MLZ_HW1_AAC_BACKUP.ipynb

{
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   "cell_type": "code",
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   "id": "b69a6f2d-103d-47ca-820f-e2b1d5683f65",
   "metadata": {},
   "outputs": [],
   "source": [
    " #  Data Talks Club - Machine Learning Zoomcamp\n",
    " #  Session 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d54faba-5251-48d7-bc55-65346b323f20",
   "metadata": {},
   "source": [
    "Set up the environment\n",
    "You need to install Python, NumPy, Pandas, Matplotlib and Seaborn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "15a0d4e5-2d70-42d8-8ef1-62526a8285f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    " #  import matplotlib as mpl\n",
    " #  import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3adff009-943f-4894-a10d-7e065f14647a",
   "metadata": {},
   "outputs": [],
   "source": [
    " # Q1. Pandas version\r",
    " # What's the version of Pandas that you installed?\r",
    " # \n",
    " #  You can get the version information using the __version__ field:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "69b1d615-6f91-4b02-b3ee-ae92bd874da3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.12.4\n"
     ]
    }
   ],
   "source": [
    "import platform\n",
    "print(platform.python_version())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "829e3eb6-3301-4e97-8dc9-754165a79036",
   "metadata": {},
   "outputs": [],
   "source": [
    " # Getting the data\r",
    " # For this homework, we'll use the Laptops Price dataset. Download it from here.\r",
    " # \n",
    " # You can do it with wget # : # \n",
    "\r",
    " # \n",
    "wget https://raw.githubusercontent.com/alexeygrigorev/datasets/master/laptops # .c # sv # \r\n",
    "Or just open it with  your browser and click \"Save as. # . # \". #  \r\n",
    "\r\n",
    "Now read it with  ou ouPandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6e24ed64-da46-4c36-869f-102cccc7d66f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('C:/Users/arcangelaac/Downloads/laptops.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b4c3164a-60a1-411c-8faa-a63dcc3c93cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12160"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " #  Q2. Records count\r",
    " #  How many records are in the dataset?\r",
    " #   12 # \r",
    " # 000\r\n",
    " # 21 # 60 # \r\n",
    "12160"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ce4412bd-8144-4d1f-8be3-0e0b977c6229",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2160, 12)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "41d85154-fe6c-460e-9877-6969f6de98c1",
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Laptop</th>\n",
       "      <th>Status</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Model</th>\n",
       "      <th>CPU</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Storage</th>\n",
       "      <th>Storage type</th>\n",
       "      <th>GPU</th>\n",
       "      <th>Screen</th>\n",
       "      <th>Touch</th>\n",
       "      <th>Final Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ASUS ExpertBook B1 B1502CBA-EJ0436X Intel Core...</td>\n",
       "      <td>New</td>\n",
       "      <td>Asus</td>\n",
       "      <td>ExpertBook</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>8</td>\n",
       "      <td>512</td>\n",
       "      <td>SSD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.6</td>\n",
       "      <td>No</td>\n",
       "      <td>1009.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alurin Go Start Intel Celeron N4020/8GB/256GB ...</td>\n",
       "      <td>New</td>\n",
       "      <td>Alurin</td>\n",
       "      <td>Go</td>\n",
       "      <td>Intel Celeron</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>SSD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.6</td>\n",
       "      <td>No</td>\n",
       "      <td>299.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ASUS ExpertBook B1 B1502CBA-EJ0424X Intel Core...</td>\n",
       "      <td>New</td>\n",
       "      <td>Asus</td>\n",
       "      <td>ExpertBook</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>SSD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.6</td>\n",
       "      <td>No</td>\n",
       "      <td>789.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MSI Katana GF66 12UC-082XES Intel Core i7-1270...</td>\n",
       "      <td>New</td>\n",
       "      <td>MSI</td>\n",
       "      <td>Katana</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>16</td>\n",
       "      <td>1000</td>\n",
       "      <td>SSD</td>\n",
       "      <td>RTX 3050</td>\n",
       "      <td>15.6</td>\n",
       "      <td>No</td>\n",
       "      <td>1199.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HP 15S-FQ5085NS Intel Core i5-1235U/16GB/512GB...</td>\n",
       "      <td>New</td>\n",
       "      <td>HP</td>\n",
       "      <td>15S</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>16</td>\n",
       "      <td>512</td>\n",
       "      <td>SSD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.6</td>\n",
       "      <td>No</td>\n",
       "      <td>669.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2155</th>\n",
       "      <td>Razer Blade 17 FHD 360Hz Intel Core i7-11800H/...</td>\n",
       "      <td>Refurbished</td>\n",
       "      <td>Razer</td>\n",
       "      <td>Blade</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>16</td>\n",
       "      <td>1000</td>\n",
       "      <td>SSD</td>\n",
       "      <td>RTX 3060</td>\n",
       "      <td>17.3</td>\n",
       "      <td>No</td>\n",
       "      <td>2699.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2156</th>\n",
       "      <td>Razer Blade 17 FHD 360Hz Intel Core i7-11800H/...</td>\n",
       "      <td>Refurbished</td>\n",
       "      <td>Razer</td>\n",
       "      <td>Blade</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>16</td>\n",
       "      <td>1000</td>\n",
       "      <td>SSD</td>\n",
       "      <td>RTX 3070</td>\n",
       "      <td>17.3</td>\n",
       "      <td>No</td>\n",
       "      <td>2899.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2157</th>\n",
       "      <td>Razer Blade 17 FHD 360Hz Intel Core i7-11800H/...</td>\n",
       "      <td>Refurbished</td>\n",
       "      <td>Razer</td>\n",
       "      <td>Blade</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>32</td>\n",
       "      <td>1000</td>\n",
       "      <td>SSD</td>\n",
       "      <td>RTX 3080</td>\n",
       "      <td>17.3</td>\n",
       "      <td>No</td>\n",
       "      <td>3399.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158</th>\n",
       "      <td>Razer Book 13 Intel Evo Core i7-1165G7/16GB/1T...</td>\n",
       "      <td>Refurbished</td>\n",
       "      <td>Razer</td>\n",
       "      <td>Book</td>\n",
       "      <td>Intel Evo Core i7</td>\n",
       "      <td>16</td>\n",
       "      <td>1000</td>\n",
       "      <td>SSD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1899.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2159</th>\n",
       "      <td>Razer Book FHD+ Intel Evo Core i7-1165G7/16GB/...</td>\n",
       "      <td>Refurbished</td>\n",
       "      <td>Razer</td>\n",
       "      <td>Book</td>\n",
       "      <td>Intel Evo Core i7</td>\n",
       "      <td>16</td>\n",
       "      <td>256</td>\n",
       "      <td>SSD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1699.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2160 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Laptop       Status   Brand  \\\n",
       "0     ASUS ExpertBook B1 B1502CBA-EJ0436X Intel Core...          New    Asus   \n",
       "1     Alurin Go Start Intel Celeron N4020/8GB/256GB ...          New  Alurin   \n",
       "2     ASUS ExpertBook B1 B1502CBA-EJ0424X Intel Core...          New    Asus   \n",
       "3     MSI Katana GF66 12UC-082XES Intel Core i7-1270...          New     MSI   \n",
       "4     HP 15S-FQ5085NS Intel Core i5-1235U/16GB/512GB...          New      HP   \n",
       "...                                                 ...          ...     ...   \n",
       "2155  Razer Blade 17 FHD 360Hz Intel Core i7-11800H/...  Refurbished   Razer   \n",
       "2156  Razer Blade 17 FHD 360Hz Intel Core i7-11800H/...  Refurbished   Razer   \n",
       "2157  Razer Blade 17 FHD 360Hz Intel Core i7-11800H/...  Refurbished   Razer   \n",
       "2158  Razer Book 13 Intel Evo Core i7-1165G7/16GB/1T...  Refurbished   Razer   \n",
       "2159  Razer Book FHD+ Intel Evo Core i7-1165G7/16GB/...  Refurbished   Razer   \n",
       "\n",
       "           Model                CPU  RAM  Storage Storage type       GPU  \\\n",
       "0     ExpertBook      Intel Core i5    8      512          SSD       NaN   \n",
       "1             Go      Intel Celeron    8      256          SSD       NaN   \n",
       "2     ExpertBook      Intel Core i3    8      256          SSD       NaN   \n",
       "3         Katana      Intel Core i7   16     1000          SSD  RTX 3050   \n",
       "4            15S      Intel Core i5   16      512          SSD       NaN   \n",
       "...          ...                ...  ...      ...          ...       ...   \n",
       "2155       Blade      Intel Core i7   16     1000          SSD  RTX 3060   \n",
       "2156       Blade      Intel Core i7   16     1000          SSD  RTX 3070   \n",
       "2157       Blade      Intel Core i7   32     1000          SSD  RTX 3080   \n",
       "2158        Book  Intel Evo Core i7   16     1000          SSD       NaN   \n",
       "2159        Book  Intel Evo Core i7   16      256          SSD       NaN   \n",
       "\n",
       "      Screen Touch  Final Price  \n",
       "0       15.6    No      1009.00  \n",
       "1       15.6    No       299.00  \n",
       "2       15.6    No       789.00  \n",
       "3       15.6    No      1199.00  \n",
       "4       15.6    No       669.01  \n",
       "...      ...   ...          ...  \n",
       "2155    17.3    No      2699.99  \n",
       "2156    17.3    No      2899.99  \n",
       "2157    17.3    No      3399.99  \n",
       "2158    13.4   Yes      1899.99  \n",
       "2159    13.4   Yes      1699.99  \n",
       "\n",
       "[2160 rows x 12 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7060be58-4ac7-45a9-800f-9bfad5acb33d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=2160, step=1)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
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    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ee63405-32a0-4985-83a5-badb5f6cb9dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Q3. Laptop brands\r",
    "# How many laptop brands are presented in the dataset?\r",
    "# \r\n",
    "# 12\r",
    " \n",
    "\n",
    "7 \n",
    "22# # 8\r\n",
    "2160"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8ce8d65f-f6bf-486f-9618-6de7f99131e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_group_brand = df.groupby('Brand')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2d8708ee-ebbb-4d2a-9a6c-e7a6c1a0d97b",
   "metadata": {},
   "outputs": [
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       "      <th colspan=\"8\" halign=\"left\">RAM</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Storage</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Screen</th>\n",
       "      <th colspan=\"8\" halign=\"left\">Final Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Acer</th>\n",
       "      <td>137.0</td>\n",
       "      <td>12.204380</td>\n",
       "      <td>6.218023</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>536.291971</td>\n",
       "      <td>...</td>\n",
       "      <td>15.600</td>\n",
       "      <td>17.3</td>\n",
       "      <td>137.0</td>\n",
       "      <td>1001.285766</td>\n",
       "      <td>671.175377</td>\n",
       "      <td>264.14</td>\n",
       "      <td>499.0000</td>\n",
       "      <td>729.000</td>\n",
       "      <td>1349.0000</td>\n",
       "      <td>3691.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alurin</th>\n",
       "      <td>29.0</td>\n",
       "      <td>9.655172</td>\n",
       "      <td>3.298007</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>352.551724</td>\n",
       "      <td>...</td>\n",
       "      <td>15.600</td>\n",
       "      <td>15.6</td>\n",
       "      <td>29.0</td>\n",
       "      <td>484.701379</td>\n",
       "      <td>136.876704</td>\n",
       "      <td>239.00</td>\n",
       "      <td>399.0000</td>\n",
       "      <td>499.000</td>\n",
       "      <td>549.0000</td>\n",
       "      <td>869.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apple</th>\n",
       "      <td>116.0</td>\n",
       "      <td>13.034483</td>\n",
       "      <td>7.480910</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>508.620690</td>\n",
       "      <td>...</td>\n",
       "      <td>15.075</td>\n",
       "      <td>16.2</td>\n",
       "      <td>116.0</td>\n",
       "      <td>1578.227672</td>\n",
       "      <td>857.160470</td>\n",
       "      <td>299.00</td>\n",
       "      <td>882.1425</td>\n",
       "      <td>1491.330</td>\n",
       "      <td>1905.4825</td>\n",
       "      <td>3849.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asus</th>\n",
       "      <td>415.0</td>\n",
       "      <td>16.404819</td>\n",
       "      <td>9.757939</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>634.775904</td>\n",
       "      <td>...</td>\n",
       "      <td>15.600</td>\n",
       "      <td>18.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>1269.380699</td>\n",
       "      <td>948.473100</td>\n",
       "      <td>239.25</td>\n",
       "      <td>651.2750</td>\n",
       "      <td>901.990</td>\n",
       "      <td>1589.0000</td>\n",
       "      <td>5758.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deep Gaming</th>\n",
       "      <td>8.0</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>16.561573</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1250.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>17.300</td>\n",
       "      <td>17.3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1505.377500</td>\n",
       "      <td>101.288428</td>\n",
       "      <td>1334.00</td>\n",
       "      <td>1458.5000</td>\n",
       "      <td>1511.000</td>\n",
       "      <td>1565.5075</td>\n",
       "      <td>1639.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dell</th>\n",
       "      <td>84.0</td>\n",
       "      <td>11.380952</td>\n",
       "      <td>4.583640</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>395.142857</td>\n",
       "      <td>...</td>\n",
       "      <td>15.600</td>\n",
       "      <td>17.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>1153.839881</td>\n",
       "      <td>671.795071</td>\n",
       "      <td>379.00</td>\n",
       "      <td>699.0000</td>\n",
       "      <td>1003.000</td>\n",
       "      <td>1313.8100</td>\n",
       "      <td>3936.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denver</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>14.000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>329.950000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>329.95</td>\n",
       "      <td>329.9500</td>\n",
       "      <td>329.950</td>\n",
       "      <td>329.9500</td>\n",
       "      <td>329.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dynabook Toshiba</th>\n",
       "      <td>19.0</td>\n",
       "      <td>10.526316</td>\n",
       "      <td>3.820535</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>404.210526</td>\n",
       "      <td>...</td>\n",
       "      <td>15.600</td>\n",
       "      <td>15.6</td>\n",
       "      <td>19.0</td>\n",
       "      <td>999.197895</td>\n",
       "      <td>456.023431</td>\n",
       "      <td>397.29</td>\n",
       "      <td>716.8200</td>\n",
       "      <td>889.000</td>\n",
       "      <td>1346.4900</td>\n",
       "      <td>1805.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gigabyte</th>\n",
       "      <td>48.0</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>5.128145</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>860.166667</td>\n",
       "      <td>...</td>\n",
       "      <td>16.000</td>\n",
       "      <td>17.3</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1698.488958</td>\n",
       "      <td>703.457605</td>\n",
       "      <td>799.00</td>\n",
       "      <td>1124.1400</td>\n",
       "      <td>1555.255</td>\n",
       "      <td>2235.9025</td>\n",
       "      <td>3799.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HP</th>\n",
       "      <td>368.0</td>\n",
       "      <td>11.967391</td>\n",
       "      <td>6.336523</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>479.260870</td>\n",
       "      <td>...</td>\n",
       "      <td>15.600</td>\n",
       "      <td>17.3</td>\n",
       "      <td>368.0</td>\n",
       "      <td>952.628478</td>\n",
       "      <td>697.770160</td>\n",
       "      <td>210.14</td>\n",
       "      <td>521.3125</td>\n",
       "      <td>727.950</td>\n",
       "      <td>1201.2425</td>\n",
       "      <td>5368.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Innjoo</th>\n",
       "      <td>6.0</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>1.505545</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>15.225</td>\n",
       "      <td>15.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>347.888333</td>\n",
       "      <td>66.087823</td>\n",
       "      <td>251.40</td>\n",
       "      <td>312.7825</td>\n",
       "      <td>350.315</td>\n",
       "      <td>390.3150</td>\n",
       "      <td>431.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jetwing</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>15.600</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>469.270000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>469.27</td>\n",
       "      <td>469.2700</td>\n",
       "      <td>469.270</td>\n",
       "      <td>469.2700</td>\n",
       "      <td>469.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LG</th>\n",
       "      <td>32.0</td>\n",
       "      <td>21.750000</td>\n",
       "      <td>8.911754</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>703.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>17.000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1730.028438</td>\n",
       "      <td>484.884581</td>\n",
       "      <td>609.04</td>\n",
       "      <td>1517.7975</td>\n",
       "      <td>1814.010</td>\n",
       "      <td>2024.0000</td>\n",
       "      <td>2399.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lenovo</th>\n",
       "      <td>366.0</td>\n",
       "      <td>12.382514</td>\n",
       "      <td>5.800909</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>471.475410</td>\n",
       "      <td>...</td>\n",
       "      <td>15.600</td>\n",
       "      <td>17.3</td>\n",
       "      <td>366.0</td>\n",
       "      <td>1087.723880</td>\n",
       "      <td>715.780097</td>\n",
       "      <td>239.00</td>\n",
       "      <td>599.0000</td>\n",
       "      <td>888.040</td>\n",
       "      <td>1280.2250</td>\n",
       "      <td>5018.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSI</th>\n",
       "      <td>308.0</td>\n",
       "      <td>25.168831</td>\n",
       "      <td>13.776629</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>956.441558</td>\n",
       "      <td>...</td>\n",
       "      <td>17.000</td>\n",
       "      <td>17.3</td>\n",
       "      <td>308.0</td>\n",
       "      <td>1913.451006</td>\n",
       "      <td>1030.673940</td>\n",
       "      <td>444.19</td>\n",
       "      <td>1146.3075</td>\n",
       "      <td>1703.985</td>\n",
       "      <td>2355.0425</td>\n",
       "      <td>7150.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medion</th>\n",
       "      <td>32.0</td>\n",
       "      <td>10.875000</td>\n",
       "      <td>7.753251</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>494.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>15.600</td>\n",
       "      <td>17.3</td>\n",
       "      <td>32.0</td>\n",
       "      <td>905.331875</td>\n",
       "      <td>852.520784</td>\n",
       "      <td>246.89</td>\n",
       "      <td>423.8100</td>\n",
       "      <td>546.310</td>\n",
       "      <td>1006.5000</td>\n",
       "      <td>3799.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Microsoft</th>\n",
       "      <td>77.0</td>\n",
       "      <td>12.103896</td>\n",
       "      <td>5.655888</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>394.285714</td>\n",
       "      <td>...</td>\n",
       "      <td>14.400</td>\n",
       "      <td>15.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>1595.543247</td>\n",
       "      <td>600.917041</td>\n",
       "      <td>599.01</td>\n",
       "      <td>1134.2100</td>\n",
       "      <td>1576.270</td>\n",
       "      <td>2014.5800</td>\n",
       "      <td>3747.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Millenium</th>\n",
       "      <td>2.0</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>15.600</td>\n",
       "      <td>15.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1976.845000</td>\n",
       "      <td>474.984838</td>\n",
       "      <td>1640.98</td>\n",
       "      <td>1808.9125</td>\n",
       "      <td>1976.845</td>\n",
       "      <td>2144.7775</td>\n",
       "      <td>2312.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PcCom</th>\n",
       "      <td>24.0</td>\n",
       "      <td>20.666667</td>\n",
       "      <td>7.428890</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>688.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>15.600</td>\n",
       "      <td>17.3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1606.762917</td>\n",
       "      <td>247.374780</td>\n",
       "      <td>1049.00</td>\n",
       "      <td>1449.6750</td>\n",
       "      <td>1624.450</td>\n",
       "      <td>1799.9275</td>\n",
       "      <td>1949.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Primux</th>\n",
       "      <td>8.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.851640</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>15.600</td>\n",
       "      <td>15.6</td>\n",
       "      <td>8.0</td>\n",
       "      <td>339.618750</td>\n",
       "      <td>127.760370</td>\n",
       "      <td>208.99</td>\n",
       "      <td>261.6850</td>\n",
       "      <td>311.115</td>\n",
       "      <td>369.3250</td>\n",
       "      <td>599.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prixton</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>85.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>14.850</td>\n",
       "      <td>15.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>292.280000</td>\n",
       "      <td>42.026857</td>\n",
       "      <td>246.95</td>\n",
       "      <td>273.4450</td>\n",
       "      <td>299.940</td>\n",
       "      <td>314.9450</td>\n",
       "      <td>329.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Razer</th>\n",
       "      <td>37.0</td>\n",
       "      <td>21.621622</td>\n",
       "      <td>7.743640</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>980.540541</td>\n",
       "      <td>...</td>\n",
       "      <td>17.300</td>\n",
       "      <td>17.3</td>\n",
       "      <td>37.0</td>\n",
       "      <td>3265.701622</td>\n",
       "      <td>888.122168</td>\n",
       "      <td>1232.74</td>\n",
       "      <td>2866.5700</td>\n",
       "      <td>3299.990</td>\n",
       "      <td>3899.9900</td>\n",
       "      <td>4999.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Realme</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>512.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>14.000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>999.00</td>\n",
       "      <td>999.0000</td>\n",
       "      <td>999.000</td>\n",
       "      <td>999.0000</td>\n",
       "      <td>999.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Samsung</th>\n",
       "      <td>22.0</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>6.141196</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>470.545455</td>\n",
       "      <td>...</td>\n",
       "      <td>16.000</td>\n",
       "      <td>16.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1595.511364</td>\n",
       "      <td>810.431303</td>\n",
       "      <td>365.90</td>\n",
       "      <td>985.9875</td>\n",
       "      <td>1580.000</td>\n",
       "      <td>2025.7400</td>\n",
       "      <td>3699.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thomson</th>\n",
       "      <td>4.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.309401</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>14.475</td>\n",
       "      <td>15.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>316.860000</td>\n",
       "      <td>118.163991</td>\n",
       "      <td>201.05</td>\n",
       "      <td>223.3850</td>\n",
       "      <td>314.915</td>\n",
       "      <td>408.3900</td>\n",
       "      <td>436.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toshiba</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>14.000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>799.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>799.00</td>\n",
       "      <td>799.0000</td>\n",
       "      <td>799.000</td>\n",
       "      <td>799.0000</td>\n",
       "      <td>799.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vant</th>\n",
       "      <td>11.0</td>\n",
       "      <td>19.636364</td>\n",
       "      <td>10.346717</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>681.818182</td>\n",
       "      <td>...</td>\n",
       "      <td>14.000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1047.455455</td>\n",
       "      <td>210.523523</td>\n",
       "      <td>632.00</td>\n",
       "      <td>1058.5000</td>\n",
       "      <td>1123.000</td>\n",
       "      <td>1162.0000</td>\n",
       "      <td>1217.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    RAM                                                       \\\n",
       "                  count       mean        std   min   25%   50%   75%    max   \n",
       "Brand                                                                          \n",
       "Acer              137.0  12.204380   6.218023   4.0   8.0   8.0  16.0   32.0   \n",
       "Alurin             29.0   9.655172   3.298007   8.0   8.0   8.0   8.0   16.0   \n",
       "Apple             116.0  13.034483   7.480910   4.0   8.0   8.0  16.0   32.0   \n",
       "Asus              415.0  16.404819   9.757939   4.0   8.0  16.0  16.0   64.0   \n",
       "Deep Gaming         8.0  44.000000  16.561573  32.0  32.0  32.0  64.0   64.0   \n",
       "Dell               84.0  11.380952   4.583640   4.0   8.0   8.0  16.0   32.0   \n",
       "Denver              1.0   4.000000        NaN   4.0   4.0   4.0   4.0    4.0   \n",
       "Dynabook Toshiba   19.0  10.526316   3.820535   8.0   8.0   8.0  16.0   16.0   \n",
       "Gigabyte           48.0  17.500000   5.128145   8.0  16.0  16.0  16.0   32.0   \n",
       "HP                368.0  11.967391   6.336523   4.0   8.0   8.0  16.0   32.0   \n",
       "Innjoo              6.0   6.333333   1.505545   4.0   6.0   6.0   7.5    8.0   \n",
       "Jetwing             1.0   8.000000        NaN   8.0   8.0   8.0   8.0    8.0   \n",
       "LG                 32.0  21.750000   8.911754   8.0  16.0  16.0  32.0   32.0   \n",
       "Lenovo            366.0  12.382514   5.800909   4.0   8.0   8.0  16.0   32.0   \n",
       "MSI               308.0  25.168831  13.776629   8.0  16.0  16.0  32.0  128.0   \n",
       "Medion             32.0  10.875000   7.753251   4.0   8.0   8.0  10.0   32.0   \n",
       "Microsoft          77.0  12.103896   5.655888   4.0   8.0   8.0  16.0   32.0   \n",
       "Millenium           2.0  16.000000   0.000000  16.0  16.0  16.0  16.0   16.0   \n",
       "PcCom              24.0  20.666667   7.428890  16.0  16.0  16.0  32.0   32.0   \n",
       "Primux              8.0   7.000000   1.851640   4.0   7.0   8.0   8.0    8.0   \n",
       "Prixton             3.0   4.000000   0.000000   4.0   4.0   4.0   4.0    4.0   \n",
       "Razer              37.0  21.621622   7.743640  16.0  16.0  16.0  32.0   32.0   \n",
       "Realme              1.0   8.000000        NaN   8.0   8.0   8.0   8.0    8.0   \n",
       "Samsung            22.0  14.000000   6.141196   4.0  10.0  16.0  16.0   32.0   \n",
       "Thomson             4.0   6.000000   2.309401   4.0   4.0   6.0   8.0    8.0   \n",
       "Toshiba             1.0   8.000000        NaN   8.0   8.0   8.0   8.0    8.0   \n",
       "Vant               11.0  19.636364  10.346717   8.0  16.0  16.0  16.0   40.0   \n",
       "\n",
       "                 Storage               ...  Screen       Final Price  \\\n",
       "                   count         mean  ...     75%   max       count   \n",
       "Brand                                  ...                             \n",
       "Acer               137.0   536.291971  ...  15.600  17.3       137.0   \n",
       "Alurin              29.0   352.551724  ...  15.600  15.6        29.0   \n",
       "Apple              116.0   508.620690  ...  15.075  16.2       116.0   \n",
       "Asus               415.0   634.775904  ...  15.600  18.0       415.0   \n",
       "Deep Gaming          8.0  1250.000000  ...  17.300  17.3         8.0   \n",
       "Dell                84.0   395.142857  ...  15.600  17.0        84.0   \n",
       "Denver               1.0   256.000000  ...  14.000  14.0         1.0   \n",
       "Dynabook Toshiba    19.0   404.210526  ...  15.600  15.6        19.0   \n",
       "Gigabyte            48.0   860.166667  ...  16.000  17.3        48.0   \n",
       "HP                 368.0   479.260870  ...  15.600  17.3       368.0   \n",
       "Innjoo               6.0   192.000000  ...  15.225  15.6         6.0   \n",
       "Jetwing              1.0   256.000000  ...  15.600  15.6         1.0   \n",
       "LG                  32.0   703.000000  ...  17.000  17.0        32.0   \n",
       "Lenovo             366.0   471.475410  ...  15.600  17.3       366.0   \n",
       "MSI                308.0   956.441558  ...  17.000  17.3       308.0   \n",
       "Medion              32.0   494.000000  ...  15.600  17.3        32.0   \n",
       "Microsoft           77.0   394.285714  ...  14.400  15.0        77.0   \n",
       "Millenium            2.0   500.000000  ...  15.600  15.6         2.0   \n",
       "PcCom               24.0   688.000000  ...  15.600  17.3        24.0   \n",
       "Primux               8.0   288.000000  ...  15.600  15.6         8.0   \n",
       "Prixton              3.0    85.333333  ...  14.850  15.6         3.0   \n",
       "Razer               37.0   980.540541  ...  17.300  17.3        37.0   \n",
       "Realme               1.0   512.000000  ...  14.000  14.0         1.0   \n",
       "Samsung             22.0   470.545455  ...  16.000  16.0        22.0   \n",
       "Thomson              4.0   224.000000  ...  14.475  15.6         4.0   \n",
       "Toshiba              1.0   256.000000  ...  14.000  14.0         1.0   \n",
       "Vant                11.0   681.818182  ...  14.000  14.0        11.0   \n",
       "\n",
       "                                                                          \\\n",
       "                         mean          std      min        25%       50%   \n",
       "Brand                                                                      \n",
       "Acer              1001.285766   671.175377   264.14   499.0000   729.000   \n",
       "Alurin             484.701379   136.876704   239.00   399.0000   499.000   \n",
       "Apple             1578.227672   857.160470   299.00   882.1425  1491.330   \n",
       "Asus              1269.380699   948.473100   239.25   651.2750   901.990   \n",
       "Deep Gaming       1505.377500   101.288428  1334.00  1458.5000  1511.000   \n",
       "Dell              1153.839881   671.795071   379.00   699.0000  1003.000   \n",
       "Denver             329.950000          NaN   329.95   329.9500   329.950   \n",
       "Dynabook Toshiba   999.197895   456.023431   397.29   716.8200   889.000   \n",
       "Gigabyte          1698.488958   703.457605   799.00  1124.1400  1555.255   \n",
       "HP                 952.628478   697.770160   210.14   521.3125   727.950   \n",
       "Innjoo             347.888333    66.087823   251.40   312.7825   350.315   \n",
       "Jetwing            469.270000          NaN   469.27   469.2700   469.270   \n",
       "LG                1730.028438   484.884581   609.04  1517.7975  1814.010   \n",
       "Lenovo            1087.723880   715.780097   239.00   599.0000   888.040   \n",
       "MSI               1913.451006  1030.673940   444.19  1146.3075  1703.985   \n",
       "Medion             905.331875   852.520784   246.89   423.8100   546.310   \n",
       "Microsoft         1595.543247   600.917041   599.01  1134.2100  1576.270   \n",
       "Millenium         1976.845000   474.984838  1640.98  1808.9125  1976.845   \n",
       "PcCom             1606.762917   247.374780  1049.00  1449.6750  1624.450   \n",
       "Primux             339.618750   127.760370   208.99   261.6850   311.115   \n",
       "Prixton            292.280000    42.026857   246.95   273.4450   299.940   \n",
       "Razer             3265.701622   888.122168  1232.74  2866.5700  3299.990   \n",
       "Realme             999.000000          NaN   999.00   999.0000   999.000   \n",
       "Samsung           1595.511364   810.431303   365.90   985.9875  1580.000   \n",
       "Thomson            316.860000   118.163991   201.05   223.3850   314.915   \n",
       "Toshiba            799.000000          NaN   799.00   799.0000   799.000   \n",
       "Vant              1047.455455   210.523523   632.00  1058.5000  1123.000   \n",
       "\n",
       "                                      \n",
       "                        75%      max  \n",
       "Brand                                 \n",
       "Acer              1349.0000  3691.00  \n",
       "Alurin             549.0000   869.00  \n",
       "Apple             1905.4825  3849.00  \n",
       "Asus              1589.0000  5758.14  \n",
       "Deep Gaming       1565.5075  1639.01  \n",
       "Dell              1313.8100  3936.00  \n",
       "Denver             329.9500   329.95  \n",
       "Dynabook Toshiba  1346.4900  1805.01  \n",
       "Gigabyte          2235.9025  3799.00  \n",
       "HP                1201.2425  5368.77  \n",
       "Innjoo             390.3150   431.38  \n",
       "Jetwing            469.2700   469.27  \n",
       "LG                2024.0000  2399.00  \n",
       "Lenovo            1280.2250  5018.14  \n",
       "MSI               2355.0425  7150.47  \n",
       "Medion            1006.5000  3799.00  \n",
       "Microsoft         2014.5800  3747.91  \n",
       "Millenium         2144.7775  2312.71  \n",
       "PcCom             1799.9275  1949.90  \n",
       "Primux             369.3250   599.41  \n",
       "Prixton            314.9450   329.95  \n",
       "Razer             3899.9900  4999.01  \n",
       "Realme             999.0000   999.00  \n",
       "Samsung           2025.7400  3699.01  \n",
       "Thomson            408.3900   436.56  \n",
       "Toshiba            799.0000   799.00  \n",
       "Vant              1162.0000  1217.01  \n",
       "\n",
       "[27 rows x 32 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group_brand.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a699748d-5456-42a9-9274-21676efb8104",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Laptop          2160\n",
       "Status             2\n",
       "Brand             27\n",
       "Model            121\n",
       "CPU               28\n",
       "RAM                9\n",
       "Storage           12\n",
       "Storage type       2\n",
       "GPU               44\n",
       "Screen            29\n",
       "Touch              2\n",
       "Final Price     1440\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac7ded53-18ee-4142-b8c2-b5240003197d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Q4. Missing values\n",
    "# How many columns in the dataset have missing values?\n",
    "\n",
    "# 0\n",
    "# 1\n",
    "# 2\n",
    "# 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4f2fb2d7-20d7-4cb2-adab-e714a36c7f32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Laptop             0\n",
       "Status             0\n",
       "Brand              0\n",
       "Model              0\n",
       "CPU                0\n",
       "RAM                0\n",
       "Storage            0\n",
       "Storage type      42\n",
       "GPU             1371\n",
       "Screen             4\n",
       "Touch              0\n",
       "Final Price        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b7dbbd3-97d3-4e3a-8314-6c3c18abc3aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Q5. Maximum final price\r",
    "# \n",
    "#  What's the maximum final price of Dell notebooks in the dataset?\r",
    "# \n",
    "8#8 69\n",
    "33# 691\r\n",
    "# 389\n",
    "\n",
    "#  I see the solution in a previous step (3936), but I want to ask directly to get the value\r\n",
    "3936"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "dbed4cd0-ed10-4743-a651-baa0851a699e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={'Final Price' : 'Final_Price'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "e68cc295-c091-488a-82e0-ed995aa0c8f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3936.0"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Brand').Final_Price.max().loc['Dell']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "b5545b29-c15b-4295-adc3-3ae3445287a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3936.0"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Brand']=='Dell']['Final_Price'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "442a9311-5d36-435c-8842-76a85cce62ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Q6. Median value of Screen\r",
    "# \n",
    "# Find the median value of Screen column in the dataset.# \r",
    "# \n",
    "Next, calculate the most frequent value of the same Screen column# .# \r\n",
    "Use fillna method to fill the missing values in Screen column with the most frequent value from the previous ste# p# .\r\n",
    "Now, calculate the median value of Screen once aga# i# n.\r\n",
    "Has it chang# e# d?\r\n",
    "\r\n",
    "Hint: refer to existing mode and median functions to complete the # tas# k# .# \r\n",
    "\r\n",
    "Yes\r\n",
    "No"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c82f253f-80c7-42bb-bb6c-3fd287353773",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the median value of Screen column in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "c06eff62-baab-4a66-8f9a-f34cfc975f3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " The median of \"Screen\" column is : 15.6\n"
     ]
    }
   ],
   "source": [
    "df2 = df['Screen'].median()\n",
    "print(' The median of \"Screen\" column is :', df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13f7fd20-bf00-43d1-af35-ceccc433e70f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Next, calculate the most frequent value of the same Screen column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "2fd6b481-2642-4f86-a7ce-c9dbee94ef8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " The mode of \"Screen\" column is : Series([], Name: Screen, dtype: object)\n"
     ]
    }
   ],
   "source": [
    "df3 = df['Screen'].mode()\n",
    "print(' The mode of \"Screen\" column is :', df3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cf1ef19-ba3c-4250-a363-026e8d0c658e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use fillna method to fill the missing values in Screen column with the most frequent value from the previous step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "964255a4-889f-4e47-8e84-bf69e363bf42",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Screen\"] = df[\"Screen\"].fillna(df['Screen'].mode())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "b3e5c6e1-e25c-49f3-a4f2-5d1273eae7dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Laptop             0\n",
       "Status             0\n",
       "Brand              0\n",
       "Model              0\n",
       "CPU                0\n",
       "RAM                0\n",
       "Storage            0\n",
       "Storage type      42\n",
       "GPU             1371\n",
       "Screen          2160\n",
       "Touch              0\n",
       "Final_Price        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "658f386a-9515-4218-9369-f3cc3d414399",
   "metadata": {},
   "outputs": [],
   "source": [
    "  # I made one error, I will read again the source using the suuggestion in FAQ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "ea451f4f-259f-49a0-a0a2-a4bc4e6fe2dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://raw.githubusercontent.com/alexeygrigorev/datasets/master/laptops.csv\"\n",
    "df_new = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "19a386dc-18d2-4ba4-871b-2ebf1579a807",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['Screen'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "4dbd92c5-8820-4a06-a3d7-343dbba19b6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: Screen, dtype: object)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "e216a42f-18ab-4e56-bc49-f3a62300528d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    15.6\n",
       "Name: Screen, dtype: float64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.Screen.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "85e055d5-848a-489f-a5b6-4d07ecf765a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.Screen.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "853be961-cc6e-46f6-bc16-8920ca13c163",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    15.6\n",
       "Name: Screen, dtype: float64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.Screen.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "d36bcc5d-8526-4651-a43d-1cba1cbe5073",
   "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>Laptop</th>\n",
       "      <th>Status</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Model</th>\n",
       "      <th>CPU</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Storage</th>\n",
       "      <th>Storage type</th>\n",
       "      <th>GPU</th>\n",
       "      <th>Touch</th>\n",
       "      <th>Final Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Screen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10.10</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.50</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.95</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.60</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.00</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.30</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.40</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.50</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.00</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.30</th>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>126</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.40</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.50</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.60</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.90</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.00</th>\n",
       "      <td>392</td>\n",
       "      <td>392</td>\n",
       "      <td>392</td>\n",
       "      <td>392</td>\n",
       "      <td>392</td>\n",
       "      <td>392</td>\n",
       "      <td>392</td>\n",
       "      <td>386</td>\n",
       "      <td>48</td>\n",
       "      <td>392</td>\n",
       "      <td>392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.10</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.20</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.40</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.50</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.00</th>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.30</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.40</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.60</th>\n",
       "      <td>1009</td>\n",
       "      <td>1009</td>\n",
       "      <td>1009</td>\n",
       "      <td>1009</td>\n",
       "      <td>1009</td>\n",
       "      <td>1009</td>\n",
       "      <td>1009</td>\n",
       "      <td>997</td>\n",
       "      <td>370</td>\n",
       "      <td>1009</td>\n",
       "      <td>1009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.00</th>\n",
       "      <td>174</td>\n",
       "      <td>174</td>\n",
       "      <td>174</td>\n",
       "      <td>174</td>\n",
       "      <td>174</td>\n",
       "      <td>174</td>\n",
       "      <td>174</td>\n",
       "      <td>173</td>\n",
       "      <td>135</td>\n",
       "      <td>174</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.10</th>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.20</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.00</th>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>24</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.30</th>\n",
       "      <td>161</td>\n",
       "      <td>161</td>\n",
       "      <td>161</td>\n",
       "      <td>161</td>\n",
       "      <td>161</td>\n",
       "      <td>161</td>\n",
       "      <td>161</td>\n",
       "      <td>161</td>\n",
       "      <td>144</td>\n",
       "      <td>161</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.00</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Laptop  Status  Brand  Model   CPU   RAM  Storage  Storage type  GPU  \\\n",
       "Screen                                                                         \n",
       "10.10        1       1      1      1     1     1        1             1    0   \n",
       "10.50        7       7      7      7     7     7        7             7    0   \n",
       "10.95        1       1      1      1     1     1        1             1    0   \n",
       "11.60       16      16     16     16    16    16       16            12    0   \n",
       "12.00        4       4      4      4     4     4        4             0    0   \n",
       "12.30       13      13     13     13    13    13       13            10    0   \n",
       "12.40        6       6      6      6     6     6        6             5    0   \n",
       "12.50        1       1      1      1     1     1        1             1    0   \n",
       "13.00       27      27     27     27    27    27       27            23    0   \n",
       "13.30      131     131    131    131   131   131      131           126    0   \n",
       "13.40       19      19     19     19    19    19       19            19    8   \n",
       "13.50       19      19     19     19    19    19       19            19    0   \n",
       "13.60       11      11     11     11    11    11       11            11    0   \n",
       "13.90        2       2      2      2     2     2        2             2    0   \n",
       "14.00      392     392    392    392   392   392      392           386   48   \n",
       "14.10       11      11     11     11    11    11       11            11    0   \n",
       "14.20       14      14     14     14    14    14       14            14    0   \n",
       "14.40        6       6      6      6     6     6        6             6    4   \n",
       "14.50        3       3      3      3     3     3        3             3    0   \n",
       "15.00       21      21     21     21    21    21       21            20    0   \n",
       "15.30        8       8      8      8     8     8        8             8    0   \n",
       "15.40        5       5      5      5     5     5        5             5    3   \n",
       "15.60     1009    1009   1009   1009  1009  1009     1009           997  370   \n",
       "16.00      174     174    174    174   174   174      174           173  135   \n",
       "16.10       48      48     48     48    48    48       48            47   47   \n",
       "16.20       10      10     10     10    10    10       10            10    0   \n",
       "17.00       33      33     33     33    33    33       33            33   24   \n",
       "17.30      161     161    161    161   161   161      161           161  144   \n",
       "18.00        3       3      3      3     3     3        3             3    3   \n",
       "\n",
       "        Touch  Final Price  \n",
       "Screen                      \n",
       "10.10       1            1  \n",
       "10.50       7            7  \n",
       "10.95       1            1  \n",
       "11.60      16           16  \n",
       "12.00       4            4  \n",
       "12.30      13           13  \n",
       "12.40       6            6  \n",
       "12.50       1            1  \n",
       "13.00      27           27  \n",
       "13.30     131          131  \n",
       "13.40      19           19  \n",
       "13.50      19           19  \n",
       "13.60      11           11  \n",
       "13.90       2            2  \n",
       "14.00     392          392  \n",
       "14.10      11           11  \n",
       "14.20      14           14  \n",
       "14.40       6            6  \n",
       "14.50       3            3  \n",
       "15.00      21           21  \n",
       "15.30       8            8  \n",
       "15.40       5            5  \n",
       "15.60    1009         1009  \n",
       "16.00     174          174  \n",
       "16.10      48           48  \n",
       "16.20      10           10  \n",
       "17.00      33           33  \n",
       "17.30     161          161  \n",
       "18.00       3            3  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.groupby(['Screen']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "7a25769e-1cae-4449-b905-eba004e73a58",
   "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>Laptop</th>\n",
       "      <th>Status</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Model</th>\n",
       "      <th>CPU</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Storage</th>\n",
       "      <th>Storage type</th>\n",
       "      <th>GPU</th>\n",
       "      <th>Screen</th>\n",
       "      <th>Touch</th>\n",
       "      <th>Final Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Laptop, Status, Brand, Model, CPU, RAM, Storage, Storage type, GPU, Screen, Touch, Final Price]\n",
       "Index: []"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new[df_new['Screen']==' ']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68b7d8b0-cfb8-4e65-9a9e-1b4c1afd544f",
   "metadata": {},
   "outputs": [],
   "source": [
    " # Using FAQ suggestion about MODE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "49224caf-a8c3-475d-9f37-3a31af168e20",
   "metadata": {},
   "outputs": [],
   "source": [
    "mode = df_new['Screen'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "c4d56be1-323e-442c-8246-b2bd2c08e257",
   "metadata": {},
   "outputs": [],
   "source": [
    "single_valor_mode = mode[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "e96917cd-1f99-40b9-8cc4-6eebb7389613",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.6"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_valor_mode       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a4f9b22-9764-4ac3-a87d-c120764285ec",
   "metadata": {},
   "outputs": [],
   "source": [
    " # FILLNA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "80d55680-7e99-4498-97d3-0374e3b9f4c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new[\"Screen\"] = df_new[\"Screen\"].fillna(single_valor_mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "63a336db-768b-44ab-9140-a3e1450fdd52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['Screen'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "de9908c3-1bd3-477a-8dd6-6a5d68026d81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       15.6\n",
       "1       15.6\n",
       "2       15.6\n",
       "3       15.6\n",
       "4       15.6\n",
       "        ... \n",
       "2155    17.3\n",
       "2156    17.3\n",
       "2157    17.3\n",
       "2158    13.4\n",
       "2159    13.4\n",
       "Name: Screen, Length: 2160, dtype: float64"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new[\"Screen\"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "110acbf5-46a5-4e4e-b57c-d8f3b0d0f51f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.6"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['Screen'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88bda143-1d97-482b-bee3-dcff77d9b508",
   "metadata": {},
   "outputs": [],
   "source": [
    " # Q7. Sum of weights\r",
    " # 1. Select all the \"Innjoo\" laptops from the dataset. # 2. \r",
    " # 2. \n",
    "Select only columns RAM, Storage, Screen # 3. . # 3. \r\n",
    "Get the underlying  N umPy array. Let's call it  # 4. X # 4. .\r\n",
    "Compute matrix-matrix multiplication between the transpose of X and X. To get the transpose, use X.T. Let's call the result X # 5. T # 5. X.\r\n",
    "Compute the inverse 6. X # 6. TX.\r\n",
    "Create an array y with values [1100, 1300, 800, 900, 1000, 1 # 7. 1 # 7. 00].\r\n",
    "Multiply the inverse of XTX with the transpose of X, and then multiply the result by y. Call the res # 8. u # 8. lt w.\r\n",
    "What's the sum of all the elements of the  # r # esult?\r\n",
    "Note: You just implemented linear regression. We'll talk about it in the next  # lesso # n.\r\n",
    "\r\n",
    " # 0.43 # \r\n",
    " # 4 # 5.29\r\n",
    "0\n",
    "45.58\r\n",
    "91.30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "076ab8f1-d240-4033-aa01-34f53846efc3",
   "metadata": {},
   "outputs": [],
   "source": [
    " # 1. Select all the \"Innjoo\" laptops from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "be835785-9367-4d0c-b797-f9cf1827d83d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Injoo = df_new[df_new['Brand']=='Innjoo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "1dc04788-8286-45af-8f9e-529203c875c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Laptop</th>\n",
       "      <th>Status</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Model</th>\n",
       "      <th>CPU</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Storage</th>\n",
       "      <th>Storage type</th>\n",
       "      <th>GPU</th>\n",
       "      <th>Screen</th>\n",
       "      <th>Touch</th>\n",
       "      <th>Final Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1478</th>\n",
       "      <td>InnJoo Voom Excellence Intel Celeron N4020/8GB...</td>\n",
       "      <td>New</td>\n",
       "      <td>Innjoo</td>\n",
       "      <td>Voom</td>\n",
       "      <td>Intel Celeron</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>SSD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.6</td>\n",
       "      <td>No</td>\n",
       "      <td>311.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1479</th>\n",
       "      <td>InnJoo Voom Excellence Pro Intel Celeron N4020...</td>\n",
       "      <td>New</td>\n",
       "      <td>Innjoo</td>\n",
       "      <td>Voom</td>\n",
       "      <td>Intel Celeron</td>\n",
       "      <td>8</td>\n",
       "      <td>512</td>\n",
       "      <td>SSD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.6</td>\n",
       "      <td>No</td>\n",
       "      <td>392.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1480</th>\n",
       "      <td>Innjoo Voom Intel Celeron N3350/4GB/64GB eMMC/...</td>\n",
       "      <td>New</td>\n",
       "      <td>Innjoo</td>\n",
       "      <td>Voom</td>\n",
       "      <td>Intel Celeron</td>\n",
       "      <td>4</td>\n",
       "      <td>64</td>\n",
       "      <td>eMMC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.1</td>\n",
       "      <td>No</td>\n",
       "      <td>251.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1481</th>\n",
       "      <td>Innjoo Voom Laptop Max Intel Celeron N3350/6GB...</td>\n",
       "      <td>New</td>\n",
       "      <td>Innjoo</td>\n",
       "      <td>Voom</td>\n",
       "      <td>Intel Celeron</td>\n",
       "      <td>6</td>\n",
       "      <td>64</td>\n",
       "      <td>eMMC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.1</td>\n",
       "      <td>No</td>\n",
       "      <td>383.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1482</th>\n",
       "      <td>Innjoo Voom Laptop Pro Intel Celeron N3350/6GB...</td>\n",
       "      <td>New</td>\n",
       "      <td>Innjoo</td>\n",
       "      <td>Voom</td>\n",
       "      <td>Intel Celeron</td>\n",
       "      <td>6</td>\n",
       "      <td>128</td>\n",
       "      <td>SSD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.1</td>\n",
       "      <td>No</td>\n",
       "      <td>317.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1483</th>\n",
       "      <td>Innjoo Voom Pro Intel Celeron N3350/6GB/128GB ...</td>\n",
       "      <td>New</td>\n",
       "      <td>Innjoo</td>\n",
       "      <td>Voom</td>\n",
       "      <td>Intel Celeron</td>\n",
       "      <td>6</td>\n",
       "      <td>128</td>\n",
       "      <td>eMMC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.1</td>\n",
       "      <td>No</td>\n",
       "      <td>431.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Laptop Status   Brand Model  \\\n",
       "1478  InnJoo Voom Excellence Intel Celeron N4020/8GB...    New  Innjoo  Voom   \n",
       "1479  InnJoo Voom Excellence Pro Intel Celeron N4020...    New  Innjoo  Voom   \n",
       "1480  Innjoo Voom Intel Celeron N3350/4GB/64GB eMMC/...    New  Innjoo  Voom   \n",
       "1481  Innjoo Voom Laptop Max Intel Celeron N3350/6GB...    New  Innjoo  Voom   \n",
       "1482  Innjoo Voom Laptop Pro Intel Celeron N3350/6GB...    New  Innjoo  Voom   \n",
       "1483  Innjoo Voom Pro Intel Celeron N3350/6GB/128GB ...    New  Innjoo  Voom   \n",
       "\n",
       "                CPU  RAM  Storage Storage type  GPU  Screen Touch  Final Price  \n",
       "1478  Intel Celeron    8      256          SSD  NaN    15.6    No       311.37  \n",
       "1479  Intel Celeron    8      512          SSD  NaN    15.6    No       392.55  \n",
       "1480  Intel Celeron    4       64         eMMC  NaN    14.1    No       251.40  \n",
       "1481  Intel Celeron    6       64         eMMC  NaN    14.1    No       383.61  \n",
       "1482  Intel Celeron    6      128          SSD  NaN    14.1    No       317.02  \n",
       "1483  Intel Celeron    6      128         eMMC  NaN    14.1    No       431.38  "
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Injoo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2dc7bb2f-00ac-49de-8a2e-6834430dd587",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. Select only columns RAM, Storage, Screen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "668aa344-cdcd-4f34-bf4b-dea6810f5c7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_q7 = df_Injoo.loc[ : ,[ 'RAM', 'Storage','Screen'] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "43dee0d1-dd74-4e3f-a251-9ad7af3d6a99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RAM</th>\n",
       "      <th>Storage</th>\n",
       "      <th>Screen</th>\n",
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       "      <th>1478</th>\n",
       "      <td>8</td>\n",
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       "      <td>15.6</td>\n",
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       "    <tr>\n",
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       "      <td>8</td>\n",
       "      <td>512</td>\n",
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       "    <tr>\n",
       "      <th>1480</th>\n",
       "      <td>4</td>\n",
       "      <td>64</td>\n",
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       "    <tr>\n",
       "      <th>1481</th>\n",
       "      <td>6</td>\n",
       "      <td>64</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1482</th>\n",
       "      <td>6</td>\n",
       "      <td>128</td>\n",
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       "    <tr>\n",
       "      <th>1483</th>\n",
       "      <td>6</td>\n",
       "      <td>128</td>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      RAM  Storage  Screen\n",
       "1478    8      256    15.6\n",
       "1479    8      512    15.6\n",
       "1480    4       64    14.1\n",
       "1481    6       64    14.1\n",
       "1482    6      128    14.1\n",
       "1483    6      128    14.1"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_q7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b07b8a6f-7a85-4883-ae9c-b2b386132f5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. Get the underlying NumPy array. Let's call it X."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "51fac80c-cc6b-4bba-9664-f3bff075a190",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_q7.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "eb13e25f-3d69-49a1-be6b-ad339efdf0fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  8. , 256. ,  15.6],\n",
       "       [  8. , 512. ,  15.6],\n",
       "       [  4. ,  64. ,  14.1],\n",
       "       [  6. ,  64. ,  14.1],\n",
       "       [  6. , 128. ,  14.1],\n",
       "       [  6. , 128. ,  14.1]])"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "201e2fe9-c4a1-423a-8bb3-d215a56c814a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4. Compute matrix-matrix multiplication between the transpose of X and X. To get the transpose, use X.T. Let's call the result XTX."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "99ea139a-aaba-46f4-9c3d-9384f39ef1e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "XT = X.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "d5a0bd67-4b24-45ae-87c5-67be9708d5fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  8. ,   8. ,   4. ,   6. ,   6. ,   6. ],\n",
       "       [256. , 512. ,  64. ,  64. , 128. , 128. ],\n",
       "       [ 15.6,  15.6,  14.1,  14.1,  14.1,  14.1]])"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "577bafda-bd35-42a3-ae70-db7aa69b929b",
   "metadata": {},
   "outputs": [],
   "source": [
    "XTX = XT.dot(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "ceed9792-b723-4ae8-87de-acfe2eb494fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.52000e+02, 8.32000e+03, 5.59800e+02],\n",
       "       [8.32000e+03, 3.68640e+05, 1.73952e+04],\n",
       "       [5.59800e+02, 1.73952e+04, 1.28196e+03]])"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XTX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53479768-85e7-4bb7-ab43-e7e14ef95847",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5. Compute the inverse of XTX."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "ac4d2106-7893-45d4-a0ae-65c738df0aa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "XTX_inv = np.linalg.inv(XTX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "43641cb4-811c-43c5-b266-ebc8be8e7214",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.78025381e-01, -1.51791334e-03, -1.00809855e-01],\n",
       "       [-1.51791334e-03,  1.58286725e-05,  4.48052175e-04],\n",
       "       [-1.00809855e-01,  4.48052175e-04,  3.87214888e-02]])"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XTX_inv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab0ba134-9c3d-4bb5-842b-29b951e49a10",
   "metadata": {},
   "outputs": [],
   "source": [
    " # 6. Create an array y with values [1100, 1300, 800, 900, 1000, 1100]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "cf7482cb-35d1-4afa-8656-6dae04588b97",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array([1100, 1300, 800, 900, 1000, 1100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "30484749-e351-45a2-8434-e64bf719836a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1100, 1300,  800,  900, 1000, 1100])"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c9d5ff3-23ea-441a-867d-43eb90430c8c",
   "metadata": {},
   "outputs": [],
   "source": [
    " # 7. Multiply the inverse of XTX with the transpose of X, and then multiply the result by y. Call the result w."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "5fc482a0-aed2-4b1b-bc9e-e5c576eb4432",
   "metadata": {},
   "outputs": [],
   "source": [
    "  w = XTX_inv.dot(XT) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "07e21f11-6356-4eb5-bd4e-91576652632f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.26298349, -0.12560233, -0.40646389,  0.14958687,  0.05244042,\n",
       "         0.05244042],\n",
       "       [-0.00110155,  0.00295059,  0.00125892, -0.00177691, -0.00076387,\n",
       "        -0.00076387],\n",
       "       [-0.08772226,  0.0269791 ,  0.17140891, -0.0302108 , -0.00153546,\n",
       "        -0.00153546]])"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "167fb72c-521d-4e40-a871-6ca63a1357bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "   w = w.dot(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "aa162fa7-15d1-4abb-abbc-64d3a8f8eb38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([45.58076606,  0.42783519, 45.29127938])"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e75031cf-135a-4e4b-86b5-044b75d3b473",
   "metadata": {},
   "outputs": [],
   "source": [
    " # 8. What's the sum of all the elements of the result?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "a6a1205f-bb98-4211-ab9d-cc1ceb1e9099",
   "metadata": {},
   "outputs": [],
   "source": [
    "sum_w = w.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "c78ad7ae-7936-4168-b99c-e92f8127e518",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91.29988062995815"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "162a78d0-dd60-407a-ad68-b227f73f706a",
   "metadata": {},
   "outputs": [],
   "source": [
    " # END"
   ]
  }
 ],
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