Hw4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QH4MTNLJF0cX"
   },
   "source": [
    "# Лінійна регресія: перенавчання і регуляризація"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_4Ozl-xzF0cb"
   },
   "source": [
    "У цьому завданні ми на прикладах побачимо, як перенавчаються лінійні моделі, розберемо, чому так відбувається, і з'ясуємо, як діагностувати та контролювати перенавчання.\n",
    "\n",
    "В усіх комірках, де вказаний коментар з інструкціями, потрібно написати код, який виконує ці інструкції. Решту комірок із кодом (без коментарів) треба просто виконати. Крім того, у завданні необхідно відповідати на запитання; відповіді потрібно вписувати після виділеного слова \"__Відповідь:__\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "Q7qwUYsiF0cc"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lAIZa5LXF0ce"
   },
   "source": [
    "Ми будемо працювати з датасетом __\"bikes_rent.csv\"__, у якому по днях записані календарна інформація та погодні умови, що характеризують автоматизовані пункти прокату велосипедів, а також кількість прокатів у цей день. Останнє ми будемо передбачати; таким чином, ми будемо розв'язувати завдання регресії."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bL3VD-Z9F0ce"
   },
   "source": [
    "### Ознайомлення з даними"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PlxEpVFqghIe"
   },
   "source": [
    "## Завдання 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5HYQjYnVF0cf"
   },
   "source": [
    "Завантажте датасет за допомогою функції __pandas.read_csv__ у змінну __df__. Виведіть перші 5 рядків, щоб переконатися в коректному зчитуванні даних:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "moafDqLOF0cf",
    "outputId": "b46e6708-e144-446d-8b36-aaa5f5685959"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   season  yr  mnth  holiday  weekday  workingday  weathersit       temp  \\\n",
      "0       1   0     1        0        6           0           2  14.110847   \n",
      "1       1   0     1        0        0           0           2  14.902598   \n",
      "2       1   0     1        0        1           1           1   8.050924   \n",
      "3       1   0     1        0        2           1           1   8.200000   \n",
      "4       1   0     1        0        3           1           1   9.305237   \n",
      "\n",
      "      atemp      hum  windspeed(mph)  windspeed(ms)   cnt  \n",
      "0  18.18125  80.5833       10.749882       4.805490   985  \n",
      "1  17.68695  69.6087       16.652113       7.443949   801  \n",
      "2   9.47025  43.7273       16.636703       7.437060  1349  \n",
      "3  10.60610  59.0435       10.739832       4.800998  1562  \n",
      "4  11.46350  43.6957       12.522300       5.597810  1600  \n"
     ]
    }
   ],
   "source": [
    "# Load data from file \n",
    "url = \"/Users/olgatsyban/Downloads/bikes_rent.csv\"\n",
    "df = pd.read_csv(url)\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "arebsdFoF0ch"
   },
   "source": [
    "Для кожного дня прокату відомі такі ознаки:\n",
    "* _season_: 1 — весна, 2 — літо, 3 — осінь, 4 — зима\n",
    "* _yr_: 0 — 2011, 1 — 2012\n",
    "* _mnth_: від 1 до 12\n",
    "* _holiday_: 0 — немає свята, 1 — є свято\n",
    "* _weekday_: від 0 до 6\n",
    "* _workingday_: 0 — неробочий день, 1 — робочий день\n",
    "* _weathersit_: оцінка сприятливості погоди від 1 (чистий, ясний день) до 4 (злива, туман)\n",
    "* _temp_: температура в Цельсіях\n",
    "* _atemp_: температура за відчуттями в Цельсіях\n",
    "* _hum_: вологість\n",
    "* _windspeed(mph)_: швидкість вітру в милях за годину\n",
    "* _windspeed(ms)_: швидкість вітру в метрах за секунду\n",
    "* _cnt_: кількість орендованих велосипедів (це цільова ознака, її ми будемо передбачати)\n",
    "\n",
    "Отже, у нас є речові, бінарні та номінальні (порядкові) ознаки, і з усіма з них можна працювати як з речовими. З номінальними ознаками теж можна працювати як з речовими, тому що на них заданий порядок. Давайте подивимося на графіках, як цільова ознака залежить від решти."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w_bf05QXe60r"
   },
   "source": [
    "Побудуйте графіки залежностей атрибутів від кількості поїздок. Іншими словами, вам потрібно побудувати 12 графіків. На кожному має бути зображена залежність чергового атрибута від змінної **cnt**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "N72yVVRYF0ci"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x1500 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# build diagrams dependence of attributes on cnt\n",
    "\n",
    "attributes = ['season', 'yr', 'mnth', 'holiday', 'weekday', 'workingday', \n",
    "              'weathersit', 'temp', 'atemp', 'hum', 'windspeed(mph)', 'windspeed(ms)']\n",
    "\n",
    "num_cols = 3\n",
    "num_rows = len(attributes) // num_cols + (1 if len(attributes) % num_cols != 0 else 0)\n",
    "\n",
    "fig, axs = plt.subplots(num_rows, num_cols, figsize=(15, 15))\n",
    "\n",
    "for i, attribute in enumerate(attributes):\n",
    "    row = i // num_cols\n",
    "    col = i % num_cols\n",
    "    axs[row, col].bar(df[attribute], df['cnt'], alpha=0.5)\n",
    "    axs[row, col].set_title('Dependence of cnt on {}'.format(attribute))\n",
    "    axs[row, col].set_xlabel(attribute)\n",
    "    axs[row, col].set_ylabel('Count of trips')\n",
    "    axs[row, col].grid(True)\n",
    "\n",
    "# Hide empty subplots\n",
    "for i in range(len(attributes), num_rows * num_cols):\n",
    "    row = i // num_cols\n",
    "    col = i % num_cols\n",
    "    axs[row, col].axis('off')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1g8iwiimF0cj"
   },
   "source": [
    "__Запитання:__\n",
    "1. Яким є характер залежності кількості прокатів від місяця?\n",
    ">Відповідь: Залежність буде мати сезонний характер, оскільки погода та інші фактори можуть впливати на популярність прокату велосипедів у різні місяці року. На нашому графіку помітно , що кількість орендованих велосипедів збільшується у теплі пори року (весна, літо та перші місяці осені). Також варто зазначити,що кількість свят має вплив на оренду, якщо є свято, то кількість орендованих велосипедів менша.\n",
    "\n",
    "1. Вкажіть одну або дві ознаки, від яких кількість прокатів скоріше за все залежить лінійно.\n",
    ">Відповідь: кількість прокатів від вітру (швидкість вітру - windspeed) та погоди (weathersit) може залежати лінійно."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rB2NCSXbF0cj"
   },
   "source": [
    "## Завдання 2\n",
    "\n",
    "Давайте більш строго оцінимо рівень лінійної залежності між ознаками та цільовою змінною. Гарною мірою лінійної залежності між двома векторами є кореляція Пірсона. Нам уже доводилося мати з нею справу раніше. Для її обчислення в pandas можна використовувати методи датафрейму: corr і corrwith."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ykR_leNKF0ck"
   },
   "source": [
    "Порахуйте кореляції всіх ознак, окрім останньої, з останньою за допомогою методу `corrwith`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "v3RlugT3F0ck"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed(mph)</th>\n",
       "      <th>windspeed(ms)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>season</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001844</td>\n",
       "      <td>0.831440</td>\n",
       "      <td>-0.010537</td>\n",
       "      <td>-0.003080</td>\n",
       "      <td>0.012485</td>\n",
       "      <td>0.019211</td>\n",
       "      <td>0.334315</td>\n",
       "      <td>0.342876</td>\n",
       "      <td>0.205445</td>\n",
       "      <td>-0.229046</td>\n",
       "      <td>-0.229046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr</th>\n",
       "      <td>-0.001844</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001792</td>\n",
       "      <td>0.007954</td>\n",
       "      <td>-0.005461</td>\n",
       "      <td>-0.002013</td>\n",
       "      <td>-0.048727</td>\n",
       "      <td>0.047604</td>\n",
       "      <td>0.046106</td>\n",
       "      <td>-0.110651</td>\n",
       "      <td>-0.011817</td>\n",
       "      <td>-0.011817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mnth</th>\n",
       "      <td>0.831440</td>\n",
       "      <td>-0.001792</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.019191</td>\n",
       "      <td>0.009509</td>\n",
       "      <td>-0.005901</td>\n",
       "      <td>0.043528</td>\n",
       "      <td>0.220205</td>\n",
       "      <td>0.227459</td>\n",
       "      <td>0.222204</td>\n",
       "      <td>-0.207502</td>\n",
       "      <td>-0.207502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>holiday</th>\n",
       "      <td>-0.010537</td>\n",
       "      <td>0.007954</td>\n",
       "      <td>0.019191</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101960</td>\n",
       "      <td>-0.253023</td>\n",
       "      <td>-0.034627</td>\n",
       "      <td>-0.028556</td>\n",
       "      <td>-0.032507</td>\n",
       "      <td>-0.015937</td>\n",
       "      <td>0.006292</td>\n",
       "      <td>0.006292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <td>-0.003080</td>\n",
       "      <td>-0.005461</td>\n",
       "      <td>0.009509</td>\n",
       "      <td>-0.101960</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.035790</td>\n",
       "      <td>0.031087</td>\n",
       "      <td>-0.000170</td>\n",
       "      <td>-0.007537</td>\n",
       "      <td>-0.052232</td>\n",
       "      <td>0.014282</td>\n",
       "      <td>0.014282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workingday</th>\n",
       "      <td>0.012485</td>\n",
       "      <td>-0.002013</td>\n",
       "      <td>-0.005901</td>\n",
       "      <td>-0.253023</td>\n",
       "      <td>0.035790</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.061200</td>\n",
       "      <td>0.052660</td>\n",
       "      <td>0.052182</td>\n",
       "      <td>0.024327</td>\n",
       "      <td>-0.018796</td>\n",
       "      <td>-0.018796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weathersit</th>\n",
       "      <td>0.019211</td>\n",
       "      <td>-0.048727</td>\n",
       "      <td>0.043528</td>\n",
       "      <td>-0.034627</td>\n",
       "      <td>0.031087</td>\n",
       "      <td>0.061200</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.120602</td>\n",
       "      <td>-0.121583</td>\n",
       "      <td>0.591045</td>\n",
       "      <td>0.039511</td>\n",
       "      <td>0.039511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp</th>\n",
       "      <td>0.334315</td>\n",
       "      <td>0.047604</td>\n",
       "      <td>0.220205</td>\n",
       "      <td>-0.028556</td>\n",
       "      <td>-0.000170</td>\n",
       "      <td>0.052660</td>\n",
       "      <td>-0.120602</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.991702</td>\n",
       "      <td>0.126963</td>\n",
       "      <td>-0.157944</td>\n",
       "      <td>-0.157944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>atemp</th>\n",
       "      <td>0.342876</td>\n",
       "      <td>0.046106</td>\n",
       "      <td>0.227459</td>\n",
       "      <td>-0.032507</td>\n",
       "      <td>-0.007537</td>\n",
       "      <td>0.052182</td>\n",
       "      <td>-0.121583</td>\n",
       "      <td>0.991702</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.139988</td>\n",
       "      <td>-0.183643</td>\n",
       "      <td>-0.183643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hum</th>\n",
       "      <td>0.205445</td>\n",
       "      <td>-0.110651</td>\n",
       "      <td>0.222204</td>\n",
       "      <td>-0.015937</td>\n",
       "      <td>-0.052232</td>\n",
       "      <td>0.024327</td>\n",
       "      <td>0.591045</td>\n",
       "      <td>0.126963</td>\n",
       "      <td>0.139988</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.248489</td>\n",
       "      <td>-0.248489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windspeed(mph)</th>\n",
       "      <td>-0.229046</td>\n",
       "      <td>-0.011817</td>\n",
       "      <td>-0.207502</td>\n",
       "      <td>0.006292</td>\n",
       "      <td>0.014282</td>\n",
       "      <td>-0.018796</td>\n",
       "      <td>0.039511</td>\n",
       "      <td>-0.157944</td>\n",
       "      <td>-0.183643</td>\n",
       "      <td>-0.248489</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windspeed(ms)</th>\n",
       "      <td>-0.229046</td>\n",
       "      <td>-0.011817</td>\n",
       "      <td>-0.207502</td>\n",
       "      <td>0.006292</td>\n",
       "      <td>0.014282</td>\n",
       "      <td>-0.018796</td>\n",
       "      <td>0.039511</td>\n",
       "      <td>-0.157944</td>\n",
       "      <td>-0.183643</td>\n",
       "      <td>-0.248489</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  season        yr      mnth   holiday   weekday  workingday  \\\n",
       "season          1.000000 -0.001844  0.831440 -0.010537 -0.003080    0.012485   \n",
       "yr             -0.001844  1.000000 -0.001792  0.007954 -0.005461   -0.002013   \n",
       "mnth            0.831440 -0.001792  1.000000  0.019191  0.009509   -0.005901   \n",
       "holiday        -0.010537  0.007954  0.019191  1.000000 -0.101960   -0.253023   \n",
       "weekday        -0.003080 -0.005461  0.009509 -0.101960  1.000000    0.035790   \n",
       "workingday      0.012485 -0.002013 -0.005901 -0.253023  0.035790    1.000000   \n",
       "weathersit      0.019211 -0.048727  0.043528 -0.034627  0.031087    0.061200   \n",
       "temp            0.334315  0.047604  0.220205 -0.028556 -0.000170    0.052660   \n",
       "atemp           0.342876  0.046106  0.227459 -0.032507 -0.007537    0.052182   \n",
       "hum             0.205445 -0.110651  0.222204 -0.015937 -0.052232    0.024327   \n",
       "windspeed(mph) -0.229046 -0.011817 -0.207502  0.006292  0.014282   -0.018796   \n",
       "windspeed(ms)  -0.229046 -0.011817 -0.207502  0.006292  0.014282   -0.018796   \n",
       "\n",
       "                weathersit      temp     atemp       hum  windspeed(mph)  \\\n",
       "season            0.019211  0.334315  0.342876  0.205445       -0.229046   \n",
       "yr               -0.048727  0.047604  0.046106 -0.110651       -0.011817   \n",
       "mnth              0.043528  0.220205  0.227459  0.222204       -0.207502   \n",
       "holiday          -0.034627 -0.028556 -0.032507 -0.015937        0.006292   \n",
       "weekday           0.031087 -0.000170 -0.007537 -0.052232        0.014282   \n",
       "workingday        0.061200  0.052660  0.052182  0.024327       -0.018796   \n",
       "weathersit        1.000000 -0.120602 -0.121583  0.591045        0.039511   \n",
       "temp             -0.120602  1.000000  0.991702  0.126963       -0.157944   \n",
       "atemp            -0.121583  0.991702  1.000000  0.139988       -0.183643   \n",
       "hum               0.591045  0.126963  0.139988  1.000000       -0.248489   \n",
       "windspeed(mph)    0.039511 -0.157944 -0.183643 -0.248489        1.000000   \n",
       "windspeed(ms)     0.039511 -0.157944 -0.183643 -0.248489        1.000000   \n",
       "\n",
       "                windspeed(ms)  \n",
       "season              -0.229046  \n",
       "yr                  -0.011817  \n",
       "mnth                -0.207502  \n",
       "holiday              0.006292  \n",
       "weekday              0.014282  \n",
       "workingday          -0.018796  \n",
       "weathersit           0.039511  \n",
       "temp                -0.157944  \n",
       "atemp               -0.183643  \n",
       "hum                 -0.248489  \n",
       "windspeed(mph)       1.000000  \n",
       "windspeed(ms)        1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the correlation\n",
    "df[attributes].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season            0.406100\n",
       "yr                0.566710\n",
       "mnth              0.279977\n",
       "holiday          -0.068348\n",
       "weekday           0.067443\n",
       "workingday        0.061156\n",
       "weathersit       -0.297391\n",
       "temp              0.627494\n",
       "atemp             0.631066\n",
       "hum              -0.100659\n",
       "windspeed(mph)   -0.234545\n",
       "windspeed(ms)    -0.234545\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the correlation with the last attribute <cnt>\n",
    "df[attributes].corrwith(df['cnt'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ovV_DO35F0cl"
   },
   "source": [
    "У вибірці є ознаки, що корелюють із цільовою, а отже, завдання можна розв'язувати лінійними методами."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r0dre43ZF0cl"
   },
   "source": [
    "За графіками видно, що деякі ознаки схожі між собою. Тому давайте також порахуємо кореляції між речовими ознаками."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Wga1WJDkF0cl"
   },
   "source": [
    "## Завдання 3\n",
    "\n",
    "Порахуйте попарні кореляції між ознаками temp, atemp, hum, windspeed(mph), windspeed(ms) і cnt\n",
    "за допомогою методу corr:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "1rSOhITgF0cm"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    temp     atemp       hum  windspeed(mph)  windspeed(ms)  \\\n",
      "temp            1.000000  0.991702  0.126963       -0.157944      -0.157944   \n",
      "atemp           0.991702  1.000000  0.139988       -0.183643      -0.183643   \n",
      "hum             0.126963  0.139988  1.000000       -0.248489      -0.248489   \n",
      "windspeed(mph) -0.157944 -0.183643 -0.248489        1.000000       1.000000   \n",
      "windspeed(ms)  -0.157944 -0.183643 -0.248489        1.000000       1.000000   \n",
      "cnt             0.627494  0.631066 -0.100659       -0.234545      -0.234545   \n",
      "\n",
      "                     cnt  \n",
      "temp            0.627494  \n",
      "atemp           0.631066  \n",
      "hum            -0.100659  \n",
      "windspeed(mph) -0.234545  \n",
      "windspeed(ms)  -0.234545  \n",
      "cnt             1.000000  \n"
     ]
    }
   ],
   "source": [
    "# find correlation between 'temp', 'atemp', 'hum', 'windspeed(mph)', 'windspeed(ms)', 'cnt'\n",
    "some_features = ['temp', 'atemp', 'hum', 'windspeed(mph)', 'windspeed(ms)', 'cnt']\n",
    "correlations = df[some_features].corr()\n",
    "print(correlations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ooc4syvaF0cm"
   },
   "source": [
    "На діагоналях, як і належить, стоять одиниці. Однак у матриці є ще дві пари сильно корелюючих стовпців: temp і atemp (корелюють за своєю природою) і два windspeed (тому що це просто переведення одних одиниць в інші). Далі ми побачимо, що цей факт негативно позначається на навчанні лінійної моделі."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t-w2-4DbF0cm"
   },
   "source": [
    "Насамкінець подивимося середні ознак (метод mean), щоб оцінити масштаб ознак і частки 1 у бінарних ознак."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ml3jlosnF0cm"
   },
   "source": [
    "## Завдання 4\n",
    "\n",
    "Виведіть середні ознак."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "zg7yBN9YF0cn"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season               2.496580\n",
       "yr                   0.500684\n",
       "mnth                 6.519836\n",
       "holiday              0.028728\n",
       "weekday              2.997264\n",
       "workingday           0.683995\n",
       "weathersit           1.395349\n",
       "temp                20.310776\n",
       "atemp               23.717699\n",
       "hum                 62.789406\n",
       "windspeed(mph)      12.762576\n",
       "windspeed(ms)        5.705220\n",
       "cnt               4504.348837\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the mean of the attribute\n",
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2i9pGW4TF0cn"
   },
   "source": [
    "Ознаки мають різний масштаб, отже, для подальшої роботи нам краще нормувати матрицю об'єкти-ознаки."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-RTwV3e_F0cn"
   },
   "source": [
    "### Проблема перша: колінеарні ознаки"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SSmpjxmrF0co"
   },
   "source": [
    "Отже, у наших даних одна ознака дублює іншу, і є ще дві дуже схожі. Звичайно, ми могли б одразу видалити дублікати, але давайте подивимося, як відбувалося б навчання моделі, якби ми не помітили цю проблему.\n",
    "\n",
    "Для початку проведемо масштабування, або стандартизацію ознак: з кожної ознаки віднімемо її середнє і поділимо на стандартне відхилення. Це можна зробити за допомогою методу scale.\n",
    "\n",
    "Крім того, необхідно перемішати вибірку, це буде потрібно для крос-валідації."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "jkGjoC9QF0co"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import scale\n",
    "from sklearn.utils import shuffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 198
    },
    "id": "9JnmTmeKF0co",
    "outputId": "ff5429b9-c017-43d6-9a24-b13b2475f75b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X:  [[ 1.354375    0.99863295  1.58866019 ...  0.74372737 -0.21121111\n",
      "  -0.21121111]\n",
      " [-1.34821315 -1.00136893 -1.31026791 ... -1.33538868  1.12681211\n",
      "   1.12681211]\n",
      " [ 1.354375   -1.00136893  1.00887457 ... -0.29551449  0.50349108\n",
      "   0.50349108]\n",
      " ...\n",
      " [ 1.354375   -1.00136893  0.71898176 ...  1.55169951 -0.5404777\n",
      "  -0.5404777 ]\n",
      " [-1.34821315  0.99863295 -1.0203751  ... -1.06046036  0.41449871\n",
      "   0.41449871]\n",
      " [-0.44735043 -1.00136893 -0.73048229 ...  1.34385394  0.77643372\n",
      "   0.77643372]]\n",
      "y:  703    6606\n",
      "33     1550\n",
      "300    3747\n",
      "456    6041\n",
      "633    7538\n",
      "       ... \n",
      "71     2417\n",
      "106    3744\n",
      "270    3907\n",
      "435    4911\n",
      "102    2162\n",
      "Name: cnt, Length: 731, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df_shuffled = shuffle(df, random_state=42)\n",
    "X = scale(df_shuffled[df_shuffled.columns[:-1]])\n",
    "y = df_shuffled[\"cnt\"]\n",
    "\n",
    "print(\"X: \", X)\n",
    "print(\"y: \", y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wEvKSbrFF0co"
   },
   "source": [
    "Давайте навчимо лінійну регресію на наших даних і подивимося на ваги ознак."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "Q9DAkdS1F0cp"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-8Cc-xhqF0cp"
   },
   "source": [
    "## Завдання 5\n",
    "\n",
    "Створіть об'єкт лінійного регресора, навчіть його на всіх даних і виведіть ваги моделі (ваги зберігаються у змінній `coef_` класу регресора). Можна виводити пари (назва ознаки, вага), скориставшись функцією `zip`, вбудованою в мову python. Назви ознак зберігаються у змінній `df.columns`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "EYFEkeI0F0cp"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "season: 570.8698404201848\n",
      "yr: 1021.9666808385737\n",
      "mnth: -141.30637067066573\n",
      "holiday: -86.76062843476119\n",
      "weekday: 137.22937294082004\n",
      "workingday: 56.39076518684175\n",
      "weathersit: -330.2330445706409\n",
      "temp: 367.4651342506935\n",
      "atemp: 585.5644765605813\n",
      "hum: -145.60373463359957\n",
      "windspeed(mph): 12460406274500.416\n",
      "windspeed(ms): -12460406274698.867\n"
     ]
    }
   ],
   "source": [
    "# find weigth\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X, y)\n",
    "\n",
    "for feature , value in zip(df.columns, regressor.coef_):\n",
    "    print(f'{feature}: {value}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4504.354529707314"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Represents the intercept (also known as bias term) of the fitted model\n",
    "regressor.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EXHziiV-F0cp"
   },
   "source": [
    "Ми бачимо, що ваги при лінійно-залежних ознаках за модулем значно більші, ніж при інших ознаках. Щоб зрозуміти, чому так сталося, згадаємо аналітичну формулу, за якою обчислюються ваги лінійної моделі в методі найменших квадратів:\n",
    "\n",
    "$$w = (X^TX)^{-1} X^T y$$\n",
    "\n",
    "Якщо в $X$ є колінеарні (лінійно-залежні) стовпці, матриця $X^TX$ стає виродженою, і формула перестає бути коректною. Чим більш залежні ознаки, тим менший визначник цієї матриці й тим гірша апроксимація $Xw \\approx y$. Таку ситуацію називають _проблемою мультиколінеарності_."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PIX0I5LQF0cq"
   },
   "source": [
    "З парою (temp, atemp) трохи менше корелюючих змінних такого не сталося, однак на практиці завжди варто уважно стежити за коефіцієнтами при схожих ознаках."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vkXwN1tKF0cq"
   },
   "source": [
    "Для того щоб розв'язати проблему мультиколінеарності, потрібно скористатися регуляризатором. До оптимізуючого функціоналу додають $L_1$ або $L_2$ норму ваг, помножену на коефіцієнт регуляризації $\\alpha$. У першому випадку метод називається Lasso, а у другому — Ridge."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FT2oVbm1F0cq"
   },
   "source": [
    "### Завдання 6\n",
    "Давайте спробуємо навчити лінійні моделі з $L_1$ і $L_2$-регуляризацією, а далі порівняємо їхні ваги. Навчіть регресори Ridge і Lasso з параметрами за замовчуванням і переконайтеся, що проблема з вагами вирішилась."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Rw0SDNemF0cq"
   },
   "source": [
    "Навчіть лінійну модель з $L_1$-регуляризацією (клас Lasso) і виведіть ваги."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso, Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "A2U5BrZ9F0cr",
    "outputId": "1f75758c-599b-4175-9428-bd9cd7d57cf1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Lasso()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Studying with Lasso\n",
    "model_lasso = Lasso()\n",
    "model_lasso.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "season: 560.2416160308865\n",
      "yr: 1019.4634940657198\n",
      "mnth: -128.73062703678733\n",
      "holiday: -86.15278133371098\n",
      "weekday: 137.347893904963\n",
      "workingday: 55.212370641356785\n",
      "weathersit: -332.3698569623486\n",
      "temp: 376.36323620969347\n",
      "atemp: 576.5307935045563\n",
      "hum: -144.12915500348578\n",
      "windspeed(mph): -197.13968940248455\n",
      "windspeed(ms): -2.805103823944435e-08\n"
     ]
    }
   ],
   "source": [
    "# Find the weights (using Lasso)\n",
    "for feature , value in zip(df.columns, model_lasso.coef_):\n",
    "    print(f'{feature}: {value}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4504.3488372093025"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Represents the intercept (also known as bias term) of the fitted model\n",
    "model_lasso.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h7MpUkWyF0cr"
   },
   "source": [
    "Навчіть лінійну модель з $L_2$-регуляризацією (клас Ridge) і виведіть ваги."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "u4BnooUuF0cr",
    "outputId": "876aa324-1171-4adb-d744-18d4ac008e95"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Ridge()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Studying with Ridge\n",
    "model_ridge = Ridge()\n",
    "model_ridge.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "season: 563.0645722520163\n",
      "yr: 1018.948378787527\n",
      "mnth: -131.87332028247576\n",
      "holiday: -86.74609799709245\n",
      "weekday: 138.0051111787192\n",
      "workingday: 55.90311037506427\n",
      "weathersit: -332.3497884990726\n",
      "temp: 386.45788919191114\n",
      "atemp: 566.3470470600643\n",
      "hum: -145.07132729867305\n",
      "windspeed(mph): -99.25944108182756\n",
      "windspeed(ms): -99.25944115430536\n"
     ]
    }
   ],
   "source": [
    "# Find the weights (using Ridge)\n",
    "for feature, value in zip(df.columns, model_ridge.coef_):\n",
    "    print(f'{feature}: {value}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4504.3488372093025"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Represents the intercept (also known as bias term) of the fitted model\n",
    "model_ridge.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OmpSEJ3wF0cr"
   },
   "source": [
    "### Завдання 7\n",
    "\n",
    "На відміну від $L_2$-регуляризації, $L_1$ обнуляє ваги при деяких ознаках. Давайте поспостерігаємо, як змінюються ваги зі збільшенням коефіцієнта регуляризації $\\alpha$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ho9_PTbHF0cs"
   },
   "source": [
    "Для кожного значення коефіцієнта з `alphas` навчіть регресор `Lasso` і запишіть ваги у список `coefs_lasso`, а потім навчіть `Ridge` і запишіть ваги у список`coefs_ridge`. Конвертуйте отримані списки в `np.array`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "HiizakIyF0cs"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coef Lasso:  [[ 5.60241616e+02  1.01946349e+03 -1.28730627e+02 -8.61527813e+01\n",
      "   1.37347894e+02  5.52123706e+01 -3.32369857e+02  3.76363236e+02\n",
      "   5.76530794e+02 -1.44129155e+02 -1.97139689e+02 -2.80510382e-08]\n",
      " [ 4.10969632e+02  9.77019409e+02 -0.00000000e+00 -5.34489688e+01\n",
      "   9.19434374e+01  1.75372118e+01 -3.18125568e+02  3.22829934e+02\n",
      "   6.10031512e+02 -9.10689615e+01 -1.45066095e+02 -2.29895576e-08]\n",
      " [ 3.70077089e+02  9.35945490e+02  0.00000000e+00 -1.21619360e+01\n",
      "   4.88886342e+01  0.00000000e+00 -3.08805664e+02  2.69417263e+02\n",
      "   6.32502623e+02 -2.75042876e+01 -9.37749037e+01 -2.41636842e-08]\n",
      " [ 3.32835717e+02  8.91870058e+02  0.00000000e+00 -0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00 -2.79616688e+02  2.11052030e+02\n",
      "   6.62920880e+02 -0.00000000e+00 -5.01551472e+01 -2.62778724e-08]\n",
      " [ 2.98134448e+02  8.45652857e+02  0.00000000e+00 -0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00 -2.35571345e+02  1.24144807e+02\n",
      "   7.25379483e+02 -0.00000000e+00 -1.26461769e+01 -2.78799567e-08]\n",
      " [ 2.58927201e+02  7.99237273e+02  0.00000000e+00 -0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00 -1.90821549e+02  7.20759379e+01\n",
      "   7.50363107e+02 -0.00000000e+00 -0.00000000e+00 -0.00000000e+00]\n",
      " [ 2.17427513e+02  7.52720808e+02  0.00000000e+00 -0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00 -1.45713398e+02  3.77153655e+01\n",
      "   7.56296866e+02 -0.00000000e+00 -0.00000000e+00 -0.00000000e+00]\n",
      " [ 1.75929845e+02  7.06203788e+02  0.00000000e+00 -0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00 -1.00605389e+02  3.66093817e+00\n",
      "   7.61926337e+02 -0.00000000e+00 -0.00000000e+00 -0.00000000e+00]\n",
      " [ 1.34628497e+02  6.59632535e+02  0.00000000e+00 -0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00 -5.55110633e+01  0.00000000e+00\n",
      "   7.37348061e+02 -0.00000000e+00 -0.00000000e+00 -0.00000000e+00]\n",
      " [ 9.33517423e+01  6.13054639e+02  0.00000000e+00 -0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00 -1.04185690e+01  0.00000000e+00\n",
      "   7.09130877e+02 -0.00000000e+00 -0.00000000e+00 -0.00000000e+00]]\n",
      "Coef Ridge:  [[ 563.06457225 1018.94837879 -131.87332028  -86.746098    138.00511118\n",
      "    55.90311038 -332.3497885   386.45788919  566.34704706 -145.0713273\n",
      "   -99.25944108  -99.25944115]\n",
      " [ 461.1786324   954.30774189  -41.56545001  -84.91347027  126.60366663\n",
      "    54.2516911  -313.27515141  458.90147709  481.44350031 -151.29101033\n",
      "  -101.62731482 -101.62731483]\n",
      " [ 403.97653045  898.08362212    5.67424346  -81.91106159  117.94141144\n",
      "    52.72848389 -298.40898086  455.28986839  467.43141748 -152.68605059\n",
      "  -102.10167084 -102.10167084]\n",
      " [ 366.60423851  848.46346471   34.02728278  -78.77196479  110.68012006\n",
      "    51.25719208 -286.12515935  447.47982533  455.75402966 -151.48320603\n",
      "  -102.00537592 -102.00537593]\n",
      " [ 339.74528956  804.25104797   52.48987295  -75.71740756  104.40346414\n",
      "    49.84189502 -275.48609488  438.50968939  444.7642744  -148.94396978\n",
      "  -101.58638458 -101.58638458]\n",
      " [ 319.15864133  764.5614337    65.15200737  -72.81970821   98.87880449\n",
      "    48.4845255  -266.00255564  429.21412336  434.23523472 -145.6981149\n",
      "  -100.96471143 -100.96471143]\n",
      " [ 302.63606049  728.70854018   74.13828141  -70.09898324   93.95581101\n",
      "    47.18533838 -257.390629    419.9261517   424.12192175 -142.08934531\n",
      "  -100.20862041 -100.20862041]\n",
      " [ 288.91327631  696.14581646   80.660457    -67.55485237   89.52867543\n",
      "    45.94346702 -249.47139541  410.80018411  414.40937279 -138.31653824\n",
      "   -99.36066117  -99.36066117]\n",
      " [ 277.21321566  666.42974721   85.45925409  -65.17870596   85.51866655\n",
      "    44.75730348 -242.12439914  401.91194457  405.08585263 -134.4990383\n",
      "   -98.44906427  -98.44906427]\n",
      " [ 267.03109023  639.19536755   89.01232285  -62.95890597   81.86490933\n",
      "    43.62477037 -235.26399722  393.29798599  396.13815546 -130.70987223\n",
      "   -97.49343362  -97.49343362]]\n"
     ]
    }
   ],
   "source": [
    "alphas = np.arange(1, 500, 50)\n",
    "\n",
    "coefs_lasso = []\n",
    "coefs_ridge = []\n",
    "\n",
    "for alpha in alphas:\n",
    "    model_lasso.set_params(alpha=alpha)\n",
    "    model_lasso.fit(X,y)\n",
    "    coefs_lasso.append(model_lasso.coef_)\n",
    "    \n",
    "    model_ridge.set_params(alpha=alpha)\n",
    "    model_ridge.fit(X,y)\n",
    "    coefs_ridge.append(model_ridge.coef_)\n",
    "    \n",
    "coefs_lasso = np.array(coefs_lasso)\n",
    "coefs_ridge = np.array(coefs_ridge)\n",
    "    \n",
    "print(\"Coef Lasso: \", coefs_lasso)\n",
    "print(\"Coef Ridge: \", coefs_ridge)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vR3hawHAF0cs"
   },
   "source": [
    "Проаналізуйте динаміку ваг при збільшенні параметра регуляризації:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "rijH4f-VF0cs",
    "outputId": "250a46ca-6722-4642-af12-31e6e4447ff6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "\n",
    "for coef, feature in zip(coefs_lasso.T, df.columns):\n",
    "    plt.plot(alphas, coef, label=feature, color=np.random.rand(3))\n",
    "\n",
    "plt.legend(loc=\"upper right\", bbox_to_anchor=(1.4, 0.95))\n",
    "plt.xlabel(\"alpha\")\n",
    "plt.ylabel(\"feature weight\")\n",
    "plt.title(\"Lasso\")\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "for coef, feature in zip(coefs_ridge.T, df.columns):\n",
    "    plt.plot(alphas, coef, label=feature, color=np.random.rand(3))\n",
    "\n",
    "plt.legend(loc=\"upper right\", bbox_to_anchor=(1.4, 0.95))\n",
    "plt.xlabel(\"alpha\")\n",
    "plt.ylabel(\"feature weight\")\n",
    "plt.title(\"Ridge\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wDCadrLbF0cs"
   },
   "source": [
    "Проаналізуйте графіки та дайте відповіді на такі запитання.\n",
    "\n",
    "1. Який регуляризатор (Ridge або Lasso) агресивніше зменшує ваги при одному й тому самому alpha?\n",
    "> Відповідь: Lasso\n",
    "1. Що станеться з вагами Lasso, якщо alpha зробити дуже великим? Поясніть, чому так відбувається.\n",
    "> Відповідь: При збільшені alpha ваги Lasso наближаються до 0. Данне явище відюувається через так звану регуляцію. При дуже великих значеннях alpha функція штрафу за великі значення коефіцієнтів стає набагато більшою, ніж втрати функції втрати (наприклад, середньоквадратична помилка). Це призводить до того, що модель старається знайти найменшу кількість впливових ознак, або навіть ігнорує їх зовсім, щоб зменшити загальну штрафну функцію. Данну закономірність можемо побачити на графіку.\n",
    "1. Чи можна стверджувати, що `Lasso` виключає одну з ознак `windspeed` при будь-якому значенні `alpha > 0`? А Ridge? Вважається, що регуляризатор виключає ознаку, якщо коефіцієнт при ньому менший $10^{-3}$.\n",
    "> Відповідь: так, можна стверджувати, що модель Lasso виключить одну з ознак windspeed при будь-якому значенні alpha > 0. Оскільки Lasso використовує L1-регуляризацію, яка включає штраф за суму абсолютних значень коефіцієнтів. Отже, деякі коефіцієнти можуть бути зменшені до нуля, що ефективно виключає відповідні ознаки з моделі. Ні, не можна стверджувати, що модель Ridge виключить одну з ознак windspeed. Оскільки модель Ridge використовує L2-регуляризацію, яка включає штраф за квадрат суми коефіцієнтів. Данний тип регуляризації має тенденцію зменшувати значення коефіцієнтів, але не призводить до виключення ознак з моделі тобто прирівнювання до нуля.\n",
    "1. Який із регуляризаторів підійде для відбору неінформативних ознак?\n",
    "> Відповідь: Lasso, бо використовує L1-регуляризацію і може зменьшувати деякі коефіцієнти до 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IEmB7CPtF0ct"
   },
   "source": [
    "### Завдання 8\n",
    "\n",
    "Далі будемо працювати з `Lasso`.\n",
    "\n",
    "Отже, ми бачимо, що при зміні alpha модель по-різному підбирає коефіцієнти ознак. Нам потрібно вибрати найкраще alpha.\n",
    "\n",
    "Для цього, по-перше, нам потрібна метрика якості. Будемо використовувати як метрику сам оптимізований функціонал методу найменших квадратів, тобто `Mean Square Error`.\n",
    "\n",
    "По-друге, потрібно зрозуміти, на яких даних цю метрику рахувати. Не можна вибирати `alpha` за значенням MSE на навчальній вибірці, тому що тоді ми не зможемо оцінити, як модель буде робити передбачення на нових для неї даних. Якщо ми виберемо одне розбиття вибірки на навчальну та тестову (це називається holdout), то налаштуємося на конкретні \"нові\" дані, і знову можемо перенавчитися. Тому будемо робити декілька розбиттів вибірки, на кожному пробувати різні значення alpha, а потім усереднювати MSE. Найзручніше робити такі розбиття крос-валідацією, тобто розділити вибірку на $K$ частин, і кожного разу брати одну з них як тестову, а з блоків, що залишилися, складати навчальну вибірку."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wyOFogflF0ct"
   },
   "source": [
    "Робити крос-валідацію для регресії в sklearn зовсім просто: для цього є спеціальний регресор, __LassoCV__, який бере на вхід список із alpha і для кожного з них обчислює MSE на крос-валідації. Після навчання (якщо залишити параметр cv=3 за замовчуванням) регресор буде містити змінну __mse\\_path\\___, матрицю розміру len(alpha) x k, k = 3 (число блоків у крос-валідації), що містить значення MSE на тесті для відповідних запусків. Крім того, у змінній alpha\\_ буде зберігатися вибране значення параметра регуляризації, а в coef\\_, традиційно, навчені ваги, що відповідають цьому alpha_.\n",
    "\n",
    "Зверніть увагу, що регресор може змінювати порядок, у якому він проходить по alphas; для зіставлення з матрицею MSE краще використовувати змінну регресора alphas_."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_aUw0GmkF0ct"
   },
   "source": [
    "Навчіть регресор `LassoCV` на всіх параметрах регуляризації з alpha. Побудуйте графік _усередненого_ за рядками `MSE` в залежності від `alpha` (використовуйте для цього функцію `create_plot`).\n",
    "\n",
    "Виведіть вибране `alpha`, а також пари \"ознака-коефіцієнт\" для навченого вектора коефіцієнтів."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "id": "L5MVKdVPF0cu"
   },
   "outputs": [],
   "source": [
    "def create_plot(data, title, xlabel, ylabel, figsize=None, min=None):\n",
    "    size = figsize or (15, 5)\n",
    "    plt.figure(figsize=size)\n",
    "\n",
    "    x = list(map(lambda e: e[0], data))\n",
    "    y = list(map(lambda e: e[1], data))\n",
    "    plt.plot(x, y, marker='o', linestyle='-', color='b')\n",
    "    if min is not None:\n",
    "      plt.text(min[0], min[1], f\"min: {int(min[0])}x{int(min[1])}\", ha=\"center\")\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "\n",
    "    plt.grid()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "id": "lYIKXzG5F0cu",
    "outputId": "ca17b1f0-e361-4496-cb72-d6a7680bcdf9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected alpha: 6\n",
      "[(96, 851990.7226862232), (91, 846392.1588364622), (86, 840822.7945299605), (81, 835574.3424234892), (76, 830286.6292664969), (71, 825198.4820010142), (66, 820159.7018859917), (61, 815491.2384371455), (56, 811192.9043818455), (51, 807264.7026273537), (46, 803575.5866471971), (41, 800211.2474620757), (36, 797217.5699930107), (31, 794594.560667227), (26, 792342.1957655508), (21, 789464.6632025442), (16, 786750.8408386442), (11, 783634.3772912041), (6, 781531.0908199241), (1, 785055.1467870097)]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find the best alpha\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "alphas = np.arange(1, 100, 5)\n",
    "\n",
    "regressor_lasso = LassoCV(alphas=alphas, cv=3)\n",
    "regressor_lasso.fit(X, y)\n",
    "\n",
    "selected_alpha = regressor_lasso.alpha_\n",
    "print(\"Selected alpha:\", selected_alpha)\n",
    "\n",
    "mse_values = np.mean(regressor_lasso.mse_path_, axis=1)\n",
    "data = list(zip(regressor_lasso.alphas_, mse_values))\n",
    "print(data)\n",
    "title = \"MSE vs. Alpha for Lasso\"\n",
    "create_plot(data, title, xlabel='Lambda', ylabel='MSE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hRoEnDtfF0cu"
   },
   "source": [
    "Отже, ми вибрали певний параметр регуляризації. Давайте подивимося, які б ми вибирали alpha, якби ділили вибірку лише один раз на навчальну та тестову, тобто розглянемо траєкторії MSE, що відповідають окремим блокам вибірки."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Jvl3XiPmF0cu"
   },
   "source": [
    "### Завдання 9\n",
    "\n",
    "Виведіть значення `alpha`, що відповідають мінімумам `MSE` на кожному розбитті (тобто за стовпцями).\n",
    "На трьох окремих графіках візуалізуйте стовпці `mse_path_`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "cpiFOLHHF0cu",
    "outputId": "f47bd59d-2e0e-4f63-b715-7ef199d5b382"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20, 3)\n"
     ]
    }
   ],
   "source": [
    "print(regressor_lasso.mse_path_.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min_values=array([733725.76040522, 799433.16736917, 796979.89551936])\n",
      "min_idx=array([18, 19,  9])\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_values = np.min(regressor_lasso.mse_path_, axis=0)\n",
    "print(f\"{min_values=}\")\n",
    "min_idx = np.argmin(regressor_lasso.mse_path_, axis=0)\n",
    "print(f\"{min_idx=}\")\n",
    "\n",
    "def create_alphas_diagram(data, title, xlabel, ylabel, min):\n",
    "    size = (15, 5)\n",
    "    plt.figure(figsize=size)\n",
    "\n",
    "    x = data[0]\n",
    "    y = data[1]\n",
    "    plt.plot(x, y, marker='o', linestyle='-', color='purple')\n",
    "\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "\n",
    "    if min is not None:\n",
    "      plt.text(min[0], min[1], f\"min: {int(min[0])}x{int(min[1])}\", ha=\"center\")\n",
    "\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "\n",
    "name = \"Depends MSE on alhpa\"\n",
    "for col in range(regressor_lasso.mse_path_.shape[1]):\n",
    "  data = np.vstack((regressor_lasso.alphas_, regressor_lasso.mse_path_[:,col]))\n",
    "  create_alphas_diagram(data,f\"{name} {col+1}\",\"Alpha\",\"MSE\",min=(alphas[col], min_values[col]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UEzEyHA1F0cv"
   },
   "source": [
    "На кожному розбитті оптимальне значення `alpha` своє, і йому відповідає велике MSE на інших розбиттях. Виходить, що ми налаштовуємося на конкретні навчальні та контрольні вибірки. При виборі `alpha` на крос-валідації ми вибираємо дещо \"середнє\", що даватиме прийнятне значення метрики на різних розбиттях вибірки."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B6rE566oF0cv"
   },
   "source": [
    "### Завдання 10\n",
    "\n",
    "Нарешті, як прийнято в аналізі даних, давайте проінтерпретуємо результат. Дайте відповіді на наступні запитання."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SSCcePDSF0cw"
   },
   "source": [
    "#### Запитання 1\n",
    "\n",
    "В останній навченій моделі виберіть 4 ознаки з найбільшими (позитивними) коефіцієнтами (і випишіть їх), подивіться на візуалізації залежностей `cnt` від цих ознак, які ми зображали у блоці \"Ознайомлення з даними\". Чи видно зростаючу лінійну залежність `cnt` від цих ознак за графіками? Чи логічно стверджувати (виходячи зі здорового глузду), що чим більше значення цих ознак, тим більше людей захочуть взяти велосипеди?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2ikKprKqF0c0"
   },
   "source": [
    "__Відповідь:__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "id": "W1MTuRXuF0c0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "regressor_lasso.coef_.shape=(12,)\n",
      "season         : 532.019\n",
      "yr             : 1015.0602\n",
      "mnth           : -100.0395\n",
      "holiday        : -83.294\n",
      "weekday        : 132.5045\n",
      "workingday     : 51.5571\n",
      "weathersit     : -330.5599\n",
      "temp           : 370.6799\n",
      "atemp          : 581.3969\n",
      "hum            : -140.0074\n",
      "windspeed(mph) : -191.7714\n",
      "windspeed(ms)  : -0.0\n"
     ]
    }
   ],
   "source": [
    "print(f\"{regressor_lasso.coef_.shape=}\")\n",
    "for f,w in zip(df.columns, regressor_lasso.coef_):\n",
    "  print(f\"{f:15}: {w.round(4)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 1) yr 1015.06 ні не можно сказати , що наявна залежність, бо не обовязково при збільшені року буде збільшуватися число прокатів. \n",
    "> 2) atemp 581.39 - можливо припустити, що погода має сплив на кількість оренд. \n",
    "> 3) season 532.019 - помітна закономірність, що сезон має сплив на кількість оренд.\n",
    "> 4) temp 370 - наявна закономірність, що температура має сплив на кількість оренд."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DkQTu53pF0c0"
   },
   "source": [
    "#### Запитання 2\n",
    "\n",
    "Виберіть 3 ознаки з найбільшими за модулем негативними коефіцієнтами (і випишіть їх), подивіться на відповідні візуалізації. Чи видно лінійну залежність, що зменшується? Чи логічно стверджувати, що чим більша величина цих ознак, тим менше людей захочуть взяти велосипеди?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DUN8I7shF0c0"
   },
   "source": [
    "__Відповідь:__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jv1CGs-8F0c1"
   },
   "source": [
    "> 1) weathersit: -330.5599 Лінійну залежність наявна та можно стверджувати , що при збільшенні ознаки кількість оренд зменьшиться \n",
    "> 2) windspeed(mph): -191.7714 Лінійну залежність наявна та можно стверджувати , що при збільшенні ознаки кількість оренд зменьшиться\n",
    "> 3) hum: -140.0074 Лінійну залежність наявна та можно стверджувати , що при збільшенні ознаки кількість оренд зменьшиться"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vrbD3fNSF0c1"
   },
   "source": [
    "#### Запитання 3\n",
    "\n",
    "Випишіть ознаки з коефіцієнтами, близькими до нуля (< 1e-3). Як ви думаєте, чому модель виключила їх із моделі (знову подивіться на графіки)? Чи правда, що вони ніяк не впливають на попит на велосипеди?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tFhvV-jcF0c1"
   },
   "source": [
    "__Відповідь:__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "id": "TwnJNeaIF0c1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>season</th>\n",
       "      <td>5.320190e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr</th>\n",
       "      <td>1.015060e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mnth</th>\n",
       "      <td>-1.000395e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>holiday</th>\n",
       "      <td>-8.329396e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <td>1.325045e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workingday</th>\n",
       "      <td>5.155709e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weathersit</th>\n",
       "      <td>-3.305599e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp</th>\n",
       "      <td>3.706799e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>atemp</th>\n",
       "      <td>5.813969e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hum</th>\n",
       "      <td>-1.400074e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windspeed(mph)</th>\n",
       "      <td>-1.917714e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windspeed(ms)</th>\n",
       "      <td>-2.685698e-08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Weight\n",
       "season          5.320190e+02\n",
       "yr              1.015060e+03\n",
       "mnth           -1.000395e+02\n",
       "holiday        -8.329396e+01\n",
       "weekday         1.325045e+02\n",
       "workingday      5.155709e+01\n",
       "weathersit     -3.305599e+02\n",
       "temp            3.706799e+02\n",
       "atemp           5.813969e+02\n",
       "hum            -1.400074e+02\n",
       "windspeed(mph) -1.917714e+02\n",
       "windspeed(ms)  -2.685698e-08"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_coef = pd.DataFrame(regressor_lasso.coef_,index=df.columns[:-1], columns=[\"Weight\"])\n",
    "data_coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "        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>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>windspeed(ms)</th>\n",
       "      <td>-2.685698e-08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Weight\n",
       "windspeed(ms) -2.685698e-08"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_coef[data_coef.abs() < 1e-03].dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> windspeed(ms): -2.685698e-08 Дана ознака слабо впливає на цільову змінну (кількість арендованих велосипедів), що є причиною незначної кореляції. Тому при регуляції Lasso ця характеристика була наближена (прирівнена) до 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Wg0VQz5_F0c2"
   },
   "source": [
    "### Висновок\n",
    "Отже, ми подивилися, як можна стежити за адекватністю лінійної моделі, як відбирати ознаки і як грамотно, за можливості не налаштовуючись на якусь конкретну порцію даних, підбирати коефіцієнт регуляризації.\n",
    "\n",
    "Варто зазначити, що за допомогою крос-валідації зручно підбирати лише невелику кількість параметрів. (1, 2, максимум 3), тому що для кожної допустимої їх комбінації нам доводиться декілька разів навчати модель. Такий процес займає багато часу, особливо якщо треба навчатися на великих обсягах даних."
   ]
  }
 ],
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