AIM-PIbd-32-Tyorkin-D-V/lab_3/lab3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Лабораторная работа №3\n",
    "\n",
    "*Вариант задания:* Товары Jio Mart (вариант - 23)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для выполнения лабораторной работы по датасету 'jio mart product items', приведу пример двух бизнес-целей:\n",
    "\n",
    "### Бизнес-цели:\n",
    "\n",
    "1. **Оптимизация цен на товары**\n",
    "   \n",
    "   **Цель:** Снизить издержки и увеличить продажи за счет оптимизации цен на товары.\n",
    "\n",
    "   **Техническая цель:** Создать модель машинного обучения, которая будет прогнозировать, является ли товар излишне дорогим для свой категории или нет.\n",
    "   \n",
    "\n",
    "2. **Распределение товаров по категориям**\n",
    "   \n",
    "   **Цель:** Оптимизировать распределение товаров по категориям.\n",
    "\n",
    "    **Техническая цель:** Создать модель машинного обучения, которая будет прогнозировать оптимальные цены на товары на основе их категорий, подкатегорий и текущих цен.\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['category', 'sub_category', 'href', 'items', 'price'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "import seaborn as sns\n",
    "\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\"..//static//csv//jio_mart_items.csv\")\n",
    "\n",
    "# Срез данных, первые 15000 строк\n",
    "df = df.iloc[:15000]\n",
    "\n",
    "# Вывод\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>category</th>\n",
       "      <th>sub_category</th>\n",
       "      <th>href</th>\n",
       "      <th>items</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Groceries</td>\n",
       "      <td>Fruits &amp; Vegetables</td>\n",
       "      <td>https://www.jiomart.com/c/groceries/fruits-veg...</td>\n",
       "      <td>Fresh Dates (Pack) (Approx 450 g - 500 g)</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Groceries</td>\n",
       "      <td>Fruits &amp; Vegetables</td>\n",
       "      <td>https://www.jiomart.com/c/groceries/fruits-veg...</td>\n",
       "      <td>Tender Coconut Cling Wrapped (1 pc) (Approx 90...</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Groceries</td>\n",
       "      <td>Fruits &amp; Vegetables</td>\n",
       "      <td>https://www.jiomart.com/c/groceries/fruits-veg...</td>\n",
       "      <td>Mosambi 1 kg</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Groceries</td>\n",
       "      <td>Fruits &amp; Vegetables</td>\n",
       "      <td>https://www.jiomart.com/c/groceries/fruits-veg...</td>\n",
       "      <td>Orange Imported 1 kg</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Groceries</td>\n",
       "      <td>Fruits &amp; Vegetables</td>\n",
       "      <td>https://www.jiomart.com/c/groceries/fruits-veg...</td>\n",
       "      <td>Banana Robusta 6 pcs (Box) (Approx 800 g - 110...</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    category         sub_category  \\\n",
       "0  Groceries  Fruits & Vegetables   \n",
       "1  Groceries  Fruits & Vegetables   \n",
       "2  Groceries  Fruits & Vegetables   \n",
       "3  Groceries  Fruits & Vegetables   \n",
       "4  Groceries  Fruits & Vegetables   \n",
       "\n",
       "                                                href  \\\n",
       "0  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
       "1  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
       "2  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
       "3  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
       "4  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
       "\n",
       "                                               items  price  \n",
       "0          Fresh Dates (Pack) (Approx 450 g - 500 g)  109.0  \n",
       "1  Tender Coconut Cling Wrapped (1 pc) (Approx 90...   49.0  \n",
       "2                                       Mosambi 1 kg   69.0  \n",
       "3                               Orange Imported 1 kg  125.0  \n",
       "4  Banana Robusta 6 pcs (Box) (Approx 800 g - 110...   44.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Для наглядности\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>373.427633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>463.957949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>123.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>250.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>446.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price\n",
       "count  15000.000000\n",
       "mean     373.427633\n",
       "std      463.957949\n",
       "min        5.000000\n",
       "25%      123.000000\n",
       "50%      250.000000\n",
       "75%      446.000000\n",
       "max    14999.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Описание данных (основные статистические показатели)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category        0\n",
      "sub_category    0\n",
      "href            0\n",
      "items           0\n",
      "price           0\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "category        False\n",
       "sub_category    False\n",
       "href            False\n",
       "items           False\n",
       "price           False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Процент пропущенных значений признаков\n",
    "for i in df.columns:\n",
    "    null_rate = df[i].isnull().sum() / len(df) * 100\n",
    "    if null_rate > 0:\n",
    "        print(f'{i} Процент пустых значений: %{null_rate:.2f}')\n",
    "\n",
    "# Проверка на пропущенные данные\n",
    "print(df.isnull().sum())\n",
    "\n",
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Нет пропущенных данных."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Разбиваем на выборки (обучающую, тестовую, контрольную)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размеры выборок:\n",
      "Обучающая выборка: 9000 записей\n",
      "Валидационная выборка: 3000 записей\n",
      "Тестовая выборка: 3000 записей\n"
     ]
    },
    {
     "data": {
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+Q4cONQzDMN544w1DkrFgwYJieZf0Go8cOdKoVq2akZ2dfcX5AsDfUUm1psi7775rODo6Gjt37rTav2TJEkOSsWvXLsMwDOPo0aOGo6OjMWDAgGKfpS6tQ0Uurw+XWrNmjSHJePHFF632P/DAA4aDg4Px008/mftK+sx8zz33GE2bNjUf5+fnGzk5OVYxZ8+eNXx8fIzHHnvM3Hfx4kXD0dHRGDlypFXstXzGNQzrGlPkkUceMapVq2Y+vpbP388995zVz6PocY8ePYz8/Hxzf1H9K6pjubm5Rp06dYw77rjDuHjxohm3bt06Q5IRFRVlNSdJxpNPPmnuKywsNMLCwgxnZ2fj999/t8p3+fLlRmFhoREeHm5Uq1bNSEhIKJb39byXBC7HsiqwO+fOnZMk1ahR46pxReMWi8Xcd9ddd6lLly7mYzc3Nz3xxBNKS0sz/5obHBwsPz8/q7VUDx06pAMHDlhdKVenTh1lZGQoNzf3qnm4ubmZ/87Ly9Mff/yhhg0bysvLq8S/IGdlZSkjI0NxcXEKDQ1Vs2bNzLGaNWvq0UcfVWJiotLT06963mt1aX4XLlzQ6dOndc8998gwDH377bfm2Llz5+To6Fjqq72kv9aqPX36tH7++WfNmTNHVapUsfp5FDl9+rROnz5d4hV3JbmWn+uXX34pX19f80pySapatarGjh2r8+fPa/v27VbH7N+/v9XNSu6++261a9dOX375ZbHz//zzzwoLC1PLli317rvvytHR+lfzpa9xdna2Tp8+rfbt20vSNV9FAAC2sGjRIsXFxSkuLk7vvfeeunXrpscff1yffvqpVdylv+fOnj2rrKwsderUqcTfcUV1s+gqpWvN448//tBzzz13xZjs7Gy5urr+z2NdT66lNWXKFLVu3VoPPvig1f5NmzYpNzdX48aNs6oVI0aMkIeHh9U33qS/rsoqqolF259//lniOfPy8orFllRHL51/0TcDunTpop9//llZWVnm2Llz53TLLbeU+ttNhYWFZh5JSUl65513VLdu3WI3aCvK+48//lB+fv41HXvYsGGqWbOm+bhRo0bq16+fYmNjVVBQoIKCAm3cuFH9+/c37z0jSXXr1tUjjzyir7/+2ur9ofTX8iiXXrU/evRoOTk5lVj3P//8cz3xxBOaOHFiiUvvXPoanzt3zrwfzp9//qkffvjhmuYIADeD1atXKzAwUE2aNLGqT927d5ckc2mSNWvWqLCwUFFRUcU+S11vHfryyy9VpUoVjR071mr/008/LcMw9NVXX1nt//PPP3X69GmlpaXpk08+0XfffacePXqY41WqVJGzs7Okv2rbmTNnlJ+fr7Zt21q9d7hw4YIKCwut6tP1ysnJ0enTp81+wJYtW6xyuTzns2fPXvNa7xMmTLD6ZvzgwYPl4+NjvvfYv3+/MjIy9MQTT1i9nwoLC1OTJk2KvUeRZFUDi5ajy83N1aZNm4rFTpw4UTExMfroo4909913FxuviPdnuHnRHIfdKWp6FzXJr6SkJnqTJk2KxRV9SCtau9PR0VHh4eFas2aN+QE0JiZGrq6uVh9y77nnHmVnZ2vq1Kn69ddfzUJ/uYsXLyoqKspc76xWrVqqXbu2MjMzrT6EFunfv798fHxksVjUuHHj/5nvjUpNTdWjjz4qb29vubu7q3bt2maj+dL8goKCVFhYqKeeekrHjh0zi/H1aN26tWrXrq3bb79dy5Yt08KFC4sVxgsXLqh27dqqXbu23NzcVK9ePc2bN++qx72Wn+svv/yiRo0aFXuzVRT3yy+/WO1v1KhRsWP+85//LPa6X7hwQaGhoUpPT9eZM2dKfPN25swZPfXUU/Lx8ZGbm5tq166tgIAASSrxvwEAqCzuvvtuBQcHKzg4WOHh4Vq/fr2aNm1qfvgpsm7dOrVv316urq7y9vZW7dq1tXjx4hJ/xxWt43kt629Kf/2efPnllzVhwoQrLmtVUFCgzMxMeXp6/s/jXU+upfH1119r7dq1mjFjRrGaUFRrLq/vzs7Ouu2224rVoh9++MGsiUXblf5AsHHjxmKxb7/9drG4Xbt2KTg4WNWrV5eXl5dq166t//73v5KK1/34+Hi98cYbOnnypE6fPm1+nf1anDhxwsyjVatWOnbsmD755JNiP/eivGvVqiVXV1e1bt3aXF/2ckWv55XqftEf+X///Xf9+eefV3wfVVhYqBMnTljtv7zuu7u7q27dusXqflJSkh5++GEVFBSUuFar9NeSLgMGDJCnp6c8PDxUu3Zt8wIL6j4Ae3L06FElJycXq09FS2BlZGRIko4dOyZHR0c1bdr0hs/5yy+/yM/Pr9jFdFf63Ddr1izVrl1bdevW1QMPPKBOnTppxowZVjErV65UixYt5Orqqpo1a6p27dpav3691e/0mjVrqlGjRnrrrbe0ceNGZWRk6PTp0yWurX4lH3zwgWrXri0fHx+FhITI39+/2H26pL+W9apdu7a8vb1VrVo1hYWF6ejRoyUe80q1s0qVKmrUqJHV52Wp+HuUoude/ro5Ojpa/QFakvlzvbx2vvHGG3r11Vcl6Yo9hPJ+f4abG2uOw+54enqqbt26OnDgwFXjDhw4oH/84x/y8PCQZP2XyP9lyJAhmjVrltasWaOHH35Yq1atUp8+faw+dPfr10+PPfaYZs2addX1I5988kktX75c48aNU1BQkDw9PeXg4KBBgwapsLCwWPzs2bPVqFEj3Xfffdecb2kVFBTo3nvv1ZkzZzR58mQ1adJE1atX18mTJ/Xoo49a5Tdo0CB98803WrBgwTWvN3q59957Tz4+PsrOztaWLVsUGRkpV1dXq5tbubq6au3atZL++gPHsmXLNG7cONWtW1cPPfRQsWNez8+1PJw+fVrVq1fX2rVr1b9/f0VHRxdrXDz00EPavXu3Jk6cqJYtW8rd3V2FhYXq2bNnif8NAEBl5ejoqG7dumnevHk6evSomjVrpp07d6pfv37q3LmzXn/9ddWtW1dVq1bV8uXLtWrVqmLHSEtLk7u7u6pXr35N55wxY4YcHR01ceLEK97IODU1VYWFhSXeKPtS15traUyePFmhoaHq3r17iTcOvR4NGjTQm2++abVv9erVJdbhdu3a6cUXX7Tat3DhQn3++efm42PHjqlHjx5q0qSJXnvtNfn7+8vZ2Vlffvml5syZY1WToqOjdfLkSY0aNapUufv4+Oi9996T9FdDeNmyZerZs6e+/vprNW/evMS8T506pRkzZmjAgAFKTk4udkxb13xJ+u6779SrVy/16NFDEydO1L///W+r9cYzMzPVpUsXeXh4aPr06br99tvl6uqqb775RpMnT6buA7ArhYWFat68uV577bUSx/39/Ss4o+IGDx6sIUOGqLCwUD///LNeeOEF9enTR5s2bZKDg4Pee+89Pfroo+rfv78mTpyoOnXqqEqVKoqOjtaxY8esjvXhhx8qPDxcoaGhVvuv9T1PSEiIJk6cKOmv+3XMmDFD3bp10/79+61qYEREhB588EEVFBTo8OHDmjZtmvr3719pa+eePXv00ksvad++fRo/frx69uxp3ndGqpj3Z7i50RyHXerTp4/efPNNff311+rYsWOx8Z07d+r48eMaOXKkuS8gIEBHjhwpFlv09dZLP1DfcccdatWqlWJiYnTrrbcqNTVVCxYsKPbct99+W1FRUTp27Jj5Yefee++1ivn44481dOhQ8y+l0l9f/b70DtiXatOmjbp06SJ3d/drzre0Dh48qB9//FErV67UkCFDzP1xcXHFYh0dHTV79mwdPHhQKSkpev3115Wenl7spmxX06FDBzPvPn36KDk5WdHR0VbN8SpVqljdoDMsLEze3t6KjY0tsTl+rT/X+vXr68CBAyosLLS6erworn79+lbPL+kv7z/++GOx171atWqKjY1VkyZNNH78eL388st66KGHzCsTzp49q82bN+v555+3ujHclf6yDwCVXdHSF0VXEX/yySdydXXVhg0brJZKWb58eYnP//7774strXElp06d0rx58xQdHa0aNWpcsTm+f/9+SVLbtm2verzrzfV6rVmzRvHx8Vf8CnBRrTly5IjV1Va5ublKSUkpdoPq6tWrF9uXlJRU4rFr1apVLPbSG51Jf93QPCcnR1988YXq1atn7i/6WvulatasqXfffVfNmjVTx44dNXLkSG3cuPGabyjp6upqlU+/fv3k7e2thQsX6o033rhi3g0bNlSHDh20Y8cOqxwlmd+6ulLdr169uvlhu1q1aleMc3R0LNaQOXr0qLp162Y+Pn/+vH777Tf17t3bKq558+ZavXq13NzctHr1akVEROjAgQPmV9C3bdumP/74Q59++qnVzdNTUlKu8EoBwM3r9ttvN5cpudryKLfffrsKCwv1/fffW93gsjTq16+vTZs26dy5c1ZXj1/pc99tt91mVYc8PT31yCOPaM+ePQoKCtLHH3+s2267TZ9++qnVHEr6JlerVq305ptvqlOnTpo+fbrat2+vWbNmadeuXdeUe926da1yady4se655x7zor0ijRo1MuNCQ0P1559/6v/+7/+Umppa7JiX1s5L33sUFhbq6NGjatWqldXrcuTIEXPZmyJHjhwp9roV/TGh6Gpx6a/Py1LxXsVjjz2m//73vzp16pSaNm2q8ePHmzc8lcr//RlufiyrArs0ceJEubm5aeTIkcU+KJ85c0ajRo1StWrVzL+6SlLv3r21d+9e7d6929yXnZ2txYsXy9fXV23atLE6zuDBg7Vx40bNnTtXNWvWVK9evUrMpX79+urevbv5tfPLValSpdg6YAsWLFBBQcEV5+fg4KCQkBBt2LBBhw8ftprbypUr1bZt2yt+tfx6FK05dml+hmFccRmTBQsWaMuWLYqJiVFwcLA6dOhwQ+e/ePHi//yaWVFul66Pdqlr/bn27t1baWlp+vDDD824/Px8LViwQO7u7sXWPl+zZo1OnjxpPt67d68SEhK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      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Разделение признаков (features) и целевой переменной (target)\n",
    "X = df.drop(columns=['price'])  # Признаки (все столбцы, кроме 'price')\n",
    "y = df['price']  # Целевая переменная (price)\n",
    "\n",
    "# Разбиение на обучающую (60%), валидационную (20%) и тестовую (20%) выборки\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n",
    "\n",
    "# Проверка размеров выборок\n",
    "print(f\"Размеры выборок:\")\n",
    "print(f\"Обучающая выборка: {X_train.shape[0]} записей\")\n",
    "print(f\"Валидационная выборка: {X_val.shape[0]} записей\")\n",
    "print(f\"Тестовая выборка: {X_test.shape[0]} записей\")\n",
    "\n",
    "# Визуализация распределения цен в каждой выборке\n",
    "plt.figure(figsize=(18, 6))\n",
    "\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.hist(y_train, bins=30, color='blue', alpha=0.7)\n",
    "plt.title('Обучающая выборка')\n",
    "plt.xlabel('Цена')\n",
    "plt.ylabel('Количество')\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.hist(y_val, bins=30, color='green', alpha=0.7)\n",
    "plt.title('Валидационная выборка')\n",
    "plt.xlabel('Цена')\n",
    "plt.ylabel('Количество')\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.hist(y_test, bins=30, color='red', alpha=0.7)\n",
    "plt.title('Тестовая выборка')\n",
    "plt.xlabel('Цена')\n",
    "plt.ylabel('Количество')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Балансировка выборок**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размеры выборок:\n",
      "Обучающая выборка: 9000 записей\n",
      "Валидационная выборка: 3000 записей\n",
      "Тестовая выборка: 3000 записей\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Разделение признаков (features) и целевой переменной (target)\n",
    "X = df.drop(columns=['price'])  # Признаки (все столбцы, кроме 'price')\n",
    "y = df['price']  # Целевая переменная (цена)\n",
    "\n",
    "# Применение one-hot encoding для категориальных признаков\n",
    "X = pd.get_dummies(X, drop_first=True)\n",
    "\n",
    "# Разбиение на обучающую (60%), валидационную (20%) и тестовую (20%) выборки\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n",
    "\n",
    "# Проверка размеров выборок\n",
    "print(f\"Размеры выборок:\")\n",
    "print(f\"Обучающая выборка: {X_train.shape[0]} записей\")\n",
    "print(f\"Валидационная выборка: {X_val.shape[0]} записей\")\n",
    "print(f\"Тестовая выборка: {X_test.shape[0]} записей\")\n",
    "\n",
    "# Удаление выбросов (цены выше 95-го процентиля)\n",
    "upper_limit = y_train.quantile(0.95)\n",
    "X_train = X_train[y_train <= upper_limit]\n",
    "y_train = y_train[y_train <= upper_limit]\n",
    "\n",
    "# Логарифмическое преобразование целевой переменной\n",
    "y_train_log = np.log1p(y_train)\n",
    "y_val_log = np.log1p(y_val)\n",
    "y_test_log = np.log1p(y_test)\n",
    "\n",
    "# Стандартизация признаков\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_val_scaled = scaler.transform(X_val)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Визуализация распределения цен в сбалансированной выборке\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(y_train_log, bins=30, color='orange', alpha=0.7)\n",
    "plt.title('Сбалансированная обучающая выборка (логарифмическое преобразование)')\n",
    "plt.xlabel('Логарифм цены')\n",
    "plt.ylabel('Количество')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Унитарное кодирование категориальных признаков**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Данные до унитарного кодирования:\n",
      "    category         sub_category  \\\n",
      "0  Groceries  Fruits & Vegetables   \n",
      "1  Groceries  Fruits & Vegetables   \n",
      "2  Groceries  Fruits & Vegetables   \n",
      "3  Groceries  Fruits & Vegetables   \n",
      "4  Groceries  Fruits & Vegetables   \n",
      "\n",
      "                                                href  \\\n",
      "0  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
      "1  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
      "2  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
      "3  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
      "4  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
      "\n",
      "                                               items  price  \n",
      "0          Fresh Dates (Pack) (Approx 450 g - 500 g)  109.0  \n",
      "1  Tender Coconut Cling Wrapped (1 pc) (Approx 90...   49.0  \n",
      "2                                       Mosambi 1 kg   69.0  \n",
      "3                               Orange Imported 1 kg  125.0  \n",
      "4  Banana Robusta 6 pcs (Box) (Approx 800 g - 110...   44.0  \n",
      "\n",
      "Данные после унитарного кодирования:\n",
      "   price  sub_category_Fruits & Vegetables  sub_category_Premium Fruits  \\\n",
      "0  109.0                              True                        False   \n",
      "1   49.0                              True                        False   \n",
      "2   69.0                              True                        False   \n",
      "3  125.0                              True                        False   \n",
      "4   44.0                              True                        False   \n",
      "\n",
      "   sub_category_Snacks & Branded Foods  sub_category_Staples  \\\n",
      "0                                False                 False   \n",
      "1                                False                 False   \n",
      "2                                False                 False   \n",
      "3                                False                 False   \n",
      "4                                False                 False   \n",
      "\n",
      "   href_https://www.jiomart.com/c/groceries/dairy-bakery/bakery-snacks/281  \\\n",
      "0                                              False                         \n",
      "1                                              False                         \n",
      "2                                              False                         \n",
      "3                                              False                         \n",
      "4                                              False                         \n",
      "\n",
      "   href_https://www.jiomart.com/c/groceries/dairy-bakery/batter-chutney/407  \\\n",
      "0                                              False                          \n",
      "1                                              False                          \n",
      "2                                              False                          \n",
      "3                                              False                          \n",
      "4                                              False                          \n",
      "\n",
      "   href_https://www.jiomart.com/c/groceries/dairy-bakery/breads-and-buns/267  \\\n",
      "0                                              False                           \n",
      "1                                              False                           \n",
      "2                                              False                           \n",
      "3                                              False                           \n",
      "4                                              False                           \n",
      "\n",
      "   href_https://www.jiomart.com/c/groceries/dairy-bakery/cakes-muffins/125  \\\n",
      "0                                              False                         \n",
      "1                                              False                         \n",
      "2                                              False                         \n",
      "3                                              False                         \n",
      "4                                              False                         \n",
      "\n",
      "   href_https://www.jiomart.com/c/groceries/dairy-bakery/cheese/1569  ...  \\\n",
      "0                                              False                  ...   \n",
      "1                                              False                  ...   \n",
      "2                                              False                  ...   \n",
      "3                                              False                  ...   \n",
      "4                                              False                  ...   \n",
      "\n",
      "   items_sUpazon Instant Idli Mix, 400g (Ragi Idli)  \\\n",
      "0                                             False   \n",
      "1                                             False   \n",
      "2                                             False   \n",
      "3                                             False   \n",
      "4                                             False   \n",
      "\n",
      "   items_sUpazon Instant Idli Mix, 400g (Rawa Idli)  \\\n",
      "0                                             False   \n",
      "1                                             False   \n",
      "2                                             False   \n",
      "3                                             False   \n",
      "4                                             False   \n",
      "\n",
      "   items_shivanyamart Roasted Peanuts - 1 kg  \\\n",
      "0                                      False   \n",
      "1                                      False   \n",
      "2                                      False   \n",
      "3                                      False   \n",
      "4                                      False   \n",
      "\n",
      "   items_shivanyamart Special Bombay Mixer - 200 g (Pack of 2)  \\\n",
      "0                                              False             \n",
      "1                                              False             \n",
      "2                                              False             \n",
      "3                                              False             \n",
      "4                                              False             \n",
      "\n",
      "   items_tasty tongue - Haldi ka Achar with Lime decoction, 190 gms Glass Jar  \\\n",
      "0                                              False                            \n",
      "1                                              False                            \n",
      "2                                              False                            \n",
      "3                                              False                            \n",
      "4                                              False                            \n",
      "\n",
      "   items_tasty tongue - Homemade Baingan Ka Achar, Certified, No Added preservatives |190 GMS Glass Jar  \\\n",
      "0                                              False                                                      \n",
      "1                                              False                                                      \n",
      "2                                              False                                                      \n",
      "3                                              False                                                      \n",
      "4                                              False                                                      \n",
      "\n",
      "   items_tasty tongue - Homemade Heeng wala Nimbu ka teekha Achar , Certified | 190 GMS Glass Jar  \\\n",
      "0                                              False                                                \n",
      "1                                              False                                                \n",
      "2                                              False                                                \n",
      "3                                              False                                                \n",
      "4                                              False                                                \n",
      "\n",
      "   items_tasty tongue - Homemade Kacche Aam ka Achar, Certified | 350 GMS Glass Jar  \\\n",
      "0                                              False                                  \n",
      "1                                              False                                  \n",
      "2                                              False                                  \n",
      "3                                              False                                  \n",
      "4                                              False                                  \n",
      "\n",
      "   items_tasty tongue - Homemade Khatta Meetha Nimbu ka Achar ,Certified | 350 GMS Glass Jar  \\\n",
      "0                                              False                                           \n",
      "1                                              False                                           \n",
      "2                                              False                                           \n",
      "3                                              False                                           \n",
      "4                                              False                                           \n",
      "\n",
      "   items_xThe Whole Truth - 71% Dark Chocolate Combo - (Pack of 3) - 1 - 71% ,1 - Sea-Salt , 1 - Orange - No Added Sugar - Sweetened Only with Dates - 71% Cocoa - 29% Dates  \n",
      "0                                              False                                                                                                                          \n",
      "1                                              False                                                                                                                          \n",
      "2                                              False                                                                                                                          \n",
      "3                                              False                                                                                                                          \n",
      "4                                              False                                                                                                                          \n",
      "\n",
      "[5 rows x 14769 columns]\n"
     ]
    }
   ],
   "source": [
    "print(\"Данные до унитарного кодирования:\")\n",
    "print(df.head())\n",
    "\n",
    "# Применение унитарного кодирования для категориальных признаков\n",
    "df_encoded = pd.get_dummies(df, drop_first=True)\n",
    "\n",
    "print(\"\\nДанные после унитарного кодирования:\")\n",
    "print(df_encoded.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Дискретизация числовых признаков**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Данные до дискретизации:\n",
      "    category         sub_category  \\\n",
      "0  Groceries  Fruits & Vegetables   \n",
      "1  Groceries  Fruits & Vegetables   \n",
      "2  Groceries  Fruits & Vegetables   \n",
      "3  Groceries  Fruits & Vegetables   \n",
      "4  Groceries  Fruits & Vegetables   \n",
      "\n",
      "                                                href  \\\n",
      "0  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
      "1  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
      "2  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
      "3  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
      "4  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
      "\n",
      "                                               items  price price_bins  \n",
      "0          Fresh Dates (Pack) (Approx 450 g - 500 g)  109.0    100-500  \n",
      "1  Tender Coconut Cling Wrapped (1 pc) (Approx 90...   49.0      0-100  \n",
      "2                                       Mosambi 1 kg   69.0      0-100  \n",
      "3                               Orange Imported 1 kg  125.0    100-500  \n",
      "4  Banana Robusta 6 pcs (Box) (Approx 800 g - 110...   44.0      0-100  \n",
      "\n",
      "Данные после дискретизации:\n",
      "   price price_bins\n",
      "0  109.0    100-500\n",
      "1   49.0      0-100\n",
      "2   69.0      0-100\n",
      "3  125.0    100-500\n",
      "4   44.0      0-100\n"
     ]
    }
   ],
   "source": [
    "print(\"Данные до дискретизации:\")\n",
    "print(df.head())\n",
    "\n",
    "# Определение интервалов и меток для дискретизации\n",
    "bins = [0, 100, 500, 1000, 5000, float('inf')]\n",
    "labels = ['0-100', '100-500', '500-1000', '1000-5000', '5000+']\n",
    "\n",
    "# Применение дискретизации\n",
    "df['price_bins'] = pd.cut(df['price'], bins=bins, labels=labels, right=False)\n",
    "\n",
    "print(\"\\nДанные после дискретизации:\")\n",
    "print(df[['price', 'price_bins']].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**«Ручной» синтез признаков**\n",
    "\n",
    "Создание новых признаков на основе экспертных знаний и логики предметной области. К примеру, для данных о продаже домов можно создать признак цена за единицу товара."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Данные до синтеза признака:\n",
      "    category         sub_category  \\\n",
      "0  Groceries  Fruits & Vegetables   \n",
      "1  Groceries  Fruits & Vegetables   \n",
      "2  Groceries  Fruits & Vegetables   \n",
      "3  Groceries  Fruits & Vegetables   \n",
      "4  Groceries  Fruits & Vegetables   \n",
      "\n",
      "                                                href  \\\n",
      "0  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
      "1  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
      "2  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
      "3  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
      "4  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
      "\n",
      "                                               items  price price_bins  \n",
      "0          Fresh Dates (Pack) (Approx 450 g - 500 g)  109.0    100-500  \n",
      "1  Tender Coconut Cling Wrapped (1 pc) (Approx 90...   49.0      0-100  \n",
      "2                                       Mosambi 1 kg   69.0      0-100  \n",
      "3                               Orange Imported 1 kg  125.0    100-500  \n",
      "4  Banana Robusta 6 pcs (Box) (Approx 800 g - 110...   44.0      0-100  \n",
      "\n",
      "Данные после синтеза признака 'relative_price':\n",
      "   price   category  relative_price\n",
      "0  109.0  Groceries        0.291891\n",
      "1   49.0  Groceries        0.131217\n",
      "2   69.0  Groceries        0.184775\n",
      "3  125.0  Groceries        0.334737\n",
      "4   44.0  Groceries        0.117827\n"
     ]
    }
   ],
   "source": [
    "# Проверка первых строк данных\n",
    "print(\"Данные до синтеза признака:\")\n",
    "print(df.head())\n",
    "\n",
    "# Вычисление средней цены по категориям\n",
    "mean_price_by_category = df.groupby('category')['price'].transform('mean')\n",
    "\n",
    "# Создание нового признака 'relative_price' (относительная цена)\n",
    "df['relative_price'] = df['price'] / mean_price_by_category\n",
    "\n",
    "# Проверка первых строк данных после синтеза признака\n",
    "print(\"\\nДанные после синтеза признака 'relative_price':\")\n",
    "print(df[['price', 'category', 'relative_price']].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Масштабирование признаков на основе нормировки и стандартизации**\n",
    "\n",
    "Масштабирование признаков - это процесс преобразования числовых признаков таким образом, чтобы они имели одинаковый масштаб. Это важно для многих алгоритмов машинного обучения, которые чувствительны к масштабу признаков, таких как линейная регрессия, метод опорных векторов (SVM) и нейронные сети."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Данные до масштабирования:\n",
      "   price  relative_price\n",
      "0  109.0        0.291891\n",
      "1   49.0        0.131217\n",
      "2   69.0        0.184775\n",
      "3  125.0        0.334737\n",
      "4   44.0        0.117827\n",
      "\n",
      "Данные после нормировки:\n",
      "      price  relative_price\n",
      "0  0.006936        0.006936\n",
      "1  0.002935        0.002935\n",
      "2  0.004268        0.004268\n",
      "3  0.008003        0.008003\n",
      "4  0.002601        0.002601\n",
      "\n",
      "Данные после стандартизации:\n",
      "      price  relative_price\n",
      "0 -0.569958       -0.569958\n",
      "1 -0.699284       -0.699284\n",
      "2 -0.656175       -0.656175\n",
      "3 -0.535471       -0.535471\n",
      "4 -0.710061       -0.710061\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "\n",
    "# Создание нового признака 'relative_price' (цена относительно средней цены в категории)\n",
    "mean_price_by_category = df.groupby('category')['price'].transform('mean')\n",
    "df['relative_price'] = df['price'] / mean_price_by_category\n",
    "\n",
    "# Проверка первых строк данных до масштабирования\n",
    "print(\"Данные до масштабирования:\")\n",
    "print(df[['price', 'relative_price']].head())\n",
    "\n",
    "# Масштабирование признаков на основе нормировки\n",
    "min_max_scaler = MinMaxScaler()\n",
    "df[['price', 'relative_price']] = min_max_scaler.fit_transform(df[['price', 'relative_price']])\n",
    "\n",
    "# Проверка первых строк данных после нормировки\n",
    "print(\"\\nДанные после нормировки:\")\n",
    "print(df[['price', 'relative_price']].head())\n",
    "\n",
    "# Стандартизация признаков\n",
    "standard_scaler = StandardScaler()\n",
    "df[['price', 'relative_price']] = standard_scaler.fit_transform(df[['price', 'relative_price']])\n",
    "\n",
    "# Проверка первых строк данных после стандартизации\n",
    "print(\"\\nДанные после стандартизации:\")\n",
    "print(df[['price', 'relative_price']].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Конструирование признаков с применением фреймворка Featuretools**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/featuretools/synthesis/deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Built 7 features\n",
      "Elapsed: 00:00 | Progress: 100%|██████████\n",
      "Новые признаки, созданные с помощью Featuretools:\n",
      "        category         sub_category     price price_bins  relative_price  \\\n",
      "index                                                                        \n",
      "0      Groceries  Fruits & Vegetables -0.569958    100-500    6.281321e+15   \n",
      "1      Groceries  Fruits & Vegetables -0.699284      0-100    7.706585e+15   \n",
      "2      Groceries  Fruits & Vegetables -0.656175      0-100    7.231497e+15   \n",
      "3      Groceries  Fruits & Vegetables -0.535471    100-500    5.901250e+15   \n",
      "4      Groceries  Fruits & Vegetables -0.710061      0-100    7.825357e+15   \n",
      "\n",
      "       NUM_CHARACTERS(items)  NUM_WORDS(items)  \n",
      "index                                           \n",
      "0                         41                 8  \n",
      "1                         59                11  \n",
      "2                         12                 3  \n",
      "3                         20                 4  \n",
      "4                         50                10  \n"
     ]
    }
   ],
   "source": [
    "import featuretools as ft\n",
    "\n",
    "# Создание нового признака 'relative_price'\n",
    "mean_price_by_category = df.groupby('category')['price'].transform('mean')\n",
    "df['relative_price'] = df['price'] / mean_price_by_category\n",
    "\n",
    "# Создание EntitySet\n",
    "es = ft.EntitySet(id='jio_mart_items')\n",
    "\n",
    "# Добавление данных с явным указанием индексного столбца\n",
    "es = es.add_dataframe(dataframe_name='items_data', dataframe=df, index='index', make_index=True)\n",
    "\n",
    "# Конструирование признаков\n",
    "features, feature_defs = ft.dfs(entityset=es, target_dataframe_name='items_data', verbose=True)\n",
    "\n",
    "# Проверка первых строк новых признаков\n",
    "print(\"Новые признаки, созданные с помощью Featuretools:\")\n",
    "print(features.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Оценка качества**\n",
    "\n",
    "*Предсказательная способность Метрики:* RMSE, MAE, R²  \n",
    "\n",
    "*Методы:* Обучение модели на обучающей выборке и оценка на контрольной и тестовой выборках.  \n",
    "\n",
    "*Скорость вычисления Методы:* Измерение времени выполнения генерации признаков и обучения модели.  \n",
    "\n",
    "*Надежность Методы:* Кросс-валидация, анализ чувствительности модели к изменениям в данных.  \n",
    "\n",
    "*Корреляция Методы:* Анализ корреляционной матрицы признаков, удаление мультиколлинеарных признаков.  \n",
    "\n",
    "*Цельность Методы:* Проверка логической связи между признаками и целевой переменной, интерпретация результатов модели.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 534.0885949291326\n",
      "R²: 0.6087611252156747\n",
      "MAE: 28.697400000000002\n",
      "Training Time: 1.323014259338379 seconds\n",
      "Cross-validated RMSE: 133.74731704254154\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/rd/3q9k4y6x0mn6ztd0mby6zx3r0000gn/T/ipykernel_96452/3211138617.py:70: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x='Importance', y='Feature', data=importance_df_top, palette='viridis')\n"
     ]
    },
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      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train RMSE: 50.92770420271637\n",
      "Train R²: 0.9845578370650323\n",
      "Train MAE: 1.9114281249999987\n",
      "Корреляция: 0.82\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import time\n",
    "import numpy as np\n",
    "\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\"..//static//csv//jio_mart_items.csv\").head(2000)\n",
    "\n",
    "# Создание нового признака 'relative_price'\n",
    "mean_price_by_category = df.groupby('category')['price'].transform('mean')\n",
    "df['relative_price'] = df['price'] / mean_price_by_category\n",
    "\n",
    "# Предобработка данных\n",
    "# Преобразуем категориальные переменные в числовые\n",
    "df = pd.get_dummies(df, drop_first=True)\n",
    "\n",
    "# Разделение данных на признаки и целевую переменную\n",
    "X = df.drop('price', axis=1)\n",
    "y = df['price']\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Выбор модели\n",
    "model = RandomForestRegressor(random_state=42)\n",
    "\n",
    "# Измерение времени обучения и предсказания\n",
    "start_time = time.time()\n",
    "\n",
    "# Обучение модели\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Предсказание и оценка\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "end_time = time.time()\n",
    "training_time = end_time - start_time\n",
    "\n",
    "rmse = mean_squared_error(y_test, y_pred, squared=False)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "\n",
    "print(f\"RMSE: {rmse}\")\n",
    "print(f\"R²: {r2}\")\n",
    "print(f\"MAE: {mae}\")\n",
    "print(f\"Training Time: {training_time} seconds\")\n",
    "\n",
    "# Кросс-валидация\n",
    "scores = cross_val_score(model, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
    "rmse_cv = (-scores.mean())**0.5\n",
    "print(f\"Cross-validated RMSE: {rmse_cv}\")\n",
    "\n",
    "# Анализ важности признаков\n",
    "feature_importances = model.feature_importances_\n",
    "feature_names = X_train.columns\n",
    "\n",
    "importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': feature_importances})\n",
    "importance_df = importance_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Отобразим только топ-20 признаков\n",
    "top_n = 20\n",
    "importance_df_top = importance_df.head(top_n)\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.barplot(x='Importance', y='Feature', data=importance_df_top, palette='viridis')\n",
    "plt.title(f'Top {top_n} Feature Importance')\n",
    "plt.xlabel('Importance')\n",
    "plt.ylabel('Feature')\n",
    "plt.show()\n",
    "\n",
    "# Проверка на переобучение\n",
    "y_train_pred = model.predict(X_train)\n",
    "\n",
    "rmse_train = mean_squared_error(y_train, y_train_pred, squared=False)\n",
    "r2_train = r2_score(y_train, y_train_pred)\n",
    "mae_train = mean_absolute_error(y_train, y_train_pred)\n",
    "\n",
    "print(f\"Train RMSE: {rmse_train}\")\n",
    "print(f\"Train R²: {r2_train}\")\n",
    "print(f\"Train MAE: {mae_train}\")\n",
    "\n",
    "correlation = np.corrcoef(y_test, y_pred)[0, 1]\n",
    "print(f\"Корреляция: {correlation:.2f}\")\n",
    "\n",
    "# Визуализация результатов\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_test, y_pred, alpha=0.5)\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n",
    "plt.xlabel('Actual Price')\n",
    "plt.ylabel('Predicted Price')\n",
    "plt.title('Actual vs Predicted Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Выводы и итог  \n",
    "\n",
    "**Время обучения:**\n",
    "\n",
    "Время обучения модели составляет 1.32 секунды, что является средним. Это указывает на то, что модель обучается быстро и может эффективно обрабатывать данные.\n",
    "\n",
    "**Предсказательная способность:**\n",
    "\n",
    "MAE (Mean Absolute Error): 28.6974 — это средняя абсолютная ошибка предсказаний модели. Значение MAE  невелико, что означает, что предсказанные значения в среднем отклоняются от реальных на 28.6974. Это может быть приемлемым уровнем ошибки.\n",
    "\n",
    "RMSE (Mean Squared Error): 534.088 — это среднее значение квадратов ошибок. Хотя MSE высокое, оно также может быть связано с большими значениями целевой переменной (цен).\n",
    "\n",
    "R² (коэффициент детерминации): 0.609 — это средний уровень, указывающий на то, что модель объясняет 60,9% вариации целевой переменной. Это свидетельствует о средней предсказательной способности модели.\n",
    "\n",
    "**Корреляция:**\n",
    "\n",
    "Корреляция (0.82) между предсказанными и реальными значениями говорит о том, что предсказания модели имеют  сильную линейную зависимость с реальными значениями. Это подтверждает, что модель хорошо обучена и делает точные прогнозы.\n",
    "\n",
    "**Надежность (кросс-валидация):**\n",
    "\n",
    "Среднее RMSE (кросс-валидация): 133.75 — это значительно ниже, чем обычное RMSE, что указывает на отсутствие проблем с переобучением - что и подтверждается тестом переобучением. \n",
    "\n",
    "Результаты визуализации важности признаков, полученные из линейной регрессии, помогают понять, какие из входных переменных наибольшим образом влияют на целевую переменную (price). Это может быть полезным для дальнейшего анализа и при принятии бизнес-решений, связанных с управлением и ценообразованием в Jio Mart."
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