last_sale_reg.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cfcb8ae2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import datetime\n",
    "from scipy import stats\n",
    "import requests\n",
    "from requests import get\n",
    "from bs4 import BeautifulSoup\n",
    "import xgboost as xgb\n",
    "from sklearn.model_selection import RepeatedKFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from numpy import absolute\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics\n",
    "from linearmodels import PooledOLS\n",
    "import statsmodels.api as sm\n",
    "from linearmodels import PanelOLS\n",
    "from linearmodels import RandomEffects\n",
    "from sklearn.decomposition import PCA\n",
    "import prince\n",
    "import mca\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import RepeatedKFold\n",
    "from sklearn.linear_model import Lasso\n",
    "from numpy import mean\n",
    "from numpy import std\n",
    "from numpy import absolute\n",
    "from sklearn.linear_model import LassoCV\n",
    "from numpy import arange\n",
    "from statsmodels.tools.eval_measures import rmse\n",
    "from statsmodels.tools.eval_measures import meanabs\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f5b12735",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>punk_id</th>\n",
       "      <th>type</th>\n",
       "      <th>from</th>\n",
       "      <th>to</th>\n",
       "      <th>date</th>\n",
       "      <th>amount_in_eth</th>\n",
       "      <th>amount_in_dol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</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>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Bid Withdrawn</td>\n",
       "      <td>0x983ace</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-05-10</td>\n",
       "      <td>250.0</td>\n",
       "      <td>1030000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Bid</td>\n",
       "      <td>0x983ace</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-04-11</td>\n",
       "      <td>250.0</td>\n",
       "      <td>537,615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>Bid Withdrawn</td>\n",
       "      <td>0xd7510a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-03-03</td>\n",
       "      <td>100.0</td>\n",
       "      <td>160,973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>Bid</td>\n",
       "      <td>0xd7510a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-02-20</td>\n",
       "      <td>100.0</td>\n",
       "      <td>188,897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Bid Withdrawn</td>\n",
       "      <td>natealex</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-09-16</td>\n",
       "      <td>69.0</td>\n",
       "      <td>25,532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   punk_id           type      from   to        date amount_in_eth  \\\n",
       "0                                                                    \n",
       "0        0  Bid Withdrawn  0x983ace  NaN  2021-05-10         250.0   \n",
       "1        0            Bid  0x983ace  NaN  2021-04-11         250.0   \n",
       "2        0  Bid Withdrawn  0xd7510a  NaN  2021-03-03         100.0   \n",
       "3        0            Bid  0xd7510a  NaN  2021-02-20         100.0   \n",
       "4        0  Bid Withdrawn  natealex  NaN  2020-09-16          69.0   \n",
       "\n",
       "  amount_in_dol  \n",
       "0                \n",
       "0     1030000.0  \n",
       "1       537,615  \n",
       "2       160,973  \n",
       "3       188,897  \n",
       "4        25,532  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = 'https://raw.githubusercontent.com/kalinakalina/DEDA_Cryptopunks/main/data/transaction.csv'\n",
    "df_tr = pd.read_csv(file, header = None, index_col = 0)\n",
    "df_tr.columns = ['type', 'from', 'to', 'date', 'punk_id', 'amount_in_eth', 'amount_in_dol']\n",
    "df_tr = df_tr[ ['punk_id'] + [ col for col in df_tr.columns if col != 'punk_id' ] ]\n",
    "df_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "70533600",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fix_df(x):\n",
    "    x['punk_id'] = x['punk_id'].astype('category')\n",
    "    x['type'] = x['type'].astype('category')\n",
    "    x['from'] = x['from'].astype(str)\n",
    "    x['to'] = x['to'].astype(str)\n",
    "    x['date'] = pd.to_datetime(x['date'], format = '%Y.%m.%d')\n",
    "    \n",
    "    x['amount_in_eth'] = x['amount_in_eth'].astype(str)\n",
    "    x['amount_in_eth'] = x['amount_in_eth'].str.replace('Y', '')\n",
    "    x['amount_in_eth'] = x['amount_in_eth'].str.replace('Z', '')\n",
    "    x['amount_in_eth'] = x['amount_in_eth'].str.replace('M', '')\n",
    "    x['amount_in_eth'] = x['amount_in_eth'].str.replace('T', '')\n",
    "    x['amount_in_eth'] = x['amount_in_eth'].str.replace('B', '')\n",
    "    x['amount_in_eth'] = x['amount_in_eth'].replace(',', '', regex=True)\n",
    "    x['amount_in_eth'] = x['amount_in_eth'].astype(float)\n",
    "\n",
    "    x['amount_in_dol'] = x['amount_in_dol'].astype(str)\n",
    "    x['amount_in_dol'] = x['amount_in_dol'].str.replace('Y', '')\n",
    "    x['amount_in_dol'] = x['amount_in_dol'].str.replace('B', '')\n",
    "    x['amount_in_dol'] = x['amount_in_dol'].str.replace('T', '')\n",
    "    x['amount_in_dol'] = x['amount_in_dol'].str.replace('P', '')\n",
    "    x['amount_in_dol'] = x['amount_in_dol'].str.replace('Z', '')\n",
    "    x['amount_in_dol'] = x['amount_in_dol'].replace('<', '', regex=True)\n",
    "    x['amount_in_dol'] = x['amount_in_dol'].replace(',', '', regex=True)\n",
    "    x['amount_in_dol'] = x['amount_in_dol'].astype(float)\n",
    "    \n",
    "    x['amount_in_eth'] = x['amount_in_eth'].fillna(0)\n",
    "    x['amount_in_dol'] = x['amount_in_dol'].fillna(0)\n",
    "\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d6d3d714",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>punk_id</th>\n",
       "      <th>date</th>\n",
       "      <th>amount_in_dol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2018-11-30</td>\n",
       "      <td>2822.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2017-07-07</td>\n",
       "      <td>386.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2017-06-23</td>\n",
       "      <td>320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2020-11-30</td>\n",
       "      <td>36305.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-04-06</td>\n",
       "      <td>5155.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  punk_id       date  amount_in_dol\n",
       "0       0 2018-11-30         2822.0\n",
       "1       0 2017-07-07          386.0\n",
       "2       0 2017-06-23          320.0\n",
       "3       1 2020-11-30        36305.0\n",
       "4       1 2019-04-06         5155.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tr = fix_df(df_tr)\n",
    "\n",
    "sales = df_tr[df_tr['type'].str.contains(\"Sold\")].reset_index(drop=True)\n",
    "sales = sales.drop(['from', 'to', 'type', 'amount_in_eth'], axis=1)\n",
    "sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "62460994",
   "metadata": {},
   "outputs": [],
   "source": [
    "ids = sales.punk_id.unique().tolist()\n",
    "punks = []\n",
    "check = []\n",
    "for i in ids:\n",
    "    try:\n",
    "        var = sales.loc[sales.punk_id==i,'amount_in_dol'].values[1]\n",
    "    except IndexError:\n",
    "        var_exists = 0\n",
    "    else:\n",
    "        var_exists = var\n",
    "    punks.append(i)\n",
    "    check.append(var_exists)\n",
    "\n",
    "sec_sales = dict(zip(punks, check))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "60d8f1b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "ids = sales.punk_id.unique().tolist()\n",
    "punks = []\n",
    "check = []\n",
    "for i in ids:\n",
    "    sale = sales.loc[sales.punk_id==i,'amount_in_dol'].values[0]\n",
    "    punks.append(i)\n",
    "    check.append(sale)\n",
    "\n",
    "last_sale = dict(zip(punks, check))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "18beb63b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_last_sale = pd.DataFrame(last_sale.items())\n",
    "dict_name={0:'punk_id', 1:  'last_sale'}\n",
    "df_last_sale.rename(columns=dict_name,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f6cc2fe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sec_sale = pd.DataFrame(sec_sales.items())\n",
    "dict_name={0:'punk_id', 1:  'sec_sale',}\n",
    "df_sec_sale.rename(columns=dict_name,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c8514dab",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>punk_id</th>\n",
       "      <th>last_sale</th>\n",
       "      <th>sec_sale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2822.0</td>\n",
       "      <td>386.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>36305.0</td>\n",
       "      <td>5155.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>234.0</td>\n",
       "      <td>351.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>105.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53</td>\n",
       "      <td>42803.0</td>\n",
       "      <td>26612.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5586</th>\n",
       "      <td>9980</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5587</th>\n",
       "      <td>9989</td>\n",
       "      <td>140.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5588</th>\n",
       "      <td>9990</td>\n",
       "      <td>44981.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5589</th>\n",
       "      <td>9997</td>\n",
       "      <td>169770.0</td>\n",
       "      <td>21062.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5590</th>\n",
       "      <td>9998</td>\n",
       "      <td>61170.0</td>\n",
       "      <td>9499.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5591 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      punk_id  last_sale  sec_sale\n",
       "0           0     2822.0     386.0\n",
       "1           1    36305.0    5155.0\n",
       "2          14      234.0     351.0\n",
       "3          33      105.0       0.0\n",
       "4          53    42803.0   26612.0\n",
       "...       ...        ...       ...\n",
       "5586     9980       70.0       0.0\n",
       "5587     9989      140.0       0.0\n",
       "5588     9990    44981.0       0.0\n",
       "5589     9997   169770.0   21062.0\n",
       "5590     9998    61170.0    9499.0\n",
       "\n",
       "[5591 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df_last_sale, df_sec_sale, on=\"punk_id\", how='left')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d9d108ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "file2 = 'https://raw.githubusercontent.com/kalinakalina/DEDA_Cryptopunks/main/data/accessories.csv'\n",
    "df_acc = pd.read_csv(file2, header = None, sep='\\n')\n",
    "df_acc = df_acc[0].str.split(',', expand=True)\n",
    "df_acc.drop(0, axis=1, inplace=True)\n",
    "\n",
    "dict={1:'gender', \n",
    "      2:  'acc_1',\n",
    "      3:'acc_2', \n",
    "      4: 'acc_3',\n",
    "      5:'acc_4',\n",
    "      6: 'acc_5',\n",
    "      7:'acc_6',\n",
    "      8:'acc_7'}\n",
    "df_acc.rename(columns=dict,\n",
    "          inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fd43487c",
   "metadata": {},
   "outputs": [],
   "source": [
    "types = df_acc['gender']\n",
    "types_dummies = pd.get_dummies(types)\n",
    "types_dummies['punk_id'] = types_dummies.index\n",
    "types_dummies = types_dummies[ ['punk_id'] + [ col for col in types_dummies.columns if col != 'punk_id' ] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4b4e301b",
   "metadata": {},
   "outputs": [],
   "source": [
    "file3 = 'https://raw.githubusercontent.com/kalinakalina/DEDA_Cryptopunks/main/data/accessories_transformed_full.csv'\n",
    "acc = pd.read_csv(file3, index_col = 0)\n",
    "acc['punk_id'] = acc.index\n",
    "acc = acc[ ['punk_id'] + [ col for col in acc.columns if col != 'punk_id' ] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "49b29769",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>last_sale</th>\n",
       "      <th>sec_sale</th>\n",
       "      <th>Alien</th>\n",
       "      <th>Ape</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>Zombie</th>\n",
       "      <th>Beanie</th>\n",
       "      <th>Choker</th>\n",
       "      <th>Pilot Helmet</th>\n",
       "      <th>...</th>\n",
       "      <th>Regular Shades</th>\n",
       "      <th>Horned Rim Glasses</th>\n",
       "      <th>Big Shades</th>\n",
       "      <th>Nerd Glasses</th>\n",
       "      <th>Black Lipstick</th>\n",
       "      <th>Mole</th>\n",
       "      <th>Purple Lipstick</th>\n",
       "      <th>Hot Lipstick</th>\n",
       "      <th>Cigarette</th>\n",
       "      <th>Earring</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>punk_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2822.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36305.0</td>\n",
       "      <td>5155.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>234.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>105.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>42803.0</td>\n",
       "      <td>26612.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <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>9980</th>\n",
       "      <td>70.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9989</th>\n",
       "      <td>140.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9990</th>\n",
       "      <td>44981.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>169770.0</td>\n",
       "      <td>21062.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>61170.0</td>\n",
       "      <td>9499.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5591 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         last_sale  sec_sale  Alien  Ape  Female  Male  Zombie  Beanie  \\\n",
       "punk_id                                                                  \n",
       "0           2822.0     386.0      0    0       1     0       0     0.0   \n",
       "1          36305.0    5155.0      0    0       0     1       0     0.0   \n",
       "14           234.0     351.0      0    0       1     0       0     0.0   \n",
       "33           105.0       0.0      0    0       0     1       0     0.0   \n",
       "53         42803.0   26612.0      0    0       1     0       0     0.0   \n",
       "...            ...       ...    ...  ...     ...   ...     ...     ...   \n",
       "9980          70.0       0.0      0    0       1     0       0     0.0   \n",
       "9989         140.0       0.0      0    0       1     0       0     0.0   \n",
       "9990       44981.0       0.0      0    0       0     1       0     0.0   \n",
       "9997      169770.0   21062.0      0    0       0     0       1     0.0   \n",
       "9998       61170.0    9499.0      0    0       1     0       0     0.0   \n",
       "\n",
       "         Choker  Pilot Helmet  ...  Regular Shades  Horned Rim Glasses  \\\n",
       "punk_id                        ...                                       \n",
       "0           0.0           0.0  ...             0.0                 0.0   \n",
       "1           0.0           0.0  ...             0.0                 0.0   \n",
       "14          0.0           1.0  ...             0.0                 0.0   \n",
       "33          0.0           0.0  ...             0.0                 0.0   \n",
       "53          0.0           0.0  ...             0.0                 0.0   \n",
       "...         ...           ...  ...             ...                 ...   \n",
       "9980        0.0           0.0  ...             0.0                 0.0   \n",
       "9989        0.0           1.0  ...             0.0                 0.0   \n",
       "9990        0.0           0.0  ...             0.0                 1.0   \n",
       "9997        0.0           0.0  ...             0.0                 0.0   \n",
       "9998        0.0           0.0  ...             0.0                 0.0   \n",
       "\n",
       "         Big Shades  Nerd Glasses  Black Lipstick  Mole  Purple Lipstick  \\\n",
       "punk_id                                                                    \n",
       "0               0.0           0.0             0.0   0.0              0.0   \n",
       "1               0.0           0.0             0.0   0.0              0.0   \n",
       "14              0.0           0.0             0.0   0.0              0.0   \n",
       "33              0.0           0.0             0.0   0.0              0.0   \n",
       "53              1.0           0.0             0.0   0.0              0.0   \n",
       "...             ...           ...             ...   ...              ...   \n",
       "9980            0.0           0.0             1.0   0.0              0.0   \n",
       "9989            0.0           0.0             0.0   0.0              0.0   \n",
       "9990            0.0           0.0             0.0   0.0              0.0   \n",
       "9997            0.0           0.0             0.0   0.0              0.0   \n",
       "9998            0.0           0.0             1.0   0.0              0.0   \n",
       "\n",
       "         Hot Lipstick  Cigarette  Earring  \n",
       "punk_id                                    \n",
       "0                 0.0        0.0      1.0  \n",
       "1                 0.0        0.0      0.0  \n",
       "14                1.0        0.0      0.0  \n",
       "33                0.0        0.0      0.0  \n",
       "53                0.0        0.0      0.0  \n",
       "...               ...        ...      ...  \n",
       "9980              0.0        1.0      0.0  \n",
       "9989              1.0        1.0      0.0  \n",
       "9990              0.0        0.0      0.0  \n",
       "9997              0.0        0.0      0.0  \n",
       "9998              0.0        0.0      0.0  \n",
       "\n",
       "[5591 rows x 94 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df, types_dummies, on=\"punk_id\", how='left')\n",
    "df = pd.merge(df, acc, on=\"punk_id\", how='left')\n",
    "\n",
    "df = df.set_index(['punk_id'])\n",
    "df = df.fillna(0)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d3c16620",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['last_sale']\n",
    "y = y.round(decimals=-2)\n",
    "\n",
    "X = df.iloc[:,1:]\n",
    "X['sec_sale'] = X['sec_sale'].round(decimals=-2)\n",
    "X = X.astype(int)\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=888)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "455adc6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              last_sale   R-squared:                       0.431\n",
      "Model:                            OLS   Adj. R-squared:                  0.418\n",
      "Method:                 Least Squares   F-statistic:                     31.50\n",
      "Date:                Tue, 13 Jul 2021   Prob (F-statistic):               0.00\n",
      "Time:                        22:13:19   Log-Likelihood:                -51859.\n",
      "No. Observations:                3913   AIC:                         1.039e+05\n",
      "Df Residuals:                    3820   BIC:                         1.045e+05\n",
      "Df Model:                          92                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "========================================================================================\n",
      "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------\n",
      "const                 5.884e+05   1.81e+04     32.487      0.000    5.53e+05    6.24e+05\n",
      "sec_sale                 1.1569      0.081     14.330      0.000       0.999       1.315\n",
      "Alien                 2.602e+06    5.3e+04     49.124      0.000     2.5e+06    2.71e+06\n",
      "Ape                   -4.42e+05   3.58e+04    -12.355      0.000   -5.12e+05   -3.72e+05\n",
      "Female               -5.482e+05   1.49e+04    -36.838      0.000   -5.77e+05   -5.19e+05\n",
      "Male                 -5.625e+05   1.41e+04    -39.795      0.000    -5.9e+05   -5.35e+05\n",
      "Zombie               -4.605e+05   2.52e+04    -18.296      0.000    -5.1e+05   -4.11e+05\n",
      "Beanie                5.527e+04   3.27e+04      1.688      0.091   -8907.762    1.19e+05\n",
      "Choker                1.814e+04    3.1e+04      0.585      0.558   -4.26e+04    7.89e+04\n",
      "Pilot Helmet          3.716e+04    3.1e+04      1.198      0.231   -2.37e+04     9.8e+04\n",
      "Tiara                 2477.3317   3.56e+04      0.070      0.944   -6.73e+04    7.22e+04\n",
      "Orange Side           3.968e+04   3.01e+04      1.318      0.188   -1.94e+04    9.87e+04\n",
      "Buck Teeth            2.517e+04   2.63e+04      0.956      0.339   -2.64e+04    7.68e+04\n",
      "Welding Goggles       4306.4140   3.29e+04      0.131      0.896   -6.02e+04    6.88e+04\n",
      "Pigtails             -1815.1219   2.91e+04     -0.062      0.950   -5.88e+04    5.52e+04\n",
      "Pink With Hat        -1.305e+04   3.02e+04     -0.433      0.665   -7.22e+04    4.61e+04\n",
      "Top Hat               1.742e+04   2.51e+04      0.693      0.489   -3.19e+04    6.67e+04\n",
      "Spots                 1.034e+04   2.13e+04      0.487      0.627   -3.13e+04     5.2e+04\n",
      "Rosy Cheeks           7611.8701   2.08e+04      0.366      0.714   -3.31e+04    4.83e+04\n",
      "Blonde Short          -2.11e+04   2.88e+04     -0.733      0.464   -7.75e+04    3.53e+04\n",
      "Wild White Hair      -1.492e+04   2.54e+04     -0.587      0.558   -6.48e+04     3.5e+04\n",
      "Cowboy Hat            1407.5674   2.31e+04      0.061      0.951   -4.39e+04    4.67e+04\n",
      "Wild Blonde           7898.4160   2.88e+04      0.274      0.784   -4.86e+04    6.44e+04\n",
      "Straight Hair Blonde  1.392e+04   2.62e+04      0.532      0.595   -3.74e+04    6.52e+04\n",
      "Big Beard             6540.1754   1.82e+04      0.359      0.719   -2.91e+04    4.22e+04\n",
      "Red Mohawk           -5921.6348    2.3e+04     -0.258      0.797    -5.1e+04    3.91e+04\n",
      "Half Shaved           -1.48e+04   2.78e+04     -0.532      0.595   -6.94e+04    3.98e+04\n",
      "Blonde Bob           -2.844e+04   2.73e+04     -1.041      0.298    -8.2e+04    2.51e+04\n",
      "Vampire Hair         -1.334e+04   2.19e+04     -0.608      0.543   -5.64e+04    2.97e+04\n",
      "Clown Hair Green     -6692.3826   2.26e+04     -0.297      0.767   -5.09e+04    3.75e+04\n",
      "Straight Hair Dark    1.275e+04   2.67e+04      0.477      0.634   -3.97e+04    6.52e+04\n",
      "Straight Hair        -3.229e+04   2.45e+04     -1.319      0.187   -8.03e+04    1.57e+04\n",
      "Silver Chain          9578.2436   1.84e+04      0.520      0.603   -2.66e+04    4.57e+04\n",
      "Dark Hair            -1.954e+04   2.48e+04     -0.788      0.431   -6.82e+04    2.91e+04\n",
      "Purple Hair          -7795.4054   2.34e+04     -0.333      0.739   -5.37e+04    3.81e+04\n",
      "Gold Chain           -2101.1335   1.81e+04     -0.116      0.908   -3.77e+04    3.35e+04\n",
      "Medical Mask         -4.411e+04    1.7e+04     -2.595      0.010   -7.74e+04   -1.08e+04\n",
      "Tassle Hat           -2.398e+04   2.41e+04     -0.995      0.320   -7.12e+04    2.33e+04\n",
      "Fedora               -1.313e+04   2.14e+04     -0.613      0.540   -5.51e+04    2.89e+04\n",
      "Police Cap            9991.8568   2.11e+04      0.472      0.637   -3.15e+04    5.15e+04\n",
      "Clown Nose            -464.8361    1.7e+04     -0.027      0.978   -3.38e+04    3.28e+04\n",
      "Smile                 2.537e+04   1.49e+04      1.704      0.088   -3823.717    5.46e+04\n",
      "Cap Forward           5.795e+04   1.81e+04      3.205      0.001    2.25e+04    9.34e+04\n",
      "Hoodie                1.786e+04   2.01e+04      0.889      0.374   -2.15e+04    5.72e+04\n",
      "Front Beard Dark      4370.1181   1.81e+04      0.242      0.809    -3.1e+04    3.98e+04\n",
      "Frown                 1.323e+04   1.36e+04      0.971      0.331   -1.35e+04    3.99e+04\n",
      "Purple Eye Shadow    -4025.0435   1.52e+04     -0.264      0.792   -3.39e+04    2.59e+04\n",
      "Handlebars            1293.7582   1.43e+04      0.091      0.928   -2.67e+04    2.93e+04\n",
      "Blue Eye Shadow       -249.6001   1.53e+04     -0.016      0.987   -3.03e+04    2.98e+04\n",
      "Green Eye Shadow      8136.6416   1.56e+04      0.523      0.601   -2.24e+04    3.87e+04\n",
      "Vape                  3916.7926   1.46e+04      0.268      0.789   -2.47e+04    3.26e+04\n",
      "Front Beard            833.1972   1.33e+04      0.063      0.950   -2.52e+04    2.69e+04\n",
      "Chinstrap             5274.3637   1.27e+04      0.415      0.678   -1.96e+04    3.02e+04\n",
      "3D Glasses            4.519e+04   1.47e+04      3.067      0.002    1.63e+04    7.41e+04\n",
      "Luxurious Beard       1.587e+04   1.49e+04      1.065      0.287   -1.34e+04    4.51e+04\n",
      "Mustache             -3995.3117   1.34e+04     -0.297      0.766   -3.03e+04    2.23e+04\n",
      "Normal Beard Black    2335.7309   1.85e+04      0.126      0.899   -3.39e+04    3.85e+04\n",
      "Normal Beard          5096.4123   1.31e+04      0.389      0.698   -2.06e+04    3.08e+04\n",
      "Eye Mask              3414.4525   1.32e+04      0.259      0.796   -2.24e+04    2.93e+04\n",
      "Goat                 -5187.6633   1.31e+04     -0.397      0.691   -3.08e+04    2.04e+04\n",
      "Do-rag               -2.553e+04   1.91e+04     -1.340      0.180   -6.29e+04    1.18e+04\n",
      "Shaved Head          -2.371e+04   1.86e+04     -1.272      0.204   -6.03e+04    1.28e+04\n",
      "Muttonchops           2655.1616    1.3e+04      0.205      0.838   -2.27e+04     2.8e+04\n",
      "Peak Spike           -1.524e+04   1.97e+04     -0.774      0.439   -5.38e+04    2.34e+04\n",
      "Pipe                  4.331e+04   1.25e+04      3.475      0.001    1.89e+04    6.77e+04\n",
      "VR                    1.143e+04   1.38e+04      0.826      0.409   -1.57e+04    3.86e+04\n",
      "Cap                  -3.842e+04   1.91e+04     -2.015      0.044   -7.58e+04   -1038.128\n",
      "Small Shades           3.52e+04   1.17e+04      2.996      0.003    1.22e+04    5.82e+04\n",
      "Clown Eyes Green      1796.4434   1.24e+04      0.145      0.885   -2.25e+04    2.61e+04\n",
      "Clown Eyes Blue       -795.4082   1.29e+04     -0.062      0.951   -2.61e+04    2.45e+04\n",
      "Headband              2771.7045   1.81e+04      0.153      0.879   -3.28e+04    3.84e+04\n",
      "Crazy Hair             -1.1e+04   1.85e+04     -0.593      0.553   -4.73e+04    2.53e+04\n",
      "Knitted Cap          -3468.6744   1.64e+04     -0.212      0.832   -3.55e+04    2.86e+04\n",
      "Mohawk Dark           -808.5178   1.54e+04     -0.053      0.958    -3.1e+04    2.93e+04\n",
      "Mohawk               -2.057e+04    1.8e+04     -1.140      0.254   -5.59e+04    1.48e+04\n",
      "Mohawk Thin          -1629.2986   1.46e+04     -0.111      0.911   -3.03e+04    2.71e+04\n",
      "Frumpy Hair          -2.697e+04   1.79e+04     -1.508      0.132    -6.2e+04    8092.897\n",
      "Wild Hair            -1.399e+04   1.82e+04     -0.770      0.441   -4.96e+04    2.16e+04\n",
      "Messy Hair            -2.19e+04   1.79e+04     -1.225      0.220   -5.69e+04    1.31e+04\n",
      "Eye Patch             8384.8978   1.13e+04      0.741      0.459   -1.38e+04    3.06e+04\n",
      "Stringy Hair         -2.079e+04   1.78e+04     -1.171      0.242   -5.56e+04     1.4e+04\n",
      "Bandana              -3.304e+04   1.73e+04     -1.905      0.057    -6.7e+04     972.370\n",
      "Classic Shades        4162.8562   1.02e+04      0.407      0.684   -1.59e+04    2.42e+04\n",
      "Shadow Beard          3117.3943   1.05e+04      0.298      0.766   -1.74e+04    2.36e+04\n",
      "Regular Shades       -1843.3411    1.1e+04     -0.168      0.866   -2.33e+04    1.96e+04\n",
      "Horned Rim Glasses    5895.7037   1.02e+04      0.577      0.564   -1.41e+04    2.59e+04\n",
      "Big Shades            6814.4267   1.01e+04      0.672      0.502   -1.31e+04    2.67e+04\n",
      "Nerd Glasses         -1585.4310      1e+04     -0.158      0.874   -2.12e+04     1.8e+04\n",
      "Black Lipstick       -4989.5134   1.11e+04     -0.451      0.652   -2.67e+04    1.67e+04\n",
      "Mole                  -665.5674   8951.234     -0.074      0.941   -1.82e+04    1.69e+04\n",
      "Purple Lipstick      -3750.5684   1.09e+04     -0.343      0.732   -2.52e+04    1.77e+04\n",
      "Hot Lipstick         -1445.6146   1.06e+04     -0.137      0.891   -2.22e+04    1.93e+04\n",
      "Cigarette             2801.4155   7758.546      0.361      0.718   -1.24e+04     1.8e+04\n",
      "Earring               -674.0522   5200.262     -0.130      0.897   -1.09e+04    9521.504\n",
      "==============================================================================\n",
      "Omnibus:                     6202.698   Durbin-Watson:                   2.001\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):         69848573.497\n",
      "Skew:                           8.919   Prob(JB):                         0.00\n",
      "Kurtosis:                     657.287   Cond. No.                     3.17e+20\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The smallest eigenvalue is 3.7e-29. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n"
     ]
    }
   ],
   "source": [
    "sm_reg = sm.OLS(y_train, X_train).fit()\n",
    "print(sm_reg.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "65bdfd27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 35347.738735701205\n",
      "Root Mean Squared Error: 129733.59000172261\n"
     ]
    }
   ],
   "source": [
    "y_pred = sm_reg.predict(X_test)\n",
    "print('Mean Absolute Error:', meanabs(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', rmse(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1d9de2a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['last_sale']\n",
    "y = y.round(decimals=-2)\n",
    "\n",
    "X = df.iloc[:,1:]\n",
    "X['sec_sale'] = X['sec_sale'].round(decimals=-2)\n",
    "X = X.astype(int)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=888)\n",
    "\n",
    "sm_reg = LinearRegression()\n",
    "sm_reg.fit(X_train, y_train)\n",
    "y_pred = sm_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a932f014",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result for Last Sale Regression \n",
      "\n",
      "Adjusted Rsquared Train:  0.417\n",
      "Test R^2:  -1.316\n",
      "Adjusted Rsquared Test:  -1.452\n",
      "Mean Absolute Error: 35347.739\n",
      "Mean Squared Error: 16830804360.586\n",
      "Root Mean Squared Error: 129733.59\n"
     ]
    }
   ],
   "source": [
    "print('Result for Last Sale Regression \\n')\n",
    "print('Adjusted Rsquared Train: ', (1 - (1-sm_reg.score(X_train, y_train))*(len(y_train)-1)/(len(y_train)-X_train.shape[1]-1)).round(decimals=3))\n",
    "print('Test R^2: ', sm_reg.score(X_test, y_test).round(decimals=3))\n",
    "print('Adjusted Rsquared Test: ', (1 - (1-sm_reg.score(X_test, y_test))*(len(y_test)-1)/(len(y_test)-X_test.shape[1]-1)).round(decimals=3))\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred).round(decimals=3))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred).round(decimals=3))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)).round(decimals=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "69781dcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = y_test - y_pred\n",
    "plt.rcParams['figure.figsize'] = (10,6)\n",
    "plt.plot(y_test, res, 'o', color='darkblue')\n",
    "plt.title(\"Last Sale Regression Residual Plot\")\n",
    "plt.xlabel(\"Amount in Dollars\")\n",
    "plt.ylabel(\"Residual\")\n",
    "plt.xlim(0, 500000)\n",
    "plt.savefig('Last Sale Regression Residual Plot', transparent=True)\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae303c8c",
   "metadata": {},
   "source": [
    "# Last Sale Reg with Selected Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7b9d2299",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['last_sale']\n",
    "y = y.round(decimals=-2)\n",
    "\n",
    "X = df.iloc[:,1:]\n",
    "X['sec_sale'] = X['sec_sale'].round(decimals=-2)\n",
    "X = X.astype(int)\n",
    "cols = ['Zombie', 'Orange Side', '3D Glasses', 'Top Hat', 'Hoodie', 'Pigtails', 'Buck Teeth', 'Pink With Hat', 'sec_sale']\n",
    "new_X = X[cols]\n",
    "X_train, X_test, y_train, y_test = train_test_split(new_X, y, test_size=0.3, random_state=888)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d3dd036e",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_reg = LinearRegression()\n",
    "new_reg.fit(X_train, y_train)\n",
    "y_pred = new_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5816cb14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result for Last Sale Regression \n",
      "\n",
      "Adjusted Rsquared Train:  0.035\n",
      "Test R^2:  0.336\n",
      "Adjusted Rsquared Test:  0.332\n",
      "Mean Absolute Error: 31129.421\n",
      "Mean Squared Error: 4824208825.177\n",
      "Root Mean Squared Error: 69456.525\n"
     ]
    }
   ],
   "source": [
    "print('Result for Last Sale Regression \\n')\n",
    "print('Adjusted Rsquared Train: ', (1 - (1-new_reg.score(X_train, y_train))*(len(y_train)-1)/(len(y_train)-X_train.shape[1]-1)).round(decimals=3))\n",
    "print('Test R^2: ', new_reg.score(X_test, y_test).round(decimals=3))\n",
    "print('Adjusted Rsquared Test: ', (1 - (1-new_reg.score(X_test, y_test))*(len(y_test)-1)/(len(y_test)-X_test.shape[1]-1)).round(decimals=3))\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred).round(decimals=3))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred).round(decimals=3))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)).round(decimals=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "60c65b48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = y_test - y_pred\n",
    "plt.rcParams['figure.figsize'] = (10,6)\n",
    "plt.plot(y_test, res, 'o', color='darkblue')\n",
    "plt.title(\"Last Sale Regression Residual Plot with Selected Features\")\n",
    "plt.xlabel(\"Amount in Dollars\")\n",
    "plt.ylabel(\"Residual\")\n",
    "plt.xlim(0, 500000)\n",
    "plt.savefig('Last Sale Regression Residual Plot with Selected Features', transparent=True)\n",
    "plt.show()\n",
    "plt.close()"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}