notebooks/price_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "20a19e97-2c50-48f7-b934-f1bd00909bb7",
   "metadata": {},
   "source": [
    "# USD AAI 500 final project - Group 3\n",
    "Group members\n",
    "\n",
    "- Victoria Dorn\n",
    "- Victor Hugo Germano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9a351c61-8861-4331-bfcc-9b2c9b6ff7d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load necessary libraries\n",
    "import pandas as pd \n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt \n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "from matplotlib import gridspec\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import math\n",
    "\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b56ba716-1fee-4577-acc1-1a5ae4f53907",
   "metadata": {},
   "source": [
    "# Data Cleaning and Organization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "97f0b4af-9603-4174-bbb0-0c0708c2fb7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = 'primary'\n",
    "data = pd.read_csv(f'../dataset/mapped_{dataset}.csv', sep=\",\")\n",
    "data = data.drop(columns='uuid')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de865f3b-2e4d-4ed1-80e1-e5331384d513",
   "metadata": {},
   "source": [
    "# Artists affect prices?\n",
    "\n",
    "Assuming USD only for analysis\n",
    "\n",
    "- Null hypothesis $H_0$: artists have no impact on prices\n",
    "- Alt hypothesis $H_A$: artists do have an impact\n",
    "\n",
    "## Intepretation\n",
    "\n",
    "The F-statistic of 2.101 suggests that there is a significant amount of variance between the average prices of cards from different artists compared to the variance within each artist's prices.\n",
    "\n",
    "Since the P-value is significantly less than 0.05, you can reject the null hypothesis (H0), which states that artists have no impact on prices.\n",
    "\n",
    "\n",
    "### Implications\n",
    "**Artist Impact:** The results suggest that the artist associated with a card does influence its price, meaning that some artists may produce cards that are valued more highly in the market than others.\n",
    "\n",
    "**Market Insights:** For collectors and sellers, understanding these differences can inform buying and pricing strategies.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0fd0d4ae-b869-48c7-a7ed-07bdf97417fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hypothesis H0: Artists have no impact on prices\n",
    "\n",
    "# groups mean prices by artists\n",
    "artists = data['artist'].unique()\n",
    "\n",
    "# Create a group of prices for each unique artist to performing a ANOVA test\n",
    "artist_price_groups = [ data[data['artist'] == artist]['price'].to_numpy()\n",
    "    for artist in artists if len(data[data['artist'] == artist]['price']) > 1 ]\n",
    "\n",
    "# Removing Zero Variance artist prices\n",
    "filtered_prices = [row for row in artist_price_groups if np.var(row) > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0efd102c-ee96-4e4a-a013-18dd58983d02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Can artist impact prices: \n",
      " Data: Paper cards, no outliers, Q1_Q3, only in USD\n",
      "T-statistic ANOVA: 5.0445\n",
      "P-value: 0.000000\n",
      "Reject the null hypothesis. There is a statistically significant difference.\n"
     ]
    }
   ],
   "source": [
    "# Conduct the one-way ANOVA\n",
    "# Paper cards, no outliers, Q1_Q3, only in USD\n",
    "t_stat, p_value = stats.f_oneway(*filtered_prices)\n",
    "\n",
    "print(\"Can artist impact prices: \\n Data: Paper cards, no outliers, Q1_Q3, only in USD\")\n",
    "print(\"T-statistic ANOVA: %.4f\" % t_stat)\n",
    "print(\"P-value: %.6f\" % p_value);\n",
    "\n",
    "if p_value < 0.05:\n",
    "    print(\"Reject the null hypothesis. There is a statistically significant difference.\")\n",
    "else:\n",
    "    print(\"Fail to reject the null hypothesis. There is no statistically significant difference.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42b44237-88fc-4926-b401-e4806f2bd185",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# Prediction Exercises\n",
    "\n",
    "Trying to understand what affects the price the most"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9362370f-7c24-40dd-bc53-d248882c4776",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Create linear regression model and use it based on selected feature and target\n",
    "def prediction(features, target):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)\n",
    "    \n",
    "    poly = PolynomialFeatures(degree=2)\n",
    "    X_train_poly = poly.fit_transform(X_train)\n",
    "    X_test_poly = poly.transform(X_test)\n",
    "    \n",
    "    model = LinearRegression().fit(X_train_poly, y_train)\n",
    "    \n",
    "    y_pred = model.predict(X_test_poly)\n",
    "\n",
    "    # Calculate metrics\n",
    "    mse = mean_squared_error(y_test, y_pred)\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "    return y_test, y_pred, mse, r2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff2a0767-f523-48cb-8c72-f4c52b352abb",
   "metadata": {},
   "source": [
    "# Price Prediction model - using categorical data\n",
    "\n",
    "- Mean Squared Error: 1.970043342975583\n",
    "- R-squared: 0.2172273808523122\n",
    "\n",
    "Not the best model result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3f283256-b2c3-4b16-9f43-eafdb22256c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 1.91355490272543\n",
      "R-squared: 0.35104788760622274\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "# Price Prediction based on Rank\n",
    "\n",
    "target = data['price']\n",
    "features = data.copy()\n",
    "features = features.drop(columns='price')\n",
    "\n",
    "# Polinomial Linear Regression\n",
    "y_test, y_pred, mse, r2 = prediction(features, target)\n",
    "\n",
    "print(f'Mean Squared Error: {mse}')\n",
    "print(f'R-squared: {r2}')\n",
    "\n",
    "# Ploting results\n",
    "sns.scatterplot(x=y_test, y=y_pred)\n",
    "plt.title(f'Actual vs Predicted Prices ')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2a09768-0422-4849-83be-768fcf7ed037",
   "metadata": {},
   "source": [
    "# OLS data interpretation\n",
    "\n",
    "## Key Metrics Interpretation\n",
    "\n",
    "### R-squared and Adjusted R-squared\n",
    "- **R-squared (0.418)**: Approximately **41.8%** of the variability in card prices can be explained by the independent variables included in the model. \n",
    "- **Adjusted R-squared (0.417)**: indicates that the model's explanatory power remains consistent even after accounting for additional variables.\n",
    "\n",
    "### F-statistic and Prob (F-statistic)\n",
    "- **F-statistic (550.1)**: Sugests that at least one predictor variable significantly contributes to explaining the variability in price.\n",
    "- **Prob (F-statistic) (0.00)**: Model is statistically significant, where independent variables collectively have a significant effect on card prices.\n",
    "\n",
    "## Conclusions\n",
    "\n",
    "Highest Contributors to price increse\n",
    "\n",
    "- **Finishes Encoded**: Coefficient: **0.1251** suggests that cards with different finishes (like foil) tend to be priced higher by about $0.13 on average.\n",
    "- **Price Provider Encoded**: - Coefficient: **0.3187** indicates that different price providers contribute to higher prices on average, which is statistically significant.\n",
    "\n",
    "The OLS regression results suggest that several factors significantly influence card prices, including rarity, power, artist identity, finish type, EDHREC rank, price provider, and set code.\n",
    "\n",
    "1) The negative impact of rarity on price may indicate market dynamics where rarer cards are less frequently sold or valued differently.\n",
    "2) The positive relationship between power and price suggests that more powerful cards are valued higher by collectors and players.\n",
    "3) Collectors and sellers can leverage these insights to inform pricing strategies based on card attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "25f51505-5020-451e-a7dd-1a562ca00e88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared (uncentered):</th>      <td>   0.498</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.497</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   654.9</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 12 Oct 2024</td> <th>  Prob (F-statistic):</th>           <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>06:18:59</td>     <th>  Log-Likelihood:    </th>          <td> -27745.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 15214</td>      <th>  AIC:               </th>          <td>5.554e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 15191</td>      <th>  BIC:               </th>          <td>5.571e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    23</td>      <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>artist</th>           <td>   -0.0001</td> <td> 9.52e-05</td> <td>   -1.390</td> <td> 0.164</td> <td>   -0.000</td> <td> 5.43e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cardFinish</th>       <td>   -0.3984</td> <td>    0.022</td> <td>  -17.710</td> <td> 0.000</td> <td>   -0.443</td> <td>   -0.354</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>colorIdentity</th>    <td>    0.0150</td> <td>    0.006</td> <td>    2.652</td> <td> 0.008</td> <td>    0.004</td> <td>    0.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>colors</th>           <td>   -0.0137</td> <td>    0.006</td> <td>   -2.406</td> <td> 0.016</td> <td>   -0.025</td> <td>   -0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>edhrecRank</th>       <td>-4.789e-05</td> <td> 2.71e-06</td> <td>  -17.658</td> <td> 0.000</td> <td>-5.32e-05</td> <td>-4.26e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>edhrecSaltiness</th>  <td>    0.8681</td> <td>    0.043</td> <td>   20.391</td> <td> 0.000</td> <td>    0.785</td> <td>    0.952</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>gameAvailability</th> <td>    1.3893</td> <td>    0.049</td> <td>   28.612</td> <td> 0.000</td> <td>    1.294</td> <td>    1.485</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>isReprint</th>        <td>   -0.1448</td> <td>    0.027</td> <td>   -5.333</td> <td> 0.000</td> <td>   -0.198</td> <td>   -0.092</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>layout</th>           <td>    0.2547</td> <td>    0.029</td> <td>    8.863</td> <td> 0.000</td> <td>    0.198</td> <td>    0.311</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>manaCost</th>         <td>    0.0003</td> <td>    0.000</td> <td>    1.657</td> <td> 0.098</td> <td>-4.67e-05</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>manaValue</th>        <td>    0.0621</td> <td>    0.010</td> <td>    6.258</td> <td> 0.000</td> <td>    0.043</td> <td>    0.082</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>name</th>             <td>-1.338e-05</td> <td> 3.18e-05</td> <td>   -0.420</td> <td> 0.674</td> <td>-7.58e-05</td> <td>  4.9e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>number</th>           <td> 8.447e-05</td> <td> 6.24e-05</td> <td>    1.354</td> <td> 0.176</td> <td>-3.78e-05</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>originalType</th>     <td>   -0.0010</td> <td>    0.000</td> <td>   -6.922</td> <td> 0.000</td> <td>   -0.001</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>power</th>            <td>   -0.0038</td> <td>    0.007</td> <td>   -0.553</td> <td> 0.580</td> <td>   -0.017</td> <td>    0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>priceProvider</th>    <td>   -0.1241</td> <td>    0.017</td> <td>   -7.138</td> <td> 0.000</td> <td>   -0.158</td> <td>   -0.090</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>providerListing</th>  <td>   -0.4133</td> <td>    0.038</td> <td>  -10.768</td> <td> 0.000</td> <td>   -0.489</td> <td>   -0.338</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rarity</th>           <td>   -0.4203</td> <td>    0.013</td> <td>  -31.138</td> <td> 0.000</td> <td>   -0.447</td> <td>   -0.394</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>setCode</th>          <td>    0.0012</td> <td>    0.000</td> <td>    4.969</td> <td> 0.000</td> <td>    0.001</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>supertypes</th>       <td>    0.2266</td> <td>    0.055</td> <td>    4.153</td> <td> 0.000</td> <td>    0.120</td> <td>    0.334</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>toughness</th>        <td>   -0.0193</td> <td>    0.009</td> <td>   -2.244</td> <td> 0.025</td> <td>   -0.036</td> <td>   -0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>type</th>             <td>    0.0006</td> <td>    0.000</td> <td>    3.581</td> <td> 0.000</td> <td>    0.000</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>types</th>            <td>    0.4023</td> <td>    0.056</td> <td>    7.231</td> <td> 0.000</td> <td>    0.293</td> <td>    0.511</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>4828.653</td> <th>  Durbin-Watson:     </th> <td>   1.191</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>13067.893</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 1.723</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 5.957</td>  <th>  Cond. No.          </th> <td>3.99e+04</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[3] The condition number is large, 3.99e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &      price       & \\textbf{  R-squared (uncentered):}      &     0.498   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared (uncentered):} &     0.497   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       }          &     654.9   \\\\\n",
       "\\textbf{Date:}             & Sat, 12 Oct 2024 & \\textbf{  Prob (F-statistic):}          &     0.00    \\\\\n",
       "\\textbf{Time:}             &     06:18:59     & \\textbf{  Log-Likelihood:    }          &   -27745.   \\\\\n",
       "\\textbf{No. Observations:} &       15214      & \\textbf{  AIC:               }          & 5.554e+04   \\\\\n",
       "\\textbf{Df Residuals:}     &       15191      & \\textbf{  BIC:               }          & 5.571e+04   \\\\\n",
       "\\textbf{Df Model:}         &          23      & \\textbf{                     }          &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     }          &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                          & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{artist}           &      -0.0001  &     9.52e-05     &    -1.390  &         0.164        &       -0.000    &     5.43e-05     \\\\\n",
       "\\textbf{cardFinish}       &      -0.3984  &        0.022     &   -17.710  &         0.000        &       -0.443    &       -0.354     \\\\\n",
       "\\textbf{colorIdentity}    &       0.0150  &        0.006     &     2.652  &         0.008        &        0.004    &        0.026     \\\\\n",
       "\\textbf{colors}           &      -0.0137  &        0.006     &    -2.406  &         0.016        &       -0.025    &       -0.003     \\\\\n",
       "\\textbf{edhrecRank}       &   -4.789e-05  &     2.71e-06     &   -17.658  &         0.000        &    -5.32e-05    &    -4.26e-05     \\\\\n",
       "\\textbf{edhrecSaltiness}  &       0.8681  &        0.043     &    20.391  &         0.000        &        0.785    &        0.952     \\\\\n",
       "\\textbf{gameAvailability} &       1.3893  &        0.049     &    28.612  &         0.000        &        1.294    &        1.485     \\\\\n",
       "\\textbf{isReprint}        &      -0.1448  &        0.027     &    -5.333  &         0.000        &       -0.198    &       -0.092     \\\\\n",
       "\\textbf{layout}           &       0.2547  &        0.029     &     8.863  &         0.000        &        0.198    &        0.311     \\\\\n",
       "\\textbf{manaCost}         &       0.0003  &        0.000     &     1.657  &         0.098        &    -4.67e-05    &        0.001     \\\\\n",
       "\\textbf{manaValue}        &       0.0621  &        0.010     &     6.258  &         0.000        &        0.043    &        0.082     \\\\\n",
       "\\textbf{name}             &   -1.338e-05  &     3.18e-05     &    -0.420  &         0.674        &    -7.58e-05    &      4.9e-05     \\\\\n",
       "\\textbf{number}           &    8.447e-05  &     6.24e-05     &     1.354  &         0.176        &    -3.78e-05    &        0.000     \\\\\n",
       "\\textbf{originalType}     &      -0.0010  &        0.000     &    -6.922  &         0.000        &       -0.001    &       -0.001     \\\\\n",
       "\\textbf{power}            &      -0.0038  &        0.007     &    -0.553  &         0.580        &       -0.017    &        0.010     \\\\\n",
       "\\textbf{priceProvider}    &      -0.1241  &        0.017     &    -7.138  &         0.000        &       -0.158    &       -0.090     \\\\\n",
       "\\textbf{providerListing}  &      -0.4133  &        0.038     &   -10.768  &         0.000        &       -0.489    &       -0.338     \\\\\n",
       "\\textbf{rarity}           &      -0.4203  &        0.013     &   -31.138  &         0.000        &       -0.447    &       -0.394     \\\\\n",
       "\\textbf{setCode}          &       0.0012  &        0.000     &     4.969  &         0.000        &        0.001    &        0.002     \\\\\n",
       "\\textbf{supertypes}       &       0.2266  &        0.055     &     4.153  &         0.000        &        0.120    &        0.334     \\\\\n",
       "\\textbf{toughness}        &      -0.0193  &        0.009     &    -2.244  &         0.025        &       -0.036    &       -0.002     \\\\\n",
       "\\textbf{type}             &       0.0006  &        0.000     &     3.581  &         0.000        &        0.000    &        0.001     \\\\\n",
       "\\textbf{types}            &       0.4023  &        0.056     &     7.231  &         0.000        &        0.293    &        0.511     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 4828.653 & \\textbf{  Durbin-Watson:     } &     1.191  \\\\\n",
       "\\textbf{Prob(Omnibus):} &   0.000  & \\textbf{  Jarque-Bera (JB):  } & 13067.893  \\\\\n",
       "\\textbf{Skew:}          &   1.723  & \\textbf{  Prob(JB):          } &      0.00  \\\\\n",
       "\\textbf{Kurtosis:}      &   5.957  & \\textbf{  Cond. No.          } &  3.99e+04  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] R² is computed without centering (uncentered) since the model does not contain a constant. \\newline\n",
       " [2] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
       " [3] The condition number is large, 3.99e+04. This might indicate that there are \\newline\n",
       " strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                  price   R-squared (uncentered):                   0.498\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.497\n",
       "Method:                 Least Squares   F-statistic:                              654.9\n",
       "Date:                Sat, 12 Oct 2024   Prob (F-statistic):                        0.00\n",
       "Time:                        06:18:59   Log-Likelihood:                         -27745.\n",
       "No. Observations:               15214   AIC:                                  5.554e+04\n",
       "Df Residuals:                   15191   BIC:                                  5.571e+04\n",
       "Df Model:                          23                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "artist              -0.0001   9.52e-05     -1.390      0.164      -0.000    5.43e-05\n",
       "cardFinish          -0.3984      0.022    -17.710      0.000      -0.443      -0.354\n",
       "colorIdentity        0.0150      0.006      2.652      0.008       0.004       0.026\n",
       "colors              -0.0137      0.006     -2.406      0.016      -0.025      -0.003\n",
       "edhrecRank       -4.789e-05   2.71e-06    -17.658      0.000   -5.32e-05   -4.26e-05\n",
       "edhrecSaltiness      0.8681      0.043     20.391      0.000       0.785       0.952\n",
       "gameAvailability     1.3893      0.049     28.612      0.000       1.294       1.485\n",
       "isReprint           -0.1448      0.027     -5.333      0.000      -0.198      -0.092\n",
       "layout               0.2547      0.029      8.863      0.000       0.198       0.311\n",
       "manaCost             0.0003      0.000      1.657      0.098   -4.67e-05       0.001\n",
       "manaValue            0.0621      0.010      6.258      0.000       0.043       0.082\n",
       "name             -1.338e-05   3.18e-05     -0.420      0.674   -7.58e-05     4.9e-05\n",
       "number            8.447e-05   6.24e-05      1.354      0.176   -3.78e-05       0.000\n",
       "originalType        -0.0010      0.000     -6.922      0.000      -0.001      -0.001\n",
       "power               -0.0038      0.007     -0.553      0.580      -0.017       0.010\n",
       "priceProvider       -0.1241      0.017     -7.138      0.000      -0.158      -0.090\n",
       "providerListing     -0.4133      0.038    -10.768      0.000      -0.489      -0.338\n",
       "rarity              -0.4203      0.013    -31.138      0.000      -0.447      -0.394\n",
       "setCode              0.0012      0.000      4.969      0.000       0.001       0.002\n",
       "supertypes           0.2266      0.055      4.153      0.000       0.120       0.334\n",
       "toughness           -0.0193      0.009     -2.244      0.025      -0.036      -0.002\n",
       "type                 0.0006      0.000      3.581      0.000       0.000       0.001\n",
       "types                0.4023      0.056      7.231      0.000       0.293       0.511\n",
       "==============================================================================\n",
       "Omnibus:                     4828.653   Durbin-Watson:                   1.191\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            13067.893\n",
       "Skew:                           1.723   Prob(JB):                         0.00\n",
       "Kurtosis:                       5.957   Cond. No.                     3.99e+04\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[3] The condition number is large, 3.99e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking statsmodel OLS\n",
    "model = sm.OLS(target,features) \n",
    "fit = model.fit()\n",
    "fit.summary()"
   ]
  }
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