cohorts/2024/01-intro/homework_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "251d40a3",
   "metadata": {
    "id": "251d40a3"
   },
   "source": [
    "## Homework #1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1cfc63c",
   "metadata": {
    "id": "b1cfc63c"
   },
   "source": [
    "### Set up the environment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "545d3529",
   "metadata": {
    "id": "545d3529"
   },
   "source": [
    "You need to install Python, NumPy, Pandas, Matplotlib and Seaborn. For that, you can the instructions from <a href='https://github.com/alexeygrigorev/mlbookcamp-code/blob/master/course-zoomcamp/01-intro/06-environment.md'>06-environment.md</a>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "92a54c41",
   "metadata": {
    "executionInfo": {
     "elapsed": 706,
     "status": "ok",
     "timestamp": 1694208135401,
     "user": {
      "displayName": "Timur Kamaliev",
      "userId": "12270392991844786490"
     },
     "user_tz": -180
    },
    "id": "92a54c41",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "351c8283",
   "metadata": {
    "id": "351c8283"
   },
   "source": [
    "### Question 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8024830",
   "metadata": {
    "id": "e8024830"
   },
   "source": [
    "What's the version of Pandas that you installed?\n",
    "\n",
    "You can get the version information using the `__version__` field:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cd014841",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1694208136053,
     "user": {
      "displayName": "Timur Kamaliev",
      "userId": "12270392991844786490"
     },
     "user_tz": -180
    },
    "id": "cd014841",
    "outputId": "69d7c606-95a6-4a40-98a0-0f85927705e2",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.0.3'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ceefa5c",
   "metadata": {
    "id": "4ceefa5c"
   },
   "source": [
    "### Getting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cf7db9d",
   "metadata": {
    "id": "2cf7db9d"
   },
   "source": [
    "For this homework, we'll use the California Housing Prices dataset. Download it from <a href='https://raw.githubusercontent.com/alexeygrigorev/datasets/master/housing.csv'>here</a>.\n",
    "\n",
    "You can do it with wget:\n",
    "```bash\n",
    "wget https://raw.githubusercontent.com/alexeygrigorev/datasets/master/housing.csv\n",
    "```\n",
    "Or just open it with your browser and click \"Save as...\".\n",
    "\n",
    "Now read it with Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1fa83ea3",
   "metadata": {
    "executionInfo": {
     "elapsed": 284,
     "status": "ok",
     "timestamp": 1694208138879,
     "user": {
      "displayName": "Timur Kamaliev",
      "userId": "12270392991844786490"
     },
     "user_tz": -180
    },
    "id": "1fa83ea3",
    "tags": []
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"https://raw.githubusercontent.com/alexeygrigorev/datasets/master/laptops.csv\")\n",
    "# data = pd.read_csv(\"data/laptops.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cf2aa356",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1694208139499,
     "user": {
      "displayName": "Timur Kamaliev",
      "userId": "12270392991844786490"
     },
     "user_tz": -180
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    "id": "cf2aa356",
    "outputId": "319e25c2-3ce0-45bd-e707-8b124a62fd3d",
    "tags": []
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "  </tbody>\n",
       "</table>\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",
       "        Model            CPU  RAM  Storage Storage type       GPU  Screen  \\\n",
       "0  ExpertBook  Intel Core i5    8      512          SSD       NaN    15.6   \n",
       "1          Go  Intel Celeron    8      256          SSD       NaN    15.6   \n",
       "2  ExpertBook  Intel Core i3    8      256          SSD       NaN    15.6   \n",
       "3      Katana  Intel Core i7   16     1000          SSD  RTX 3050    15.6   \n",
       "4         15S  Intel Core i5   16      512          SSD       NaN    15.6   \n",
       "\n",
       "  Touch  Final Price  \n",
       "0    No      1009.00  \n",
       "1    No       299.00  \n",
       "2    No       789.00  \n",
       "3    No      1199.00  \n",
       "4    No       669.01  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aecac649",
   "metadata": {
    "id": "aecac649"
   },
   "source": [
    "### Question 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab65645c",
   "metadata": {
    "id": "ab65645c"
   },
   "source": [
    "How many records are in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c73d4d30",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 286,
     "status": "ok",
     "timestamp": 1694208141779,
     "user": {
      "displayName": "Timur Kamaliev",
      "userId": "12270392991844786490"
     },
     "user_tz": -180
    },
    "id": "c73d4d30",
    "outputId": "e16c4908-c136-49ff-b6a5-b9168e06f18c",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2160"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5d128d0",
   "metadata": {
    "id": "b5d128d0"
   },
   "source": [
    "### Question 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "511821c3-8513-4648-bd75-7955bb362fce",
   "metadata": {},
   "source": [
    "How many laptop brands are presented in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ed9eaa6e-6184-4d05-ace5-b2507a008ccf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Brand.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d88d5778-9334-4b49-9a2b-47d375fefa41",
   "metadata": {
    "tags": []
   },
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90b3262c",
   "metadata": {
    "id": "90b3262c"
   },
   "source": [
    "### Question 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "JtB8acmV4jrf",
   "metadata": {
    "id": "JtB8acmV4jrf"
   },
   "source": [
    "How many columns in the dataset have missing values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7e6b2e07-871d-405c-a7aa-bc957ed4e530",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 301,
     "status": "ok",
     "timestamp": 1694208145679,
     "user": {
      "displayName": "Timur Kamaliev",
      "userId": "12270392991844786490"
     },
     "user_tz": -180
    },
    "id": "7e6b2e07-871d-405c-a7aa-bc957ed4e530",
    "outputId": "6d9697ce-752f-4a9c-aebc-486be364ed88",
    "tags": []
   },
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ctux01eQsN2p",
   "metadata": {
    "id": "ctux01eQsN2p"
   },
   "source": [
    "3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "987507a8",
   "metadata": {
    "id": "987507a8"
   },
   "source": [
    "### Question 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "n0kM9M3O3Zuz",
   "metadata": {
    "id": "n0kM9M3O3Zuz"
   },
   "source": [
    "What's the maximum final price of Dell notebooks in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "SgnkG0qUvJMd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 237
    },
    "executionInfo": {
     "elapsed": 247,
     "status": "ok",
     "timestamp": 1694208167900,
     "user": {
      "displayName": "Timur Kamaliev",
      "userId": "12270392991844786490"
     },
     "user_tz": -180
    },
    "id": "SgnkG0qUvJMd",
    "outputId": "110e64c6-859b-4a16-9f3f-3fd0b4163c6e"
   },
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brand</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Acer</th>\n",
       "      <td>264.14</td>\n",
       "      <td>1001.285766</td>\n",
       "      <td>3691.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alurin</th>\n",
       "      <td>239.00</td>\n",
       "      <td>484.701379</td>\n",
       "      <td>869.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apple</th>\n",
       "      <td>299.00</td>\n",
       "      <td>1578.227672</td>\n",
       "      <td>3849.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asus</th>\n",
       "      <td>239.25</td>\n",
       "      <td>1269.380699</td>\n",
       "      <td>5758.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deep Gaming</th>\n",
       "      <td>1334.00</td>\n",
       "      <td>1505.377500</td>\n",
       "      <td>1639.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dell</th>\n",
       "      <td>379.00</td>\n",
       "      <td>1153.839881</td>\n",
       "      <td>3936.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denver</th>\n",
       "      <td>329.95</td>\n",
       "      <td>329.950000</td>\n",
       "      <td>329.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dynabook Toshiba</th>\n",
       "      <td>397.29</td>\n",
       "      <td>999.197895</td>\n",
       "      <td>1805.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gigabyte</th>\n",
       "      <td>799.00</td>\n",
       "      <td>1698.488958</td>\n",
       "      <td>3799.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HP</th>\n",
       "      <td>210.14</td>\n",
       "      <td>952.628478</td>\n",
       "      <td>5368.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Innjoo</th>\n",
       "      <td>251.40</td>\n",
       "      <td>347.888333</td>\n",
       "      <td>431.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jetwing</th>\n",
       "      <td>469.27</td>\n",
       "      <td>469.270000</td>\n",
       "      <td>469.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LG</th>\n",
       "      <td>609.04</td>\n",
       "      <td>1730.028438</td>\n",
       "      <td>2399.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lenovo</th>\n",
       "      <td>239.00</td>\n",
       "      <td>1087.723880</td>\n",
       "      <td>5018.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSI</th>\n",
       "      <td>444.19</td>\n",
       "      <td>1913.451006</td>\n",
       "      <td>7150.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medion</th>\n",
       "      <td>246.89</td>\n",
       "      <td>905.331875</td>\n",
       "      <td>3799.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Microsoft</th>\n",
       "      <td>599.01</td>\n",
       "      <td>1595.543247</td>\n",
       "      <td>3747.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Millenium</th>\n",
       "      <td>1640.98</td>\n",
       "      <td>1976.845000</td>\n",
       "      <td>2312.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PcCom</th>\n",
       "      <td>1049.00</td>\n",
       "      <td>1606.762917</td>\n",
       "      <td>1949.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Primux</th>\n",
       "      <td>208.99</td>\n",
       "      <td>339.618750</td>\n",
       "      <td>599.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prixton</th>\n",
       "      <td>246.95</td>\n",
       "      <td>292.280000</td>\n",
       "      <td>329.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Razer</th>\n",
       "      <td>1232.74</td>\n",
       "      <td>3265.701622</td>\n",
       "      <td>4999.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Realme</th>\n",
       "      <td>999.00</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>999.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Samsung</th>\n",
       "      <td>365.90</td>\n",
       "      <td>1595.511364</td>\n",
       "      <td>3699.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thomson</th>\n",
       "      <td>201.05</td>\n",
       "      <td>316.860000</td>\n",
       "      <td>436.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toshiba</th>\n",
       "      <td>799.00</td>\n",
       "      <td>799.000000</td>\n",
       "      <td>799.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vant</th>\n",
       "      <td>632.00</td>\n",
       "      <td>1047.455455</td>\n",
       "      <td>1217.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      min         mean      max\n",
       "Brand                                          \n",
       "Acer               264.14  1001.285766  3691.00\n",
       "Alurin             239.00   484.701379   869.00\n",
       "Apple              299.00  1578.227672  3849.00\n",
       "Asus               239.25  1269.380699  5758.14\n",
       "Deep Gaming       1334.00  1505.377500  1639.01\n",
       "Dell               379.00  1153.839881  3936.00\n",
       "Denver             329.95   329.950000   329.95\n",
       "Dynabook Toshiba   397.29   999.197895  1805.01\n",
       "Gigabyte           799.00  1698.488958  3799.00\n",
       "HP                 210.14   952.628478  5368.77\n",
       "Innjoo             251.40   347.888333   431.38\n",
       "Jetwing            469.27   469.270000   469.27\n",
       "LG                 609.04  1730.028438  2399.00\n",
       "Lenovo             239.00  1087.723880  5018.14\n",
       "MSI                444.19  1913.451006  7150.47\n",
       "Medion             246.89   905.331875  3799.00\n",
       "Microsoft          599.01  1595.543247  3747.91\n",
       "Millenium         1640.98  1976.845000  2312.71\n",
       "PcCom             1049.00  1606.762917  1949.90\n",
       "Primux             208.99   339.618750   599.41\n",
       "Prixton            246.95   292.280000   329.95\n",
       "Razer             1232.74  3265.701622  4999.01\n",
       "Realme             999.00   999.000000   999.00\n",
       "Samsung            365.90  1595.511364  3699.01\n",
       "Thomson            201.05   316.860000   436.56\n",
       "Toshiba            799.00   799.000000   799.00\n",
       "Vant               632.00  1047.455455  1217.01"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Brand')['Final Price'].agg(['min', 'mean', 'max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "McMWSfTPtqDR",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 272,
     "status": "ok",
     "timestamp": 1694208169667,
     "user": {
      "displayName": "Timur Kamaliev",
      "userId": "12270392991844786490"
     },
     "user_tz": -180
    },
    "id": "McMWSfTPtqDR",
    "outputId": "a1782e85-9771-4e22-f882-85e1bcd4ab72"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3936.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.Brand == 'Dell']['Final Price'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5758929",
   "metadata": {
    "id": "a5758929"
   },
   "source": [
    "### Question 6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c359a2a",
   "metadata": {
    "id": "0c359a2a"
   },
   "source": [
    "\n",
    "\n",
    "1. Find the median value of `Screen` column in the dataset.\n",
    "2. Next, calculate the most frequent value of the same `Screen` column.\n",
    "3. Use `fillna` method to fill the missing values in `Screen` column with the most frequent value from the previous step.\n",
    "4. Now, calculate the median value of `Screen` once again.\n",
    "\n",
    "Has it changed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "h50zj0Na5p6y",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 277,
     "status": "ok",
     "timestamp": 1694210300971,
     "user": {
      "displayName": "Timur Kamaliev",
      "userId": "12270392991844786490"
     },
     "user_tz": -180
    },
    "id": "h50zj0Na5p6y",
    "outputId": "07067b58-ce33-45f3-f784-ff689d336544"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.6"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_screen = data['Screen'].median()\n",
    "median_screen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1e5e8f1a-20dd-4143-b205-ce949bd6992e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.6"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mode_screen = data['Screen'].mode()[0]\n",
    "mode_screen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "lW-cYPtprtGX",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 246,
     "status": "ok",
     "timestamp": 1694208397959,
     "user": {
      "displayName": "Timur Kamaliev",
      "userId": "12270392991844786490"
     },
     "user_tz": -180
    },
    "id": "lW-cYPtprtGX",
    "outputId": "1566f283-0337-463f-f5b2-7dc795f48114"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.6"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Screen'].fillna(mode_screen).median()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b1263c0",
   "metadata": {
    "id": "7b1263c0"
   },
   "source": [
    "No, it's left the same."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f44769a",
   "metadata": {
    "id": "3f44769a"
   },
   "source": [
    "### Question 7"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe2faf20",
   "metadata": {
    "id": "fe2faf20"
   },
   "source": [
    "1. Select all the \"Innjoo\" laptops from the dataset.\n",
    "2. Select only columns `RAM`, `Storage`, `Screen`.\n",
    "3. Get the underlying NumPy array. Let's call it `X`.\n",
    "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`.\n",
    "5. Invert `XTX`.\n",
    "6. Create an array `y` with values `[1100, 1300, 800, 900, 1000, 1100]`.\n",
    "7. Multiply the inverse of `XTX` with the transpose of `X`, and then multiply the result by `y`. Call the result `w`.\n",
    "8. What's the sum of all the elements of the result?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "HhziJLnz6azH",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1694210142110,
     "user": {
      "displayName": "Timur Kamaliev",
      "userId": "12270392991844786490"
     },
     "user_tz": -180
    },
    "id": "HhziJLnz6azH",
    "outputId": "0e8337e0-4942-4fba-c841-9d5999e5cf69"
   },
   "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>RAM</th>\n",
       "      <th>Storage</th>\n",
       "      <th>Screen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1478</th>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>15.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1479</th>\n",
       "      <td>8</td>\n",
       "      <td>512</td>\n",
       "      <td>15.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1480</th>\n",
       "      <td>4</td>\n",
       "      <td>64</td>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1481</th>\n",
       "      <td>6</td>\n",
       "      <td>64</td>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1482</th>\n",
       "      <td>6</td>\n",
       "      <td>128</td>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "    <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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "innjoo_df = data[data.Brand == 'Innjoo']\n",
    "innjoo_df = innjoo_df[['RAM', 'Storage', 'Screen']]\n",
    "innjoo_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "BMSgjlTV6a48",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 241,
     "status": "ok",
     "timestamp": 1694209443845,
     "user": {
      "displayName": "Timur Kamaliev",
      "userId": "12270392991844786490"
     },
     "user_tz": -180
    },
    "id": "BMSgjlTV6a48",
    "outputId": "94d51bb0-019c-4180-97da-201c850da575"
   },
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = innjoo_df.values\n",
    "XTX = X.T.dot(X)\n",
    "\n",
    "XTX_inv = np.linalg.inv(XTX)\n",
    "XTX_inv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0AoSPilS6a7S",
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1694209445937,
     "user": {
      "displayName": "Timur Kamaliev",
      "userId": "12270392991844786490"
     },
     "user_tz": -180
    },
    "id": "0AoSPilS6a7S"
   },
   "outputs": [],
   "source": [
    "y = np.array([1100, 1300, 800, 900, 1000, 1100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dz3BmJfIvfdp",
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1694209446791,
     "user": {
      "displayName": "Timur Kamaliev",
      "userId": "12270392991844786490"
     },
     "user_tz": -180
    },
    "id": "dz3BmJfIvfdp"
   },
   "outputs": [],
   "source": [
    "w = (XTX_inv @ X.T) @ y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5e62860c-4afe-4a33-942f-ec238924b027",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([45.58076606,  0.42783519, 45.29127938])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ouoIQygN6bD3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1694209447851,
     "user": {
      "displayName": "Timur Kamaliev",
      "userId": "12270392991844786490"
     },
     "user_tz": -180
    },
    "id": "ouoIQygN6bD3",
    "outputId": "895ebf5f-51b4-4ec5-ae5b-dce89abb2cb5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91.29988062995815"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(w)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70e3eaa9",
   "metadata": {
    "id": "70e3eaa9"
   },
   "source": [
    "> **Note**: we just implemented normal equation\n",
    "\n",
    "\n",
    "$$w = (X^T X)^{-1} X^T y$$\n",
    "\n",
    "\n",
    "We'll talk about it more in the next week (Machine Learning for Regression)"
   ]
  }
 ],
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