ML-For-Beginners/translations/vi/2-Regression/2-Data/solution/notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hồi Quy Tuyến Tính cho Bí Ngô - Bài Học 2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>City Name</th>\n",
       "      <th>Type</th>\n",
       "      <th>Package</th>\n",
       "      <th>Variety</th>\n",
       "      <th>Sub Variety</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Date</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Mostly Low</th>\n",
       "      <th>...</th>\n",
       "      <th>Unit of Sale</th>\n",
       "      <th>Quality</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Appearance</th>\n",
       "      <th>Storage</th>\n",
       "      <th>Crop</th>\n",
       "      <th>Repack</th>\n",
       "      <th>Trans Mode</th>\n",
       "      <th>Unnamed: 24</th>\n",
       "      <th>Unnamed: 25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>BALTIMORE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1 1/9 bushel cartons</td>\n",
       "      <td>PIE TYPE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9/24/16</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>BALTIMORE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1 1/9 bushel cartons</td>\n",
       "      <td>PIE TYPE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9/24/16</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>BALTIMORE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1 1/9 bushel cartons</td>\n",
       "      <td>PIE TYPE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10/1/16</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>BALTIMORE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1 1/9 bushel cartons</td>\n",
       "      <td>PIE TYPE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10/1/16</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>BALTIMORE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1 1/9 bushel cartons</td>\n",
       "      <td>PIE TYPE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10/8/16</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    City Name Type               Package   Variety Sub Variety  Grade  \\\n",
       "70  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
       "71  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
       "72  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
       "73  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
       "74  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
       "\n",
       "       Date  Low Price  High Price  Mostly Low  ...  Unit of Sale Quality  \\\n",
       "70  9/24/16       15.0        15.0        15.0  ...           NaN     NaN   \n",
       "71  9/24/16       18.0        18.0        18.0  ...           NaN     NaN   \n",
       "72  10/1/16       18.0        18.0        18.0  ...           NaN     NaN   \n",
       "73  10/1/16       17.0        17.0        17.0  ...           NaN     NaN   \n",
       "74  10/8/16       15.0        15.0        15.0  ...           NaN     NaN   \n",
       "\n",
       "   Condition Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  \\\n",
       "70       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
       "71       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
       "72       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
       "73       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
       "74       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
       "\n",
       "    Unnamed: 25  \n",
       "70          NaN  \n",
       "71          NaN  \n",
       "72          NaN  \n",
       "73          NaN  \n",
       "74          NaN  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
    "\n",
    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
    "\n",
    "pumpkins.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City Name            0\n",
       "Type               406\n",
       "Package              0\n",
       "Variety              0\n",
       "Sub Variety        167\n",
       "Grade              415\n",
       "Date                 0\n",
       "Low Price            0\n",
       "High Price           0\n",
       "Mostly Low          24\n",
       "Mostly High         24\n",
       "Origin               0\n",
       "Origin District    396\n",
       "Item Size          114\n",
       "Color              145\n",
       "Environment        415\n",
       "Unit of Sale       404\n",
       "Quality            415\n",
       "Condition          415\n",
       "Appearance         415\n",
       "Storage            415\n",
       "Crop               415\n",
       "Repack               0\n",
       "Trans Mode         415\n",
       "Unnamed: 24        415\n",
       "Unnamed: 25        391\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pumpkins.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Month               Package  Low Price  High Price  Price\n",
      "70        9  1 1/9 bushel cartons      15.00        15.0  13.50\n",
      "71        9  1 1/9 bushel cartons      18.00        18.0  16.20\n",
      "72       10  1 1/9 bushel cartons      18.00        18.0  16.20\n",
      "73       10  1 1/9 bushel cartons      17.00        17.0  15.30\n",
      "74       10  1 1/9 bushel cartons      15.00        15.0  13.50\n",
      "...     ...                   ...        ...         ...    ...\n",
      "1738      9    1/2 bushel cartons      15.00        15.0  30.00\n",
      "1739      9    1/2 bushel cartons      13.75        15.0  28.75\n",
      "1740      9    1/2 bushel cartons      10.75        15.0  25.75\n",
      "1741      9    1/2 bushel cartons      12.00        12.0  24.00\n",
      "1742      9    1/2 bushel cartons      12.00        12.0  24.00\n",
      "\n",
      "[415 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# A set of new columns for a new dataframe. Filter out nonmatching columns\n",
    "columns_to_select = ['Package', 'Low Price', 'High Price', 'Date']\n",
    "pumpkins = pumpkins.loc[:, columns_to_select]\n",
    "\n",
    "# Get an average between low and high price for the base pumpkin price\n",
    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
    "\n",
    "# Convert the date to its month only\n",
    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
    "\n",
    "# Create a new dataframe with this basic data\n",
    "new_pumpkins = pd.DataFrame({'Month': month, 'Package': pumpkins['Package'], 'Low Price': pumpkins['Low Price'],'High Price': pumpkins['High Price'], 'Price': price})\n",
    "\n",
    "# Convert the price if the Package contains fractional bushel values\n",
    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/(1 + 1/9)\n",
    "\n",
    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price/(1/2)\n",
    "\n",
    "print(new_pumpkins)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "price = new_pumpkins.Price\n",
    "month = new_pumpkins.Month\n",
    "plt.scatter(price, month)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Pumpkin Price')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "new_pumpkins.groupby(['Month'])['Price'].mean().plot(kind='bar')\n",
    "plt.ylabel(\"Pumpkin Price\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trực quan hóa với Seaborn\n",
    "\n",
    "[Seaborn](https://seaborn.pydata.org/) được xây dựng trên Matplotlib và hoạt động trực tiếp với dataframes, giúp nhanh chóng tạo các biểu đồ thống kê hấp dẫn chỉ với rất ít mã.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Biểu đồ phân tán để hiển thị mối quan hệ\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.relplot(x=\"Price\", y=\"Month\", data=new_pumpkins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.relplot(x=\"Price\", y=\"Month\", kind=\"line\", data=new_pumpkins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Biểu đồ cột để hiển thị phân phối\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.catplot(x=\"Month\", y=\"Price\", data=new_pumpkins, kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bản đồ nhiệt để hiển thị tương quan\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "correlations = new_pumpkins[['Month', 'Low Price', 'High Price', 'Price']].corr()\n",
    "sns.heatmap(correlations, annot=True, cmap=\"coolwarm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n\n<!-- CO-OP TRANSLATOR DISCLAIMER START -->\n**Tuyên bố miễn trừ trách nhiệm**:\nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng bản dịch tự động có thể chứa lỗi hoặc sai sót. Tài liệu gốc bằng ngôn ngữ gốc nên được coi là nguồn tin chính thức. Đối với thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp bởi con người. Chúng tôi không chịu trách nhiệm về bất kỳ hiểu lầm hoặc giải thích sai nào phát sinh từ việc sử dụng bản dịch này.\n<!-- CO-OP TRANSLATOR DISCLAIMER END -->\n"
   ]
  }
 ],
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