Milestone3-Preprocessing-Model1.ipynb

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    {
     "name": "stdout",
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     "text": [
      "Requirement already satisfied: imblearn in c:\\users\\david kim\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (0.0)\n",
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      "Requirement already satisfied: scipy>=1.5.0 in c:\\users\\david kim\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from imbalanced-learn->imblearn) (1.14.1)\n",
      "Requirement already satisfied: scikit-learn>=1.0.2 in c:\\users\\david kim\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from imbalanced-learn->imblearn) (1.5.2)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\david kim\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from imbalanced-learn->imblearn) (1.4.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\david kim\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from imbalanced-learn->imblearn) (3.5.0)\n"
     ]
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     "name": "stderr",
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     "text": [
      "DEPRECATION: Loading egg at c:\\users\\david kim\\appdata\\local\\programs\\python\\python311\\lib\\site-packages\\async_generator-1.10-py3.11.egg is deprecated. pip 25.1 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\n",
      "DEPRECATION: Loading egg at c:\\users\\david kim\\appdata\\local\\programs\\python\\python311\\lib\\site-packages\\attrs-22.2.0-py3.11.egg is deprecated. pip 25.1 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\n",
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     ]
    }
   ],
   "source": [
    "!pip install imblearn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cf97ecc-7864-4d8f-ac6a-4293f010575d",
   "metadata": {},
   "source": [
    "# Milestone 3: Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bb94dbf-3c8b-4110-bfac-2eaec3fd4514",
   "metadata": {},
   "source": [
    "In the previous milestone, we merged the **TCG** and **Price Guide** datasets. We saved the resulting dataframe as a `.csv` file. For this milestone, we will first begin by normalizing our data and encoding our categorical variables. We will then train and evaluate our first model. Let's begin my importing important libraries. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd2a3201-640f-4944-b19d-ae9550a3d939",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "831f5596-50ce-450f-9c4c-e4502d135b3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "from scipy.stats import zscore\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.metrics import classification_report, mean_absolute_error, mean_squared_error, r2_score\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, learning_curve\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import KFold, cross_val_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a888dc43-7041-4060-bb82-5875b133ae45",
   "metadata": {},
   "source": [
    "### Data Loading\n",
    "\n",
    "We will begin by loading our merged dataset from the previous milestone. Then, we use `.dropna()` to drop rows that contain any null values. Then, we have printed some statistics about the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c57ec2d8-d1e3-471f-8188-fb0f3809ae13",
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    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>loose-price</th>\n",
       "      <th>graded-price</th>\n",
       "      <th>box-only-price</th>\n",
       "      <th>manual-only-price</th>\n",
       "      <th>bgs-10-price</th>\n",
       "      <th>condition-17-price</th>\n",
       "      <th>condition-18-price</th>\n",
       "      <th>sales-volume</th>\n",
       "      <th>hp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.030000e+04</td>\n",
       "      <td>30132.000000</td>\n",
       "      <td>23242.000000</td>\n",
       "      <td>23345.000000</td>\n",
       "      <td>22502.000000</td>\n",
       "      <td>22504.000000</td>\n",
       "      <td>22653.000000</td>\n",
       "      <td>22507.000000</td>\n",
       "      <td>30300.000000</td>\n",
       "      <td>25631.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.815578e+06</td>\n",
       "      <td>9.113746</td>\n",
       "      <td>40.473400</td>\n",
       "      <td>50.029936</td>\n",
       "      <td>169.867231</td>\n",
       "      <td>260.751890</td>\n",
       "      <td>144.303368</td>\n",
       "      <td>101.864726</td>\n",
       "      <td>48.108482</td>\n",
       "      <td>96.110569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.610918e+06</td>\n",
       "      <td>57.197150</td>\n",
       "      <td>203.630556</td>\n",
       "      <td>280.753070</td>\n",
       "      <td>1698.911993</td>\n",
       "      <td>2705.228337</td>\n",
       "      <td>641.144056</td>\n",
       "      <td>1019.301576</td>\n",
       "      <td>102.838685</td>\n",
       "      <td>52.140362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.522800e+04</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.887088e+05</td>\n",
       "      <td>1.340000</td>\n",
       "      <td>12.060000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>34.910000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>60.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.596655e+05</td>\n",
       "      <td>1.990000</td>\n",
       "      <td>16.065000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>42.720000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.254008e+06</td>\n",
       "      <td>4.292500</td>\n",
       "      <td>29.807500</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>90.045000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>99.990000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>120.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.747251e+06</td>\n",
       "      <td>4223.260000</td>\n",
       "      <td>17033.540000</td>\n",
       "      <td>27550.000000</td>\n",
       "      <td>221599.740000</td>\n",
       "      <td>332400.000000</td>\n",
       "      <td>42316.000000</td>\n",
       "      <td>132960.000000</td>\n",
       "      <td>1837.000000</td>\n",
       "      <td>340.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                 id   loose-price  graded-price  box-only-price  \\\n",
       "count  3.030000e+04  30132.000000  23242.000000    23345.000000   \n",
       "mean   1.815578e+06      9.113746     40.473400       50.029936   \n",
       "std    1.610918e+06     57.197150    203.630556      280.753070   \n",
       "min    2.522800e+04      0.010000      0.140000        0.990000   \n",
       "25%    8.887088e+05      1.340000     12.060000       13.000000   \n",
       "50%    9.596655e+05      1.990000     16.065000       18.000000   \n",
       "75%    2.254008e+06      4.292500     29.807500       33.000000   \n",
       "max    7.747251e+06   4223.260000  17033.540000    27550.000000   \n",
       "\n",
       "       manual-only-price   bgs-10-price  condition-17-price  \\\n",
       "count       22502.000000   22504.000000        22653.000000   \n",
       "mean          169.867231     260.751890          144.303368   \n",
       "std          1698.911993    2705.228337          641.144056   \n",
       "min             0.980000       0.990000            1.000000   \n",
       "25%            34.910000      52.000000           44.000000   \n",
       "50%            42.720000      65.000000           50.000000   \n",
       "75%            90.045000     137.000000           99.990000   \n",
       "max        221599.740000  332400.000000        42316.000000   \n",
       "\n",
       "       condition-18-price  sales-volume            hp  \n",
       "count        22507.000000  30300.000000  25631.000000  \n",
       "mean           101.864726     48.108482     96.110569  \n",
       "std           1019.301576    102.838685     52.140362  \n",
       "min              1.000000      1.000000     10.000000  \n",
       "25%             21.000000      5.000000     60.000000  \n",
       "50%             26.000000     12.000000     80.000000  \n",
       "75%             54.000000     38.000000    120.000000  \n",
       "max         132960.000000   1837.000000    340.000000  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_guide_tcg = pd.read_csv(\"price_guide_tcg_cleaned.csv\", low_memory=False)\n",
    "price_guide_tcg.dropna()\n",
    "price_guide_tcg.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baed812f-fb45-4a6b-a5cb-13e11569123e",
   "metadata": {},
   "source": [
    "Below, we can see that there are 29 features in our dataset. names for these features are printed below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "647016cf-9d0c-405d-8a27-54266ed3ff0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of features 29\n",
      "Index(['tcg_id', 'id', 'console-name', 'product-name', 'loose-price',\n",
      "       'graded-price', 'box-only-price', 'manual-only-price', 'bgs-10-price',\n",
      "       'condition-17-price', 'condition-18-price', 'sales-volume',\n",
      "       'release-date', 'set', 'series', 'publisher', 'generation',\n",
      "       'release_date', 'artist', 'name', 'set_num', 'types', 'supertype',\n",
      "       'subtypes', 'hp', 'weaknesses', 'rarity', 'legalities', 'resistances'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print('Number of features', price_guide_tcg.shape[1])\n",
    "print(price_guide_tcg.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f214856-3d7f-4365-845d-42928ac19e5d",
   "metadata": {},
   "source": [
    "# Preprocessing: Categorical Variables\n",
    "\n",
    "## 1. Dropping categories with very few observations\n",
    "\n",
    "Below, we have shown the value counts for `types`, `generation` and `rarity`. One can notice that for some categories within each of these categorical variables, we have very few observations. For instance, there are only two observations where a Pokemon's type is `['Water', 'Fire']`. This may lead to a lot of noise in our dataset. \n",
    "\n",
    "This could negatively impact our training because there is a very insufficient representation of these categories in the dataset.\n",
    "\n",
    "#### Value counts for `types`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0fce8c91-b912-43b2-aac1-8c4e676f4d31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "types\n",
       "['Water']                    3787\n",
       "['Grass']                    3706\n",
       "['Psychic']                  3331\n",
       "['Colorless']                3324\n",
       "['Fighting']                 2853\n",
       "['Fire']                     2251\n",
       "['Lightning']                2177\n",
       "['Darkness']                 1602\n",
       "['Metal']                    1275\n",
       "['Dragon']                    666\n",
       "['Fairy']                     392\n",
       "['Grass', 'Darkness']          21\n",
       "['Fighting', 'Darkness']       19\n",
       "['Lightning', 'Metal']         18\n",
       "['Darkness', 'Metal']          15\n",
       "['Water', 'Darkness']          15\n",
       "['Fire', 'Metal']              13\n",
       "['Water', 'Metal']             12\n",
       "['Psychic', 'Metal']           11\n",
       "['Fire', 'Darkness']           11\n",
       "['Lightning', 'Darkness']      10\n",
       "['Fighting', 'Metal']           8\n",
       "['Psychic', 'Darkness']         6\n",
       "['Fire', 'Grass']               6\n",
       "['Grass', 'Metal']              6\n",
       "['Fire', 'Water']               4\n",
       "['Metal', 'Fighting']           2\n",
       "['Fairy', 'Psychic']            2\n",
       "['Lightning', 'Grass']          2\n",
       "['Fairy', 'Water']              2\n",
       "['Darkness', 'Psychic']         2\n",
       "['Water', 'Fighting']           2\n",
       "['Colorless', 'Psychic']        2\n",
       "['Fire', 'Lightning']           2\n",
       "['Lightning', 'Water']          2\n",
       "['Water', 'Fire']               2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_guide_tcg['types'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0cf4375-f925-4049-a8c4-a8a3fbac7ff4",
   "metadata": {},
   "source": [
    "#### Value counts for `rarity`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "994af2e8-6710-4bba-bdf9-1b165b284c68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rarity\n",
       "Common                       8768\n",
       "Uncommon                     8549\n",
       "Rare                         4782\n",
       "Rare Holo                    3380\n",
       "Promo                        1115\n",
       "Rare Ultra                    776\n",
       "Rare Holo EX                  335\n",
       "Rare Secret                   331\n",
       "Rare Rainbow                  325\n",
       "Rare Holo V                   280\n",
       "Rare Holo GX                  172\n",
       "Rare Shiny                    149\n",
       "Illustration Rare             123\n",
       "Rare Holo VMAX                122\n",
       "Ultra Rare                    102\n",
       "Double Rare                    96\n",
       "Rare Holo LV.X                 57\n",
       "Rare Holo VSTAR                56\n",
       "Special Illustration Rare      53\n",
       "Rare Shiny GX                  35\n",
       "Trainer Gallery Rare Holo      34\n",
       "Hyper Rare                     29\n",
       "Radiant Rare                   28\n",
       "Rare BREAK                     27\n",
       "Rare Prism Star                27\n",
       "Rare Holo Star                 26\n",
       "Rare Prime                     26\n",
       "Rare Shining                   26\n",
       "LEGEND                         18\n",
       "Rare ACE                       13\n",
       "Amazing Rare                   11\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_guide_tcg['rarity'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fe06406-620a-4e3a-a0aa-fd68a474be51",
   "metadata": {},
   "source": [
    "#### Value counts for `generation`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "56605fbb-3b67-4eaf-8d63-e9f847353bc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "generation\n",
       "Eighth     5888\n",
       "Seventh    5015\n",
       "Third      3412\n",
       "Sixth      3148\n",
       "Fifth      2887\n",
       "Fourth     2668\n",
       "Ninth      2282\n",
       "Second     1989\n",
       "First      1507\n",
       "Fourth     1204\n",
       "Other       161\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_guide_tcg['generation'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e343df2-2319-4450-a9d9-a0ef763ec408",
   "metadata": {},
   "source": [
    "### Copying relevant parts of our dataset into a new Dataframe\n",
    "\n",
    "For the purposes of our model described in later cells, we will copy over relevant features from our dataset to a new DataFrame. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "fe7e2890-1c1c-4e49-ba07-e89b7fae8479",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         rarity          types generation  bgs-10-price  graded-price     hp  \\\n",
      "0      Uncommon            NaN      Sixth           NaN           NaN    NaN   \n",
      "1      Uncommon            NaN      Sixth           NaN           NaN    NaN   \n",
      "2      Uncommon            NaN      Sixth           NaN           NaN    NaN   \n",
      "3      Uncommon            NaN      Sixth          58.0         15.11    NaN   \n",
      "4  Rare Holo EX  ['Lightning']      Sixth          62.0         28.09  170.0   \n",
      "\n",
      "   sales-volume  \n",
      "0           5.0  \n",
      "1           1.0  \n",
      "2           1.0  \n",
      "3           7.0  \n",
      "4          81.0  \n"
     ]
    }
   ],
   "source": [
    "subset_df = price_guide_tcg[['rarity', 'types', 'generation', 'bgs-10-price', 'graded-price', 'hp', 'sales-volume']].copy()\n",
    "print(subset_df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28b79e1c-c2e0-48d8-81bf-1acb435aacc3",
   "metadata": {},
   "source": [
    "### Setting thresholds for the minimum counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fc06383c-e97c-4317-91e0-aed8eb54b4da",
   "metadata": {},
   "outputs": [],
   "source": [
    "thresholds = {\n",
    "    'types': 1000  ,        # Minimum count for 'types'\n",
    "    'rarity': 1000  ,    # Minimum count for 'rarity'\n",
    "    'generation':1500\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa27d3be-4733-4bac-9dc1-0d229a2f1ce6",
   "metadata": {},
   "source": [
    "### Removing rows with rare categories for each column based on the thresholds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3fc08df7-eb44-45e2-82c7-5a9856072985",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col, threshold in thresholds.items():\n",
    "    counts = subset_df[col].value_counts()\n",
    "    valid_categories = counts[counts >= threshold].index\n",
    "    subset_df = subset_df[subset_df[col].isin(valid_categories)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f07d07c7",
   "metadata": {},
   "source": [
    "### Merging output classes and removing outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4646f6f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "top1 = subset_df['graded-price'].quantile(0.99)\n",
    "subset_df = subset_df[subset_df['graded-price'] < top1]\n",
    "\n",
    "subset_df['rarity'] = subset_df['rarity'].replace('Common', \"Common/Uncommon\")\n",
    "subset_df['rarity'] = subset_df['rarity'].replace('Uncommon', \"Common/Uncommon\")\n",
    "\n",
    "subset_df['rarity'] = subset_df['rarity'].replace('Rare', \"Rare/Rare Holo\")\n",
    "subset_df['rarity'] = subset_df['rarity'].replace('Rare Holo', \"Rare/Rare Holo\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e357f66f-2054-4412-b5d2-473a556347d9",
   "metadata": {},
   "source": [
    "### Checking if the removal was successul"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c3a0a216-9d98-4be2-b932-3b78f8e043c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "types\n",
       "['Water']        2127\n",
       "['Grass']        1983\n",
       "['Colorless']    1860\n",
       "['Psychic']      1800\n",
       "['Fighting']     1621\n",
       "['Fire']         1275\n",
       "['Lightning']    1163\n",
       "['Darkness']      898\n",
       "['Metal']         687\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_df['types'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "34f1b0d2-fd0e-4385-b2e6-0e2103cfb973",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rarity\n",
       "Common/Uncommon    8067\n",
       "Rare/Rare Holo     5347\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_df['rarity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "100a9d3d-26e1-478f-a482-a5f6b4f87d6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "generation\n",
       "Eighth     2891\n",
       "Third      2416\n",
       "Seventh    1681\n",
       "Second     1522\n",
       "Ninth      1458\n",
       "Fourth     1455\n",
       "Sixth      1251\n",
       "Fifth       740\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_df['generation'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edcbdb62-5ffe-4e60-962b-656ad8f53e85",
   "metadata": {},
   "source": [
    "Thus, we can see that for each of these categorical variables, we have ensured that there is a high density of observations present in our dataset. \n",
    "\n",
    "Next, we will verify the shape of this new DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0e519e4d-5480-4d74-be79-f5d8d1d20425",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13414, 7)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67ac3a0a-e9af-4b9f-9983-307d1fe6b14e",
   "metadata": {},
   "source": [
    "We will also drop any rows with null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "13921f77-bf8f-4666-829a-fdbf86f896b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "subset_df = subset_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e2bfed28-52cc-4411-94ff-4c57eb8c3ef6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12637, 7)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81ebf039-d7eb-4fba-b0b4-6a0d4e58ead1",
   "metadata": {},
   "source": [
    "### First few columns of the new DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2526118b-32bc-4ef1-ba04-1655d3d881ec",
   "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>rarity</th>\n",
       "      <th>types</th>\n",
       "      <th>generation</th>\n",
       "      <th>bgs-10-price</th>\n",
       "      <th>graded-price</th>\n",
       "      <th>hp</th>\n",
       "      <th>sales-volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Common/Uncommon</td>\n",
       "      <td>['Grass']</td>\n",
       "      <td>Sixth</td>\n",
       "      <td>58.0</td>\n",
       "      <td>15.01</td>\n",
       "      <td>70.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Common/Uncommon</td>\n",
       "      <td>['Psychic']</td>\n",
       "      <td>Sixth</td>\n",
       "      <td>36.0</td>\n",
       "      <td>9.28</td>\n",
       "      <td>60.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Common/Uncommon</td>\n",
       "      <td>['Psychic']</td>\n",
       "      <td>Sixth</td>\n",
       "      <td>90.0</td>\n",
       "      <td>16.34</td>\n",
       "      <td>60.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Common/Uncommon</td>\n",
       "      <td>['Grass']</td>\n",
       "      <td>Sixth</td>\n",
       "      <td>58.0</td>\n",
       "      <td>23.00</td>\n",
       "      <td>120.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Common/Uncommon</td>\n",
       "      <td>['Grass']</td>\n",
       "      <td>Sixth</td>\n",
       "      <td>58.0</td>\n",
       "      <td>15.01</td>\n",
       "      <td>120.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             rarity        types generation  bgs-10-price  graded-price  \\\n",
       "9   Common/Uncommon    ['Grass']      Sixth          58.0         15.01   \n",
       "12  Common/Uncommon  ['Psychic']      Sixth          36.0          9.28   \n",
       "13  Common/Uncommon  ['Psychic']      Sixth          90.0         16.34   \n",
       "16  Common/Uncommon    ['Grass']      Sixth          58.0         23.00   \n",
       "17  Common/Uncommon    ['Grass']      Sixth          58.0         15.01   \n",
       "\n",
       "       hp  sales-volume  \n",
       "9    70.0           7.0  \n",
       "12   60.0          29.0  \n",
       "13   60.0          14.0  \n",
       "16  120.0          24.0  \n",
       "17  120.0          12.0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05d4a982-3689-4cad-994c-44f0f59a4ddc",
   "metadata": {},
   "source": [
    "### Examining relationships in our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "77ce027e-aee0-4160-8ec4-980926390c71",
   "metadata": {},
   "outputs": [],
   "source": [
    "type_means = subset_df.groupby('types')['bgs-10-price'].mean().sort_values()\n",
    "rarity_means = subset_df.groupby('rarity')['bgs-10-price'].mean().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "de2fab48-96d4-4bbd-bd58-9e31888fd59d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "\n",
    "plt.subplot(1, 2, 1)  # 1 row, 2 columns, first plot\n",
    "plt.bar(type_means.index, type_means, color='pink', edgecolor='black')\n",
    "plt.title('Average Graded Price by Type', fontsize=12)\n",
    "plt.xlabel('Type', fontsize=12)\n",
    "plt.ylabel('Average Graded Price', fontsize=12)\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "plt.subplot(1, 2, 2)  # 1 row, 2 columns, second plot\n",
    "plt.bar(rarity_means.index, rarity_means, color='skyblue', edgecolor='black')\n",
    "plt.title('Average Graded Price by Rarity', fontsize=12)\n",
    "plt.xlabel('Rarity', fontsize=12)\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2351f82c-7caa-4778-8cbf-774e32deff3e",
   "metadata": {},
   "source": [
    "# Model 1: Random Forest\n",
    "\n",
    "In this model, we are using a **Random Forest Classifier** to predict the **rarity** of Pokemon cards based on a combination of numerical as well as categorical features.\n",
    "\n",
    "#### Features :\n",
    "1. **Categorical Features**:\n",
    "   - `types`: Represents the type/category of the item.\n",
    "   - `generation`: Refers to the generation or version of the item.\n",
    "\n",
    "2. **Numerical Features**:\n",
    "   - `bgs-10-price`: The graded price of the item in mint condition.\n",
    "   - `graded-price`: The general graded price of the item.\n",
    "   - `hp`: Represents the item's hit points (a measure of power or health).\n",
    "   - `sales-volume`: Indicates the volume of sales for the item.\n",
    "\n",
    "#### Target Variable :\n",
    "- **rarity**: The classification label indicating how rare the item is.\n",
    "\n",
    "**We will first conduct our categorical variable encoding:**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "186dfa23-8663-4606-ac59-15e256acb49e",
   "metadata": {},
   "source": [
    "#### One-hot encoding on categorical variables (`types` and `generation`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "65472ea5-38f9-449d-b7b6-4414f4c85529",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoded_df = pd.get_dummies(subset_df, columns=['types', 'generation'], drop_first=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "548ec73f-7cf5-44ba-94c6-0af896e72f4e",
   "metadata": {},
   "source": [
    "#### Defining features and target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "9e881d2c-7f35-4167-b3bf-541ac802d640",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['bgs-10-price', 'graded-price', 'hp', 'sales-volume'] + \\\n",
    "           [col for col in encoded_df.columns if col.startswith('types_') or col.startswith('generation_')]\n",
    "target = 'rarity'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "258be5d4-320d-4a10-8404-dcc567743635",
   "metadata": {},
   "source": [
    "#### Separating features and target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a2958b69-1c4c-4c0d-9988-12dbd7e40cd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = encoded_df[features]\n",
    "y = encoded_df[target]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2869c05-cc52-40d7-a078-252a0480e837",
   "metadata": {},
   "source": [
    "#### Next, we will create our Train-Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b1c1b093-08ab-4408-bf9e-0d8edaffa582",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a564990-ab27-4cf4-b55a-3115b60e7627",
   "metadata": {},
   "source": [
    "### Balancing the Dataset using SMOTE Oversampling\n",
    "\n",
    "To further address the class imbalance mentioned before, we use overampling --- `SMOTE (Synthetic Minority Oversampling Technique)` . \n",
    "\n",
    "It creates synthetic samples for the minority class based on its nearest neighbors to ensure the classes are balanced. For more information, look at: https://imbalanced-learn.org/stable/references/generated/imblearn.over_sampling.SMOTE.html\n",
    "\n",
    "Here, X_train and y_train are resampled to create a balanced dataset (X_resampled and y_resampled) for improved model training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "550bdd72-93c2-4c38-948f-561fd15a7bf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "sm = SMOTE(random_state=42)\n",
    "X_resampled, y_resampled = sm.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a641c2e2-1cae-443d-b6c8-9a4065895b0e",
   "metadata": {},
   "source": [
    "### Model Training\n",
    "The **Random Forest Classifier** is trained on the processed data using k-fold cross-validation (5 splits) to evaluate its performance and reduce overfitting.\n",
    "\n",
    "First, we iniitialize our model with a fixed random seed (random_state=42) for reproducibility. Then we fit the Random Forest model to the resampled dataset (`X_resampled` and `y_resample`d) created using SMOTE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e3b77a59-cb09-4043-882f-aa8b2fcc59bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  --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",
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       "    --sklearn-color-icon: #878787;\n",
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       "\n",
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       "  color: var(--sklearn-color-text);\n",
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       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 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-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=10, min_samples_split=4, n_estimators=220,\n",
       "                       n_jobs=-1, random_state=123)</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=10, min_samples_split=4, n_estimators=220,\n",
       "                       n_jobs=-1, random_state=123)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=10, min_samples_split=4, n_estimators=220,\n",
       "                       n_jobs=-1, random_state=123)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier(random_state=123,n_estimators=220, max_depth=10, min_samples_split=4, n_jobs=-1)\n",
    "model.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6432528-3a41-477f-bc2a-09b30e6568ad",
   "metadata": {},
   "source": [
    "### K - fold validation\n",
    "We did the followng: \n",
    "- Splits the data into 5 folds for training and validation.\n",
    "- We train and evaluate model on different subsets of the data to prevent overfitting\n",
    "- `cv_scores` stores accuracy scores for each fold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "fdb00866-b695-4b45-9ae9-0d8a04c2a068",
   "metadata": {},
   "outputs": [],
   "source": [
    "kf = KFold(n_splits=5, shuffle=True, random_state=123)\n",
    "cv_scores = cross_val_score(model, X_resampled, y_resampled, cv=kf, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90587538-6fe1-4047-974e-8df1679b1329",
   "metadata": {},
   "source": [
    "### Cross Validation Accuracy Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f55e61f0-1fd0-4cbd-8ec0-6732203b918c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.86129568 0.87660989 0.85666805 0.86414624 0.85334441]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(cv_scores)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(range(1, len(cv_scores) + 1), cv_scores, marker='o', linestyle='-', label='CV Scores')\n",
    "plt.axhline(np.mean(cv_scores), color='red', linestyle='--', label='Mean CV Score')\n",
    "plt.title('Cross-Validation Scores')\n",
    "plt.xlabel('Fold')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "531d67e8-67a3-493f-84ea-046f347fa267",
   "metadata": {},
   "source": [
    "Retraining the model on the entire resampled dataset after cross-validation for final predictions and evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "cb4c7107-7f99-410e-9d35-635e6a588296",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\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-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 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-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=10, min_samples_split=4, n_estimators=220,\n",
       "                       n_jobs=-1, random_state=123)</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=10, min_samples_split=4, n_estimators=220,\n",
       "                       n_jobs=-1, random_state=123)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=10, min_samples_split=4, n_estimators=220,\n",
       "                       n_jobs=-1, random_state=123)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efd46e79-c72b-4476-9eb7-9be25392a122",
   "metadata": {},
   "source": [
    "### Training Curve\n",
    "\n",
    "Here, we can analyze the training and testing accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "0955146c-cf2c-409a-806b-41f76ae92ca2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_sizes, train_scores, test_scores = learning_curve(\n",
    "    estimator=model,\n",
    "    X=X_resampled,\n",
    "    y=y_resampled,\n",
    "    train_sizes=np.linspace(0.1, 1.0, 10),\n",
    "    cv=5,\n",
    "    scoring='accuracy',\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "train_mean = np.mean(train_scores, axis=1)\n",
    "train_std = np.std(train_scores, axis=1)\n",
    "test_mean = np.mean(test_scores, axis=1)\n",
    "test_std = np.std(test_scores, axis=1)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(train_sizes, train_mean, 'o-', color='blue', label='Training Accuracy')\n",
    "plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, alpha=0.1, color='blue')\n",
    "plt.plot(train_sizes, test_mean, 'o-', color='green', label='Cross-Validation Accuracy')\n",
    "plt.fill_between(train_sizes, test_mean - test_std, test_mean + test_std, alpha=0.1, color='green')\n",
    "plt.title('Learning Curve')\n",
    "plt.xlabel('Training Set Size')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(loc='best')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "fd26a3aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final training: 0.8957208142916494\n"
     ]
    }
   ],
   "source": [
    "print(f'Final training: {train_mean[-1]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90ba2550-fb48-4515-be16-c8d3e099310e",
   "metadata": {},
   "source": [
    "### Evaluating our Random Forest\n",
    "\n",
    "#### **Classification Report**:\n",
    "- **Class-Specific Metrics**:\n",
    "  - **Common**:\n",
    "    - Precision: 0.82 → 82% of predictions for \"Common\" are correct.\n",
    "    - Recall: 0.85 → 85% of actual \"Common\" instances are identified.\n",
    "    - F1-Score: 0.84 → Balanced performance in terms of precision and recall.\n",
    "  - **Rare**:\n",
    "    - Lower precision (0.70) and recall (0.67) indicate more difficulty in predicting this class accurately.\n",
    "  - **Rare Holo**:\n",
    "    - Strong performance with F1-score of 0.82, suggesting the model predicts this class well.\n",
    "  - **Uncommon**:\n",
    "    - Moderate performance with F1-score of 0.71.\n",
    "\n",
    "- **Overall Metrics**:\n",
    "  - Accuracy: 0.76 → The model correctly predicts 76% of test samples.\n",
    "  - Macro Avg:\n",
    "    - Averages metrics across all classes equally, resulting in 0.76 for precision, recall, and F1-score.\n",
    "  - Weighted Avg:\n",
    "    - Averages metrics, weighted by class occurrences, yielding the same 0.76 because the classes are reasonably balanced.\n",
    "\n",
    "#### **Confusion Matrix**:\n",
    "- Rows: Actual classes.\n",
    "- Columns: Predicted classes.\n",
    "- Example Interpretations:\n",
    "  - \"Common\" (row 1):\n",
    "    - Correctly predicted: 653 instances.\n",
    "    - Misclassified as \"Rare\": 27 instances.\n",
    "    - Misclassified as \"Rare Holo\": 10 instances.\n",
    "    - Misclassified as \"Uncommon\": 76 instances.\n",
    "  - The matrix indicates where the model struggles, such as confusion between \"Rare\" and \"Uncommon\" or \"Rare\" and \"Rare Holo\".\n",
    "\n",
    "#### **Accuracy**:\n",
    "- While a 76.48% accuracy is decent, the confusion in predicting \"Rare\" and \"Uncommon\" suggests room for improvement.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9b1b9a15-a029-496e-ad65-2826a9c16aa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "964d925a-0b73-46ae-8922-0bd12300ae89",
   "metadata": {},
   "source": [
    "### We get the following results from our classification report\n",
    "\n",
    "1. **Accuracy (0.67)**:\n",
    "   - The model correctly predicts the rarity of Pokémon cards **67% of the time**.\n",
    "2. **Macro Average**:\n",
    "   - **Precision, Recall, F1-Score (0.64)**:\n",
    "3. **Weighted Average**:\n",
    "   - **Precision, Recall, F1-Score (0.67)**:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "d0f36836-949b-4a3a-b848-aef8a4cae50b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "Common/Uncommon       0.87      0.86      0.87      1510\n",
      " Rare/Rare Holo       0.80      0.80      0.80      1018\n",
      "\n",
      "       accuracy                           0.84      2528\n",
      "      macro avg       0.83      0.83      0.83      2528\n",
      "   weighted avg       0.84      0.84      0.84      2528\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf6eeff4-92ab-4ee3-bcce-48f897dc67cf",
   "metadata": {},
   "source": [
    "### Confusion Matrix:\n",
    "\n",
    "1. **Common**:\n",
    "   - Most are correctly predicted (756), but some are misclassified as `Uncommon `(91) or other classes.\n",
    "   - The model performs best on `Common` cards because they dominate the dataset and are easier to distinguish.\n",
    "\n",
    "2. **Rare**:\n",
    "   - 316 are correctly predicted, but many are confused with `Rare Holo` (119) and `Uncommon` (125).\n",
    "   - This suggests the model struggles with finer distinctions between \"Rare\" and similar classes.\n",
    "\n",
    "3. **Rare Holo**:\n",
    "   - 300 are correctly predicted, but a significant number are misclassified as \"Rare\" (125).\n",
    "   - This confusion is likely due to overlapping features between \"Rare Holo\" and \"Rare.\"\n",
    "\n",
    "4. **Uncommon**:\n",
    "   - 334 are correctly identified, but there is confusion with `Common` (91) and \"Rare\" (116).\n",
    "   - This indicates the model has difficulty separating `Uncommon` from other classes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a7aaefa2-2f54-436c-b013-feba8733d424",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1306  204]\n",
      " [ 199  819]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1684be29-29a2-4f39-8cc6-111d5e6487ee",
   "metadata": {},
   "source": [
    "### Final Accuracy\n",
    "\n",
    "As mentioned before, the model correctly predicts the rarity of Pokémon cards for 67 cards oout of a 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "276f8b96-733b-4faf-b064-a7dcf2b7a1e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8405854430379747\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {accuracy}\")\n"
   ]
  }
 ],
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