error.\n",
    "\n",
    "```Results summary\n",
    "Results in tunerStore\\tunerInstance5\n",
    "Showing 10 best trials\n",
    "Objective(name=\"val_loss\", direction=\"min\")\n",
    "\n",
    "Trial 13 summary\n",
    "Hyperparameters:\n",
    "units1: 3\n",
    "acttype: relu\n",
    "lr: 0.0936101501762388\n",
    "Score: 220.14390563964844\n",
    "\n",
    "Trial 47 summary\n",
    "Hyperparameters:\n",
    "units1: 8\n",
    "acttype: relu\n",
    "lr: 0.07825422900366437\n",
    "Score: 226.53050231933594\n",
    "\n",
    "Trial 44 summary\n",
    "Hyperparameters:\n",
    "units1: 8\n",
    "acttype: relu\n",
    "lr: 0.05\n",
    "Score: 228.0859375\n",
    "\n",
    "Trial 20 summary\n",
    "Hyperparameters:\n",
    "units1: 3\n",
    "acttype: relu\n",
    "lr: 0.2742923480285484\n",
    "Score: 228.7980499267578\n",
    "\n",
    "Trial 11 summary\n",
    "Hyperparameters:\n",
    "units1: 3\n",
    "acttype: relu\n",
    "lr: 0.05\n",
    "Score: 231.3817138671875\n",
    "\n",
    "Trial 49 summary\n",
    "Hyperparameters:\n",
    "units1: 8\n",
    "acttype: relu\n",
    "lr: 0.13395323057981742\n",
    "Score: 235.16546630859375\n",
    "\n",
    "Trial 17 summary\n",
    "Hyperparameters:\n",
    "units1: 3\n",
    "acttype: relu\n",
    "lr: 0.1602390336065017\n",
    "Score: 235.59889221191406\n",
    "\n",
    "Trial 48 summary\n",
    "Hyperparameters:\n",
    "units1: 8\n",
    "acttype: relu\n",
    "lr: 0.06541731153543581\n",
    "Score: 240.52723693847656\n",
    "\n",
    "Trial 12 summary\n",
    "Hyperparameters:\n",
    "units1: 3\n",
    "acttype: relu\n",
    "lr: 0.11197938216997383\n",
    "Score: 245.84762573242188\n",
    "\n",
    "Trial 15 summary\n",
    "Hyperparameters:\n",
    "units1: 3\n",
    "acttype: relu\n",
    "lr: 0.06541731153543581\n",
    "Score: 252.85423278808594\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Fold Cross Validation: Combatting Overfitting/Underfitting\n",
    "Based on the results of the best model's MSE: the MSE for the training data:  407.6795840293022, the MSE for the testing data:  344.07788384916205, and the MSE for the validation data:  222.8351758414751. We can conclude that the model is not overfitting since the MSE for the training data is not lower than the testing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 1 MSE for training: 800.3483\n",
      "Fold 1 MSE for testing: 659.3152\n",
      "Fold 2 MSE for training: 781.0257\n",
      "Fold 2 MSE for testing: 831.5279\n",
      "Fold 3 MSE for training: 780.2587\n",
      "Fold 3 MSE for testing: 838.4422\n",
      "Fold 4 MSE for training: 757.5928\n",
      "Fold 4 MSE for testing: 1042.7502\n",
      "Fold 5 MSE for training: 766.5234\n",
      "Fold 5 MSE for testing: 962.2502\n",
      "Fold 6 MSE for training: 759.6024\n",
      "Fold 6 MSE for testing: 1024.6353\n",
      "Fold 7 MSE for training: 793.1397\n",
      "Fold 7 MSE for testing: 722.3335\n",
      "Fold 8 MSE for training: 801.9471\n",
      "Fold 8 MSE for testing: 642.9446\n",
      "Fold 9 MSE for training: 809.1474\n",
      "Fold 9 MSE for testing: 578.0418\n",
      "Fold 10 MSE for training: 811.1258\n",
      "Fold 10 MSE for testing: 560.2088\n",
      "Average MSE for training: 786.0711\n",
      "Average MSE for training: 786.2450\n"
     ]
    }
   ],
   "source": [
    "# Define the KFold parameters\n",
    "kfold = RepeatedKFold(n_splits=10, n_repeats=1, random_state=1)\n",
    "\n",
    "# Lists to store MSE scores for each fold\n",
    "mse_per_fold_training, mse_per_fold_test = list(), list()\n",
    "\n",
    "y_train_np = y_train.values\n",
    "y_test_np = y_test.values\n",
    "\n",
    "j = 0\n",
    "# KFold Cross Validation\n",
    "for train, test in kfold.split(X_train, y_train_np):\n",
    "    # Create and compile the model\n",
    "    model = build_nn()\n",
    "\n",
    "    # Fit the model\n",
    "    estimator = KerasRegressor(model=build_nn, epochs=50, batch_size=5, verbose=0)\n",
    "    results = estimator.fit(X_train[train], y_train_np[train])\n",
    "\n",
    "    # Predict and calculate MSE\n",
    "    predictions_train = results.predict(X_train[train])\n",
    "    predictions_test = results.predict(X_train[test])\n",
    "\n",
    "    mse_train = mean_squared_error(y_train_np[train], predictions_train)\n",
    "    mse_test = mean_squared_error(y_train_np[test], predictions_test)\n",
    "\n",
    "    # Print metrics for this fold\n",
    "    # print(f\"Fold Accuracy: {acc*100:.2f}%\")\n",
    "    print(f\"Fold {j+1} MSE for training: {mse_train:.4f}\")\n",
    "    print(f\"Fold {j+1} MSE for testing: {mse_test:.4f}\")\n",
    "\n",
    "\n",
    "    # Append to the lists\n",
    "    # acc_per_fold.append(acc * 100)\n",
    "    mse_per_fold_test.append(mse_test)\n",
    "    mse_per_fold_training.append(mse_train)\n",
    "    j += 1\n",
    "\n",
    "# Calculate and print average metrics\n",
    "print(f\"Average MSE for training: {np.mean(mse_per_fold_training):.4f}\")\n",
    "print(f\"Average MSE for testing: {np.mean(mse_per_fold_test):.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results from Cross Validation\n",
    "Based on the training and testing MSE of the original model, the Testing Mean Squared Error was 584.9784896957286 and the Training Mean Squared Error was 786.0688282061325. This indicates that the model is not overfitting so the cross validation results do not benefit the model. Since cross validation is primarily helpful when a model is overfitting, cross validation does not benefit the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model 3: Random Forest Regressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we will be seeing if an ensemble-based method would work well for our data. Our model of choice is the Random Forest Regressor, which fits the data to a number of Decision Trees and averages out the results in hopes of a higher accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a RandomForestRegressor model\n",
    "rf = RandomForestRegressor(random_state = 42, max_depth = 10, min_samples_split=10)\n",
    "\n",
    "# Fit the model to the training data\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# Try to predict the the training and testing targets given the fitted model\n",
    "rf_predictions_train = rf.predict(X_train)\n",
    "rf_predictions_test = rf.predict(X_test)\n",
    "\n",
    "# Store the MSE between the model's predictions and the ground truth\n",
    "rf_mse_train = mean_squared_error(rf_predictions_train, y_train)\n",
    "rf_mse_test = mean_squared_error(rf_predictions_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(129.86997059798847, 267.5180215681688)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's look at our training and testing MSE!\n",
    "rf_mse_train, rf_mse_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_estimators = [int(x) for x in np.linspace(start = 50, stop = 150, num = 10)]\n",
    "max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\n",
    "max_depth.append(None)\n",
    "min_samples_split = [2, 5, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grid search!\n",
    "\n",
    "bestMSE = None\n",
    "bestEstimator = 0\n",
    "bestDepth = 0\n",
    "bestSamples = 0\n",
    "mses = []\n",
    "\n",
    "for estimator in n_estimators:\n",
    "    temp1 = []\n",
    "    for depth in max_depth:\n",
    "        temp2 = []\n",
    "        for samples_split in min_samples_split:\n",
    "            rf = RandomForestRegressor(n_estimators=estimator, max_depth=depth, min_samples_split=samples_split, random_state = 42)\n",
    "            rf.fit(X_train, y_train)\n",
    "            predictions_test = rf.predict(X_test)\n",
    "            mse_test = mean_squared_error(predictions_test, y_test)\n",
    "            temp2.append(mse_test)\n",
    "            if bestMSE is None or mse_test < bestMSE:\n",
    "                bestMSE = mse_test\n",
    "                bestEstimator = estimator\n",
    "                bestDepth = depth\n",
    "                bestSamples = samples_split\n",
    "        temp1.append(temp2)\n",
    "    mses.append(temp1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(258.0549903355085, 94, 20, 2)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bestMSE, bestEstimator, bestDepth, bestSamples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_depth=20, n_estimators=94, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(max_depth=20, n_estimators=94, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_depth=20, n_estimators=94, random_state=42)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_rf = RandomForestRegressor(random_state = 42, max_depth = bestDepth, min_samples_split=bestSamples, n_estimators=bestEstimator)\n",
    "best_rf.fit(X_train, y_train)\n",
    "best_rf_predictions_train = best_rf.predict(X_train)\n",
    "best_rf_mse_test = bestMSE\n",
    "best_rf_mse_train = mean_squared_error(best_rf_predictions_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "258.0549903355085 108.01476698619791\n"
     ]
    }
   ],
   "source": [
    "best_rf_mse_train, best_rf_mse_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.22719850e-01, 6.54526911e-02, 4.61280721e-01, 1.02299273e-01,\n",
       "       1.47627936e-02, 1.77321578e-02, 1.41713092e-02, 2.03459195e-03,\n",
       "       3.82785196e-03, 1.44252338e-02, 2.35115365e-06, 2.47396609e-03,\n",
       "       3.44121146e-03, 1.39745396e-02, 1.35993340e-03, 8.18381827e-05,\n",
       "       1.36213254e-02, 1.58013101e-02, 1.82614156e-04, 8.05528118e-03,\n",
       "       1.32272976e-05, 2.04275772e-03, 1.16992609e-03, 3.23110003e-03,\n",
       "       4.74528741e-04, 1.33990170e-02, 8.56230666e-04, 1.34819972e-02,\n",
       "       1.64546250e-04, 1.01088250e-03, 5.19224037e-04, 9.88402999e-03,\n",
       "       3.26543642e-02, 4.74424983e-03, 3.45334876e-04, 5.16681435e-04,\n",
       "       8.10372902e-03, 8.49587487e-03, 3.98363166e-03, 2.03650436e-03,\n",
       "       1.81422519e-03, 3.36715140e-04, 9.64876115e-04, 1.13718415e-03,\n",
       "       7.81255253e-06, 3.06821823e-05, 3.34224475e-03, 2.78784119e-03,\n",
       "       2.02519958e-03, 2.71315835e-03, 1.13783532e-05])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importance = rf.feature_importances_\n",
    "importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_layoffs_imp = importance[0]\n",
    "stock_delta_imp = importance[1]\n",
    "company_size_imp = importance[2]\n",
    "money_raised_imp = importance[3]\n",
    "industry_imp = sum([importance[i] for i in range(4, 33)])\n",
    "stage_imp = sum([importance[i] for i in range(33, 37)])\n",
    "region_imp = sum([importance[i] for i in range(37, 51)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "importances = [date_layoffs_imp, stock_delta_imp, company_size_imp, money_raised_imp, industry_imp, stage_imp, region_imp]\n",
    "categories = ['Date Layoffs', 'Stock Delta', 'Company Size', 'Money Raised', 'Industry', 'Stage', 'Region']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x29485f190>,\n",
       "  <matplotlib.patches.Wedge at 0x29485f0d0>,\n",
       "  <matplotlib.patches.Wedge at 0x29485f910>,\n",
       "  <matplotlib.patches.Wedge at 0x29485fd90>,\n",
       "  <matplotlib.patches.Wedge at 0x2956b8250>,\n",
       "  <matplotlib.patches.Wedge at 0x2956b86d0>,\n",
       "  <matplotlib.patches.Wedge at 0x295681910>],\n",
       " [Text(1.019256795503398, 0.4136611956905611, 'Date Layoffs'),\n",
       "  Text(0.6157386996249076, 0.9115184330468737, 'Stock Delta'),\n",
       "  Text(-0.9599579555268108, 0.5371040156438841, 'Company Size'),\n",
       "  Text(-0.3359532374832638, -1.0474423240563242, 'Money Raised'),\n",
       "  Text(0.6696975705082394, -0.8726426324993307, 'Industry'),\n",
       "  Text(1.0711327085740132, -0.2503491973681932, 'Stage'),\n",
       "  Text(1.0952193188469364, -0.10244336788905721, 'Region')])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(importances, labels=categories)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure above shows importance of each feature in prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mses_vs_estimators = [x[max_depth.index(bestDepth)][min_samples_split.index(bestSamples)] for x in mses]\n",
    "plt.scatter(n_estimators, mses_vs_estimators)\n",
    "plt.xlabel(\"Number of estimators\")\n",
    "plt.ylabel(\"MSE\")\n",
    "plt.title(\"MSE vs n_estimators\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For constant depth and min number of samples, we can plot the MSE vs the number of estimators. Here, we set depth and min number of samples as the best values found by Grid Search. Clearly, we can see that there is an optimal number of estimators at `n_estimators = 94` where the MSE is minimized."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}