Session #1 Homework.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "506b9577",
   "metadata": {},
   "source": [
    "# Session #1 Homework"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4cf837b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting wget\n",
      "  Downloading wget-3.2.zip (10 kB)\n",
      "Building wheels for collected packages: wget\n",
      "  Building wheel for wget (setup.py): started\n",
      "  Building wheel for wget (setup.py): finished with status 'done'\n",
      "  Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9675 sha256=71611960be62f437aeb26fdecaac54607fd62c1f2910842cbd35611545043876\n",
      "  Stored in directory: c:\\users\\darkcorp\\appdata\\local\\pip\\cache\\wheels\\04\\5f\\3e\\46cc37c5d698415694d83f607f833f83f0149e49b3af9d0f38\n",
      "Successfully built wget\n",
      "Installing collected packages: wget\n",
      "Successfully installed wget-3.2\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install wget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0d80d678",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import wget"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3296553",
   "metadata": {},
   "source": [
    "### Question 1\n",
    "What's the version of NumPy that you installed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3c77005d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.21.5'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Q1\n",
    "np.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da6655c1",
   "metadata": {},
   "source": [
    "### Getting the data\n",
    "For this homework, we'll use the Car price dataset. Download it from here.\n",
    "\n",
    "You can do it with wget:\n",
    "\n",
    "`wget https://raw.githubusercontent.com/alexeygrigorev/mlbookcamp-code/master/chapter-02-car-price/data.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": 7,
   "id": "fbb293c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/alexeygrigorev/mlbookcamp-code/master/chapter-02-car-price/data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "75172a88",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Year</th>\n",
       "      <th>Engine Fuel Type</th>\n",
       "      <th>Engine HP</th>\n",
       "      <th>Engine Cylinders</th>\n",
       "      <th>Transmission Type</th>\n",
       "      <th>Driven_Wheels</th>\n",
       "      <th>Number of Doors</th>\n",
       "      <th>Market Category</th>\n",
       "      <th>Vehicle Size</th>\n",
       "      <th>Vehicle Style</th>\n",
       "      <th>highway MPG</th>\n",
       "      <th>city mpg</th>\n",
       "      <th>Popularity</th>\n",
       "      <th>MSRP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series M</td>\n",
       "      <td>2011</td>\n",
       "      <td>premium unleaded (required)</td>\n",
       "      <td>335.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Factory Tuner,Luxury,High-Performance</td>\n",
       "      <td>Compact</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>3916</td>\n",
       "      <td>46135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>premium unleaded (required)</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Luxury,Performance</td>\n",
       "      <td>Compact</td>\n",
       "      <td>Convertible</td>\n",
       "      <td>28</td>\n",
       "      <td>19</td>\n",
       "      <td>3916</td>\n",
       "      <td>40650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>premium unleaded (required)</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Luxury,High-Performance</td>\n",
       "      <td>Compact</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "      <td>3916</td>\n",
       "      <td>36350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>premium unleaded (required)</td>\n",
       "      <td>230.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Luxury,Performance</td>\n",
       "      <td>Compact</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>3916</td>\n",
       "      <td>29450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>premium unleaded (required)</td>\n",
       "      <td>230.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Compact</td>\n",
       "      <td>Convertible</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>3916</td>\n",
       "      <td>34500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Make       Model  Year             Engine Fuel Type  Engine HP  \\\n",
       "0  BMW  1 Series M  2011  premium unleaded (required)      335.0   \n",
       "1  BMW    1 Series  2011  premium unleaded (required)      300.0   \n",
       "2  BMW    1 Series  2011  premium unleaded (required)      300.0   \n",
       "3  BMW    1 Series  2011  premium unleaded (required)      230.0   \n",
       "4  BMW    1 Series  2011  premium unleaded (required)      230.0   \n",
       "\n",
       "   Engine Cylinders Transmission Type     Driven_Wheels  Number of Doors  \\\n",
       "0               6.0            MANUAL  rear wheel drive              2.0   \n",
       "1               6.0            MANUAL  rear wheel drive              2.0   \n",
       "2               6.0            MANUAL  rear wheel drive              2.0   \n",
       "3               6.0            MANUAL  rear wheel drive              2.0   \n",
       "4               6.0            MANUAL  rear wheel drive              2.0   \n",
       "\n",
       "                         Market Category Vehicle Size Vehicle Style  \\\n",
       "0  Factory Tuner,Luxury,High-Performance      Compact         Coupe   \n",
       "1                     Luxury,Performance      Compact   Convertible   \n",
       "2                Luxury,High-Performance      Compact         Coupe   \n",
       "3                     Luxury,Performance      Compact         Coupe   \n",
       "4                                 Luxury      Compact   Convertible   \n",
       "\n",
       "   highway MPG  city mpg  Popularity   MSRP  \n",
       "0           26        19        3916  46135  \n",
       "1           28        19        3916  40650  \n",
       "2           28        20        3916  36350  \n",
       "3           28        18        3916  29450  \n",
       "4           28        18        3916  34500  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "076ca7bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            BMW\n",
       "1            BMW\n",
       "2            BMW\n",
       "3            BMW\n",
       "4            BMW\n",
       "          ...   \n",
       "11909      Acura\n",
       "11910      Acura\n",
       "11911      Acura\n",
       "11912      Acura\n",
       "11913    Lincoln\n",
       "Name: Make, Length: 11914, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Make']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81f0ecc2",
   "metadata": {},
   "source": [
    "### Question 2\n",
    "How many records are in the dataset?\n",
    "\n",
    "Here you need to specify the number of rows.\n",
    "\n",
    "* 16\n",
    "* 6572\n",
    "* **11914**\n",
    "* 18990"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5293b8d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Engine HP</th>\n",
       "      <th>Engine Cylinders</th>\n",
       "      <th>Number of Doors</th>\n",
       "      <th>highway MPG</th>\n",
       "      <th>city mpg</th>\n",
       "      <th>Popularity</th>\n",
       "      <th>MSRP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11914.00</td>\n",
       "      <td>11845.00</td>\n",
       "      <td>11884.00</td>\n",
       "      <td>11908.00</td>\n",
       "      <td>11914.00</td>\n",
       "      <td>11914.00</td>\n",
       "      <td>11914.00</td>\n",
       "      <td>11914.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2010.38</td>\n",
       "      <td>249.39</td>\n",
       "      <td>5.63</td>\n",
       "      <td>3.44</td>\n",
       "      <td>26.64</td>\n",
       "      <td>19.73</td>\n",
       "      <td>1554.91</td>\n",
       "      <td>40594.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.58</td>\n",
       "      <td>109.19</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0.88</td>\n",
       "      <td>8.86</td>\n",
       "      <td>8.99</td>\n",
       "      <td>1441.86</td>\n",
       "      <td>60109.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1990.00</td>\n",
       "      <td>55.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2007.00</td>\n",
       "      <td>170.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>549.00</td>\n",
       "      <td>21000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2015.00</td>\n",
       "      <td>227.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>26.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>1385.00</td>\n",
       "      <td>29995.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>2009.00</td>\n",
       "      <td>42231.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2017.00</td>\n",
       "      <td>1001.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>354.00</td>\n",
       "      <td>137.00</td>\n",
       "      <td>5657.00</td>\n",
       "      <td>2065902.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Year  Engine HP  Engine Cylinders  Number of Doors  highway MPG  \\\n",
       "count  11914.00   11845.00          11884.00         11908.00     11914.00   \n",
       "mean    2010.38     249.39              5.63             3.44        26.64   \n",
       "std        7.58     109.19              1.78             0.88         8.86   \n",
       "min     1990.00      55.00              0.00             2.00        12.00   \n",
       "25%     2007.00     170.00              4.00             2.00        22.00   \n",
       "50%     2015.00     227.00              6.00             4.00        26.00   \n",
       "75%     2016.00     300.00              6.00             4.00        30.00   \n",
       "max     2017.00    1001.00             16.00             4.00       354.00   \n",
       "\n",
       "       city mpg  Popularity        MSRP  \n",
       "count  11914.00    11914.00    11914.00  \n",
       "mean      19.73     1554.91    40594.74  \n",
       "std        8.99     1441.86    60109.10  \n",
       "min        7.00        2.00     2000.00  \n",
       "25%       16.00      549.00    21000.00  \n",
       "50%       18.00     1385.00    29995.00  \n",
       "75%       22.00     2009.00    42231.25  \n",
       "max      137.00     5657.00  2065902.00  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "950d241d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=11914, step=1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ca1eade",
   "metadata": {},
   "source": [
    "### Question 3\n",
    "Who are the most frequent car manufacturers (top-3) according to the dataset?\n",
    "\n",
    "* Chevrolet, Volkswagen, Toyota\n",
    "* Chevrolet, Ford, Toyota\n",
    "* Ford, Volkswagen, Toyota\n",
    "* **Chevrolet, Ford, Volkswagen**\n",
    "\n",
    "Note: You should rely on \"Make\" column in this question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a28bfc8a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         11914\n",
       "unique           48\n",
       "top       Chevrolet\n",
       "freq           1123\n",
       "Name: Make, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Make'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d5094878",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Year</th>\n",
       "      <th>Engine Fuel Type</th>\n",
       "      <th>Engine HP</th>\n",
       "      <th>Engine Cylinders</th>\n",
       "      <th>Transmission Type</th>\n",
       "      <th>Driven_Wheels</th>\n",
       "      <th>Number of Doors</th>\n",
       "      <th>Market Category</th>\n",
       "      <th>Vehicle Size</th>\n",
       "      <th>Vehicle Style</th>\n",
       "      <th>highway MPG</th>\n",
       "      <th>city mpg</th>\n",
       "      <th>Popularity</th>\n",
       "      <th>MSRP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Make</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chevrolet</th>\n",
       "      <td>1123</td>\n",
       "      <td>1123</td>\n",
       "      <td>1123</td>\n",
       "      <td>1117</td>\n",
       "      <td>1121</td>\n",
       "      <td>1123</td>\n",
       "      <td>1123</td>\n",
       "      <td>1123</td>\n",
       "      <td>622</td>\n",
       "      <td>1123</td>\n",
       "      <td>1123</td>\n",
       "      <td>1123</td>\n",
       "      <td>1123</td>\n",
       "      <td>1123</td>\n",
       "      <td>1123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ford</th>\n",
       "      <td>881</td>\n",
       "      <td>881</td>\n",
       "      <td>881</td>\n",
       "      <td>868</td>\n",
       "      <td>881</td>\n",
       "      <td>881</td>\n",
       "      <td>881</td>\n",
       "      <td>881</td>\n",
       "      <td>499</td>\n",
       "      <td>881</td>\n",
       "      <td>881</td>\n",
       "      <td>881</td>\n",
       "      <td>881</td>\n",
       "      <td>881</td>\n",
       "      <td>881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Volkswagen</th>\n",
       "      <td>809</td>\n",
       "      <td>809</td>\n",
       "      <td>809</td>\n",
       "      <td>809</td>\n",
       "      <td>805</td>\n",
       "      <td>809</td>\n",
       "      <td>809</td>\n",
       "      <td>809</td>\n",
       "      <td>585</td>\n",
       "      <td>809</td>\n",
       "      <td>809</td>\n",
       "      <td>809</td>\n",
       "      <td>809</td>\n",
       "      <td>809</td>\n",
       "      <td>809</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Model  Year  Engine Fuel Type  Engine HP  Engine Cylinders  \\\n",
       "Make                                                                     \n",
       "Chevrolet    1123  1123              1123       1117              1121   \n",
       "Ford          881   881               881        868               881   \n",
       "Volkswagen    809   809               809        809               805   \n",
       "\n",
       "            Transmission Type  Driven_Wheels  Number of Doors  \\\n",
       "Make                                                            \n",
       "Chevrolet                1123           1123             1123   \n",
       "Ford                      881            881              881   \n",
       "Volkswagen                809            809              809   \n",
       "\n",
       "            Market Category  Vehicle Size  Vehicle Style  highway MPG  \\\n",
       "Make                                                                    \n",
       "Chevrolet               622          1123           1123         1123   \n",
       "Ford                    499           881            881          881   \n",
       "Volkswagen              585           809            809          809   \n",
       "\n",
       "            city mpg  Popularity  MSRP  \n",
       "Make                                    \n",
       "Chevrolet       1123        1123  1123  \n",
       "Ford             881         881   881  \n",
       "Volkswagen       809         809   809  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Make').count().sort_values('Model', ascending=False).head(n=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f71116e6",
   "metadata": {},
   "source": [
    "### Question 4\n",
    "What's the number of unique Audi car models in the dataset?\n",
    "\n",
    "* 3\n",
    "* 16\n",
    "* 26\n",
    "* **34**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e780b469",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Model'][\n",
    "            (df['Make'] == 'Audi')\n",
    "            ].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f33b3e5",
   "metadata": {},
   "source": [
    "### Question 5\n",
    "How many columns in the dataset have missing values?\n",
    "\n",
    "* **5**\n",
    "* 6\n",
    "* 7\n",
    "* 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "18307fb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                    0\n",
       "Model                   0\n",
       "Year                    0\n",
       "Engine Fuel Type        3\n",
       "Engine HP              69\n",
       "Engine Cylinders       30\n",
       "Transmission Type       0\n",
       "Driven_Wheels           0\n",
       "Number of Doors         6\n",
       "Market Category      3742\n",
       "Vehicle Size            0\n",
       "Vehicle Style           0\n",
       "highway MPG             0\n",
       "city mpg                0\n",
       "Popularity              0\n",
       "MSRP                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b234f07",
   "metadata": {},
   "source": [
    "### Question 6\n",
    "Find the median value of \"Engine Cylinders\" column in the dataset.\n",
    "\n",
    "Next, calculate the most frequent value of the same \"Engine Cylinders\".\n",
    "\n",
    "Use the fillna method to fill the missing values in \"Engine Cylinders\" with the most frequent value from the previous step.\n",
    "\n",
    "Now, calculate the median value of \"Engine Cylinders\" once again.\n",
    "\n",
    "Has it changed?\n",
    "\n",
    "Hint: refer to existing mode and median functions to complete the task.\n",
    "\n",
    "* Yes\n",
    "* **No**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1bce5ed8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eng_cyl_median = df['Engine Cylinders'].median()\n",
    "eng_cyl_median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e574d4fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4.0\n",
       "Name: Engine Cylinders, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eng_cyl_mode = df['Engine Cylinders'].mode()\n",
    "eng_cyl_mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0c72ea18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Engine Cylinders'].fillna(eng_cyl_mode).median()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be48bd6c",
   "metadata": {},
   "source": [
    "### Question 7\n",
    "1. Select all the \"Lotus\" cars from the dataset.\n",
    "2. Select only columns \"Engine HP\", \"Engine Cylinders\".\n",
    "3. Now drop all duplicated rows using drop_duplicates method (you should get a dataframe with 9 rows).\n",
    "4. Get the underlying NumPy array. Let's call it X.\n",
    "5. 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",
    "6. Invert XTX.\n",
    "7. Create an array y with values [1100, 800, 750, 850, 1300, 1000, 1000, 1300, 800].\n",
    "8. Multiply the inverse of XTX with the transpose of X, and then multiply the result by y. Call the result w.\n",
    "9. What's the value of the first element of w?\n",
    "\n",
    "Note: You just implemented linear regression. We'll talk about it in the next lesson.\n",
    "\n",
    "* -0.0723\n",
    "* **4.5949**\n",
    "* 31.6537\n",
    "* 63.5643"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "37c486cc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[189.,   4.],\n",
       "       [218.,   4.],\n",
       "       [217.,   4.],\n",
       "       [350.,   8.],\n",
       "       [400.,   6.],\n",
       "       [276.,   6.],\n",
       "       [345.,   6.],\n",
       "       [257.,   4.],\n",
       "       [240.,   4.]])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select all the \"Lotus\" cars from the dataset.\n",
    "df_lotus = df[\n",
    "                df['Make'] == 'Lotus'\n",
    "]\n",
    "\n",
    "# Select only columns \"Engine HP\", \"Engine Cylinders\".\n",
    "# Now drop all duplicated rows using drop_duplicates method (you should get a dataframe with 9 rows).\n",
    "# Get the underlying NumPy array. Let's call it X.\n",
    "X = df_lotus[['Engine HP', 'Engine Cylinders']].drop_duplicates().values\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "7efad45f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[189., 218., 217., 350., 400., 276., 345., 257., 240.],\n",
       "       [  4.,   4.,   4.,   8.,   6.,   6.,   6.,   4.,   4.]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute matrix-matrix multiplication between the transpose of X and X. To get the transpose, use X.T.\n",
    "XT = X.T\n",
    "XT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "cb1d273d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.31684e+05, 1.34100e+04],\n",
       "       [1.34100e+04, 2.52000e+02]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's call the result XTX. \n",
    "XTX = XT.dot(X)\n",
    "XTX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "cce1647a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.53084235e-05, -2.94319825e-03],\n",
       "       [-2.94319825e-03,  1.60588447e-01]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Invert XTX.\n",
    "XTX_inv = np.linalg.inv(XTX)\n",
    "XTX_inv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "aafe04c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1100,  800,  750,  850, 1300, 1000, 1000, 1300,  800])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an array y with values [1100, 800, 750, 850, 1300, 1000, 1000, 1300, 800].\n",
    "y = np.array([1100, 800, 750, 850, 1300, 1000, 1000, 1300, 800])\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "fd2e3c1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  4.59494481, -63.56432501])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Multiply the inverse of XTX with the transpose of X, and then multiply the result by y. Call the result w.\n",
    "w = XTX_inv.dot(XT).dot(y)\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "288fb6c4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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