solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wine Classifier with KNN 🍷\n",
    "Project to predict wine quality using K-Nearest Neighbors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Load the data\n",
    "url = \"https://raw.githubusercontent.com/rosinni/k-nearest-neighbors-project-tutorial/refs/heads/main/winequality-red.csv\"\n",
    "df = pd.read_csv(url, sep=\";\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "327ced73",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the 'label' column by categorizing 'quality'\n",
    "def quality_to_label(q):\n",
    "    if q <= 4:\n",
    "        return 0  # low\n",
    "    elif q <= 6:\n",
    "        return 1  # medium\n",
    "    else:\n",
    "        return 2  # high\n",
    "\n",
    "df['label'] = df['quality'].apply(quality_to_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. Separate independent variables (X) and the target variable (y)\n",
    "X = df.drop([\"label\", \"quality\"], axis=1)\n",
    "y = df[\"label\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. Split the data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4. Scale the data\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.84375\n",
      "\n",
      "Matriz de confusión:\n",
      " [[  0  11   0]\n",
      " [  1 250  11]\n",
      " [  0  27  20]]\n",
      "\n",
      "Reporte de clasificación:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00        11\n",
      "           1       0.87      0.95      0.91       262\n",
      "           2       0.65      0.43      0.51        47\n",
      "\n",
      "    accuracy                           0.84       320\n",
      "   macro avg       0.50      0.46      0.47       320\n",
      "weighted avg       0.81      0.84      0.82       320\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 5. Train the model with initial k = 5\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(X_train_scaled, y_train)\n",
    "y_pred = knn.predict(X_test_scaled)\n",
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
    "print(\"\\nConfusion Matrix:\\n\", confusion_matrix(y_test, y_pred))\n",
    "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6. Search for the best value of k\n",
    "k_range = range(1, 21)\n",
    "accuracies = []\n",
    "\n",
    "for k in k_range:\n",
    "    model = KNeighborsClassifier(n_neighbors=k)\n",
    "    model.fit(X_train_scaled, y_train)\n",
    "    y_k_pred = model.predict(X_test_scaled)\n",
    "    accuracies.append(accuracy_score(y_test, y_k_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 7. Plot accuracy vs k\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(k_range, accuracies, marker='o')\n",
    "plt.title(\"Accuracy vs Value of k\")\n",
    "plt.xlabel(\"k\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mejor valor de k: 14\n"
     ]
    }
   ],
   "source": [
    "# 8. Choose the best k\n",
    "best_k = k_range[accuracies.index(max(accuracies))]\n",
    "print(f\"Best value of k: {best_k}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=14)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=14)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=14)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 9. Train the final model\n",
    "final_knn = KNeighborsClassifier(n_neighbors=best_k)\n",
    "final_knn.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 10. Function to predict wine quality\n",
    "def predict_wine_quality(features):\n",
    "    features_scaled = scaler.transform([features])\n",
    "    pred = final_knn.predict(features_scaled)[0]\n",
    "    if pred == 0:\n",
    "        return \"This wine is likely of low quality 🍷\"\n",
    "    elif pred == 1:\n",
    "        return \"This wine is likely of medium quality 🍷\"\n",
    "    else:\n",
    "        return \"This wine is likely of high quality 🍷\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Example prediction: This wine is likely of medium quality 🍷\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/vscode/.local/lib/python3.11/site-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# 11. Test the function\n",
    "result = predict_wine_quality([7.4, 0.7, 0.0, 1.9, 0.076,\n",
    "                               11.0, 34.0, 0.9978, 3.51, 0.56, 9.4])\n",
    "print(\"\\nExample prediction:\", result)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}