Books regression model training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from math import floor\n",
    "\n",
    "import pickle\n",
    "import warnings\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor, AdaBoostRegressor\n",
    "from sklearn.svm import LinearSVR, SVR\n",
    "\n",
    "from sklearn.model_selection import KFold, GridSearchCV, train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder, PolynomialFeatures\n",
    "from sklearn.metrics import r2_score,mean_squared_error "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "np.random.seed(24680)\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training a prediction model for the average rating of a book\n",
    "---\n",
    "### Motivation\n",
    "This project will serve as an item based recommendation system for the average rating of a book. Thus no new user preferences will be taken into account. We will solely rely on the already rated books.\n",
    "\n",
    "### Data source\n",
    "The dataset is a sample from Goodreads.com and is published at: https://www.kaggle.com/jealousleopard/goodreadsbooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "books_data = pd.read_csv(\"datasets/books.csv\",\n",
    "                         error_bad_lines=False,\n",
    "                         warn_bad_lines=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Columns description: \n",
    "- **bookID** unique Identification number for each book.\n",
    "- **title** The name under which the book was published.\n",
    "- **authorsNames** of the authors of the book. Multiple authors are delimited with -.\n",
    "- **average_rating** The average rating of the book received in total.\n",
    "- **isbn** Another unique number to identify the book, the International Standard Book Number.\n",
    "- **isbn13** A 13-digit ISBN to identify the book, instead of the standard 11-digit ISBN.\n",
    "- **language_code** Helps understand what is the primary language of the book. For instance, eng is standard for English.\n",
    "- **\\# num_pages** Number of pages the book contains.\n",
    "- **ratings_count** Total number of ratings the book received.\n",
    "- **text_reviews_count** Total number of written text reviews the book received."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bookID</th>\n",
       "      <th>title</th>\n",
       "      <th>authors</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>isbn</th>\n",
       "      <th>isbn13</th>\n",
       "      <th>language_code</th>\n",
       "      <th># num_pages</th>\n",
       "      <th>ratings_count</th>\n",
       "      <th>text_reviews_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>11211</td>\n",
       "      <td>37476</td>\n",
       "      <td>Why I Hate Canadians</td>\n",
       "      <td>Will Ferguson</td>\n",
       "      <td>3.71</td>\n",
       "      <td>1550546007</td>\n",
       "      <td>9781550546002</td>\n",
       "      <td>en-GB</td>\n",
       "      <td>220</td>\n",
       "      <td>608</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12959</td>\n",
       "      <td>44739</td>\n",
       "      <td>Graphic Design Solutions</td>\n",
       "      <td>Robin Landa</td>\n",
       "      <td>4.03</td>\n",
       "      <td>1401881548</td>\n",
       "      <td>9781401881542</td>\n",
       "      <td>en-US</td>\n",
       "      <td>403</td>\n",
       "      <td>138</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7068</td>\n",
       "      <td>22188</td>\n",
       "      <td>Gossip Girl (Gossip Girl  #1)</td>\n",
       "      <td>Cecily von Ziegesar</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0316910333</td>\n",
       "      <td>9780316910330</td>\n",
       "      <td>eng</td>\n",
       "      <td>224</td>\n",
       "      <td>53230</td>\n",
       "      <td>2245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5061</td>\n",
       "      <td>15138</td>\n",
       "      <td>La mujer del viajero en el tiempo</td>\n",
       "      <td>Audrey Niffenegger-Silvia Alemany</td>\n",
       "      <td>3.96</td>\n",
       "      <td>0307344835</td>\n",
       "      <td>9780307344830</td>\n",
       "      <td>spa</td>\n",
       "      <td>608</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8853</td>\n",
       "      <td>28466</td>\n",
       "      <td>ADHD in Adulthood: A Guide to Current Theory  ...</td>\n",
       "      <td>Margaret Weiss-Gabrielle Weiss-Lily Trokenberg...</td>\n",
       "      <td>3.40</td>\n",
       "      <td>080186822X</td>\n",
       "      <td>9780801868221</td>\n",
       "      <td>eng</td>\n",
       "      <td>376</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       bookID                                              title  \\\n",
       "11211   37476                               Why I Hate Canadians   \n",
       "12959   44739                           Graphic Design Solutions   \n",
       "7068    22188                      Gossip Girl (Gossip Girl  #1)   \n",
       "5061    15138                  La mujer del viajero en el tiempo   \n",
       "8853    28466  ADHD in Adulthood: A Guide to Current Theory  ...   \n",
       "\n",
       "                                                 authors  average_rating  \\\n",
       "11211                                      Will Ferguson            3.71   \n",
       "12959                                        Robin Landa            4.03   \n",
       "7068                                 Cecily von Ziegesar            3.51   \n",
       "5061                   Audrey Niffenegger-Silvia Alemany            3.96   \n",
       "8853   Margaret Weiss-Gabrielle Weiss-Lily Trokenberg...            3.40   \n",
       "\n",
       "             isbn         isbn13 language_code  # num_pages  ratings_count  \\\n",
       "11211  1550546007  9781550546002         en-GB          220            608   \n",
       "12959  1401881548  9781401881542         en-US          403            138   \n",
       "7068   0316910333  9780316910330           eng          224          53230   \n",
       "5061   0307344835  9780307344830           spa          608             19   \n",
       "8853   080186822X  9780801868221           eng          376              5   \n",
       "\n",
       "       text_reviews_count  \n",
       "11211                  47  \n",
       "12959                  11  \n",
       "7068                 2245  \n",
       "5061                    2  \n",
       "8853                    0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books_data.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13714, 10)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bookID                  int64\n",
       "title                  object\n",
       "authors                object\n",
       "average_rating        float64\n",
       "isbn                   object\n",
       "isbn13                  int64\n",
       "language_code          object\n",
       "# num_pages             int64\n",
       "ratings_count           int64\n",
       "text_reviews_count      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books_data.isna().any().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no NA values in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "books_data.rename(columns={\"# num_pages\":\"pages_count\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bookID</th>\n",
       "      <th>title</th>\n",
       "      <th>authors</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>isbn</th>\n",
       "      <th>isbn13</th>\n",
       "      <th>language_code</th>\n",
       "      <th>pages_count</th>\n",
       "      <th>ratings_count</th>\n",
       "      <th>text_reviews_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2643</td>\n",
       "      <td>7967</td>\n",
       "      <td>Speaker for the Dead (Ender's Saga  #2)</td>\n",
       "      <td>Orson Scott Card</td>\n",
       "      <td>4.06</td>\n",
       "      <td>0812550757</td>\n",
       "      <td>9780812550757</td>\n",
       "      <td>eng</td>\n",
       "      <td>382</td>\n",
       "      <td>182235</td>\n",
       "      <td>6426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12966</td>\n",
       "      <td>44766</td>\n",
       "      <td>Purple Cane Road (Dave Robicheaux  #11)</td>\n",
       "      <td>James Lee Burke</td>\n",
       "      <td>4.15</td>\n",
       "      <td>0440224047</td>\n",
       "      <td>9780440224044</td>\n",
       "      <td>en-US</td>\n",
       "      <td>387</td>\n",
       "      <td>5304</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1636</td>\n",
       "      <td>4909</td>\n",
       "      <td>Teacher Man (Frank McCourt  #3)</td>\n",
       "      <td>Frank McCourt</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0743243781</td>\n",
       "      <td>9780743243780</td>\n",
       "      <td>eng</td>\n",
       "      <td>272</td>\n",
       "      <td>25726</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13133</td>\n",
       "      <td>45438</td>\n",
       "      <td>Legend of the Worst Boy in the World</td>\n",
       "      <td>Eoin Colfer-Glenn McCoy</td>\n",
       "      <td>3.74</td>\n",
       "      <td>0786855037</td>\n",
       "      <td>9780786855032</td>\n",
       "      <td>en-US</td>\n",
       "      <td>101</td>\n",
       "      <td>321</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13077</td>\n",
       "      <td>45251</td>\n",
       "      <td>Judas Unchained (Commonwealth Saga  #2)</td>\n",
       "      <td>Peter F. Hamilton</td>\n",
       "      <td>4.31</td>\n",
       "      <td>0345461673</td>\n",
       "      <td>9780345461674</td>\n",
       "      <td>en-US</td>\n",
       "      <td>1008</td>\n",
       "      <td>707</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       bookID                                    title  \\\n",
       "2643     7967  Speaker for the Dead (Ender's Saga  #2)   \n",
       "12966   44766  Purple Cane Road (Dave Robicheaux  #11)   \n",
       "1636     4909          Teacher Man (Frank McCourt  #3)   \n",
       "13133   45438     Legend of the Worst Boy in the World   \n",
       "13077   45251  Judas Unchained (Commonwealth Saga  #2)   \n",
       "\n",
       "                       authors  average_rating        isbn         isbn13  \\\n",
       "2643          Orson Scott Card            4.06  0812550757  9780812550757   \n",
       "12966          James Lee Burke            4.15  0440224047  9780440224044   \n",
       "1636             Frank McCourt            3.75  0743243781  9780743243780   \n",
       "13133  Eoin Colfer-Glenn McCoy            3.74  0786855037  9780786855032   \n",
       "13077        Peter F. Hamilton            4.31  0345461673  9780345461674   \n",
       "\n",
       "      language_code  pages_count  ratings_count  text_reviews_count  \n",
       "2643            eng          382         182235                6426  \n",
       "12966         en-US          387           5304                 237  \n",
       "1636            eng          272          25726                1991  \n",
       "13133         en-US          101            321                  44  \n",
       "13077         en-US         1008            707                  67  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books_data.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also bookID, isbn and isbn13 are just unique identifiers so I will drop them as they will not provide any additional information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>authors</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>language_code</th>\n",
       "      <th>pages_count</th>\n",
       "      <th>ratings_count</th>\n",
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       "      <td>8941</td>\n",
       "      <td>Ratner's Star</td>\n",
       "      <td>Don DeLillo</td>\n",
       "      <td>3.49</td>\n",
       "      <td>eng</td>\n",
       "      <td>448</td>\n",
       "      <td>1478</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6397</td>\n",
       "      <td>Moral Politics: How Liberals and Conservatives...</td>\n",
       "      <td>George Lakoff</td>\n",
       "      <td>4.03</td>\n",
       "      <td>eng</td>\n",
       "      <td>471</td>\n",
       "      <td>1215</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8705</td>\n",
       "      <td>Accordion Crimes</td>\n",
       "      <td>Annie Proulx</td>\n",
       "      <td>3.59</td>\n",
       "      <td>eng</td>\n",
       "      <td>432</td>\n",
       "      <td>5533</td>\n",
       "      <td>441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5941</td>\n",
       "      <td>The Books of Magic</td>\n",
       "      <td>Neil Gaiman-John Bolton-Scott Hampton-Charles ...</td>\n",
       "      <td>4.08</td>\n",
       "      <td>eng</td>\n",
       "      <td>200</td>\n",
       "      <td>13358</td>\n",
       "      <td>459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8528</td>\n",
       "      <td>Glass House</td>\n",
       "      <td>Philip Johnson-Toshio Nakamura</td>\n",
       "      <td>4.00</td>\n",
       "      <td>eng</td>\n",
       "      <td>256</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
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      "text/plain": [
       "                                                  title  \\\n",
       "8941                                      Ratner's Star   \n",
       "6397  Moral Politics: How Liberals and Conservatives...   \n",
       "8705                                   Accordion Crimes   \n",
       "5941                                 The Books of Magic   \n",
       "8528                                        Glass House   \n",
       "\n",
       "                                                authors  average_rating  \\\n",
       "8941                                        Don DeLillo            3.49   \n",
       "6397                                      George Lakoff            4.03   \n",
       "8705                                       Annie Proulx            3.59   \n",
       "5941  Neil Gaiman-John Bolton-Scott Hampton-Charles ...            4.08   \n",
       "8528                     Philip Johnson-Toshio Nakamura            4.00   \n",
       "\n",
       "     language_code  pages_count  ratings_count  text_reviews_count  \n",
       "8941           eng          448           1478                  98  \n",
       "6397           eng          471           1215                 129  \n",
       "8705           eng          432           5533                 441  \n",
       "5941           eng          200          13358                 459  \n",
       "8528           eng          256             14                   2  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books_data = books_data.drop([\"bookID\", \"isbn\", \"isbn13\"], axis = 1)\n",
    "books_data.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following heatmap displays the correlation between the features:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26505fd160>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"The following heatmap displays the correlation between the features:\")\n",
    "\n",
    "sns.heatmap(data=books_data.corr(),\n",
    "            linewidths=0.25, square=True,\n",
    "            linecolor=\"black\", annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see a high correlation between the ratings_count and the text_reviews_count (~ 86%). From this \n",
    "we can conclude that when a person writes a review he/she will most likely also rate the book itself."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's examine the top 10 most rated books."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>ratings_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Harry Potter and the Sorcerer's Stone (Harry P...</td>\n",
       "      <td>5629932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12243</td>\n",
       "      <td>Twilight (Twilight  #1)</td>\n",
       "      <td>4367341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2000</td>\n",
       "      <td>The Hobbit or There and Back Again</td>\n",
       "      <td>2364968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1717</td>\n",
       "      <td>The Catcher in the Rye</td>\n",
       "      <td>2318478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>340</td>\n",
       "      <td>Angels &amp; Demons (Robert Langdon  #1)</td>\n",
       "      <td>2279854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Harry Potter and the Prisoner of Azkaban (Harr...</td>\n",
       "      <td>2149872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5300</td>\n",
       "      <td>Harry Potter and the Chamber of Secrets (Harry...</td>\n",
       "      <td>2115562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2505</td>\n",
       "      <td>Animal Farm</td>\n",
       "      <td>2102616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>The Fellowship of the Ring (The Lord of the Ri...</td>\n",
       "      <td>2009749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Harry Potter and the Order of the Phoenix (Har...</td>\n",
       "      <td>1996446</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   title  ratings_count\n",
       "2      Harry Potter and the Sorcerer's Stone (Harry P...        5629932\n",
       "12243                            Twilight (Twilight  #1)        4367341\n",
       "2000                  The Hobbit or There and Back Again        2364968\n",
       "1717                              The Catcher in the Rye        2318478\n",
       "340                 Angels & Demons (Robert Langdon  #1)        2279854\n",
       "4      Harry Potter and the Prisoner of Azkaban (Harr...        2149872\n",
       "5300   Harry Potter and the Chamber of Secrets (Harry...        2115562\n",
       "2505                                         Animal Farm        2102616\n",
       "25     The Fellowship of the Ring (The Lord of the Ri...        2009749\n",
       "1      Harry Potter and the Order of the Phoenix (Har...        1996446"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_rated = books_data.sort_values(by=\"ratings_count\", ascending = False).head(10)\n",
    "\n",
    "most_rated_titles = pd.DataFrame(most_rated.title).join(pd.DataFrame(most_rated.ratings_count))\n",
    "most_rated_titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Titles')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "ax.invert_yaxis()\n",
    "plt.bar(most_rated_titles.ratings_count, most_rated_titles.title,\n",
    "        align=\"center\")\n",
    "plt.title(\"Top 10 Books: Most Rated\")\n",
    "plt.xlabel(\"Ratings count\")\n",
    "plt.ylabel(\"Titles\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scatter_plot(x, y, title, x_label, y_label):\n",
    "    plt.subplots(figsize=(8, 8))\n",
    "    plt.scatter(x,\n",
    "                y)\n",
    "    plt.title(title)\n",
    "    plt.xlabel(x_label)\n",
    "    plt.ylabel(y_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pages_count_and_average_rating_title = \"Relation between pages count and average rating\"\n",
    "average_rating_label = \"Average rating\"\n",
    "pages_count_label = \"Pages count\"\n",
    "scatter_plot(books_data.average_rating,\n",
    "             books_data.pages_count,\n",
    "            pages_count_and_average_rating_title, average_rating_label, pages_count_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a lot of outliers here. We can see that is no significant relation between average rating and the count of pages of a book.\n",
    "\n",
    "The heatmap also proved this as it displayed a ration of 0.17.\n",
    "Will drop the ourlier (with pages count >= 2000)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "books_data = books_data.drop(books_data.index[books_data[\"pages_count\"] >= 2000])\n",
    "pages_count_and_average_rating_title = \"Relation between pages count and average rating\"\n",
    "average_rating_label = \"Average rating\"\n",
    "pages_count_label = \"Pages count\"\n",
    "scatter_plot(books_data.average_rating,\n",
    "             books_data.pages_count,\n",
    "            pages_count_and_average_rating_title, average_rating_label, pages_count_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let`s focus on books ranging between 1-1500 pages.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>authors</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>language_code</th>\n",
       "      <th>pages_count</th>\n",
       "      <th>ratings_count</th>\n",
       "      <th>text_reviews_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Harry Potter and the Half-Blood Prince (Harry ...</td>\n",
       "      <td>J.K. Rowling-Mary GrandPré</td>\n",
       "      <td>4.56</td>\n",
       "      <td>eng</td>\n",
       "      <td>652</td>\n",
       "      <td>1944099</td>\n",
       "      <td>26249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Harry Potter and the Order of the Phoenix (Har...</td>\n",
       "      <td>J.K. Rowling-Mary GrandPré</td>\n",
       "      <td>4.49</td>\n",
       "      <td>eng</td>\n",
       "      <td>870</td>\n",
       "      <td>1996446</td>\n",
       "      <td>27613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Harry Potter and the Sorcerer's Stone (Harry P...</td>\n",
       "      <td>J.K. Rowling-Mary GrandPré</td>\n",
       "      <td>4.47</td>\n",
       "      <td>eng</td>\n",
       "      <td>320</td>\n",
       "      <td>5629932</td>\n",
       "      <td>70390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Harry Potter and the Chamber of Secrets (Harry...</td>\n",
       "      <td>J.K. Rowling</td>\n",
       "      <td>4.41</td>\n",
       "      <td>eng</td>\n",
       "      <td>352</td>\n",
       "      <td>6267</td>\n",
       "      <td>272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Harry Potter and the Prisoner of Azkaban (Harr...</td>\n",
       "      <td>J.K. Rowling-Mary GrandPré</td>\n",
       "      <td>4.55</td>\n",
       "      <td>eng</td>\n",
       "      <td>435</td>\n",
       "      <td>2149872</td>\n",
       "      <td>33964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13709</td>\n",
       "      <td>M Is for Magic</td>\n",
       "      <td>Neil Gaiman-Teddy Kristiansen</td>\n",
       "      <td>3.82</td>\n",
       "      <td>eng</td>\n",
       "      <td>260</td>\n",
       "      <td>11317</td>\n",
       "      <td>1060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13710</td>\n",
       "      <td>Black Orchid</td>\n",
       "      <td>Neil Gaiman-Dave McKean</td>\n",
       "      <td>3.72</td>\n",
       "      <td>eng</td>\n",
       "      <td>160</td>\n",
       "      <td>8710</td>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13711</td>\n",
       "      <td>InterWorld (InterWorld  #1)</td>\n",
       "      <td>Neil Gaiman-Michael Reaves</td>\n",
       "      <td>3.53</td>\n",
       "      <td>en-US</td>\n",
       "      <td>239</td>\n",
       "      <td>14334</td>\n",
       "      <td>1485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13712</td>\n",
       "      <td>The Faeries' Oracle</td>\n",
       "      <td>Brian Froud-Jessica Macbeth</td>\n",
       "      <td>4.43</td>\n",
       "      <td>eng</td>\n",
       "      <td>224</td>\n",
       "      <td>1550</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13713</td>\n",
       "      <td>The World of The Dark Crystal</td>\n",
       "      <td>Brian Froud</td>\n",
       "      <td>4.29</td>\n",
       "      <td>eng</td>\n",
       "      <td>132</td>\n",
       "      <td>3572</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13666 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   title  \\\n",
       "0      Harry Potter and the Half-Blood Prince (Harry ...   \n",
       "1      Harry Potter and the Order of the Phoenix (Har...   \n",
       "2      Harry Potter and the Sorcerer's Stone (Harry P...   \n",
       "3      Harry Potter and the Chamber of Secrets (Harry...   \n",
       "4      Harry Potter and the Prisoner of Azkaban (Harr...   \n",
       "...                                                  ...   \n",
       "13709                                     M Is for Magic   \n",
       "13710                                       Black Orchid   \n",
       "13711                        InterWorld (InterWorld  #1)   \n",
       "13712                                The Faeries' Oracle   \n",
       "13713                      The World of The Dark Crystal   \n",
       "\n",
       "                             authors  average_rating language_code  \\\n",
       "0         J.K. Rowling-Mary GrandPré            4.56           eng   \n",
       "1         J.K. Rowling-Mary GrandPré            4.49           eng   \n",
       "2         J.K. Rowling-Mary GrandPré            4.47           eng   \n",
       "3                       J.K. Rowling            4.41           eng   \n",
       "4         J.K. Rowling-Mary GrandPré            4.55           eng   \n",
       "...                              ...             ...           ...   \n",
       "13709  Neil Gaiman-Teddy Kristiansen            3.82           eng   \n",
       "13710        Neil Gaiman-Dave McKean            3.72           eng   \n",
       "13711     Neil Gaiman-Michael Reaves            3.53         en-US   \n",
       "13712    Brian Froud-Jessica Macbeth            4.43           eng   \n",
       "13713                    Brian Froud            4.29           eng   \n",
       "\n",
       "       pages_count  ratings_count  text_reviews_count  \n",
       "0              652        1944099               26249  \n",
       "1              870        1996446               27613  \n",
       "2              320        5629932               70390  \n",
       "3              352           6267                 272  \n",
       "4              435        2149872               33964  \n",
       "...            ...            ...                 ...  \n",
       "13709          260          11317                1060  \n",
       "13710          160           8710                 361  \n",
       "13711          239          14334                1485  \n",
       "13712          224           1550                  38  \n",
       "13713          132           3572                  33  \n",
       "\n",
       "[13666 rows x 7 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Let`s focus on books ranging between 1-1500 pages.\")\n",
    "limited_by_page_count_books = books_data[books_data.pages_count <= 1500]\n",
    "limited_by_page_count_books"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_plot(limited_by_page_count_books.average_rating,\n",
    "             limited_by_page_count_books.pages_count,\n",
    "             pages_count_and_average_rating_title,\n",
    "             average_rating_label,\n",
    "             pages_count_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([], <a list of 0 Text xticklabel objects>)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(8, 8))\n",
    "plt.title(\"People preferences in regards to pages count \")\n",
    "violionplot = plt.violinplot(limited_by_page_count_books.pages_count, showmeans=True, points=1000, widths=1)\n",
    "plt.xticks([], None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can conclude that people tend to prefer books with pages count between 200 and 400."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>authors</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>language_code</th>\n",
       "      <th>pages_count</th>\n",
       "      <th>ratings_count</th>\n",
       "      <th>text_reviews_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>339</td>\n",
       "      <td>The 5 Love Languages / The 5 Love Languages Jo...</td>\n",
       "      <td>Gary Chapman</td>\n",
       "      <td>4.71</td>\n",
       "      <td>eng</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>421</td>\n",
       "      <td>The Clan of the Cave Bear  Part 1 of 2 (Earth'...</td>\n",
       "      <td>Jean M. Auel-Donada  Peters</td>\n",
       "      <td>4.43</td>\n",
       "      <td>eng</td>\n",
       "      <td>0</td>\n",
       "      <td>611</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>959</td>\n",
       "      <td>The Tragedy of Pudd'nhead Wilson</td>\n",
       "      <td>Mark Twain-Michael Prichard</td>\n",
       "      <td>3.79</td>\n",
       "      <td>eng</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>977</td>\n",
       "      <td>The Lady and the Unicorn</td>\n",
       "      <td>Tracy Chevalier</td>\n",
       "      <td>3.68</td>\n",
       "      <td>eng</td>\n",
       "      <td>0</td>\n",
       "      <td>303</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1426</td>\n",
       "      <td>The Da Vinci Code (Robert Langdon  #2)</td>\n",
       "      <td>Dan Brown-Paul Michael</td>\n",
       "      <td>3.82</td>\n",
       "      <td>eng</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12072</td>\n",
       "      <td>Fine Lines (One-Eyed Mack  #6)</td>\n",
       "      <td>Jim Lehrer</td>\n",
       "      <td>3.23</td>\n",
       "      <td>eng</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12594</td>\n",
       "      <td>Stowaway and Milk Run: Two Unabridged Stories ...</td>\n",
       "      <td>Mary Higgins Clark-Jan Maxwell</td>\n",
       "      <td>3.40</td>\n",
       "      <td>eng</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12960</td>\n",
       "      <td>The Mask of the Enchantress</td>\n",
       "      <td>Victoria Holt</td>\n",
       "      <td>3.84</td>\n",
       "      <td>eng</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13147</td>\n",
       "      <td>Treasury of American Tall Tales: Volume 1: Dav...</td>\n",
       "      <td>David Bromberg-Jay Ungar-Molly Mason-Garrison ...</td>\n",
       "      <td>3.86</td>\n",
       "      <td>eng</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13321</td>\n",
       "      <td>Appetite for Life: The Biography of Julia Child</td>\n",
       "      <td>Noël Riley Fitch-Nadia May</td>\n",
       "      <td>4.06</td>\n",
       "      <td>eng</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   title  \\\n",
       "339    The 5 Love Languages / The 5 Love Languages Jo...   \n",
       "421    The Clan of the Cave Bear  Part 1 of 2 (Earth'...   \n",
       "959                     The Tragedy of Pudd'nhead Wilson   \n",
       "977                             The Lady and the Unicorn   \n",
       "1426              The Da Vinci Code (Robert Langdon  #2)   \n",
       "...                                                  ...   \n",
       "12072                     Fine Lines (One-Eyed Mack  #6)   \n",
       "12594  Stowaway and Milk Run: Two Unabridged Stories ...   \n",
       "12960                        The Mask of the Enchantress   \n",
       "13147  Treasury of American Tall Tales: Volume 1: Dav...   \n",
       "13321    Appetite for Life: The Biography of Julia Child   \n",
       "\n",
       "                                                 authors  average_rating  \\\n",
       "339                                         Gary Chapman            4.71   \n",
       "421                          Jean M. Auel-Donada  Peters            4.43   \n",
       "959                          Mark Twain-Michael Prichard            3.79   \n",
       "977                                      Tracy Chevalier            3.68   \n",
       "1426                              Dan Brown-Paul Michael            3.82   \n",
       "...                                                  ...             ...   \n",
       "12072                                         Jim Lehrer            3.23   \n",
       "12594                     Mary Higgins Clark-Jan Maxwell            3.40   \n",
       "12960                                      Victoria Holt            3.84   \n",
       "13147  David Bromberg-Jay Ungar-Molly Mason-Garrison ...            3.86   \n",
       "13321                         Noël Riley Fitch-Nadia May            4.06   \n",
       "\n",
       "      language_code  pages_count  ratings_count  text_reviews_count  \n",
       "339             eng            0             20                   4  \n",
       "421             eng            0            611                  34  \n",
       "959             eng            0              3                   0  \n",
       "977             eng            0            303                  24  \n",
       "1426            eng            0             90                  16  \n",
       "...             ...          ...            ...                 ...  \n",
       "12072           eng            0             17                   3  \n",
       "12594           eng            0             55                   0  \n",
       "12960           eng            0             20                   1  \n",
       "13147           eng            0             36                   9  \n",
       "13321           eng            0             37                   5  \n",
       "\n",
       "[85 rows x 7 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books_data[books_data[\"pages_count\"] == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 0 values in the pages_count column. Will replace them with the mean of the column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Boxplot of count of pages')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(8,8))\n",
    "plt.boxplot(books_data.pages_count)\n",
    "plt.xticks([1], [\"Pages count\"])\n",
    "plt.title(\"Boxplot of count of pages\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of pages count 337.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean of pages count\", float(floor(books_data.pages_count.mean())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count of 0s in pages count column: 0\n",
      "Are there any NaNs in pages count column: False\n"
     ]
    }
   ],
   "source": [
    "books_data.pages_count = books_data.pages_count.replace(0, np.nan)\n",
    "books_data.pages_count.fillna(float(floor(books_data.pages_count.mean())), inplace=True)\n",
    "\n",
    "print(\"Count of 0s in pages count column:\", len(books_data[books_data[\"pages_count\"] == 0]))\n",
    "print(\"Are there any NaNs in pages count column:\" , books_data.pages_count.isna().any().any())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>authors</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>language_code</th>\n",
       "      <th>pages_count</th>\n",
       "      <th>ratings_count</th>\n",
       "      <th>text_reviews_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Harry Potter and the Half-Blood Prince (Harry ...</td>\n",
       "      <td>J.K. Rowling-Mary GrandPré</td>\n",
       "      <td>4.56</td>\n",
       "      <td>eng</td>\n",
       "      <td>652.0</td>\n",
       "      <td>1944099</td>\n",
       "      <td>26249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Harry Potter and the Order of the Phoenix (Har...</td>\n",
       "      <td>J.K. Rowling-Mary GrandPré</td>\n",
       "      <td>4.49</td>\n",
       "      <td>eng</td>\n",
       "      <td>870.0</td>\n",
       "      <td>1996446</td>\n",
       "      <td>27613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Harry Potter and the Sorcerer's Stone (Harry P...</td>\n",
       "      <td>J.K. Rowling-Mary GrandPré</td>\n",
       "      <td>4.47</td>\n",
       "      <td>eng</td>\n",
       "      <td>320.0</td>\n",
       "      <td>5629932</td>\n",
       "      <td>70390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Harry Potter and the Chamber of Secrets (Harry...</td>\n",
       "      <td>J.K. Rowling</td>\n",
       "      <td>4.41</td>\n",
       "      <td>eng</td>\n",
       "      <td>352.0</td>\n",
       "      <td>6267</td>\n",
       "      <td>272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Harry Potter and the Prisoner of Azkaban (Harr...</td>\n",
       "      <td>J.K. Rowling-Mary GrandPré</td>\n",
       "      <td>4.55</td>\n",
       "      <td>eng</td>\n",
       "      <td>435.0</td>\n",
       "      <td>2149872</td>\n",
       "      <td>33964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13709</td>\n",
       "      <td>M Is for Magic</td>\n",
       "      <td>Neil Gaiman-Teddy Kristiansen</td>\n",
       "      <td>3.82</td>\n",
       "      <td>eng</td>\n",
       "      <td>260.0</td>\n",
       "      <td>11317</td>\n",
       "      <td>1060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13710</td>\n",
       "      <td>Black Orchid</td>\n",
       "      <td>Neil Gaiman-Dave McKean</td>\n",
       "      <td>3.72</td>\n",
       "      <td>eng</td>\n",
       "      <td>160.0</td>\n",
       "      <td>8710</td>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13711</td>\n",
       "      <td>InterWorld (InterWorld  #1)</td>\n",
       "      <td>Neil Gaiman-Michael Reaves</td>\n",
       "      <td>3.53</td>\n",
       "      <td>en-US</td>\n",
       "      <td>239.0</td>\n",
       "      <td>14334</td>\n",
       "      <td>1485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13712</td>\n",
       "      <td>The Faeries' Oracle</td>\n",
       "      <td>Brian Froud-Jessica Macbeth</td>\n",
       "      <td>4.43</td>\n",
       "      <td>eng</td>\n",
       "      <td>224.0</td>\n",
       "      <td>1550</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13713</td>\n",
       "      <td>The World of The Dark Crystal</td>\n",
       "      <td>Brian Froud</td>\n",
       "      <td>4.29</td>\n",
       "      <td>eng</td>\n",
       "      <td>132.0</td>\n",
       "      <td>3572</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13689 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   title  \\\n",
       "0      Harry Potter and the Half-Blood Prince (Harry ...   \n",
       "1      Harry Potter and the Order of the Phoenix (Har...   \n",
       "2      Harry Potter and the Sorcerer's Stone (Harry P...   \n",
       "3      Harry Potter and the Chamber of Secrets (Harry...   \n",
       "4      Harry Potter and the Prisoner of Azkaban (Harr...   \n",
       "...                                                  ...   \n",
       "13709                                     M Is for Magic   \n",
       "13710                                       Black Orchid   \n",
       "13711                        InterWorld (InterWorld  #1)   \n",
       "13712                                The Faeries' Oracle   \n",
       "13713                      The World of The Dark Crystal   \n",
       "\n",
       "                             authors  average_rating language_code  \\\n",
       "0         J.K. Rowling-Mary GrandPré            4.56           eng   \n",
       "1         J.K. Rowling-Mary GrandPré            4.49           eng   \n",
       "2         J.K. Rowling-Mary GrandPré            4.47           eng   \n",
       "3                       J.K. Rowling            4.41           eng   \n",
       "4         J.K. Rowling-Mary GrandPré            4.55           eng   \n",
       "...                              ...             ...           ...   \n",
       "13709  Neil Gaiman-Teddy Kristiansen            3.82           eng   \n",
       "13710        Neil Gaiman-Dave McKean            3.72           eng   \n",
       "13711     Neil Gaiman-Michael Reaves            3.53         en-US   \n",
       "13712    Brian Froud-Jessica Macbeth            4.43           eng   \n",
       "13713                    Brian Froud            4.29           eng   \n",
       "\n",
       "       pages_count  ratings_count  text_reviews_count  \n",
       "0            652.0        1944099               26249  \n",
       "1            870.0        1996446               27613  \n",
       "2            320.0        5629932               70390  \n",
       "3            352.0           6267                 272  \n",
       "4            435.0        2149872               33964  \n",
       "...            ...            ...                 ...  \n",
       "13709        260.0          11317                1060  \n",
       "13710        160.0           8710                 361  \n",
       "13711        239.0          14334                1485  \n",
       "13712        224.0           1550                  38  \n",
       "13713        132.0           3572                  33  \n",
       "\n",
       "[13689 rows x 7 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Boxplot of count of ratings')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(8,8))\n",
    "plt.boxplot(books_data.ratings_count)\n",
    "plt.xticks([1], [\"Ratings count\"])\n",
    "plt.title(\"Boxplot of count of ratings\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a few  ourliers here. I will leave them as they are the most popular books (i.e Harry Potter and Twilight)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I`m going to encode all the string and categorical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = LabelEncoder()\n",
    "books_data.title = encoder.fit_transform(books_data.title)\n",
    "books_data.sample(5)\n",
    "books_data.authors = encoder.fit_transform(books_data.authors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "books_data = pd.get_dummies(books_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>authors</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>pages_count</th>\n",
       "      <th>ratings_count</th>\n",
       "      <th>text_reviews_count</th>\n",
       "      <th>language_code_ale</th>\n",
       "      <th>language_code_ara</th>\n",
       "      <th>language_code_cat</th>\n",
       "      <th>language_code_dan</th>\n",
       "      <th>...</th>\n",
       "      <th>language_code_nl</th>\n",
       "      <th>language_code_nor</th>\n",
       "      <th>language_code_por</th>\n",
       "      <th>language_code_rus</th>\n",
       "      <th>language_code_spa</th>\n",
       "      <th>language_code_srp</th>\n",
       "      <th>language_code_swe</th>\n",
       "      <th>language_code_tur</th>\n",
       "      <th>language_code_wel</th>\n",
       "      <th>language_code_zho</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>9135</td>\n",
       "      <td>7205</td>\n",
       "      <td>5354</td>\n",
       "      <td>3.32</td>\n",
       "      <td>290.0</td>\n",
       "      <td>4053</td>\n",
       "      <td>660</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5810</td>\n",
       "      <td>9081</td>\n",
       "      <td>5949</td>\n",
       "      <td>4.80</td>\n",
       "      <td>339.0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>211</td>\n",
       "      <td>8375</td>\n",
       "      <td>5517</td>\n",
       "      <td>3.83</td>\n",
       "      <td>306.0</td>\n",
       "      <td>17317</td>\n",
       "      <td>1138</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5729</td>\n",
       "      <td>3168</td>\n",
       "      <td>1827</td>\n",
       "      <td>3.82</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4701</td>\n",
       "      <td>7814</td>\n",
       "      <td>4926</td>\n",
       "      <td>3.84</td>\n",
       "      <td>350.0</td>\n",
       "      <td>3740</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      title  authors  average_rating  pages_count  ratings_count  \\\n",
       "9135   7205     5354            3.32        290.0           4053   \n",
       "5810   9081     5949            4.80        339.0             20   \n",
       "211    8375     5517            3.83        306.0          17317   \n",
       "5729   3168     1827            3.82        100.0             10   \n",
       "4701   7814     4926            3.84        350.0           3740   \n",
       "\n",
       "      text_reviews_count  language_code_ale  language_code_ara  \\\n",
       "9135                 660                  0                  0   \n",
       "5810                   0                  0                  0   \n",
       "211                 1138                  0                  0   \n",
       "5729                   1                  0                  0   \n",
       "4701                  47                  0                  0   \n",
       "\n",
       "      language_code_cat  language_code_dan  ...  language_code_nl  \\\n",
       "9135                  0                  0  ...                 0   \n",
       "5810                  0                  0  ...                 0   \n",
       "211                   0                  0  ...                 0   \n",
       "5729                  0                  0  ...                 0   \n",
       "4701                  0                  0  ...                 0   \n",
       "\n",
       "      language_code_nor  language_code_por  language_code_rus  \\\n",
       "9135                  0                  0                  0   \n",
       "5810                  0                  0                  0   \n",
       "211                   0                  0                  0   \n",
       "5729                  0                  0                  0   \n",
       "4701                  0                  0                  0   \n",
       "\n",
       "      language_code_spa  language_code_srp  language_code_swe  \\\n",
       "9135                  0                  0                  0   \n",
       "5810                  0                  0                  0   \n",
       "211                   0                  0                  0   \n",
       "5729                  0                  0                  0   \n",
       "4701                  0                  0                  0   \n",
       "\n",
       "      language_code_tur  language_code_wel  language_code_zho  \n",
       "9135                  0                  0                  0  \n",
       "5810                  0                  0                  0  \n",
       "211                   0                  0                  0  \n",
       "5729                  0                  0                  0  \n",
       "4701                  0                  0                  0  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books_data.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13689, 36)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Boxplot of count of pages')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(8,8))\n",
    "plt.boxplot(books_data.pages_count)\n",
    "plt.xticks([1], [\"Pages count\"])\n",
    "plt.title(\"Boxplot of count of pages\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will focus no the target (i.e the average_rating). A histogram is a good way to visualize the sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histogram of the average rating')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(8,8))\n",
    "plt.hist(books_data.average_rating,density=True)\n",
    "plt.xlabel(\"Average rating\")\n",
    "plt.ylabel(\"Density\")\n",
    "plt.title(\"Histogram of the average rating\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the histogram we see that most of ratings vary from 3 to 4.5. We can conclude that people tend to like the books they rate thus most of the ratings are very high."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chosing the best model for regression\n",
    "I'm going to try whether we can use the non-modified for predictions. If the scores are not good I will try to transform the data using a polynom and repeat the same steps and parameter tuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "book_data=StandardScaler().fit_transform(books_data)\n",
    "books_data_attributes =books_data.drop(\"average_rating\", axis =1)\n",
    "books_data_labels =books_data.average_rating\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split (books_data_attributes, books_data_labels, test_size = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_fold = list(KFold(n_splits=5, shuffle=True).split(X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def grid_search_best_model(model, params, k_fold, X_train, y_train):\n",
    "    grid_search = GridSearchCV(model,\n",
    "                           params,                             \n",
    "                          cv=k_fold).fit(X_train,y_train)\n",
    "    print(\"Best params\", grid_search.best_params_)\n",
    "    print(\"Best estimator\", grid_search.best_estimator_)\n",
    "    print(\"Best score:\", grid_search.best_score_)\n",
    "    \n",
    "    return grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_results = {}\n",
    "\n",
    "def score_model(model,X_train, X_test, y_train, y_test,\n",
    "               show_plot=True):   \n",
    "    y_pred = model.predict(X_test)  \n",
    "    print(f\"Training score: {model.score(X_train,y_train)}\")\n",
    "    print(f\"Test score: {r2_score(y_test, y_pred)}\")\n",
    "    print(\"MSE: \", mean_squared_error(y_test, y_pred))\n",
    "    \n",
    "    predictions_comparision = pd.DataFrame({'Actual': y_test.tolist(), 'Predicted': y_pred.tolist()}).sample(25)\n",
    "    if show_plot == True:\n",
    "        predictions_comparision.plot(kind=\"bar\", figsize=(12,8),title=\"Actual vs predicted values\")\n",
    "    print(predictions_comparision.sample(10))    \n",
    "    \n",
    "    \n",
    "    return {\n",
    "        \"training_score\": model.score(X_train,y_train),\n",
    "        \"test_score_r2\" : r2_score(y_test, y_pred),\n",
    "        \"test_score_mse\" : mean_squared_error(y_test, y_pred)\n",
    "    }\n",
    "\n",
    "def compare_results():\n",
    "    for key in model_results:\n",
    "        print(\"Regression: \", key)\n",
    "        print(\"Trainign score\", model_results[key][\"training_score\"])\n",
    "        print(\"R2 Test score \", model_results[key][\"test_score_r2\"])\n",
    "        print(\"MSE Test score \", model_results[key][\"test_score_mse\"])\n",
    "        print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params {'fit_intercept': True}\n",
      "Best estimator LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)\n",
      "Best score: 0.026220743850793315\n",
      "Training score: 0.05465157091881012\n",
      "Test score: 0.019113708478992963\n",
      "MSE:  0.11247859277950441\n",
      "      Actual  Predicted\n",
      "1777    4.00   4.010695\n",
      "2065    4.01   3.942140\n",
      "3148    4.04   3.882693\n",
      "1416    3.96   3.892425\n",
      "1402    3.31   3.896429\n",
      "2859    3.59   3.859084\n",
      "2857    4.24   3.865951\n",
      "3143    4.18   3.987841\n",
      "1186    4.27   4.255050\n",
      "597     4.02   3.906516\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params={\n",
    "    \"fit_intercept\":[True,False],\n",
    "}\n",
    "\n",
    "linear_regression = grid_search_best_model(LinearRegression(), params, k_fold, X_train, y_train)\n",
    "model_results[\"linear_regression\"] = score_model(linear_regression, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params {'leaf_size': 20, 'n_neighbors': 29}\n",
      "Best estimator KNeighborsRegressor(algorithm='auto', leaf_size=20, metric='minkowski',\n",
      "                    metric_params=None, n_jobs=None, n_neighbors=29, p=2,\n",
      "                    weights='uniform')\n",
      "Best score: -0.0038429003753813903\n",
      "Training score: 0.06755886981501014\n",
      "Test score: 0.002626160392752097\n",
      "MSE:  0.11436922599882397\n",
      "      Actual  Predicted\n",
      "844     4.01   4.071034\n",
      "3102    4.22   3.867931\n",
      "3227    4.02   3.881034\n",
      "1577    3.85   3.948621\n",
      "1977    3.57   3.927586\n",
      "2761    3.85   3.947931\n",
      "496     4.13   3.858621\n",
      "1623    3.78   3.898621\n",
      "1320    3.76   3.825862\n",
      "1475    4.30   4.021034\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params={\n",
    "    \"n_neighbors\": range(2, 30),\n",
    "    \"leaf_size\":[20,30,50,70]\n",
    "}\n",
    "\n",
    "knn = grid_search_best_model(KNeighborsRegressor(), params, k_fold, X_train, y_train)\n",
    "model_results[\"knn\"] = score_model(knn, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params {'max_depth': 50, 'max_leaf_nodes': 20, 'min_samples_leaf': 20, 'min_samples_split': 40}\n",
      "Best estimator DecisionTreeRegressor(criterion='mse', max_depth=50, max_features=None,\n",
      "                      max_leaf_nodes=20, min_impurity_decrease=0.0,\n",
      "                      min_impurity_split=None, min_samples_leaf=20,\n",
      "                      min_samples_split=40, min_weight_fraction_leaf=0.0,\n",
      "                      presort=False, random_state=None, splitter='best')\n",
      "Best score: 0.15273938023368386\n",
      "Training score: 0.2238261658403088\n",
      "Test score: 0.06081093588778619\n",
      "MSE:  0.10769715633545397\n",
      "      Actual  Predicted\n",
      "2684    4.26   4.040775\n",
      "3689    3.74   3.891364\n",
      "3058    3.57   1.484400\n",
      "4070    3.94   3.961019\n",
      "487     3.50   3.894562\n",
      "2030    3.93   3.894562\n",
      "278     3.40   3.848845\n",
      "501     3.89   3.894562\n",
      "3212    3.65   4.040775\n",
      "1833    4.11   3.848845\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = {    \"min_samples_split\": [10, 20, 40],\n",
    "              \"max_depth\": [2, 6, 8, 15, 50],\n",
    "              \"min_samples_leaf\": [5, 20, 30],\n",
    "              \"max_leaf_nodes\": [5, 20],\n",
    "              }\n",
    "dtr = grid_search_best_model(DecisionTreeRegressor(), params, k_fold, X_train, y_train)\n",
    "model_results[\"dtr\"] = score_model(dtr, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params {'learning_rate': 1, 'n_estimators': 1000}\n",
      "Best estimator AdaBoostRegressor(base_estimator=None, learning_rate=1, loss='linear',\n",
      "                  n_estimators=1000, random_state=None)\n",
      "Best score: 0.1167738112357136\n",
      "Training score: 0.23344257437711746\n",
      "Test score: 0.01832877116059284\n",
      "MSE:  0.11256860183127418\n",
      "      Actual  Predicted\n",
      "907     4.04   3.917230\n",
      "2181    3.88   3.910247\n",
      "2098    3.86   3.874567\n",
      "59      4.03   3.915021\n",
      "2533    4.35   3.874567\n",
      "1614    3.49   3.875060\n",
      "628     3.82   3.998397\n",
      "2424    4.16   4.020968\n",
      "2918    3.64   3.917230\n",
      "3400    3.21   3.874567\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = {\"learning_rate\":[0.3, 0.5,1],\n",
    "          \"n_estimators\": [50, 100,200,400,700,1000]\n",
    "              }\n",
    "abr = grid_search_best_model(AdaBoostRegressor(), params, k_fold, X_train, y_train)\n",
    "model_results[\"abr\"] = score_model(abr, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is normalized using the standar scaler. Will try to use relatively small C."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params {'C': 0.1, 'fit_intercept': True}\n",
      "Best estimator LinearSVR(C=0.1, dual=True, epsilon=0.0, fit_intercept=True,\n",
      "          intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=1000,\n",
      "          random_state=None, tol=0.0001, verbose=0)\n",
      "Best score: -447.3088716518204\n",
      "Training score: -52.46650719212725\n",
      "Test score: -40.487973429633215\n",
      "MSE:  4.757441213091593\n",
      "      Actual  Predicted\n",
      "1680    3.94   3.075523\n",
      "2358    4.13   4.296769\n",
      "3642    3.99   7.166097\n",
      "3544    4.06   4.320165\n",
      "912     3.74   5.215212\n",
      "3139    3.91   4.942050\n",
      "26      3.69   8.028200\n",
      "1371    3.56   3.424698\n",
      "3059    4.14   4.462631\n",
      "3882    4.22   3.645142\n"
     ]
    },
    {
     "data": {
      "image/png": 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2NtxwQ3bbbTduuummF/z/E088kcMOO4xTTjmFlVdemW9961vssssu7LrrrrzqVa9in3324atf/SozZsxgl112AWCttdbitNNOY4cdduCv/uqv2G677dhss814/etf38o6RWYO/6KIvwM+DCRwI3BIZj411OsnTZqUU6dObaWBUnUWNUfiMrrzkyQtmRkzZjBx4sTRbkYVBnsvI2JaZk4a7PXD9uRGxCbAkcDWmflkRPwAOACYvPTNlSRJKxy/7KsDIx2TOwZYPSLGAGsAs3vXJEmSJGnpDBtyM/Ne4Hjg98AfgLmZeeHA10XEYRExNSKmzpkzp/2WSpIkLYdGMjRUi7Yk7+GwITciXgT8JbA5sDGwZkR8YJDiJ2fmpMycNG7cuMVuiCRJUm1WW201HnroIYPuUshMHnroIVZbbbXF+n8jmV1hT+DuzJwDEBFnA68DTlvsVkqSJK1Axo8fz6xZs/Ao99JZbbXVGD9+/GL9n5GE3N8DO0fEGsCTwB6AUydIkiQNY+zYsWy++eaj3YwV0kjG5F4FnAlcS5k+bCXghddzkyRJkpYRI7oYRGYeAxzT47ZIkiRJrfCyvpIkSaqOIVeSJEnVMeRKkiSpOoZcSZIkVceQK0mSpOoYciVJklQdQ64kSZKqY8iVJElSdQy5kiRJqo4hV5IkSdUx5EqSJKk6hlxJkiRVx5ArSZKk6hhyJUmSVB1DriRJkqpjyJUkSVJ1DLmSJEmqjiFXkiRJ1THkSpIkqTqGXEmSJFXHkCtJkqTqGHIlSZJUHUOuJEmSqmPIlSRJUnUMuZIkSaqOIVeSJEnVMeRKkiSpOoZcSZIkVceQK0mSpOoYciVJklQdQ64kSZKqY8iVJElSdQy5kiRJqo4hV5IkSdUx5EqSJKk6Y0a7AZIkteLYdYd4fG637ZC0TLAnV5IkSdUx5EqSJKk6hlxJkiRVx5ArSZKk6gwbciPiFRExvd/tsYg4qovGSZIkSUti2NkVMvNWYDuAiFgZuBc4p8ftkiRJkpbY4g5X2AO4MzN/14vGSJIkSW1Y3JB7ADClFw2RJEmS2jLikBsRqwD7AT8c4vnDImJqREydM2dOW+2TJEmSFtvi9OTuA1ybmfcP9mRmnpyZkzJz0rhx49ppnSRJkrQEFifkHohDFSRJkrQcGFHIjYg1gLcAZ/e2OZIkSdLSG3YKMYDMfALYoMdtkSRJklrhFc8kSZJUHUOuJEmSqmPIlSRJUnUMuZIkSaqOIVeSJEnVMeRKkiSpOoZcSZIkVceQK0mSpOoYciVJklQdQ64kSZKqM6LL+kqSJC2Xjl13iMfndtsOdc6eXEmSJFXHkCtJkqTqGHIlSZJUHUOuJEmSqmPIlSRJUnUMuZIkSaqOIVeSJEnVWTHnyXXOPEmSpKqtmCFXkiSpTUN1oIGdaKPE4QqSJEmqjiFXkiRJ1THkSpIkqTqGXEmSJFXHkCtJkqTqGHIlSZJUHUOuJEmSqmPIlSRJUnUMuZIkSaqOIVeSJEnVMeRKkiSpOoZcSZIkVceQK0mSpOoYciVJklQdQ64kSZKqY8iVJElSdQy5kiRJqo4hV5IkSdUZM9oNkCRJ0jLq2HUX8dzc7tqxBOzJlSRJUnUMuZIkSarOiEJuRKwXEWdGxC0RMSMidul1wyRJkqQlNdIxuScCF2TmuyNiFWCNHrZJkiRJWirDhtyIWAd4A/AhgMx8Gni6t82SJEmSltxIhiu8FJgDfCcirouI/4qINXvcLkmSJGmJjSTkjgF2AL6VmdsDfwSOHviiiDgsIqZGxNQ5c+a03ExJkiRp5EYScmcBszLzqubnMymhdyGZeXJmTsrMSePGjWuzjZIkSdJiGTbkZuZ9wD0R8YrmoT2Am3vaKkmSJGkpjHR2hY8D329mVrgLOKR3TZIkSZKWzohCbmZOByb1uC2SJElSK7zimSRJkqpjyJUkSVJ1DLmSJEmqjiFXkiRJ1THkSpIkqTqGXEmSJFXHkCtJkqTqGHIlSZJUHUOuJEmSqmPIlSRJUnUMuZIkSaqOIVeSJEnVMeRKkiSpOoZcSZIkVceQK0mSpOoYciVJklQdQ64kSZKqY8iVJElSdcaMdgMkScCx6y7iubndtUOSKmFPriRJkqqz7PTk2oshSZKkliw7IVfLD7+QSJKkZZzDFSRJklQdQ64kSZKqY8iVJElSdQy5kiRJqo4hV5IkSdUx5EqSJKk6hlxJkiRVx5ArSZKk6hhyJUmSVB2veCZJ0uIa6sqPXvVRWmbYkytJkqTqGHIlSZJUHYcrSGrHUIdvwUO4kqTO2ZMrSZKk6hhyJUmSVB1DriRJkqpjyJUkSVJ1DLmSJEmqjiFXkiRJ1RnRFGIRMROYBzwLzM/MSb1slCRJkrQ0Fmee3Ddn5oM9a4kkSZLUEocrSJIkqTojDbkJXBgR0yLisF42SJIkSVpaIx2usGtmzo6IPwMuiohbMvOy/i9owu9hAJtuumnLzZQkSZJGbkQ9uZk5u/n3AeAcYMdBXnNyZk7KzEnjxo1rt5WSJEnSYhi2Jzci1gRWysx5zf23Ap/vecskgGPXXcRzc7trhyRJWq6MZLjCRsA5EdH3+tMz84KetkqSJElaCsOG3My8C9i2g7ZIkiRJrXAKMUmSJFXHkCtJkqTqGHIlSZJUHUOuJEmSqmPIlSRJUnVGesUzSVoxDTVXs/M0S9IyzZ5cSZIkVceeXKmPPXaSJFXDnlxJkiRVx5ArSZKk6hhyJUmSVB1DriRJkqpjyJUkSVJ1DLmSJEmqjiFXkiRJ1THkSpIkqTqGXEmSJFXHkCtJkqTqGHIlSZJUHUOuJEmSqmPIlSRJUnXGjHYDJEnSEI5ddxHPze2uHdJyyJ5cSZIkVceQK0mSpOoYciVJklQdQ64kSZKqY8iVJElSdQy5kiRJqo4hV5IkSdVxntxaOJeiJEnSAoZcqUcmHP2TQR+fuVrHDZEk1cWOrRFxuIIkSZKqY0+uJHXIHn5J6oY9uZIkSaqOPbmSJEnqxFBHswBmHve2VmsZcrVCWeQvl4eLJUmqhsMVJEmSVB1DriRJkqrjcAWpAkOesd/y+CZJveGsGxqM28XSMeRKWiZ1eXKCNBjH8EvLN4crSJIkqToj7smNiJWBqcC9mfn23jVJkobhJS0lScNYnOEKnwBmAOv0qC0aAcfnSJIkDW9EwxUiYjzwNuC/etscSZIkaemNdEzu14FPA8/1sC2SJElSK4YdrhARbwceyMxpEfGmRbzuMOAwgE033bS1BkqS1McZDySN1Eh6cncF9ouImcAZwO4RcdrAF2XmyZk5KTMnjRs3ruVmSpIkSSM3bMjNzM9k5vjMnAAcAFySmR/oecskSZKkJeTFICSpQl1eTMMLd0haFi1WyM3MS4FLe9KSljlua/nhZyVJktrmFc8kSZJUHYcrSDXzymCSpBWUPbmSJEmqTqc9ubWOvfSkC0mSpGWLwxUkrfBq/QIuSSsyQ26vDTUm0vGQkiRJPeOYXEmSJFXHnlwNysO3kiRpeWbIlTRifvmRJC0vHK4gSZKk6tiTK0mSWueRH402e3IlSZJUHXtyJUnScs1e40q0PO2qIVeSJGmEhgrUhullj8MVJEmSVB1DriRJkqrjcAVJklYQixy7etzbOmyJ1Hv25EqSJKk6hlxJkiRVx+EKkiRJK7Bap2CzJ1eSJEnVMeRKkiSpOoZcSZIkVccxuZIkaehLqsISX1ZVGk325EqSJKk6hlxJkiRVx5ArSZKk6hhyJUmSVB1DriRJkqpjyJUkSVJ1DLmSJEmqjiFXkiRJ1THkSpIkqTqGXEmSJFXHkCtJkqTqGHIlSZJUHUOuJEmSqmPIlSRJUnUMuZIkSarOmNFugCSpYseuu4jn5nbXDkkrHHtyJUmSVJ1he3IjYjXgMmDV5vVnZuYxvW6YJKlH7F2VtAIYyXCFPwG7Z+bjETEW+HVE/Cwzr+xx2yRJkqQlMmzIzcwEHm9+HNvcspeNkiRJkpbGiMbkRsTKETEdeAC4KDOvGuQ1h0XE1IiYOmfOnLbbKUmSJI3YiEJuZj6bmdsB44EdI+JVg7zm5MyclJmTxo0b13Y7JUmSpBFbrNkVMvNR4FJg7560RpIkSWrBsCE3IsZFxHrN/dWBPYFbet0wSZIkaUmNZHaFPwe+GxErU0LxDzLz/N42S5IkSVpyI5ld4QZg+w7aIkmSJLXCK55JkiSpOoZcSZIkVceQK0mSpOoYciVJklQdQ64kSZKqY8iVJElSdQy5kiRJqo4hV5IkSdUx5EqSJKk6hlxJkiRVx5ArSZKk6hhyJUmSVB1DriRJkqpjyJUkSVJ1DLmSJEmqjiFXkiRJ1THkSpIkqTqGXEmSJFXHkCtJkqTqGHIlSZJUHUOuJEmSqmPIlSRJUnUMuZIkSaqOIVeSJEnVMeRKkiSpOoZcSZIkVceQK0mSpOoYciVJklQdQ64kSZKqY8iVJElSdQy5kiRJqo4hV5IkSdUx5EqSJKk6hlxJkiRVx5ArSZKk6hhyJUmSVB1DriRJkqpjyJUkSVJ1DLmSJEmqjiFXkiRJ1THkSpIkqTrDhtyIeElE/CIiZkTEbyPiE100TJIkSVpSY0bwmvnA/8nMayNibWBaRFyUmTf3uG2SJEnSEhm2Jzcz/5CZ1zb35wEzgE163TBJkiRpSS3WmNyImABsD1w1yHOHRcTUiJg6Z86cdlonSZIkLYERh9yIWAs4CzgqMx8b+HxmnpyZkzJz0rhx49psoyRJkrRYRhRyI2IsJeB+PzPP7m2TJEmSpKUzktkVAjgFmJGZJ/S+SZIkSdLSGUlP7q7AB4HdI2J6c9u3x+2SJEmSltiwU4hl5q+B6KAtkiRJUiu84pkkSZKqY8iVJElSdQy5kiRJqo4hV5IkSdUx5EqSJKk6hlxJkiRVx5ArSZKk6hhyJUmSVB1DriRJkqpjyJUkSVJ1DLmSJEmqjiFXkiRJ1THkSpIkqTqGXEmSJFXHkCtJkqTqGHIlSZJUHUOuJEmSqmPIlSRJUnUMuZIkSaqOIVeSJEnVMeRKkiSpOoZcSZIkVceQK0mSpOoYciVJklQdQ64kSZKqY8iVJElSdQy5kiRJqo4hV5IkSdUx5EqSJKk6hlxJkiRVx5ArSZKk6hhyJUmSVB1DriRJkqpjyJUkSVJ1DLmSJEmqjiFXkiRJ1THkSpIkqTqGXEmSJFXHkCtJkqTqGHIlSZJUHUOuJEmSqjNsyI2IUyPigYi4qYsGSZIkSUtrJD25k4G9e9wOSZIkqTXDhtzMvAx4uIO2SJIkSa1obUxuRBwWEVMjYuqcOXPaWqwkSZK02FoLuZl5cmZOysxJ48aNa2uxkiRJ0mJzdgVJkiRVx5ArSZKk6oxkCrEpwG+AV0TErIg4tPfNkiRJkpbcmOFekJkHdtEQSZIkqS0OV5AkSVJ1DLmSJEmqjiFXkiRJ1THkSpIkqTqGXEmSJFXHkCtJkqTqGHIlSZJUHUOuJEmSqmPIlSRJUnUMuZIkSaqOIVeSJEnVMeRKkiSpOoZcSZIkVceQK0mSpOoYciVJklQdQ64kSZKqY8iVJElSdQy5kiRJqo4hV5IkSdUx5EqSJKk6hlxJkiRVx5ArSZKk6hhyJUmSVB1DriRJkqpjyJUkSVJ1DLmSJEmqjiFXkiRJ1THkSpIkqTqGXEmSJFXHkCtJkqTqGHIlSZJUHUOuJEmSqmPIlSRJUnUMuZIkSaqOIVeSJEnVMeRKkiSpOoZcSZIkVceQK0mSpOoYciVJklQdQ64kSZKqY8iVJElSdUYUciNi74i4NSLuiIije90oSZIkaWkMG3IjYmXgJGAfYGvgwIjYutcNkyRJkpbUSHpydwTuyMy7MvNp4AzgL3vbLEmSJGnJRWYu+gUR7wb2zswPNz9/ENgpM48Y8LrDgMOaH18B3LqYbdkQeHAx/8+SqrFWjetUa60a18lay08day1ftWpcp1pr1bhOy0OtzTJz3GBPjBnBf45BHntBMs7Mk4GTF7NhzxeJmJqZk5b0/6/otWpcp1pr1bhO1lp+6lhr+apV4zrVWqvGdVrea41kuMIs4CX9fh4PzG6rAZIkSVLbRhJyrwFeHhGbR8QqwAHAeb1tliRJkrTkhh2ukJnzI+II4OfAysCpmYxRMREAABccSURBVPnbHrRliYc6WKvTOtZafupYa/mqVeM6WWv5qWOt5aeOtUZo2BPPJEmSpOWNVzyTJElSdQy5kiRJqo4hV5IkSdUx5EqSJKk6I7kYRCciYr/M7MnUZBGxDjAuM+8c8PirM/OGXtTsWkR8KTM/24PlrpeZj7a93EXUWwvYmzI383zgduDCzHyuqzYsryJiU+CBzHwqIgL4ELADcDPw/zJzfg9qjs3MZwY8tmFmtnp1nIjYinI58U0oF6OZDZyXmTParDNI3Usyc/de1mjqbA5sD9ycmbf0ul4Xmv3uy4G7MvORHi2/uv36irIPjIgNMvOh0W7H0oiIHYHMzGsiYmvK53ZLZv605Tp907fOzsyLI+J9wOuAGcDJA/fBLdfeDdgRuCkzL+xRjZ79HRmV2RUi4l0DHwJOAg4HyMyzW6z1XuDrwAPAWOBDmXlN89y1mblDW7WGacchmfmdlpb1HwMfAj4IfA8gM49so05Taz5wKTAFOKuXgbf5rD4FXA+8GbiCcrRhG+D9mXljr2r3a8M/Z+bne7DcvYB3sHBI+1FmXtBijZuAHTPziYj4MrAFcC6wO0Bm/nWLtd4M/DewKnAdcFhmzmyea/X3KiL+ATgQOINycRooF6U5ADgjM49rqc7AYBTAljSXKM/MV7dRp6l1bma+o7n/l5R91KWUP1z/lpmT26rV1Oj5l4SIOA04KjMfbLb3/6K8dy8HPpmZP2yx1jKxX2/qbdXWF5NlYR/YtKO1v1fN8o4Djm+2jUnAD4DnKJ/dQZn5y7ZqNfV6/gUoIo4B9qF0Fl4E7ET5Hd4T+HlmfrGNOk2t7zd11gAeBdYCzgb2oOS4g1usdXVm7tjc/wjwt8A5wFuBH7e1v22W3/u/I5nZ+Y3y7fR84FTgO81tXvPvqS3Xmg78eXN/R+AW4F3Nz9d1uM6/b3FZs4DTgIOAg5vbnL77Lbf7RuDtwPeBh4AfUcLF6j14j24A1mjub0jZUQC8Grhiefuc+i3z68BPm/dtt+Z2QPPYiS3Wubnf/WnASv1+vr7ldboGeGVz/92U3qadm59b/b0CbgPGDvL4KsDtLdY5r/m92grYDJgA3NPc36zldbqu3/0rgM2b+xv24LP6h2Y/eDTwgeZ2dN9jLda5ccA6TejhOi0T+/WmXpv79lHfB7a9ToNsG78AXtvc3xKY2nKt91K+xE0HfttXq3nu2jbXiXLtgDWAx4B1msdXB25oeZ1uaP4dA9wPrNz8HD2o1X/fdA3lywLAmv0/x5Zq9fzvyGgNV9gFOK5ZwW9nZkbEmzLzkB7UWjkz/wCQmVc33xzOj4jxlB6N1gzSE7TgKWCjFktNBP6VcmjkU5l5b0Qck5nfbbFGn2cy83zKe7Y68BeUgHZSRPw8M9/XYq0Anmzu/xH4M4DMvKH5Zt5OkYjHFlF/9bbq9LNvZm45SDv+hxLgPtFSnXsiYvfMvASYSTnc+buI2KCl5fe3SjYXhcnMMyNiBnB2RBxNy79XlB6fjYHfDXj8z5vnWpGZ+0XEOymTkR+fmedFxDOZObBuK+X63R+TmXc3bXgwIto+LH0o5Q/JwMOBJ1BCQFs9MytFxDqZ+Rjlc/k9LFintv/WdLZfh0GPni14ClivzVJ0sA+ETv9eAYyNiDFZhkytnk2ve2beFhGrtlzrs8BrMvMPzXCC/46Iz2Y5Qhwt1pmfmc8CT0TEnc12T2Y+2YPf4ZWaIQtrUkL1usDDlB7QsT2o9SLKEYTIzDkAmfnH5shum3r+d2RUQm6W8StvAT4OXNIcjuzVuIl5EbFFNoctmg3/zZTu91e2XGsjYC9g4PizoPRstCIz5wFHRcRrgNMi4if07iTCBTuFzHyScpjpBxGxLuXwe5t+ClwQEb+kHAb6IUBErE+7O6dHKd/u7x/4RETc02KdPk9FxI6ZefWAx18LPNVinQ8D34uIY4G5wPSIuA54EfD3LdYBeCYiXpyZ9wFk5m8jYg/KEZotWq51FPC/EXE7pWcVYFPgZcARbRbKzHMi4kLgXyPiw5Te4l7YtvmyFcCqfe9l84ds5ZZrdfIlAfgX4BcRcRJwOfDDiPgRZbhMa8NyGl3u1wEOAf4P8KdBnjuwxTpd7QOho79XjZOAnzbDFi6IiK/z/OH26S3X6uoL0NMRsUZmPgG8pu/B5m9j2yH3FMrRipWBf6T8bt0F7EwZxtWmdSlHAgPIfvumtWh/G+z535FRv+JZRGxMOZw7KTNf2oPlbws8kZm3D3h8LPDezPx+i7VOAb6Tmb8e5LnTW+717FtuUMYy75KZH+jB8j+Zmce3vdxF1NsX2JpyePOi5rGVKIerB/sDsyQ1vkAZjzgwdBIRX87Mf2ijTr9l7gB8C1ib58eUvoRyiOvwzJzWcr2JlMOAY5p612TLJ61ExJ7AnMy8fsDj6wJHZIvj0ZrlrkQ5LL0JZUfbt17PtllnQM1tKb9X3+5VjUFqrgdMzMzftLjMvYFvUA4FvuBLQrY7LvxlwEdYePs7NzN/3laNpk5n+/VmuZcAn8vMF4S/iLg7MzdvsVbP94HNMjv9exURbwI+xvPbxj2Ucwa+M/Aow1LWuQL4YPYbj9v0gp8D7JaZrfQcR8Sqg30eEbEhZShNq+Onm6xEZs5u9hN7UoaVvODvWC9ExBrARn1HnVpa5lB/R9YD/raNvyOjHnK70O8wSd+Zq1tRzvh9eHRb1p7o4ewUoyki1q/lc4qIF9MvpPV9e+1hvZ6e3d61iBhHOeFsPnB3Zj7eYe3WTi4aDaPxJaFrvdwHNj2pTzW9dloCUU5+3AS4qv/vbkTs3fIXrU6/APVb/suAbYEZmXlzL2oMqNeTv43R8YxKg9RvdXaeUZknNyJeHBHfioiTImKDiDg2Im6IiB9ExJ+3XOtDwP0RcVtE7EMZ2P9l4PqIaPMwU1+9TZtvIUTEhIh4d0S8quUa7xp4A07ud7/NWpMi4hcRcVpEvCQiLoqIuRFxTURs33KtXSNiRkT8NiJ2ioiLgKkRcU9E7NJmrUW0YateLTsz72t6bW8BNu7bTtrSfEYbNvf3ooy3/DJl2MJ72qw1TDt+1vLyto6Ii4HfAFdRztq/MSImNz3HXWh16pyI+Ot+98dHxP9GxKMRcUVEvGD8dgvGU6Y2OguY2vzcs229T0Tc1qPlDtwH7k+P9oEAmflwFwG3y+2i7f3PMLWOpJy0/HHgpigzivT5Upu1ml7BBb2NEbFWlBkd1m75yO0v+u1vP0gZarIP8D8R8fG26jTL/1y/+1s3v1fTImJmROzUZi3gwYi4OCIO7fU2EhH7RMTdEfHriNg+In4LXBURs6IMW1h62eKZciO9UcZnfZxyhu8NlLN/N20e+1HLtW6knKW6OeXw8BbN4xvR/lmJR1N+uW6hjI+8hTKW5rfA37dYp8vZKa6m/OIeSDm89O7m8T2A3/Sg1jaUExMfpBxagjLX6+UdbZu9mF3hm/3u70Y5IecXzfu5b4t1ujy7fYchbq8B/tByrSuBVzT3dwS+29z/CHBmi3X+Y4jbfwKPtbxO1/a7/wPgo5ROh3cC/9tyra72S/Oafey8frdn+x5veZ062weOoC0/W063i/nAxZQTE9fr8Xt0I7BWc38C5YvWJ5qf256N5UOUmYBua/523QX8b7O/PbDFOjf1u38NsEFzfw3azxb9t4ufAPs093ek5Vk36HZGpemUE+l3aWr1zawwkZZmwmh9Yx7hivWfouL3A56b3vab2O/+7AHPtb0h/pZydv4GzQ63/9QbN7VY57XNL+3HeH7Iyd2j8Fm1vXPqX2vGgOfanPqlszAzsO2UcLtDc/+ltDh9TrP99U1j82sWnkLsty2v07PAJc36DLw92XKt6wf83P/9vLnFOvOAw3h+Wr7+twd7uE1MH/Bc279XXe2X/pMyV/dG/R67u8116bfczvaBzbI7+VLX8XbRZZi5ecDPa1E6u04YuJ4trVfPO7Yo87pu0tz/BbBac3/lHuxv+28X1w1sRw9rrU6Zku3sZhs5vYe17hnwXCvbxWhNIdZ/mMT3FvFcG34fEf9GOennloj4d8oHtifwh5ZrPZtl+pCnKdPAPAQLpt5orUh2OzvFUxHxVsoZlxkR78jMcyPijZSg06b+n/1nBjzX5lnuXZ0pPZh1MvNagMy8KyLaPJO+y7PbZwAfzQHj3qAnM1TcGRH/RAk176I5G7sZY9fmPuwaSugb7OSiY1usAzA+yrRUAYyLha/40/aUQF3tlz4eZcaXKRFxLuVkt57slzreB0LZNn7J4GeXt3lIt8vtosvpIe+LiO0yczpAZj4eEW+n9MRv02IdKNv7g5TD7o/n8zNw3N/m9g78HXBhRJxF+SJ5SURcALyeckShTS+NiPMo28X4eH5WB2h/u+hyRqVHI+KjwDrAIxHxd029PYFWzrkYrSuefR74Sg44cSTKwO3jMvPdLdZah3LFjqTsdPemHM74PfCv2Uw10lKtyZQwtibwBOVw0AWUkLF2Zr63rVr9avZ6dortKOM6n6P8Un+MchGK2ZSrk1zeYq39gItzwNi3iNgC2D8zv9JSnc7OlG6W+QRwB2XnMQHYNDMfiXIy0A2Z2dqY7Yh4OeWQdK/Pbn83ZXjErYM8947MPLfFWutR5r7cmnIlqOMyc16z052YmVe2VKezk4si4uABD53XbBMvBo7MFi/R3fV+qdmujwDeQ+lF27jN5Q9SbxPga/RoH9jUuAl451Bf6jLzJS3V6XK7uC4zX3BeRV+YyRbnXY8yfdf8HORk24jYteW/I+dRQufalH3GdTzfsfW6zNyrxVrrAu9j4f3tj7Llk1SbTqX+pjVfFDaiDCE8qcVanc2oFBEvAT5HyWfHUjqZDqVMd/jJbOGKjKMVco8EzsnMXsxJOmqiTHj+HsoHdiblMn8HUgL1SZn5x1FsXisi4vWUcUA3Zo+uY91rXYaZpt5mAx6anZnPNCctvCFbvIx1l5ovH++kTIc2nzJF1ZTMnDuqDdNCRmu/FOUk4psysxcXI+lUl1/qutJlmOlSlx1bWvaNVsidS7may53AFOCH2VxVowe1JgFfBe6lHAI/lTKe63ZKT+R1vajbS9Hv+tvNIdt/oATPm4AvtBne4oXXsT6cMrdh69exHqYdJ2fmYT1c/gaZ+VCvlt+FiHgp5VvxbMpVrL5GGdA/g3JlvJkt1jqSMp7vMmBfyhCCRyih9/DMvLTFWitRxsXuz8KB+ttt1hmmDT/LzH1aXF5nn9UQ9Vvf3psetIF2p4zdJjP3a7HWEcAZWa6m9jKeP+x9G/DhbHmO0mHackhmtnJ4uvlCcijlsPAmlKA2mzJe9pRscT5ZLZ0B2+AWlCEKPdkGm578YyhHVP+ZMkxnf8r+4hMtH5HuMl/0fD84KlOIUc52HE+5NO1rgJsj4oKIODgi1m651jeBr1DOSLwC+L+ZuR7ljONvtlkoylQln48yBdbciJgTEVcOcghqaU3ud/84yqTu/04ZJN72xPX9x/scBrw1M/+FEnLf32ahiFh/iNsGlCDVVp3j4vmpXyZFuXLMVRHxu0EOC7VRb52I+LeI+O+IeN+A59rcBidTxg4+TpmR4FbK2cUXUEJAmz5COcP3C5TDgFtn5j9Sek2+1nKtU4DNKNv6Lyi/y6cAn4sWp+qJiB2GuL0G2K6tOo3JLPxZ3UKPPqshtvcre7C9j6ec6HMCZX90AuVEt39vbm36WD4/l+aJwNcy80WUP8idXbyj8S8tLuu/Kdvav1D2eW9r7m8LnNZinaGm8rwxejCVZ5di8GkvH432p73svw3+B73dBicDN1NmiPgFZWz924Bf9ahWn17ni8n0ej/Yxtlri3tjwJnylCC1H6VXd07LtbqcHeBHlMMi4ymXUf0nymT83wW+1KN1mk65Eg6UMZ9tzxhxPeWysBswYCaAHrx/z1K+AN3d79b389Mt1uk/1dYvKJf4hTKuqrXZDvrVOIuys3gHcF7z86rNc23OGtHltn5jv3V4EWWMWN9zrZ2x3yzvhgE/X9n8uyoDZuFoYfvrasaITj+rfvd7tr1TOk3+DrgI2K557K4216VfrVv73b9mUdtLS/VuGOJ2I/CnXqzXIM/d1vI6dTaVZ5c3Opr2ssttcJj9RduzU3SZL3q+Hxyt2RUWOsUxyyGY84Dzopzl2aYuZweYkJmTm/snRMQ1mfmvEXEI5VtYWycNrBtlwvOgBI1nADIzI6Lt8SddXsf6LmCPzPz9wCei3TP2x8bzV8FbPTOvAcjM2yKilUs+DrBFZu7f3D83Iv6RciZua4dvG89FmTB+PWCNiJiUmVObw7ltzuIA5YIM10TElcAbKCcn9l2VrO2r8DwTEVtk5p1RLpH8NEBm/qnl7b3LGSO6/Kw62d6zXDr6axHxw+bf+2l39ov+zoxyQt3ngXMi4ijKyUV7UMZetm0jYC/KkJz+gnKEsC2PRLlwy1nN+9k3XOc9g9ReWhtl5n82NQ7PzC83j/9nRBzacq0ujc3MnwFEuUz7mQCZ+b8R0eYY5C63wUXNSNX2/qLLfNHz/eBohdy/GuqJLFNWtOlvKMMVnqPspD7WbJj3Ug65tumPEbFbZv46Iv6C5o99Zj4X0ercJb+kTPcC5bDjRlmmR3kx5SIKrcnMCUM89Rxl/GWbvk7pFRxsB9HKzAqNk4CfRsRxwAUR8XWe3zlNb7FOn1UjYqW+P1qZ+cWImEUZz7pWi3U+DfyY8tm8A/hMRLya8kWl1W09M0+MchWyicAJ2ZxNnGVs/RvarAV8ijI12lOUoz4HwIJAfX6LdY5l6CFcrV7BiA4/Kzre3jNzFvCeiHgbZfhC6zLzH6NczXIKsAWlV/8wyvkCrQ6japxPuZjBC96viLi0xToHUL4wnhQRfZdWXY/SA39Ai3Wg2+DUpU46tjreBn8UEWtl5uOZ2f/qZy+jDEtrU2f5gg72g6Ny4lnXImIisDG9v2b2qyk9XFtSBmn/ddNbMo5ypZX/aLHWTsBzWeaL3JoyFvKWzPxpWzVGQ0TsSPnS2NP1iog3UaZD65v65R7Kzuk72fLJHRHxFeDCzLx4wON7A/+ZmS9vsVb/7eKVlMN2N1ewXexCmYKo19vFVpQTfnq6r2iW2dlntYjt/dSmh3e5M2Bf8UrKNjGjgm19J8oJZ3dSvkTuTA+2i+hwKs8uRcS2PN+x1Tft5cE0HVs5yNSRS1Grs21wFPdNPc0Xvd4PVh9yo5wFfjhlQPN2lDMRf9Q8d21m7tBRO9o8A/cYyoYwhjL+bUfKt689gZ9n5hfbqNO1QdZrJ+BSerBeXe4whmmH28UwulqvZl/xt5RhCz3dVywrn1Wb21+XlpX3r23Lynotr9vFcJbX/W2UE2yPoLJ9Uye1lmZA7/Jwo8NrZg/Tjt+3uKwbKYeT1qAcDuy7lOvq9OCki44/q56vF3Ak5RDPucBM4C/7PdfaiWBuF8vXenW5r1hWPqs2t78at4kVdb2W1+2iy/Xq8rOqdd/URa3RGpPbpZWz6anLzJnNYbszo0zQ3+qJUxFxw1BPUU5caMv8zHwWeCIi7szMx6CMZ46I51qs07Wu1usjwGuyXDFmAmV7mJCZJ9L+yXRuF0uvq/XqbF9Bh59Vh9tfl9zWl1Kl20Wt+9sq901d1FoRQm6X18zu6gzcp+P5a1e/ZkGRconB5XkH39V6dbnDALeLpdXVenW5r+jys+pq++uS2/rSq3G7gDr3t7Xum3pea0UIuQdRrpC0QJYTLQ6KiP/bcq2uzsB9Q2b+CRZM29NnLGWA/fKqq/XqcocBbhdLq6v16nJf0eVn1dX21yW39aVX43YBde5va9039bxW9SeeSQNFxHjKYZL7Bnlu18y8fBSaJUmSWmTIlSRJUnWGmvhckiRJWm4ZciVJklQdQ64kSZKqY8iVJElSdf4/FRA2hONL/PcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = {\n",
    "  \"C\": [0.1,1, 5, 10, 150,500,1000,5000],\n",
    "    \"fit_intercept\":[True,False]\n",
    "}\n",
    "linear_svr = grid_search_best_model(LinearSVR(), params,k_fold, X_train, y_train)\n",
    "model_results[\"linear_svr\"] = score_model(linear_svr, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params {'learning_rate': 0.05, 'max_depth': 10, 'max_features': 0.3, 'min_samples_leaf': 5, 'n_estimators': 100}\n",
      "Best estimator GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
      "                          learning_rate=0.05, loss='ls', max_depth=10,\n",
      "                          max_features=0.3, max_leaf_nodes=None,\n",
      "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                          min_samples_leaf=5, min_samples_split=2,\n",
      "                          min_weight_fraction_leaf=0.0, n_estimators=100,\n",
      "                          n_iter_no_change=None, presort='auto',\n",
      "                          random_state=None, subsample=1.0, tol=0.0001,\n",
      "                          validation_fraction=0.1, verbose=0, warm_start=False)\n",
      "Best score: 0.20478382652742433\n",
      "Training score: 0.5486441662586472\n",
      "Test score: 0.12250123343038744\n",
      "MSE:  0.10062310716612463\n",
      "      Actual  Predicted\n",
      "278     3.40   3.785467\n",
      "3727    4.15   4.001967\n",
      "1891    3.69   3.846426\n",
      "3975    4.06   3.994848\n",
      "1317    4.31   4.103366\n",
      "1192    3.78   3.862023\n",
      "57      3.54   3.698265\n",
      "3692    3.76   3.846269\n",
      "3476    3.98   3.963292\n",
      "2354    4.05   3.925652\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params={\"n_estimators\":[20, 50, 100,200], \n",
    "        \"learning_rate\": [0.01, 0.05, 0.1, 0.3],\n",
    "         \"max_depth\":[3,5,10], \n",
    "        \"min_samples_leaf\": [3,5],\n",
    "          \"max_features\": [0.3, 1]\n",
    "       }        \n",
    "\n",
    "gbr = grid_search_best_model(GradientBoostingRegressor(), params,k_fold, X_train, y_train)\n",
    "model_results[\"gbr\"] = score_model(gbr, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params {'C': 1, 'epsilon': 0.01, 'gamma': 0.001}\n",
      "Best estimator SVR(C=1, cache_size=200, coef0=0.0, degree=3, epsilon=0.01, gamma=0.001,\n",
      "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)\n",
      "Best score: 0.01604185881811424\n",
      "Training score: 0.8025762519618768\n",
      "Test score: 0.024288337199910193\n",
      "MSE:  0.11188521619577968\n",
      "      Actual  Predicted\n",
      "3154    4.05   3.934626\n",
      "2588    3.79   3.936716\n",
      "1215    3.86   3.934626\n",
      "2628    4.21   3.934734\n",
      "2845    4.12   3.934626\n",
      "3996    3.90   3.934626\n",
      "1084    3.81   3.934626\n",
      "584     4.45   3.934626\n",
      "3782    3.66   3.934626\n",
      "1089    4.02   3.934626\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = { \n",
    "    \"C\": [1, 5, 50, 100],\n",
    "    \"gamma\": [0.001, 0.01, 0.1],\n",
    "    \"epsilon\" : [0.01, 0.1]\n",
    "}\n",
    "\n",
    "gaussian_svr = grid_search_best_model(SVR(), params, k_fold,X_train, y_train)\n",
    "model_results[\"gaussian_svr\"] = score_model(gaussian_svr, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From what we can see the score of all algorithms are not very good. Plus some overfit. I will try\n",
    "to transform the data with polynom of 2nd degree in order to improve the scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "quad_transformer= PolynomialFeatures(degree=2, interaction_only=True)\n",
    "books_data_attributes_quad_transformed = quad_transformer.fit_transform(books_data_attributes)\n",
    "X_train_quad_transformed, X_test_quad_transformed, y_train_quad_transformed, y_test_quad_transformed = train_test_split (books_data_attributes_quad_transformed, books_data_labels, test_size = 0.3)\n",
    "k_fold_quad_transformed = list(KFold(n_splits=5, shuffle=True).split(X_train_quad_transformed, y_train_quad_transformed))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params {'fit_intercept': False}\n",
      "Best estimator LinearRegression(copy_X=True, fit_intercept=False, n_jobs=None, normalize=False)\n",
      "Best score: -88.89060025978347\n",
      "Training score: 0.06345786730555081\n",
      "Test score: -1915.5158807841945\n",
      "MSE:  254.12715344416515\n",
      "      Actual  Predicted\n",
      "2683    4.18   3.940324\n",
      "1094    3.70   3.867918\n",
      "3458    3.32   3.929093\n",
      "2623    4.20   3.926336\n",
      "30      3.88   3.866363\n",
      "1824    4.22   3.828282\n",
      "694     3.99   3.974696\n",
      "932     4.28   3.879171\n",
      "2358    4.18   3.852064\n",
      "1608    3.31   3.915600\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params={\n",
    "    \"fit_intercept\":[True,False],\n",
    "}\n",
    "\n",
    "linear_regression_quad_transformed = grid_search_best_model(LinearRegression(), params, k_fold_quad_transformed,X_train_quad_transformed, y_train_quad_transformed)\n",
    "model_results[\"linear_regression_quad_transformed\"] = score_model(linear_regression_quad_transformed, X_train_quad_transformed, X_test_quad_transformed, y_train_quad_transformed, y_test_quad_transformed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params {'learning_rate': 0.5, 'n_estimators': 400}\n",
      "Best estimator AdaBoostRegressor(base_estimator=None, learning_rate=0.5, loss='linear',\n",
      "                  n_estimators=400, random_state=None)\n",
      "Best score: 0.11986968483308676\n",
      "Training score: 0.21927182034470874\n",
      "Test score: 0.11038612418830973\n",
      "MSE:  0.11796147592158285\n",
      "      Actual  Predicted\n",
      "3564    3.97   3.897465\n",
      "292     4.38   3.697470\n",
      "1018    3.54   3.897465\n",
      "880     4.05   3.897465\n",
      "706     3.98   3.897465\n",
      "764     3.57   3.897465\n",
      "1908    4.02   3.897465\n",
      "3892    4.33   2.453408\n",
      "2941    4.32   3.909103\n",
      "605     3.63   3.897465\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = {\"learning_rate\":[0.3, 0.5,1],\n",
    "          \"n_estimators\": [50, 100,200,400]}\n",
    "              \n",
    "abr_quad_transformed = grid_search_best_model(AdaBoostRegressor(), params,k_fold_quad_transformed,  X_train_quad_transformed, y_train_quad_transformed)\n",
    "model_results[\"abr_quad_transformed\"] = score_model(abr_quad_transformed, X_train_quad_transformed, X_test_quad_transformed, y_train_quad_transformed, y_test_quad_transformed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params {'learning_rate': 0.05, 'max_depth': 10, 'max_features': 0.3, 'min_samples_leaf': 5, 'n_estimators': 50}\n",
      "Best estimator GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
      "                          learning_rate=0.05, loss='ls', max_depth=10,\n",
      "                          max_features=0.3, max_leaf_nodes=None,\n",
      "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                          min_samples_leaf=5, min_samples_split=2,\n",
      "                          min_weight_fraction_leaf=0.0, n_estimators=50,\n",
      "                          n_iter_no_change=None, presort='auto',\n",
      "                          random_state=None, subsample=1.0, tol=0.0001,\n",
      "                          validation_fraction=0.1, verbose=0, warm_start=False)\n",
      "Best score: 0.19811365437545178\n",
      "Training score: 0.5302009665029186\n",
      "Test score: 0.18695256844121644\n",
      "MSE:  0.10780887936737588\n",
      "      Actual  Predicted\n",
      "1087    4.28   4.205785\n",
      "582     3.68   3.859051\n",
      "1462    3.91   3.927412\n",
      "441     3.78   3.904838\n",
      "1549    3.97   3.906189\n",
      "2638    3.89   3.844968\n",
      "1065    3.68   3.971978\n",
      "293     3.26   3.885882\n",
      "1861    3.82   3.722159\n",
      "3528    4.19   3.986447\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params={\"n_estimators\":[20, 50, 100,200,400], \n",
    "        \"learning_rate\": [0.01, 0.05, 0.1, 0.3],\n",
    "         \"max_depth\":[3,5,10], \n",
    "        \"min_samples_leaf\": [3,5],\n",
    "        \"max_features\": [0.3, 1]\n",
    "       }        \n",
    "\n",
    "gbr_quad_transformed = grid_search_best_model(GradientBoostingRegressor(), params,k_fold_quad_transformed,  X_train_quad_transformed, y_train_quad_transformed)\n",
    "model_results[\"gbr_quad_transformed\"] = score_model(gbr_quad_transformed,  X_train_quad_transformed,\n",
    "                                                    X_test_quad_transformed, y_train_quad_transformed, y_test_quad_transformed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regression:  linear_regression\n",
      "Trainign score 0.05465157091881012\n",
      "R2 Test score  0.019113708478992963\n",
      "MSE Test score  0.11247859277950441\n",
      "\n",
      "Regression:  knn\n",
      "Trainign score 0.06755886981501014\n",
      "R2 Test score  0.002626160392752097\n",
      "MSE Test score  0.11436922599882397\n",
      "\n",
      "Regression:  dtr\n",
      "Trainign score 0.2238261658403088\n",
      "R2 Test score  0.06081093588778619\n",
      "MSE Test score  0.10769715633545397\n",
      "\n",
      "Regression:  abr\n",
      "Trainign score 0.23344257437711746\n",
      "R2 Test score  0.01832877116059284\n",
      "MSE Test score  0.11256860183127418\n",
      "\n",
      "Regression:  linear_svr\n",
      "Trainign score -52.46650719212725\n",
      "R2 Test score  -40.487973429633215\n",
      "MSE Test score  4.757441213091593\n",
      "\n",
      "Regression:  gbr\n",
      "Trainign score 0.5486441662586472\n",
      "R2 Test score  0.12250123343038744\n",
      "MSE Test score  0.10062310716612463\n",
      "\n",
      "Regression:  gaussian_svr\n",
      "Trainign score 0.8025762519618768\n",
      "R2 Test score  0.024288337199910193\n",
      "MSE Test score  0.11188521619577968\n",
      "\n",
      "Regression:  linear_regression_quad_transformed\n",
      "Trainign score 0.06345786730555081\n",
      "R2 Test score  -1915.5158807841945\n",
      "MSE Test score  254.12715344416515\n",
      "\n",
      "Regression:  abr_quad_transformed\n",
      "Trainign score 0.21927182034470874\n",
      "R2 Test score  0.11038612418830973\n",
      "MSE Test score  0.11796147592158285\n",
      "\n",
      "Regression:  gbr_quad_transformed\n",
      "Trainign score 0.5302009665029186\n",
      "R2 Test score  0.18695256844121644\n",
      "MSE Test score  0.10780887936737588\n",
      "\n"
     ]
    }
   ],
   "source": [
    "compare_results()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the observations we see that a GradientBoostingRegressor with preprocessed data (using polynoms of 2nd degree) performed the best on the test data with score like ~19%. On the training data it did good - around 53%. However the overall scores are not very good but the actual vs. predicted data printed displays some satisfying results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Serializing the best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully dumped the model to books_model.pkl\n"
     ]
    }
   ],
   "source": [
    "model_dump_filename = \"books_model.pkl\"\n",
    "pickle.dump(gbr_quad_transformed, open(model_dump_filename, \"wb\"))\n",
    "print(\"Successfully dumped the model to\", model_dump_filename)"
   ]
  }
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