Python_labs_project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2729131c",
   "metadata": {
    "tags": [
     "Project_plan"
    ]
   },
   "source": [
    "Project plan\n",
    "\n",
    "1. problem formulation :  \n",
    "Context : we have been instructed to predict books ratings via a machine learning application from provided data.  \n",
    "2. Data preprocessing\n",
    "3. EDA\n",
    "4. features engineering \n",
    "5. modelling & Machine learning \n",
    "6. Model validation\n",
    "7. deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "4f6b1fb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!conda env list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "42ab7ab5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# activate environment \n",
    "#!conda init bash\n",
    "#!conda activate Project1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "ea7c92fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "002c5c79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load libraries \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "#import seaborn as sns\n",
    "#import matplotlib as plt\n",
    "#import plotly as ply\n",
    "import sklearn as skl\n",
    "import datetime as dt\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import patsy\n",
    "import re \n",
    "from IPython.display import clear_output, display, HTML, Javascript"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "b0058b45",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# load data \n",
    "df = pd.read_csv(\"books.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af0df230",
   "metadata": {},
   "source": [
    "2 Data preprocessing "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03da0d13",
   "metadata": {},
   "source": [
    "2.1  inspect data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "54080887",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "<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",
       "      <th>publication_date</th>\n",
       "      <th>publisher</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Harry Potter and the Half-Blood Prince (Harry ...</td>\n",
       "      <td>J.K. Rowling/Mary GrandPré</td>\n",
       "      <td>4.57</td>\n",
       "      <td>0439785960</td>\n",
       "      <td>9780439785969</td>\n",
       "      <td>eng</td>\n",
       "      <td>652</td>\n",
       "      <td>2095690</td>\n",
       "      <td>27591</td>\n",
       "      <td>9/16/2006</td>\n",
       "      <td>Scholastic Inc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</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>0439358078</td>\n",
       "      <td>9780439358071</td>\n",
       "      <td>eng</td>\n",
       "      <td>870</td>\n",
       "      <td>2153167</td>\n",
       "      <td>29221</td>\n",
       "      <td>9/1/2004</td>\n",
       "      <td>Scholastic Inc.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bookID                                              title  \\\n",
       "0       1  Harry Potter and the Half-Blood Prince (Harry ...   \n",
       "1       2  Harry Potter and the Order of the Phoenix (Har...   \n",
       "\n",
       "                      authors  average_rating        isbn         isbn13  \\\n",
       "0  J.K. Rowling/Mary GrandPré            4.57  0439785960  9780439785969   \n",
       "1  J.K. Rowling/Mary GrandPré            4.49  0439358078  9780439358071   \n",
       "\n",
       "  language_code    num_pages  ratings_count  text_reviews_count  \\\n",
       "0           eng          652        2095690               27591   \n",
       "1           eng          870        2153167               29221   \n",
       "\n",
       "  publication_date        publisher  \n",
       "0        9/16/2006  Scholastic Inc.  \n",
       "1         9/1/2004  Scholastic Inc.  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "79cd1ae4",
   "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>num_pages</th>\n",
       "      <th>ratings_count</th>\n",
       "      <th>text_reviews_count</th>\n",
       "      <th>publication_date</th>\n",
       "      <th>publisher</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11125</th>\n",
       "      <td>45639</td>\n",
       "      <td>Poor People</td>\n",
       "      <td>William T. Vollmann</td>\n",
       "      <td>3.72</td>\n",
       "      <td>0060878827</td>\n",
       "      <td>9780060878825</td>\n",
       "      <td>eng</td>\n",
       "      <td>434</td>\n",
       "      <td>769</td>\n",
       "      <td>139</td>\n",
       "      <td>2/27/2007</td>\n",
       "      <td>Ecco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11126</th>\n",
       "      <td>45641</td>\n",
       "      <td>Las aventuras de Tom Sawyer</td>\n",
       "      <td>Mark Twain</td>\n",
       "      <td>3.91</td>\n",
       "      <td>8497646983</td>\n",
       "      <td>9788497646987</td>\n",
       "      <td>spa</td>\n",
       "      <td>272</td>\n",
       "      <td>113</td>\n",
       "      <td>12</td>\n",
       "      <td>5/28/2006</td>\n",
       "      <td>Edimat Libros</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       bookID                        title              authors  \\\n",
       "11125   45639                  Poor People  William T. Vollmann   \n",
       "11126   45641  Las aventuras de Tom Sawyer           Mark Twain   \n",
       "\n",
       "       average_rating        isbn         isbn13 language_code    num_pages  \\\n",
       "11125            3.72  0060878827  9780060878825           eng          434   \n",
       "11126            3.91  8497646983  9788497646987           spa          272   \n",
       "\n",
       "       ratings_count  text_reviews_count publication_date      publisher  \n",
       "11125            769                 139        2/27/2007           Ecco  \n",
       "11126            113                  12        5/28/2006  Edimat Libros  "
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "9f3eb044",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11127, 12)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df shape \n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "74f74529",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['bookID', 'title', 'authors', 'average_rating', 'isbn', 'isbn13',\n",
       "       'language_code', '  num_pages', 'ratings_count', 'text_reviews_count',\n",
       "       'publication_date', 'publisher'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# columns names\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "91609d11",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "    <tr>\n",
       "      <th>bookID</th>\n",
       "      <td>11127.0</td>\n",
       "      <td>2.131094e+04</td>\n",
       "      <td>1.309336e+04</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.028700e+04</td>\n",
       "      <td>2.028700e+04</td>\n",
       "      <td>3.210450e+04</td>\n",
       "      <td>4.564100e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_rating</th>\n",
       "      <td>11127.0</td>\n",
       "      <td>3.933631e+00</td>\n",
       "      <td>3.524450e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.770000e+00</td>\n",
       "      <td>3.960000e+00</td>\n",
       "      <td>4.135000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isbn13</th>\n",
       "      <td>11127.0</td>\n",
       "      <td>9.759888e+12</td>\n",
       "      <td>4.428964e+11</td>\n",
       "      <td>8.987060e+09</td>\n",
       "      <td>9.780345e+12</td>\n",
       "      <td>9.780586e+12</td>\n",
       "      <td>9.780873e+12</td>\n",
       "      <td>9.790008e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_pages</th>\n",
       "      <td>11127.0</td>\n",
       "      <td>3.363769e+02</td>\n",
       "      <td>2.411273e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.920000e+02</td>\n",
       "      <td>2.990000e+02</td>\n",
       "      <td>4.160000e+02</td>\n",
       "      <td>6.576000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratings_count</th>\n",
       "      <td>11127.0</td>\n",
       "      <td>1.793641e+04</td>\n",
       "      <td>1.124794e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.040000e+02</td>\n",
       "      <td>7.450000e+02</td>\n",
       "      <td>4.993500e+03</td>\n",
       "      <td>4.597666e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>text_reviews_count</th>\n",
       "      <td>11127.0</td>\n",
       "      <td>5.418545e+02</td>\n",
       "      <td>2.576177e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>4.600000e+01</td>\n",
       "      <td>2.375000e+02</td>\n",
       "      <td>9.426500e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      count          mean           std           min  \\\n",
       "bookID              11127.0  2.131094e+04  1.309336e+04  1.000000e+00   \n",
       "average_rating      11127.0  3.933631e+00  3.524450e-01  0.000000e+00   \n",
       "isbn13              11127.0  9.759888e+12  4.428964e+11  8.987060e+09   \n",
       "  num_pages         11127.0  3.363769e+02  2.411273e+02  0.000000e+00   \n",
       "ratings_count       11127.0  1.793641e+04  1.124794e+05  0.000000e+00   \n",
       "text_reviews_count  11127.0  5.418545e+02  2.576177e+03  0.000000e+00   \n",
       "\n",
       "                             25%           50%           75%           max  \n",
       "bookID              1.028700e+04  2.028700e+04  3.210450e+04  4.564100e+04  \n",
       "average_rating      3.770000e+00  3.960000e+00  4.135000e+00  5.000000e+00  \n",
       "isbn13              9.780345e+12  9.780586e+12  9.780873e+12  9.790008e+12  \n",
       "  num_pages         1.920000e+02  2.990000e+02  4.160000e+02  6.576000e+03  \n",
       "ratings_count       1.040000e+02  7.450000e+02  4.993500e+03  4.597666e+06  \n",
       "text_reviews_count  9.000000e+00  4.600000e+01  2.375000e+02  9.426500e+04  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describe numeric variables\n",
    "df.describe(exclude=\"object\").T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "e0043404",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
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       "      <th>freq</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <td>11127</td>\n",
       "      <td>10352</td>\n",
       "      <td>The Brothers Karamazov</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>authors</th>\n",
       "      <td>11127</td>\n",
       "      <td>6643</td>\n",
       "      <td>Stephen King</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isbn</th>\n",
       "      <td>11127</td>\n",
       "      <td>11127</td>\n",
       "      <td>0439785960</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>language_code</th>\n",
       "      <td>11127</td>\n",
       "      <td>27</td>\n",
       "      <td>eng</td>\n",
       "      <td>8911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>publication_date</th>\n",
       "      <td>11127</td>\n",
       "      <td>3679</td>\n",
       "      <td>10/1/2005</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>publisher</th>\n",
       "      <td>11127</td>\n",
       "      <td>2292</td>\n",
       "      <td>Vintage</td>\n",
       "      <td>318</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                  count unique                     top  freq\n",
       "title             11127  10352  The Brothers Karamazov     9\n",
       "authors           11127   6643            Stephen King    40\n",
       "isbn              11127  11127              0439785960     1\n",
       "language_code     11127     27                     eng  8911\n",
       "publication_date  11127   3679               10/1/2005    56\n",
       "publisher         11127   2292                 Vintage   318"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describe non-numeric  variables\n",
    "df.describe(include=\"object\").T"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f38cba68",
   "metadata": {},
   "source": [
    "There is no duplicate ID in data as isbn is 11127 the data set number of cases. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5128da2a",
   "metadata": {},
   "source": [
    "2.2  clean data "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c261187e",
   "metadata": {},
   "source": [
    "2.2.1 Clean publication date "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "5a58c05c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no date issue\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/dz/3jfgccdn52ggwvy9spwly7y00000gn/T/ipykernel_86434/1680810485.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[\"publication_Date\"][i] = dt.date(year,month,day)\n"
     ]
    }
   ],
   "source": [
    "# change from string to datetime object \n",
    "# for loop to parse date \n",
    "df[\"publication_Date\"]= pd.Series(dtype=\"int\")\n",
    "cnt = 0\n",
    "for i in range(len(df.publication_date)) :\n",
    "    month,day,year = map(int,df.publication_date[i].split('/'))\n",
    "\n",
    "    if month in [4,6,9,11] and day > 30:\n",
    "        day = 30\n",
    "        df[\"publication_Date\"][i] = dt.date(year,month,day)\n",
    "    elif(month==2 and day > 28 and year % 4 != 0):\n",
    "        day = 28\n",
    "    df[\"publication_Date\"][i] = dt.date(year,month,day)\n",
    "    cnt +=1\n",
    "if (cnt == len(df.publication_date)) :\n",
    "    print(\"no date issue\" )\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "2c763a30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['bookID', 'title', 'authors', 'average_rating', 'isbn', 'isbn13',\n",
       "       'language_code', 'num_pages', 'ratings_count', 'text_reviews_count',\n",
       "       'publication_date', 'publisher', 'publication_Date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove heading whitespace from num_pages feature \n",
    "df = df.rename(columns = {\"  num_pages\" : \"num_pages\"})\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "cb646068",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#na check in the entire dataset \n",
    "df.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d17ae46",
   "metadata": {},
   "source": [
    "There is no NA in df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "32bd8019",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is no NA in average_rating\n"
     ]
    }
   ],
   "source": [
    "# na check in average_rating\n",
    "if (df.average_rating.isna().mean() == 0 ) :\n",
    "    print(\"There is no NA in average_rating\")\n",
    "else:\n",
    "    print(\"There are NA in average_rating\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f808cc77",
   "metadata": {},
   "source": [
    "3 EDA "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bbf210f",
   "metadata": {},
   "source": [
    "1D EDA "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed1b0bcb",
   "metadata": {},
   "source": [
    "3.1 Numerical summary and 1D plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "d0c9e4db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    11127.000000\n",
       "mean         3.933631\n",
       "std          0.352445\n",
       "min          0.000000\n",
       "25%          3.770000\n",
       "50%          3.960000\n",
       "75%          4.135000\n",
       "max          5.000000\n",
       "Name: average_rating, dtype: float64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# numerical summary : average_rating \n",
    "df.average_rating.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "5a1b9a43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram of average ratings \n",
    "df.average_rating.hist(bins=60,legend={\"\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "662fe9af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram of average ratings :range [0,3.9]\n",
    "df.average_rating.hist(bins=60,range = [0,3],legend={\"\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "2a247bb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram of average ratings :range [0,4.75]\n",
    "df.average_rating.hist(bins=30,range = [3,4.75],legend={\"\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "5a8159b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram of average ratings :range [0,3.9]\n",
    "df.average_rating.hist(bins=30,range = [4.75,5],legend={\"\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "4e73733d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(104, 13)"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of cases where averating are <= 3\n",
    "df.loc[df.average_rating <= 3].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "f8da9b2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31, 13)"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of cases where average rating >= 4.75 \n",
    "df.loc[df.average_rating > 4.75].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "a453e8d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    11127.000000\n",
       "mean         3.933631\n",
       "std          0.352445\n",
       "min          0.000000\n",
       "25%          3.770000\n",
       "50%          3.960000\n",
       "75%          4.135000\n",
       "max          5.000000\n",
       "Name: average_rating, dtype: float64"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# average ratings sumary statistics \n",
    "df.average_rating.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "dc497d64",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/dz/3jfgccdn52ggwvy9spwly7y00000gn/T/ipykernel_86434/3277316177.py:2: FutureWarning: The default value of numeric_only in DataFrame.quantile is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  IQR_average_rating = df.quantile([0.25,0.75])[[\"average_rating\"]]\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>average_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>3.770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>4.135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      average_rating\n",
       "0.25           3.770\n",
       "0.75           4.135"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#average_rating 1st and 3rd quantile \n",
    "IQR_average_rating = df.quantile([0.25,0.75])[[\"average_rating\"]]\n",
    "IQR_average_rating"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "76473b7b",
   "metadata": {},
   "source": [
    "average_ratings is almost an unimodal distribution with some outliers.\n",
    "It has median 3.96 and IQR : 0.3 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "5d1e55fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    11127.000000\n",
       "mean       336.376921\n",
       "std        241.127305\n",
       "min          0.000000\n",
       "25%        192.000000\n",
       "50%        299.000000\n",
       "75%        416.000000\n",
       "max       6576.000000\n",
       "Name: num_pages, dtype: float64"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#numerical summary num_pages\n",
    "df.num_pages.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "b27c9e61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram of num_pages \n",
    "df.num_pages.hist(bins = 60,legend={\"\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2aef50c",
   "metadata": {},
   "source": [
    "Num_pages is almost an unimodal distribution, right skewed showing that  at least 50% of books presents 299 pages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "c1d1e8e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25        104.0\n",
       "0.50        745.0\n",
       "0.75       4993.5\n",
       "1.00    4597666.0\n",
       "Name: ratings_count, dtype: float64"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# numerical summary ratings_count \n",
    "(df.ratings_count\n",
    ".quantile([0.25,0.5,0.75, 1])\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc77d5b2",
   "metadata": {},
   "source": [
    "Maximum rating count is so high that it seems suspect !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "b49a1477",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10340    Twilight (Twilight  #1)\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# which book has this ratings count ?\n",
    "df.title[df.ratings_count == max(df.ratings_count) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25a677b8",
   "metadata": {},
   "source": [
    "Twilight is the book with the highest ratings_count.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "d9129cfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# histogram of ratings_count \n",
    "df.ratings_count.hist(bins = 50,range = [0,2000],legend ={\"\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fad8750e",
   "metadata": {},
   "source": [
    "Ratings count distribution is unimodal, right skewed. At least 50% of books ratings count is greater or equal to 745."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "b1084fde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count          11127\n",
       "unique          3678\n",
       "top       2005-10-01\n",
       "freq              56\n",
       "Name: publication_Date, dtype: object"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# numerical summary publication Date\n",
    "df.publication_Date.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "d1105419",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00    1900-01-01\n",
       "0.25    1998-07-17\n",
       "0.50    2003-03-01\n",
       "0.75    2005-09-30\n",
       "1.00    2020-03-31\n",
       "Name: publication_Date, dtype: object"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df\n",
    "[\"publication_Date\"]\n",
    ".quantile([0.0,0.25,0.5,0.75,1])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "e2c09838",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram of publication date \n",
    "df.publication_Date.hist(bins = 70)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5d49a73e",
   "metadata": {},
   "source": [
    "Publication date distribution is unimodal and left skewed. At least 50% of books has been published before between 1998 and 2005."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f76fb1ad",
   "metadata": {},
   "source": [
    "How many books are written in english ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "914a096c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "language_code\n",
       "en-CA    0.00\n",
       "en-GB    0.02\n",
       "en-US    0.13\n",
       "eng      0.80\n",
       "Name: bookID, dtype: float64"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# language code \n",
    "df3 = df.groupby(\"language_code\").count()\n",
    "# round \n",
    "round(df3.bookID.filter(regex =\"^[Ee][Nn][g\\\\-]\")/df.shape[0],2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "df3b5734",
   "metadata": {},
   "source": [
    "95% of book are written in English. The english type being different.  \n",
    "It may be reasonble to turn language code into a binary variable.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39a9b1b5",
   "metadata": {},
   "source": [
    "Who are the five most prolific authors ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "7f80fb80",
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>authors</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Stephen King</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P.G. Wodehouse</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rumiko Takahashi</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orson Scott Card</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agatha Christie</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  title\n",
       "authors                \n",
       "Stephen King         40\n",
       "P.G. Wodehouse       40\n",
       "Rumiko Takahashi     39\n",
       "Orson Scott Card     35\n",
       "Agatha Christie      33"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# authors\n",
    "(df.groupby(\"authors\")\n",
    " .count()\n",
    " .sort_values(\"title\",ascending=False)\n",
    " [[\"title\"]]\n",
    " .head(5)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c79ce2b7",
   "metadata": {},
   "source": [
    "5 most prolific authors are :  \n",
    "1. S. king\n",
    "2. P.G. Wodehouse\n",
    "3. Rumiko Takahashi\n",
    "4. Orson Scott Card\n",
    "5. Agatha Christie"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "104cb125",
   "metadata": {},
   "source": [
    "Who are the 5 more prolific publisher ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "8eda2031",
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>publisher</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Vintage</th>\n",
       "      <td>318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Penguin Books</th>\n",
       "      <td>261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Penguin Classics</th>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mariner Books</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ballantine Books</th>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  title\n",
       "publisher              \n",
       "Vintage             318\n",
       "Penguin Books       261\n",
       "Penguin Classics    184\n",
       "Mariner Books       150\n",
       "Ballantine Books    144"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.groupby(\"publisher\")\n",
    " .count()\n",
    " .sort_values(\"title\",ascending=False)\n",
    " [[\"title\"]]\n",
    " .head(5)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37612350",
   "metadata": {},
   "source": [
    "The 5 most productive publisher are :   \n",
    "1. Vintage\n",
    "2. Penguin Books \n",
    "3. Penguin Classics\n",
    "4. Mariner Books\n",
    "5. Ballantine Books"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38750251",
   "metadata": {},
   "source": [
    "What are the first 5 well rated titles ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "2b2ec4c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/dz/3jfgccdn52ggwvy9spwly7y00000gn/T/ipykernel_86434/2829122476.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  (df.groupby(\"title\")\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>average_rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>The Complete Theory Fun Factory: Music Theory Puzzles and Games for the Early Grades</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Irish Anatomist: A Study of Flann O'Brien</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colossians and Philemon: A Critical and Exegetical Commentary (International Critical Commentary)</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The American Campaign: U.S. Presidential Campaigns and the National Vote</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bill Gates: Computer Legend (Famous Lives)</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    average_rating\n",
       "title                                                             \n",
       "The Complete Theory Fun Factory: Music Theory P...             5.0\n",
       "The Irish Anatomist: A Study of Flann O'Brien                  5.0\n",
       "Colossians and Philemon: A Critical and Exegeti...             5.0\n",
       "The American Campaign: U.S. Presidential Campai...             5.0\n",
       "Bill Gates: Computer Legend (Famous Lives)                     5.0"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.groupby(\"title\")\n",
    " .agg(\"mean\")\n",
    " .sort_values(by =\"average_rating\",ascending=False)\n",
    " [[\"average_rating\"]]\n",
    " .head(5)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fab72b9",
   "metadata": {},
   "source": [
    "Above are the 5 well rated books title in this dataset. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ac548d06",
   "metadata": {},
   "source": [
    "We are not going to do EDA on bookID, isbn, isbn13. \n",
    "The first 3 are identifiers and have no variation, though it won't bring enough information to the model. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16c84ae8",
   "metadata": {},
   "source": [
    "Turn language code into a binary variable and perform EDA. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "144fa32e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# bin language code  eng : 1 , other : 0\n",
    "language_code_bin = []\n",
    "for i in range(len(df.language_code)) :\n",
    "    if re.match(\"^([Ee][Nn].+)\",df.language_code[i]):\n",
    "        language_code_bin.append(1)\n",
    "    else:\n",
    "        language_code_bin.append(0) \n",
    "df[\"language_code_bin\"]  = language_code_bin"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "995db1ae",
   "metadata": {},
   "source": [
    "What is the proportion of books written in English language?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "220f9b3a",
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>language_code_bin</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.948</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   title\n",
       "language_code_bin       \n",
       "0                  0.052\n",
       "1                  0.948"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# proportion of english in language code. \n",
    "(df.groupby(\"language_code_bin\")\n",
    " .count()/len(df.language_code_bin)\n",
    " ).round(3)[[\"title\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9365265",
   "metadata": {},
   "source": [
    "English language has a proportion of 0.95 in language code. 95% of our books are written in English. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eacb7ed4",
   "metadata": {},
   "source": [
    "== Finding association between variables ==  \n",
    " 3.2 Numerical summary and  2D plot \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b37b0b1",
   "metadata": {},
   "source": [
    "Is there any association between number of pages and average rating ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "73705dac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='num_pages', ylabel='average_rating'>"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot average rating vs num_pages\n",
    "df.plot.scatter(x=\"num_pages\",y = \"average_rating\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "814c2a4b",
   "metadata": {
    "tags": [
     "correlation_coef"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# correlation cofficient between average rating and num_pages\n",
    "round(df.average_rating.corr(df.num_pages),2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4fb555e6",
   "metadata": {
    "tags": [
     "summary"
    ]
   },
   "source": [
    "The correlation coeffication is 0.15. This suggest a weak linear association between \n",
    "average rating and number of pages. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "0324b724",
   "metadata": {
    "tags": [
     "2D_plot"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='text_reviews_count', ylabel='average_rating'>"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot average_rating vs text_reviews_count \n",
    "df.plot.scatter(x=\"text_reviews_count\",y = \"average_rating\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c4e9669",
   "metadata": {},
   "source": [
    "There is not a clear pattern between these 2 variables.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "34dd7701",
   "metadata": {
    "tags": [
     "corr_coef"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# correlation coef\n",
    "round(df.average_rating.corr(df.text_reviews_count),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9478d004",
   "metadata": {},
   "source": [
    "The correlation coef is almost 0. This suggest no association between average rating and text reviews count. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "44c1d079",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='ratings_count', ylabel='average_rating'>"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot average rating. vs ratings count \n",
    "df.plot.scatter(x=\"ratings_count\",y = \"average_rating\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "112b2b91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# correlation coeff between average rating and ratings count \n",
    "round(df.average_rating.corr(df.ratings_count),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24256a43",
   "metadata": {},
   "source": [
    "The correlation coefficient between average rating and ratings count is 0.04. This suggest that there is almost no association between average rating and rating count. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2d84aca7",
   "metadata": {},
   "source": [
    "Is there any association between rating_count   and text-reviews_count ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "618175f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.87"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(df.ratings_count.corr(df.text_reviews_count),2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "90be6331",
   "metadata": {},
   "source": [
    "Rating_count and text_reviews_count are linear correlated. Therefore , we will remove one of it from the analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47d91f8d",
   "metadata": {},
   "source": [
    "what is the average rating of books written in english ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "0cfb0ce8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/dz/3jfgccdn52ggwvy9spwly7y00000gn/T/ipykernel_86434/2976189250.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  (df.groupby(\"language_code_bin\")\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>average_rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>language_code_bin</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   average_rating\n",
       "language_code_bin                \n",
       "0                            3.98\n",
       "1                            3.93"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.groupby(\"language_code_bin\")\n",
    " .agg(\"mean\")\n",
    "[[\"average_rating\"]]\n",
    ".round(2)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4580bde4",
   "metadata": {},
   "source": [
    "On average,books written in English are less well rated than books written in other languages.  but the the difference near 0. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7932af2d",
   "metadata": {},
   "source": [
    "=== 2D EDA SUMMARY ===    \n",
    "-numerical features are not strongly associated with average rating if considering a linear assosiation. In a linear modeling numerical features have no effect or a little one on average rating. Let's investigate other models i.e. tree based model or svm. \n",
    "\n",
    "Qualitative variable,i.e. language_code has on average a rating of 3.9"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7476ebae",
   "metadata": {},
   "source": [
    "4 === features engineering part1  ===  \n",
    "we are going to remove bookID, title , authors,isbn,isbn13, publisher because they have or present no variation and are less informative for our application.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "6c860b28",
   "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>bookID</th>\n",
       "      <th>title</th>\n",
       "      <th>authors</th>\n",
       "      <th>isbn</th>\n",
       "      <th>isbn13</th>\n",
       "      <th>publisher</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Harry Potter and the Half-Blood Prince (Harry ...</td>\n",
       "      <td>J.K. Rowling/Mary GrandPré</td>\n",
       "      <td>0439785960</td>\n",
       "      <td>9780439785969</td>\n",
       "      <td>Scholastic Inc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Harry Potter and the Order of the Phoenix (Har...</td>\n",
       "      <td>J.K. Rowling/Mary GrandPré</td>\n",
       "      <td>0439358078</td>\n",
       "      <td>9780439358071</td>\n",
       "      <td>Scholastic Inc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Harry Potter and the Chamber of Secrets (Harry...</td>\n",
       "      <td>J.K. Rowling</td>\n",
       "      <td>0439554896</td>\n",
       "      <td>9780439554893</td>\n",
       "      <td>Scholastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>Harry Potter and the Prisoner of Azkaban (Harr...</td>\n",
       "      <td>J.K. Rowling/Mary GrandPré</td>\n",
       "      <td>043965548X</td>\n",
       "      <td>9780439655484</td>\n",
       "      <td>Scholastic Inc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>Harry Potter Boxed Set  Books 1-5 (Harry Potte...</td>\n",
       "      <td>J.K. Rowling/Mary GrandPré</td>\n",
       "      <td>0439682584</td>\n",
       "      <td>9780439682589</td>\n",
       "      <td>Scholastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11122</th>\n",
       "      <td>45631</td>\n",
       "      <td>Expelled from Eden: A William T. Vollmann Reader</td>\n",
       "      <td>William T. Vollmann/Larry McCaffery/Michael He...</td>\n",
       "      <td>1560254416</td>\n",
       "      <td>9781560254416</td>\n",
       "      <td>Da Capo Press</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11123</th>\n",
       "      <td>45633</td>\n",
       "      <td>You Bright and Risen Angels</td>\n",
       "      <td>William T. Vollmann</td>\n",
       "      <td>0140110879</td>\n",
       "      <td>9780140110876</td>\n",
       "      <td>Penguin Books</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11124</th>\n",
       "      <td>45634</td>\n",
       "      <td>The Ice-Shirt (Seven Dreams #1)</td>\n",
       "      <td>William T. Vollmann</td>\n",
       "      <td>0140131965</td>\n",
       "      <td>9780140131963</td>\n",
       "      <td>Penguin Books</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11125</th>\n",
       "      <td>45639</td>\n",
       "      <td>Poor People</td>\n",
       "      <td>William T. Vollmann</td>\n",
       "      <td>0060878827</td>\n",
       "      <td>9780060878825</td>\n",
       "      <td>Ecco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11126</th>\n",
       "      <td>45641</td>\n",
       "      <td>Las aventuras de Tom Sawyer</td>\n",
       "      <td>Mark Twain</td>\n",
       "      <td>8497646983</td>\n",
       "      <td>9788497646987</td>\n",
       "      <td>Edimat Libros</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11127 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       bookID                                              title  \\\n",
       "0           1  Harry Potter and the Half-Blood Prince (Harry ...   \n",
       "1           2  Harry Potter and the Order of the Phoenix (Har...   \n",
       "2           4  Harry Potter and the Chamber of Secrets (Harry...   \n",
       "3           5  Harry Potter and the Prisoner of Azkaban (Harr...   \n",
       "4           8  Harry Potter Boxed Set  Books 1-5 (Harry Potte...   \n",
       "...       ...                                                ...   \n",
       "11122   45631   Expelled from Eden: A William T. Vollmann Reader   \n",
       "11123   45633                        You Bright and Risen Angels   \n",
       "11124   45634                    The Ice-Shirt (Seven Dreams #1)   \n",
       "11125   45639                                        Poor People   \n",
       "11126   45641                        Las aventuras de Tom Sawyer   \n",
       "\n",
       "                                                 authors        isbn  \\\n",
       "0                             J.K. Rowling/Mary GrandPré  0439785960   \n",
       "1                             J.K. Rowling/Mary GrandPré  0439358078   \n",
       "2                                           J.K. Rowling  0439554896   \n",
       "3                             J.K. Rowling/Mary GrandPré  043965548X   \n",
       "4                             J.K. Rowling/Mary GrandPré  0439682584   \n",
       "...                                                  ...         ...   \n",
       "11122  William T. Vollmann/Larry McCaffery/Michael He...  1560254416   \n",
       "11123                                William T. Vollmann  0140110879   \n",
       "11124                                William T. Vollmann  0140131965   \n",
       "11125                                William T. Vollmann  0060878827   \n",
       "11126                                         Mark Twain  8497646983   \n",
       "\n",
       "              isbn13        publisher  \n",
       "0      9780439785969  Scholastic Inc.  \n",
       "1      9780439358071  Scholastic Inc.  \n",
       "2      9780439554893       Scholastic  \n",
       "3      9780439655484  Scholastic Inc.  \n",
       "4      9780439682589       Scholastic  \n",
       "...              ...              ...  \n",
       "11122  9781560254416    Da Capo Press  \n",
       "11123  9780140110876    Penguin Books  \n",
       "11124  9780140131963    Penguin Books  \n",
       "11125  9780060878825             Ecco  \n",
       "11126  9788497646987    Edimat Libros  \n",
       "\n",
       "[11127 rows x 6 columns]"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a new dataframe : df1 and remove some features \n",
    "df_qualitative = df[['bookID','title','authors','isbn','isbn13',\n",
    "               'publisher']]\n",
    "df_qualitative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "d1bc9ecf",
   "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>bookID</th>\n",
       "      <th>title</th>\n",
       "      <th>authors</th>\n",
       "      <th>isbn</th>\n",
       "      <th>isbn13</th>\n",
       "      <th>publisher</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Harry Potter and the Half-Blood Prince (Harry ...</td>\n",
       "      <td>J.K. Rowling/Mary GrandPré</td>\n",
       "      <td>0439785960</td>\n",
       "      <td>9780439785969</td>\n",
       "      <td>Scholastic Inc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Harry Potter and the Order of the Phoenix (Har...</td>\n",
       "      <td>J.K. Rowling/Mary GrandPré</td>\n",
       "      <td>0439358078</td>\n",
       "      <td>9780439358071</td>\n",
       "      <td>Scholastic Inc.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bookID                                              title  \\\n",
       "0       1  Harry Potter and the Half-Blood Prince (Harry ...   \n",
       "1       2  Harry Potter and the Order of the Phoenix (Har...   \n",
       "\n",
       "                      authors        isbn         isbn13        publisher  \n",
       "0  J.K. Rowling/Mary GrandPré  0439785960  9780439785969  Scholastic Inc.  \n",
       "1  J.K. Rowling/Mary GrandPré  0439358078  9780439358071  Scholastic Inc.  "
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_qualitative.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1da42a77",
   "metadata": {},
   "source": [
    "4.1 Discretize average_rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "0edc2361",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/dz/3jfgccdn52ggwvy9spwly7y00000gn/T/ipykernel_86434/744164174.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_qualitative[\"av_cat\"] = av_cat\n"
     ]
    }
   ],
   "source": [
    "# discretize average rating \n",
    "av_cat = []\n",
    "av_cat = pd.cut(df.average_rating,bins=[0,1,2,3,4,5,6],\n",
    "                    labels = [\"0\",\"1\",\"2\",\"3\",\"4\",\"5\"],\n",
    "                    include_lowest=True)\n",
    "df_qualitative[\"av_cat\"] = av_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "a693c6f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4\n",
       "1    4\n",
       "2    4\n",
       "Name: av_cat, dtype: category\n",
       "Categories (6, object): ['0' < '1' < '2' < '3' < '4' < '5']"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# head average rating \n",
    "df_qualitative.av_cat.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "26460dcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11124    3\n",
       "11125    3\n",
       "11126    3\n",
       "Name: av_cat, dtype: category\n",
       "Categories (6, object): ['0' < '1' < '2' < '3' < '4' < '5']"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tail av rating \n",
    "df_qualitative.av_cat.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "ed26b89f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.565\n",
       "4    0.426\n",
       "2    0.006\n",
       "0    0.003\n",
       "1    0.001\n",
       "5    0.000\n",
       "Name: av_cat, dtype: float64"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# proportion of classes in the data \n",
    "round(df_qualitative.av_cat.value_counts()/ len(df_qualitative.av_cat),3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70ce0e44",
   "metadata": {},
   "source": [
    "There is 40% of data in class 4 and 56% in class 3. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "7d582e0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_qualitative.av_cat.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31cf208c",
   "metadata": {},
   "source": [
    "Discretizing average_rating  make it unbalanced data.  \n",
    "We need to fix unbalalced data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f4da1fd",
   "metadata": {},
   "source": [
    "================================ REGRESSION ========================================"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9bdbee6",
   "metadata": {},
   "source": [
    "4.2 Splitting data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "d6be1e23",
   "metadata": {},
   "outputs": [],
   "source": [
    "# selecting numerical features\n",
    "df_final = df[['average_rating', 'num_pages','text_reviews_count', \n",
    "          \"language_code_bin\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "bfcc8a94",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train0,test = train_test_split(df_final, test_size= 20,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "7c8467d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split train0 in train and valid \n",
    "train,valid = train_test_split(train0,test_size=40,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "ed52ab29",
   "metadata": {},
   "outputs": [],
   "source": [
    "# subsetting \n",
    "# train0\n",
    "X_train0 = train0.drop(\"average_rating\",axis = 1)\n",
    "y_train0 = train0.average_rating\n",
    "\n",
    "# train\n",
    "X_train = train.drop(\"average_rating\",axis = 1)\n",
    "y_train = train.average_rating\n",
    "\n",
    "# valid \n",
    "X_valid = valid.drop(\"average_rating\",axis=1)\n",
    "y_valid = valid.average_rating\n",
    "\n",
    "# test \n",
    "X_test = test.drop(\"average_rating\",axis = 1)\n",
    "y_test = test.average_rating"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91b32b14",
   "metadata": {},
   "source": [
    "5 modelling : model testing "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7a64e83",
   "metadata": {},
   "source": [
    "5.1 Linear models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f753dc5c",
   "metadata": {},
   "source": [
    "-linear model : OLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "01aefb2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-16 {color: black;background-color: white;}#sk-container-id-16 pre{padding: 0;}#sk-container-id-16 div.sk-toggleable {background-color: white;}#sk-container-id-16 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-16 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-16 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-16 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-16 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-16 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-16 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-16 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-16 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-16 div.sk-item {position: relative;z-index: 1;}#sk-container-id-16 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-16 div.sk-item::before, #sk-container-id-16 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-16 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-16 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-16 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-16 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-16 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-16 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-16 div.sk-label-container {text-align: center;}#sk-container-id-16 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-16 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" checked><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# linear model \n",
    "from sklearn import linear_model\n",
    "reg = linear_model.LinearRegression()\n",
    "reg.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7342c7a1",
   "metadata": {},
   "source": [
    "-regularized linear models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23c2792b",
   "metadata": {},
   "source": [
    "-Ridge : alpha = 0.01 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "55e71c99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-17 {color: black;background-color: white;}#sk-container-id-17 pre{padding: 0;}#sk-container-id-17 div.sk-toggleable {background-color: white;}#sk-container-id-17 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-17 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-17 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-17 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-17 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-17 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-17 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-17 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-17 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-17 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-17 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-17 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-17 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-17 div.sk-item {position: relative;z-index: 1;}#sk-container-id-17 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-17 div.sk-item::before, #sk-container-id-17 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-17 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-17 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-17 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-17 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-17 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-17 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-17 div.sk-label-container {text-align: center;}#sk-container-id-17 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-17 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-17\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge(alpha=0.01)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" checked><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge(alpha=0.01)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Ridge(alpha=0.01)"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# regularized model : ridge model, alpha = 0.1 \n",
    "reg_ridge = linear_model.Ridge(alpha=0.01)\n",
    "reg_ridge.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccf05111",
   "metadata": {},
   "source": [
    "-ridge alphas, cv = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "0d2dd4dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-18 {color: black;background-color: white;}#sk-container-id-18 pre{padding: 0;}#sk-container-id-18 div.sk-toggleable {background-color: white;}#sk-container-id-18 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-18 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-18 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-18 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-18 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-18 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-18 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-18 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-18 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-18 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-18 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-18 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-18 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-18 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-18 div.sk-item {position: relative;z-index: 1;}#sk-container-id-18 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-18 div.sk-item::before, #sk-container-id-18 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-18 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-18 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-18 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-18 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-18 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-18 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-18 div.sk-label-container {text-align: center;}#sk-container-id-18 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-18 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-18\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RidgeCV(alphas=[0.001, 0.01], cv=5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" checked><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RidgeCV</label><div class=\"sk-toggleable__content\"><pre>RidgeCV(alphas=[0.001, 0.01], cv=5)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RidgeCV(alphas=[0.001, 0.01], cv=5)"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ridge model with cross validation \n",
    "# regularized model : ridge model with cv =5\n",
    "reg_ridge_CV = linear_model.RidgeCV(alphas=[0.001,0.01],cv=5)\n",
    "reg_ridge_CV.fit(X_train0,y_train0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d921b419",
   "metadata": {},
   "source": [
    "-Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "06e11c43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-19 {color: black;background-color: white;}#sk-container-id-19 pre{padding: 0;}#sk-container-id-19 div.sk-toggleable {background-color: white;}#sk-container-id-19 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-19 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-19 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-19 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-19 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-19 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-19 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-19 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-19 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-19 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-19 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-19 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-19 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-19 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-19 div.sk-item {position: relative;z-index: 1;}#sk-container-id-19 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-19 div.sk-item::before, #sk-container-id-19 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-19 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-19 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-19 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-19 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-19 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-19 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-19 div.sk-label-container {text-align: center;}#sk-container-id-19 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-19 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-19\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso(alpha=0.5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" checked><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso(alpha=0.5)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Lasso(alpha=0.5)"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from sklearn import linear_model, alpha = 0.5\n",
    "reg_lasso = linear_model.Lasso(alpha=0.5)\n",
    "reg_lasso.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfe850d2",
   "metadata": {},
   "source": [
    "5.2 Tree based model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "80c69f25",
   "metadata": {},
   "outputs": [],
   "source": [
    "# tree based model \n",
    "from sklearn import tree\n",
    "regressor = tree.DecisionTreeRegressor(random_state=40)\n",
    "tree_reg= regressor.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0487847d",
   "metadata": {},
   "source": [
    "5.3 Ensemble models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "f63624c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-20 {color: black;background-color: white;}#sk-container-id-20 pre{padding: 0;}#sk-container-id-20 div.sk-toggleable {background-color: white;}#sk-container-id-20 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-20 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-20 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-20 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-20 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-20 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-20 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-20 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-20 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-20 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-20 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-20 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-20 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-20 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-20 div.sk-item {position: relative;z-index: 1;}#sk-container-id-20 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-20 div.sk-item::before, #sk-container-id-20 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-20 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-20 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-20 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-20 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-20 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-20 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-20 div.sk-label-container {text-align: center;}#sk-container-id-20 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-20 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-20\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" checked><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(random_state=42)"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " #random forest\n",
    "from sklearn.ensemble import RandomForestRegressor \n",
    "rf = RandomForestRegressor(n_estimators= 100,\n",
    "#max_depth = 5,\n",
    "random_state=42)\n",
    "rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8671773",
   "metadata": {},
   "source": [
    "5.4 Support vectors machine "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "c974678b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-21 {color: black;background-color: white;}#sk-container-id-21 pre{padding: 0;}#sk-container-id-21 div.sk-toggleable {background-color: white;}#sk-container-id-21 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-21 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-21 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-21 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-21 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-21 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-21 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-21 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-21 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-21 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-21 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-21 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-21 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-21 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-21 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-21 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-21 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-21 div.sk-item {position: relative;z-index: 1;}#sk-container-id-21 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-21 div.sk-item::before, #sk-container-id-21 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-21 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-21 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-21 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-21 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-21 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-21 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-21 div.sk-label-container {text-align: center;}#sk-container-id-21 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-21 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-21\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;linearsvr&#x27;,\n",
       "                 LinearSVR(max_iter=100000, random_state=0, tol=1e-05))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;linearsvr&#x27;,\n",
       "                 LinearSVR(max_iter=100000, random_state=0, tol=1e-05))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVR</label><div class=\"sk-toggleable__content\"><pre>LinearSVR(max_iter=100000, random_state=0, tol=1e-05)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('linearsvr',\n",
       "                 LinearSVR(max_iter=100000, random_state=0, tol=1e-05))])"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# linear svm\n",
    "from sklearn.svm import LinearSVR\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.datasets import make_regression\n",
    "X, y = make_regression(n_features=4, random_state=0)\n",
    "svm_regr = make_pipeline(StandardScaler(),\n",
    "                     LinearSVR(random_state=0, tol=1e-5,max_iter=100000))\n",
    "svm_regr.fit(X, y)\n",
    "\n",
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04bf098e",
   "metadata": {},
   "source": [
    "5.5 gbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "7b2602a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-22 {color: black;background-color: white;}#sk-container-id-22 pre{padding: 0;}#sk-container-id-22 div.sk-toggleable {background-color: white;}#sk-container-id-22 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-22 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-22 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-22 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-22 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-22 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-22 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-22 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-22 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-22 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-22 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-22 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-22 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-22 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-22 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-22 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-22 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-22 div.sk-item {position: relative;z-index: 1;}#sk-container-id-22 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-22 div.sk-item::before, #sk-container-id-22 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-22 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-22 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-22 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-22 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-22 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-22 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-22 div.sk-label-container {text-align: center;}#sk-container-id-22 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-22 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-22\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" checked><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor(random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingRegressor(random_state=0)"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "gbm = GradientBoostingRegressor(random_state=0)\n",
    "gbm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "c54b8566",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cross val custom function \n",
    "def model_cross_val(models={}, X_validation=None,y_validation= None,cv=None, scoring = \"neg_mean_squared_error\"):\n",
    "    from sklearn.model_selection import cross_val_score\n",
    "    table = []\n",
    "    for model in models :\n",
    "        score = abs(\n",
    "            cross_val_score(models[model],\n",
    "                            X_validation,\n",
    "                            y_validation,\n",
    "                            cv=cv,\n",
    "                            scoring=scoring ).mean()\n",
    "        )\n",
    "        score = pow(score,.5)\n",
    "        table.append(score)\n",
    "    print(\n",
    "         pd.DataFrame(data = table,index = models,columns = [\"rmse\"])\n",
    "        .sort_values(by =\"rmse\")\n",
    "        \n",
    "    )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f395fcd2",
   "metadata": {},
   "source": [
    " Models dictionary for performance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "8be2b054",
   "metadata": {},
   "outputs": [],
   "source": [
    "# models to cross-validate \n",
    "\n",
    "# models to cross validate\n",
    "models = {\"linear reg\": reg,\n",
    "          \"ridge model\":reg_ridge,\n",
    "          \"lasso model \": reg_lasso,\n",
    "          \"ridge CV\":reg_ridge_CV,\n",
    "          \"random forest\":rf,\n",
    "          \"linear svm\":svm_regr,\n",
    "           \"gbm\": gbm\n",
    "         }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "f2588bdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   rmse\n",
      "linear svm     0.363405\n",
      "lasso model    0.363797\n",
      "ridge model    0.364707\n",
      "ridge CV       0.364707\n",
      "linear reg     0.364769\n",
      "random forest  0.395679\n",
      "gbm            0.474387\n"
     ]
    }
   ],
   "source": [
    "# cross validation  \n",
    "model_cross_val(models,X_valid,y_valid,cv= 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "9f140ad4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# detect and remove outliers function\n",
    "def remove_outliers(X,bins = 50, plot = \"bool\"):\n",
    "    # q1 and q3 quantiles\n",
    "    q1,q3 = X.quantile([0.25,0.75])\n",
    "\n",
    "    #interquatile range \n",
    "    iqr = q3 - q1\n",
    "   \n",
    "\n",
    "    # remove  q1 - 1.5*iqr or  q3 + 1.5*iqr:\n",
    "    X =  X[X.between(q1 - 1.5*iqr, q3 + 1.5*iqr,inclusive = \"both\")]\n",
    "       \n",
    "\n",
    "    # histogram\n",
    "    if plot :\n",
    "          X.hist( bins = bins,legend = {\"\"})\n",
    "    return X\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "830d197d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove ouliers OR NOT\n",
    "q1,q3 = df.average_rating.quantile([.25,.75])\n",
    "iqr = q3 - q1\n",
    "#df_mdl = df_final[df_final.average_rating.between(q1 - 1.5*iqr, q3 + 1.5*iqr,inclusive = \"both\")]\n",
    "df_mdl = df[[\"average_rating\",\"num_pages\",\"language_code_bin\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "f1a694e3",
   "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>average_rating</th>\n",
       "      <th>num_pages</th>\n",
       "      <th>language_code_bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.57</td>\n",
       "      <td>652</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.49</td>\n",
       "      <td>870</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   average_rating  num_pages  language_code_bin\n",
       "0            4.57        652                  1\n",
       "1            4.49        870                  1"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mdl.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "d0f3d73e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_mdl.average_rating.hist(bins = 100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "5038acf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        4.57\n",
       "1        4.49\n",
       "2        4.42\n",
       "3        4.56\n",
       "5        3.74\n",
       "         ... \n",
       "11122    4.06\n",
       "11123    4.08\n",
       "11124    3.96\n",
       "11125    3.72\n",
       "11126    3.91\n",
       "Name: average_rating, Length: 10877, dtype: float64"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "remove_outliers(df_mdl.average_rating, bins = 30)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76cc74b1",
   "metadata": {},
   "source": [
    "Removing outliers, concentrate average rating between 3.9 and 4.6. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "0dfee356",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10877.000000\n",
       "mean         3.954758\n",
       "std          0.261329\n",
       "min          3.230000\n",
       "25%          3.790000\n",
       "50%          3.960000\n",
       "75%          4.140000\n",
       "max          4.670000\n",
       "Name: average_rating, dtype: float64"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remove_outliers(df_mdl.average_rating, plot = False).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "1f8c05c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        652\n",
       "2        352\n",
       "3        435\n",
       "5        152\n",
       "9        215\n",
       "        ... \n",
       "11122    512\n",
       "11123    635\n",
       "11124    415\n",
       "11125    434\n",
       "11126    272\n",
       "Name: num_pages, Length: 10587, dtype: int64"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BAACWQ0ABAACWQ0ABAACWQ0ABAACWQ0ABAACWQ0ABAACWQ0ABAACWQ0ABAACWQ0ABAACW41dA6dWrl2w2W4tHQUGBJOn48eMqKChQ9+7d1bVrV+Xl5am2ttanj+rqauXm5io2NlaJiYmaPHmyTpw4EbyKAABAh+dXQNm6dasOHDhgPioqKiRJN998syTp3nvv1auvvqqXXnpJ69ev1/79+3XTTTeZyzc1NSk3N1eNjY3auHGjli1bpqVLl2r69OlBLAkAAHR0fgWUHj16yOl0mo+VK1fqggsu0A9+8AMdOnRIixcv1qOPPqorr7xSQ4cO1ZIlS7Rx40Zt2rRJklReXq7du3fr2Wef1eDBgzVq1CjNnj1bZWVlamxsbJMCAQBAxxMR6IKNjY169tlnVVRUJJvNpqqqKnk8HmVlZZlt+vXrp7S0NFVWVmr48OGqrKzUwIEDlZSUZLbJycnRhAkTtGvXLg0ZMuSk63K73XK73ebzhoYGSZLH45HH4wm0hJNq7i/Y/VpVR63XHm4EtlyY4fOvvzra69Q83s5Qb0f9LAeKekNbqNbrTz0BB5QVK1aovr5eY8eOlSTV1NQoKipK8fHxPu2SkpJUU1Njtvl6OGme3zzvVEpLSzVz5swW08vLyxUbGxtoCafVvPuqs+ho9c4d1rrlZ2d4A1pu1apVrVtxO+lM9Xa0z3JrUW9oC7V6jx07dsZtAw4oixcv1qhRo5SSkhJoF2esuLhYRUVF5vOGhgalpqYqOztbDocjqOvyeDyqqKjQyJEjFRkZGdS+raij1jugZE1Ay9nDDM3O8GratjC5vTa/l99ZkhPQettL8/vbHvUG+h4Fut6O+lkOFPWGtlCtt3kPyJkIKKB88skneuONN/TXv/7VnOZ0OtXY2Kj6+nqfrSi1tbVyOp1mmy1btvj01XyWT3Obk7Hb7bLb7S2mR0ZGttkb15Z9W1FHq9fd5P+Prc/yXltAfXSk1+jr2qPe1rxHrVlvR/sstxb1hrZQq9efWgK6DsqSJUuUmJio3Nxcc9rQoUMVGRmptWvXmtP27Nmj6upquVwuSZLL5dKOHTtUV1dntqmoqJDD4VB6enogQwEAACHI7y0oXq9XS5YsUX5+viIi/m/xuLg4jRs3TkVFRUpISJDD4dDdd98tl8ul4cOHS5Kys7OVnp6u22+/XXPnzlVNTY2mTp2qgoKCk24hAQAAnZPfAeWNN95QdXW17rzzzhbz5s2bp7CwMOXl5cntdisnJ0dPPvmkOT88PFwrV67UhAkT5HK51KVLF+Xn52vWrFmtqwIAAIQUvwNKdna2DOPkpyxGR0errKxMZWVlp1y+Z8+eHfLMAAAAcPZwLx4AAGA5AZ9mDMA/vR54LeBlP56T++2NACCEsAUFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYjt8B5bPPPtNPf/pTde/eXTExMRo4cKC2bdtmzjcMQ9OnT1dycrJiYmKUlZWlvXv3+vRx8OBBjRkzRg6HQ/Hx8Ro3bpyOHDnS+moAAEBIiPCn8X/+8x+NGDFCP/zhD/X666+rR48e2rt3r8455xyzzdy5czV//nwtW7ZMvXv31rRp05STk6Pdu3crOjpakjRmzBgdOHBAFRUV8ng8uuOOOzR+/HgtX748uNUBIaLXA68FtJw93NDcYUEeDACcBX4FlN/97ndKTU3VkiVLzGm9e/c2/28Yhh577DFNnTpV119/vSTpmWeeUVJSklasWKHRo0fr/fff1+rVq7V161ZlZGRIkhYsWKBrrrlGjzzyiFJSUoJRF9AmAg0KAAD/+BVQXnnlFeXk5Ojmm2/W+vXr9Z3vfEe/+tWvdNddd0mS9u3bp5qaGmVlZZnLxMXFKTMzU5WVlRo9erQqKysVHx9vhhNJysrKUlhYmDZv3qwbb7yxxXrdbrfcbrf5vKGhQZLk8Xjk8Xj8q/hbNPcX7H6tqqPWaw83AlsuzPD5N9S1tt7WfC4CfY8CXW9H/SwHinpDW6jW6089NsMwzvhbpHkXTVFRkW6++WZt3bpVEydO1KJFi5Sfn6+NGzdqxIgR2r9/v5KTk83lbrnlFtlsNv35z3/Www8/rGXLlmnPnj0+fScmJmrmzJmaMGFCi/WWlJRo5syZLaYvX75csbGxZ1wsAABoP8eOHdNPfvITHTp0SA6H47Rt/dqC4vV6lZGRoYcffliSNGTIEO3cudMMKG2luLhYRUVF5vOGhgalpqYqOzv7Wwv0l8fjUUVFhUaOHKnIyMig9m1FHbXeASVrAlrOHmZodoZX07aFye21BXlU1tPaeneW5AS87kDfo0DX21E/y4Gi3tAWqvU27wE5E34FlOTkZKWnp/tM69+/v/7nf/5HkuR0OiVJtbW1PltQamtrNXjwYLNNXV2dTx8nTpzQwYMHzeW/yW63y263t5geGRnZZm9cW/ZtRR2tXndT68KF22trdR8dSaD1tuYz0ZrXtzXr7Wif5dai3tAWavX6U4tfpxmPGDGixa6ZDz74QD179pT01QGzTqdTa9euNec3NDRo8+bNcrlckiSXy6X6+npVVVWZbdatWyev16vMzEx/hgMAAEKUX1tQ7r33Xl122WV6+OGHdcstt2jLli166qmn9NRTT0mSbDabJk2apAcffFB9+vQxTzNOSUnRDTfcIOmrLS5XX3217rrrLi1atEgej0eFhYUaPXo0Z/AAAABJfgaUSy+9VC+//LKKi4s1a9Ys9e7dW4899pjGjBljtrn//vt19OhRjR8/XvX19br88su1evVq8wBbSXruuedUWFioq666SmFhYcrLy9P8+fODVxUAAOjQ/AooknTttdfq2muvPeV8m82mWbNmadasWadsk5CQwEXZAADAKXEvHgAAYDkEFAAAYDkEFAAAYDkEFAAAYDl+HyQLAG0tkJsycudmILSwBQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFhORHsPAIB19XrgtfYeAoBOii0oAADAcggoAADAcggoAADAcggoAADAcggoAADAcggoAADAcggoAADAcggoAADAcggoAADAcggoAADAcvwKKCUlJbLZbD6Pfv36mfOPHz+ugoICde/eXV27dlVeXp5qa2t9+qiurlZubq5iY2OVmJioyZMn68SJE8GpBgAAhAS/78Vz0UUX6Y033vi/DiL+r4t7771Xr732ml566SXFxcWpsLBQN910k/7xj39IkpqampSbmyun06mNGzfqwIED+tnPfqbIyEg9/PDDQSgHAACEAr8DSkREhJxOZ4vphw4d0uLFi7V8+XJdeeWVkqQlS5aof//+2rRpk4YPH67y8nLt3r1bb7zxhpKSkjR48GDNnj1bU6ZMUUlJiaKiolpfEQAA6PD8Dih79+5VSkqKoqOj5XK5VFpaqrS0NFVVVcnj8SgrK8ts269fP6WlpamyslLDhw9XZWWlBg4cqKSkJLNNTk6OJkyYoF27dmnIkCEnXafb7Zbb7TafNzQ0SJI8Ho88Ho+/JZxWc3/B7teqOmq99nAjsOXCDJ9/Q11nqre5xo72WQ5UR/3bDRT1hgZ/6vEroGRmZmrp0qXq27evDhw4oJkzZ+p73/uedu7cqZqaGkVFRSk+Pt5nmaSkJNXU1EiSampqfMJJ8/zmeadSWlqqmTNntpheXl6u2NhYf0o4YxUVFW3Sr1V1tHrnDmvd8rMzvMEZSAfRmertaJ/l1qLe0BZq9R47duyM2/oVUEaNGmX+f9CgQcrMzFTPnj314osvKiYmxp+u/FJcXKyioiLzeUNDg1JTU5WdnS2HwxHUdXk8HlVUVGjkyJGKjIwMat9W1FHrHVCyJqDl7GGGZmd4NW1bmNxeW5BHZT2dqd7mWjvaZzlQHfVvN1DUGxqa94CcCb938XxdfHy8vvvd7+rDDz/UyJEj1djYqPr6ep+tKLW1teYxK06nU1u2bPHpo/ksn5Md19LMbrfLbre3mB4ZGdlmb1xb9m1FHa1ed1PrfmzdXlur++hIOlO9He2z3FrUG9pCrV5/amnVdVCOHDmijz76SMnJyRo6dKgiIyO1du1ac/6ePXtUXV0tl8slSXK5XNqxY4fq6urMNhUVFXI4HEpPT2/NUAAAQAjxawvKr3/9a1133XXq2bOn9u/frxkzZig8PFy33Xab4uLiNG7cOBUVFSkhIUEOh0N33323XC6Xhg8fLknKzs5Wenq6br/9ds2dO1c1NTWaOnWqCgoKTrqFBAAAdE5+BZT//d//1W233aYvvvhCPXr00OWXX65NmzapR48ekqR58+YpLCxMeXl5crvdysnJ0ZNPPmkuHx4erpUrV2rChAlyuVzq0qWL8vPzNWvWrOBWBQAAOjS/AsoLL7xw2vnR0dEqKytTWVnZKdv07NlTq1at8me1AACgk+FePAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHJadbNAoNcDrwW87MdzcoM4EgBAKGELCgAAsBwCCgAAsBwCCgAAsBwCCgAAsBwCCgAAsBwCCgAAsBwCCgAAsBwCCgAAsBwCCgAAsBwCCgAAsBwCCgAAsBwCCgAAsBwCCgAAsBwCCgAAsBwCCgAAsBwCCgAAsBwCCgAAsBwCCgAAsBwCCgAAsBwCCgAAsJyI9h4AOq9eD7zW3kMAAFgUW1AAAIDlEFAAAIDlEFAAAIDlEFAAAIDltCqgzJkzRzabTZMmTTKnHT9+XAUFBerevbu6du2qvLw81dbW+ixXXV2t3NxcxcbGKjExUZMnT9aJEydaMxQAABBCAg4oW7du1R/+8AcNGjTIZ/q9996rV199VS+99JLWr1+v/fv366abbjLnNzU1KTc3V42Njdq4caOWLVumpUuXavr06YFXAQAAQkpAAeXIkSMaM2aM/vjHP+qcc84xpx86dEiLFy/Wo48+qiuvvFJDhw7VkiVLtHHjRm3atEmSVF5ert27d+vZZ5/V4MGDNWrUKM2ePVtlZWVqbGwMTlUAAKBDC+g6KAUFBcrNzVVWVpYefPBBc3pVVZU8Ho+ysrLMaf369VNaWpoqKys1fPhwVVZWauDAgUpKSjLb5OTkaMKECdq1a5eGDBnSYn1ut1tut9t83tDQIEnyeDzyeDyBlHBKzf0Fu1+ram299nAjmMNpc/Yww+ffUNeZ6m2ukb/d0ES9ocGfevwOKC+88ILeeecdbd26tcW8mpoaRUVFKT4+3md6UlKSampqzDZfDyfN85vnnUxpaalmzpzZYnp5ebliY2P9LeGMVFRUtEm/VhVovXOHBXkgZ8nsDG97D+Gs6kz18rcb2qi3Yzt27NgZt/UroHz66aeaOHGiKioqFB0d7ffAAlVcXKyioiLzeUNDg1JTU5WdnS2HwxHUdXk8HlVUVGjkyJGKjIwMat9W1Np6B5SsaYNRtR17mKHZGV5N2xYmt9fW3sNpc52p3uZa+dsNTdQbGpr3gJwJvwJKVVWV6urqdMkll5jTmpqatGHDBj3xxBNas2aNGhsbVV9f77MVpba2Vk6nU5LkdDq1ZcsWn36bz/JpbvNNdrtddru9xfTIyMg2e+Pasm8rCrRed1PH/NFze20dduyB6Ez1tuZvtzW3X/h4Tm7Ay7YG31WhLdTq9acWvw6Sveqqq7Rjxw5t377dfGRkZGjMmDHm/yMjI7V27VpzmT179qi6uloul0uS5HK5tGPHDtXV1ZltKioq5HA4lJ6e7s9wAABAiPJrC0q3bt00YMAAn2ldunRR9+7dzenjxo1TUVGREhIS5HA4dPfdd8vlcmn48OGSpOzsbKWnp+v222/X3LlzVVNTo6lTp6qgoOCkW0kAAEDnE/S7Gc+bN09hYWHKy8uT2+1WTk6OnnzySXN+eHi4Vq5cqQkTJsjlcqlLly7Kz8/XrFmzgj0UAADQQbU6oLz11ls+z6Ojo1VWVqaysrJTLtOzZ0+tWrWqtasGAAAhinvxAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAywn6hdoAoD0NKFnTae47BIQytqAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADL8SugLFy4UIMGDZLD4ZDD4ZDL5dLrr79uzj9+/LgKCgrUvXt3de3aVXl5eaqtrfXpo7q6Wrm5uYqNjVViYqImT56sEydOBKcaAAAQEvwKKOedd57mzJmjqqoqbdu2TVdeeaWuv/567dq1S5J077336tVXX9VLL72k9evXa//+/brpppvM5ZuampSbm6vGxkZt3LhRy5Yt09KlSzV9+vTgVgUAADq0CH8aX3fddT7PH3roIS1cuFCbNm3Seeedp8WLF2v58uW68sorJUlLlixR//79tWnTJg0fPlzl5eXavXu33njjDSUlJWnw4MGaPXu2pkyZopKSEkVFRQWvMgAA0GEFfAxKU1OTXnjhBR09elQul0tVVVXyeDzKysoy2/Tr109paWmqrKyUJFVWVmrgwIFKSkoy2+Tk5KihocHcCgMAAODXFhRJ2rFjh1wul44fP66uXbvq5ZdfVnp6urZv366oqCjFx8f7tE9KSlJNTY0kqaamxiecNM9vnncqbrdbbrfbfN7Q0CBJ8ng88ng8/pZwWs39Bbtfq2ptvfZwI5jDaXP2MMPn31DXmept71rP9ncG31WhLVTr9acevwNK3759tX37dh06dEh/+ctflJ+fr/Xr1/vbjV9KS0s1c+bMFtPLy8sVGxvbJuusqKhok36tKtB65w4L8kDOktkZ3vYewlnVmeptr1pXrVrVLuvluyq0hVq9x44dO+O2fgeUqKgoXXjhhZKkoUOHauvWrXr88cd16623qrGxUfX19T5bUWpra+V0OiVJTqdTW7Zs8emv+Syf5jYnU1xcrKKiIvN5Q0ODUlNTlZ2dLYfD4W8Jp+XxeFRRUaFp28Lk9tr8Xn5nSU5Qx9PWmusdOXKkIiMj/V5+QMmaNhhV27GHGZqd4Q34/e1oOlO97V3r2f7bb+3fbkdDvaGheQ/ImfA7oHyT1+uV2+3W0KFDFRkZqbVr1yovL0+StGfPHlVXV8vlckmSXC6XHnroIdXV1SkxMVHSV+nQ4XAoPT39lOuw2+2y2+0tpkdGRrbZG+f22uRu8v9LrqN+kAJ9LQN5jawg0Pe3o+pM9bZXre31t9+W34NWRL0dmz+1+BVQiouLNWrUKKWlpenw4cNavny53nrrLa1Zs0ZxcXEaN26cioqKlJCQIIfDobvvvlsul0vDhw+XJGVnZys9PV2333675s6dq5qaGk2dOlUFBQUnDSAAAKBz8iug1NXV6Wc/+5kOHDiguLg4DRo0SGvWrNHIkSMlSfPmzVNYWJjy8vLkdruVk5OjJ5980lw+PDxcK1eu1IQJE+RyudSlSxfl5+dr1qxZwa0KAAB0aH4FlMWLF592fnR0tMrKylRWVnbKNj179my3g8kAAEDHwL14AACA5RBQAACA5RBQAACA5bT6NGNYQ68HXgtoOXu40WEvtgYACF1sQQEAAJZDQAEAAJZDQAEAAJbDMShBFuixIJL08ZzcII4EAICOiy0oAADAcggoAADAcggoAADAcggoAADAcggoAADAcggoAADAcggoAADAcggoAADAcggoAADAcriSLAAEAVeRBoKLgAIA7SyQcGMPNzR3WBsMBrAIdvEAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADL4UJtkCQNKFkjd5OtvYcBAIAktqAAAAALIqAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADL8SuglJaW6tJLL1W3bt2UmJioG264QXv27PFpc/z4cRUUFKh79+7q2rWr8vLyVFtb69Omurpaubm5io2NVWJioiZPnqwTJ060vhoAABAS/Aoo69evV0FBgTZt2qSKigp5PB5lZ2fr6NGjZpt7771Xr776ql566SWtX79e+/fv10033WTOb2pqUm5urhobG7Vx40YtW7ZMS5cu1fTp04NXFQAA6ND8ulDb6tWrfZ4vXbpUiYmJqqqq0ve//30dOnRIixcv1vLly3XllVdKkpYsWaL+/ftr06ZNGj58uMrLy7V792698cYbSkpK0uDBgzV79mxNmTJFJSUlioqKCl51AACgQ2rVlWQPHTokSUpISJAkVVVVyePxKCsry2zTr18/paWlqbKyUsOHD1dlZaUGDhyopKQks01OTo4mTJigXbt2aciQIS3W43a75Xa7zecNDQ2SJI/HI4/H05oSWmjuzx5mBLVff9YdCHt4YONtrrM96m0P1Bu6OlOt0v/VGezvQKtqrpN6OzZ/6gk4oHi9Xk2aNEkjRozQgAEDJEk1NTWKiopSfHy8T9ukpCTV1NSYbb4eTprnN887mdLSUs2cObPF9PLycsXGxgZawmnNzvC2Sb+ns2rVqoCXnTusdetuj3rbE/WGrs5UqyRVVFS09xDOKurt2I4dO3bGbQMOKAUFBdq5c6fefvvtQLs4Y8XFxSoqKjKfNzQ0KDU1VdnZ2XI4HEFdl8fjUUVFhaZtC5PbG/r3prGHGZqd4aXeENWZ6u1MtUr/V+/IkSMVGRnZ3sNpc83fzdTbsTXvATkTAQWUwsJCrVy5Uhs2bNB5551nTnc6nWpsbFR9fb3PVpTa2lo5nU6zzZYtW3z6az7Lp7nNN9ntdtnt9hbTIyMj2+yNc3ttnermedQb2jpTvZ2pVqltvwetiHo7Nn9q8essHsMwVFhYqJdfflnr1q1T7969feYPHTpUkZGRWrt2rTltz549qq6ulsvlkiS5XC7t2LFDdXV1ZpuKigo5HA6lp6f7MxwAABCi/NqCUlBQoOXLl+tvf/ubunXrZh4zEhcXp5iYGMXFxWncuHEqKipSQkKCHA6H7r77brlcLg0fPlySlJ2drfT0dN1+++2aO3euampqNHXqVBUUFJx0KwkAAOh8/AooCxculCRdccUVPtOXLFmisWPHSpLmzZunsLAw5eXlye12KycnR08++aTZNjw8XCtXrtSECRPkcrnUpUsX5efna9asWa2rBADgl14PvBbwsh/PyQ3iSICW/AoohvHtp+9FR0errKxMZWVlp2zTs2fPVp2xAgAAQhv34gEAAJbTqgu1AQA6J3YPoa0RUACgAxtQsqZTnVaNzoNdPAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIi2nsAAACcqQEla+RusgW07MdzcoM8GrQltqAAAADLIaAAAADLIaAAAADL4RgUAMBZ1euB1/xexh5uaO6wNhgMLMvvLSgbNmzQddddp5SUFNlsNq1YscJnvmEYmj59upKTkxUTE6OsrCzt3bvXp83Bgwc1ZswYORwOxcfHa9y4cTpy5EirCgEAAKHD74By9OhRXXzxxSorKzvp/Llz52r+/PlatGiRNm/erC5duignJ0fHjx8324wZM0a7du1SRUWFVq5cqQ0bNmj8+PGBVwEAAEKK37t4Ro0apVGjRp10nmEYeuyxxzR16lRdf/31kqRnnnlGSUlJWrFihUaPHq33339fq1ev1tatW5WRkSFJWrBgga655ho98sgjSklJaUU5AACcXCC7lppxivLZF9RjUPbt26eamhplZWWZ0+Li4pSZmanKykqNHj1alZWVio+PN8OJJGVlZSksLEybN2/WjTfe2KJft9stt9ttPm9oaJAkeTweeTyeYJZg9mcPM4Lar1U110m9oakz1duZapWo92wL9m/Nma7vbK+3rflTT1ADSk1NjSQpKSnJZ3pSUpI5r6amRomJib6DiIhQQkKC2eabSktLNXPmzBbTy8vLFRsbG4yhtzA7w9sm/VoV9Ya2zlRvZ6pVot6zZdWqVe2y3oqKinZZb1s5duzYGbftEGfxFBcXq6ioyHze0NCg1NRUZWdny+FwBHVdHo9HFRUVmrYtTG5vYFcr7EjsYYZmZ3ipN0R1pno7U60S9Z5tO0tyzur6mn+LRo4cqcjIyLO67rbUvAfkTAQ1oDidTklSbW2tkpOTzem1tbUaPHiw2aaurs5nuRMnTujgwYPm8t9kt9tlt9tbTI+MjGyzN87ttQV8OeWOiHpDW2eqtzPVKlHv2dJnWnnAy7bm+JW2/J1rD/7UEtQLtfXu3VtOp1Nr1641pzU0NGjz5s1yuVySJJfLpfr6elVVVZlt1q1bJ6/Xq8zMzGAOBwAAdFB+b0E5cuSIPvzwQ/P5vn37tH37diUkJCgtLU2TJk3Sgw8+qD59+qh3796aNm2aUlJSdMMNN0iS+vfvr6uvvlp33XWXFi1aJI/Ho8LCQo0ePZozeAAAgKQAAsq2bdv0wx/+0HzefGxIfn6+li5dqvvvv19Hjx7V+PHjVV9fr8svv1yrV69WdHS0ucxzzz2nwsJCXXXVVQoLC1NeXp7mz58fhHIAAEAo8DugXHHFFTKMU5/mZbPZNGvWLM2aNeuUbRISErR8+XJ/Vw0AADoJbhYIAAAsh4ACAAAsh4ACAAAsp0NcqA0AgM5oQMmaVl33pSPfQ4iAAgBAiOrIN0hkFw8AALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAczuIBAKANBXImjT3c0NxhbTCYDoQtKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHLaNaCUlZWpV69eio6OVmZmprZs2dKewwEAABbRbgHlz3/+s4qKijRjxgy98847uvjii5WTk6O6urr2GhIAALCIdgsojz76qO666y7dcccdSk9P16JFixQbG6unn366vYYEAAAsIqI9VtrY2KiqqioVFxeb08LCwpSVlaXKysoW7d1ut9xut/n80KFDkqSDBw/K4/EEdWwej0fHjh1ThCdMTV5bUPu2ogivoWPHvNQbojpTvZ2pVol6Q50V6v3iiy+C3ufhw4clSYZhfHtjox189tlnhiRj48aNPtMnT55sDBs2rEX7GTNmGJJ48ODBgwcPHiHw+PTTT781K7TLFhR/FRcXq6ioyHzu9Xp18OBBde/eXTZbcJNlQ0ODUlNT9emnn8rhcAS1byui3tDWmertTLVK1BvqQrVewzB0+PBhpaSkfGvbdgko5557rsLDw1VbW+szvba2Vk6ns0V7u90uu93uMy0+Pr4thyiHwxFSH4pvQ72hrTPV25lqlag31IVivXFxcWfUrl0Oko2KitLQoUO1du1ac5rX69XatWvlcrnaY0gAAMBC2m0XT1FRkfLz85WRkaFhw4bpscce09GjR3XHHXe015AAAIBFtFtAufXWW/X5559r+vTpqqmp0eDBg7V69WolJSW115AkfbU7acaMGS12KYUq6g1tnanezlSrRL2hrrPVezI2wziTc30AAADOHu7FAwAALIeAAgAALIeAAgAALIeAAgAALIeA8jVlZWXq1auXoqOjlZmZqS1btrT3kAKyYcMGXXfddUpJSZHNZtOKFSt85huGoenTpys5OVkxMTHKysrS3r17fdocPHhQY8aMkcPhUHx8vMaNG6cjR46cxSrOTGlpqS699FJ169ZNiYmJuuGGG7Rnzx6fNsePH1dBQYG6d++url27Ki8vr8VFAqurq5Wbm6vY2FglJiZq8uTJOnHixNks5YwsXLhQgwYNMi/e5HK59Prrr5vzQ6nWk5kzZ45sNpsmTZpkTgulmktKSmSz2Xwe/fr1M+eHUq3NPvvsM/30pz9V9+7dFRMTo4EDB2rbtm3m/FD6vurVq1eL99dms6mgoEBSaL6/rRKMe+uEghdeeMGIiooynn76aWPXrl3GXXfdZcTHxxu1tbXtPTS/rVq1yvjtb39r/PWvfzUkGS+//LLP/Dlz5hhxcXHGihUrjPfee8/40Y9+ZPTu3dv48ssvzTZXX321cfHFFxubNm0y/v73vxsXXnihcdttt53lSr5dTk6OsWTJEmPnzp3G9u3bjWuuucZIS0szjhw5Yrb55S9/aaSmphpr1641tm3bZgwfPty47LLLzPknTpwwBgwYYGRlZRnvvvuusWrVKuPcc881iouL26Ok03rllVeM1157zfjggw+MPXv2GL/5zW+MyMhIY+fOnYZhhFat37RlyxajV69exqBBg4yJEyea00Op5hkzZhgXXXSRceDAAfPx+eefm/NDqVbDMIyDBw8aPXv2NMaOHWts3rzZ+Pe//22sWbPG+PDDD802ofR9VVdX5/PeVlRUGJKMN9980zCM0Ht/W4uA8v8bNmyYUVBQYD5vamoyUlJSjNLS0nYcVet9M6B4vV7D6XQav//9781p9fX1ht1uN55//nnDMAxj9+7dhiRj69atZpvXX3/dsNlsxmeffXbWxh6Iuro6Q5Kxfv16wzC+qi0yMtJ46aWXzDbvv/++IcmorKw0DOOrQBcWFmbU1NSYbRYuXGg4HA7D7Xaf3QICcM455xj//d//HdK1Hj582OjTp49RUVFh/OAHPzADSqjVPGPGDOPiiy8+6bxQq9UwDGPKlCnG5Zdffsr5of59NXHiROOCCy4wvF5vSL6/rcUuHkmNjY2qqqpSVlaWOS0sLExZWVmqrKxsx5EF3759+1RTU+NTa1xcnDIzM81aKysrFR8fr4yMDLNNVlaWwsLCtHnz5rM+Zn8cOnRIkpSQkCBJqqqqksfj8am3X79+SktL86l34MCBPhcJzMnJUUNDg3bt2nUWR++fpqYmvfDCCzp69KhcLldI11pQUKDc3Fyf2qTQfH/37t2rlJQUnX/++RozZoyqq6slhWatr7zyijIyMnTzzTcrMTFRQ4YM0R//+Edzfih/XzU2NurZZ5/VnXfeKZvNFpLvb2sRUCT9v//3/9TU1NTiKrZJSUmqqalpp1G1jeZ6TldrTU2NEhMTfeZHREQoISHB0q+H1+vVpEmTNGLECA0YMEDSV7VERUW1uLnkN+s92evRPM9qduzYoa5du8put+uXv/ylXn75ZaWnp4dkrZL0wgsv6J133lFpaWmLeaFWc2ZmppYuXarVq1dr4cKF2rdvn773ve/p8OHDIVerJP373//WwoUL1adPH61Zs0YTJkzQPffco2XLlkkK7e+rFStWqL6+XmPHjpUUep/lYGi3S90DwVZQUKCdO3fq7bffbu+htKm+fftq+/btOnTokP7yl78oPz9f69evb+9htYlPP/1UEydOVEVFhaKjo9t7OG1u1KhR5v8HDRqkzMxM9ezZUy+++KJiYmLacWRtw+v1KiMjQw8//LAkaciQIdq5c6cWLVqk/Pz8dh5d21q8eLFGjRqllJSU9h6KZbEFRdK5556r8PDwFkdL19bWyul0ttOo2kZzPaer1el0qq6uzmf+iRMndPDgQcu+HoWFhVq5cqXefPNNnXfeeeZ0p9OpxsZG1dfX+7T/Zr0nez2a51lNVFSULrzwQg0dOlSlpaW6+OKL9fjjj4dkrVVVVaqrq9Mll1yiiIgIRUREaP369Zo/f74iIiKUlJQUcjV/XXx8vL773e/qww8/DMn3Nzk5Wenp6T7T+vfvb+7WCtXvq08++URvvPGGfv7zn5vTQvH9bS0Cir76wh86dKjWrl1rTvN6vVq7dq1cLlc7jiz4evfuLafT6VNrQ0ODNm/ebNbqcrlUX1+vqqoqs826devk9XqVmZl51sd8OoZhqLCwUC+//LLWrVun3r17+8wfOnSoIiMjferds2ePqqurferdsWOHz5dcRUWFHA5Hiy9PK/J6vXK73SFZ61VXXaUdO3Zo+/bt5iMjI0Njxowx/x9qNX/dkSNH9NFHHyk5OTkk398RI0a0uCzABx98oJ49e0oKve+rZkuWLFFiYqJyc3PNaaH4/rZaex+laxUvvPCCYbfbjaVLlxq7d+82xo8fb8THx/scLd1RHD582Hj33XeNd99915BkPProo8a7775rfPLJJ4ZhfHXaXnx8vPG3v/3N+Oc//2lcf/31Jz1tb8iQIcbmzZuNt99+2+jTp48lT9ubMGGCERcXZ7z11ls+p+8dO3bMbPPLX/7SSEtLM9atW2ds27bNcLlchsvlMuc3n7qXnZ1tbN++3Vi9erXRo0cPS56698ADDxjr16839u3bZ/zzn/80HnjgAcNmsxnl5eWGYYRWrafy9bN4DCO0ar7vvvuMt956y9i3b5/xj3/8w8jKyjLOPfdco66uzjCM0KrVML46dTwiIsJ46KGHjL179xrPPfecERsbazz77LNmm1D6vjKMr84QTUtLM6ZMmdJiXqi9v61FQPmaBQsWGGlpaUZUVJQxbNgwY9OmTe09pIC8+eabhqQWj/z8fMMwvjp1b9q0aUZSUpJht9uNq666ytizZ49PH1988YVx2223GV27djUcDodxxx13GIcPH26Hak7vZHVKMpYsWWK2+fLLL41f/epXxjnnnGPExsYaN954o3HgwAGffj7++GNj1KhRRkxMjHHuueca9913n+HxeM5yNd/uzjvvNHr27GlERUUZPXr0MK666ioznBhGaNV6Kt8MKKFU86233mokJycbUVFRxne+8x3j1ltv9bkmSCjV2uzVV181BgwYYNjtdqNfv37GU0895TM/lL6vDMMw1qxZY0hqUYNhhOb72xo2wzCMdtl0AwAAcAocgwIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACzn/wPxf1dd5VwPuAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# remove outliers from num_pages\n",
    "remove_outliers(df_mdl.num_pages,bins = 30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "579eb540",
   "metadata": {},
   "source": [
    "We end up with 11098 cases.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "41bac2eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "# split data\n",
    "train,test = train_test_split(df_mdl, test_size= 0.4,random_state=42)\n",
    "\n",
    "# train\n",
    "X_train = train.drop(\"average_rating\",axis=1)\n",
    "y_train = train.average_rating\n",
    "\n",
    "# test\n",
    "X_test = test.drop(\"average_rating\",axis=1)\n",
    "y_test = test.average_rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "6734a8c2",
   "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>num_pages</th>\n",
       "      <th>language_code_bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>752</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1532</th>\n",
       "      <td>168</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      num_pages  language_code_bin\n",
       "237         752                  1\n",
       "1532        168                  1"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "58f9db08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-23 {color: black;background-color: white;}#sk-container-id-23 pre{padding: 0;}#sk-container-id-23 div.sk-toggleable {background-color: white;}#sk-container-id-23 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-23 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-23 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-23 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-23 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-23 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-23 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-23 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-23 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-23 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-23 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-23 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-23 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-23 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-23 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-23 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-23 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-23 div.sk-item {position: relative;z-index: 1;}#sk-container-id-23 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-23 div.sk-item::before, #sk-container-id-23 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-23 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-23 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-23 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-23 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-23 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-23 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-23 div.sk-label-container {text-align: center;}#sk-container-id-23 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-23 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-23\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" checked><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# linear model \n",
    "reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "8271087b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 OLS Regression Results                                \n",
      "=======================================================================================\n",
      "Dep. Variable:         average_rating   R-squared (uncentered):                   0.946\n",
      "Model:                            OLS   Adj. R-squared (uncentered):              0.946\n",
      "Method:                 Least Squares   F-statistic:                          5.794e+04\n",
      "Date:                Wed, 29 Mar 2023   Prob (F-statistic):                        0.00\n",
      "Time:                        13:12:14   Log-Likelihood:                         -8926.9\n",
      "No. Observations:                6676   AIC:                                  1.786e+04\n",
      "Df Residuals:                    6674   BIC:                                  1.787e+04\n",
      "Df Model:                           2                                                  \n",
      "Covariance Type:            nonrobust                                                  \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "num_pages             0.0014   4.49e-05     30.883      0.000       0.001       0.001\n",
      "language_code_bin     3.4658      0.019    182.413      0.000       3.429       3.503\n",
      "==============================================================================\n",
      "Omnibus:                     3439.790   Durbin-Watson:                   1.933\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            28596.432\n",
      "Skew:                           2.332   Prob(JB):                         0.00\n",
      "Kurtosis:                      12.002   Cond. No.                         694.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
      "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# stats model package\n",
    "X = sm.add_constant(X_train)\n",
    "mdl = sm.OLS(y_train,X_train)\n",
    "fit = mdl.fit()\n",
    "# print model \n",
    "print(fit.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88449bdb",
   "metadata": {},
   "source": [
    "In a linear regression setting, ratings_count has a negative influence on average rating  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffd933a0",
   "metadata": {},
   "source": [
    "!!! Without outliers, we do not have multicollinearity in data in the above summary table. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "d1338305",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-24 {color: black;background-color: white;}#sk-container-id-24 pre{padding: 0;}#sk-container-id-24 div.sk-toggleable {background-color: white;}#sk-container-id-24 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-24 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-24 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-24 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-24 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-24 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-24 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-24 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-24 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-24 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-24 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-24 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-24 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-24 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-24 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-24 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-24 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-24 div.sk-item {position: relative;z-index: 1;}#sk-container-id-24 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-24 div.sk-item::before, #sk-container-id-24 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-24 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-24 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-24 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-24 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-24 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-24 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-24 div.sk-label-container {text-align: center;}#sk-container-id-24 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-24 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-24\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso(alpha=0.5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" checked><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso(alpha=0.5)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Lasso(alpha=0.5)"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lasso\n",
    "reg_lasso.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "48740eb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-25 {color: black;background-color: white;}#sk-container-id-25 pre{padding: 0;}#sk-container-id-25 div.sk-toggleable {background-color: white;}#sk-container-id-25 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-25 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-25 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-25 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-25 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-25 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-25 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-25 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-25 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-25 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-25 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-25 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-25 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-25 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-25 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-25 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-25 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-25 div.sk-item {position: relative;z-index: 1;}#sk-container-id-25 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-25 div.sk-item::before, #sk-container-id-25 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-25 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-25 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-25 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-25 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-25 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-25 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-25 div.sk-label-container {text-align: center;}#sk-container-id-25 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-25 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-25\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge(alpha=0.01)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" checked><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge(alpha=0.01)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Ridge(alpha=0.01)"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ridge\n",
    "reg_ridge.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "639fda74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-26 {color: black;background-color: white;}#sk-container-id-26 pre{padding: 0;}#sk-container-id-26 div.sk-toggleable {background-color: white;}#sk-container-id-26 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-26 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-26 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-26 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-26 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-26 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-26 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-26 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-26 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-26 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-26 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-26 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-26 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-26 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-26 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-26 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-26 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-26 div.sk-item {position: relative;z-index: 1;}#sk-container-id-26 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-26 div.sk-item::before, #sk-container-id-26 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-26 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-26 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-26 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-26 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-26 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-26 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-26 div.sk-label-container {text-align: center;}#sk-container-id-26 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-26 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-26\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" checked><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(random_state=42)"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# random forest\n",
    "rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "efbab8d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-27 {color: black;background-color: white;}#sk-container-id-27 pre{padding: 0;}#sk-container-id-27 div.sk-toggleable {background-color: white;}#sk-container-id-27 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-27 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-27 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-27 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-27 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-27 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-27 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-27 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-27 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-27 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-27 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-27 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-27 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-27 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-27 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-27 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-27 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-27 div.sk-item {position: relative;z-index: 1;}#sk-container-id-27 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-27 div.sk-item::before, #sk-container-id-27 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-27 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-27 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-27 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-27 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-27 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-27 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-27 div.sk-label-container {text-align: center;}#sk-container-id-27 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-27 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-27\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;linearsvr&#x27;,\n",
       "                 LinearSVR(max_iter=100000, random_state=0, tol=1e-05))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;linearsvr&#x27;,\n",
       "                 LinearSVR(max_iter=100000, random_state=0, tol=1e-05))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVR</label><div class=\"sk-toggleable__content\"><pre>LinearSVR(max_iter=100000, random_state=0, tol=1e-05)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('linearsvr',\n",
       "                 LinearSVR(max_iter=100000, random_state=0, tol=1e-05))])"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# svm\n",
    "svm_regr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "973c1d0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-28 {color: black;background-color: white;}#sk-container-id-28 pre{padding: 0;}#sk-container-id-28 div.sk-toggleable {background-color: white;}#sk-container-id-28 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-28 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-28 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-28 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-28 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-28 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-28 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-28 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-28 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-28 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-28 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-28 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-28 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-28 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-28 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-28 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-28 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-28 div.sk-item {position: relative;z-index: 1;}#sk-container-id-28 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-28 div.sk-item::before, #sk-container-id-28 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-28 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-28 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-28 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-28 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-28 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-28 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-28 div.sk-label-container {text-align: center;}#sk-container-id-28 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-28 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-28\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" checked><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor(random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingRegressor(random_state=0)"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbm = GradientBoostingRegressor(random_state=42)\n",
    "gbm.fit(X_train, y_train)\n",
    "GradientBoostingRegressor(random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3975969",
   "metadata": {},
   "source": [
    "7 Models new evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "835ca536",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   rmse\n",
      "ridge model    0.346476\n",
      "ridge CV       0.346476\n",
      "linear reg     0.346476\n",
      "lasso model    0.346712\n",
      "linear svm     0.347041\n",
      "gbm            0.348566\n",
      "random forest  0.370773\n"
     ]
    }
   ],
   "source": [
    "model_cross_val(models,X_validation=X_test, y_validation=y_test, cv = 10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f613040f",
   "metadata": {},
   "source": [
    "== Regression application : conclusion  === \n",
    "* The models performance is better without outliers.  \n",
    "* Linear models are better than non-linear ones in general, but the difference is not so big than that. \n",
    "* The candidate models could be gbm, Lasso and ridge with CV, i.e. Regularized linear regression models."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f15d8760",
   "metadata": {},
   "source": [
    "==== model evaluation : hyper parmeters search ==="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "62b16dc7",
   "metadata": {},
   "outputs": [],
   "source": [
    " # IMPORT validation curve \n",
    "from sklearn.model_selection import validation_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "9f26c687",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 0.9,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'criterion': 'friedman_mse',\n",
       " 'init': None,\n",
       " 'learning_rate': 0.1,\n",
       " 'loss': 'squared_error',\n",
       " 'max_depth': 3,\n",
       " 'max_features': None,\n",
       " 'max_leaf_nodes': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_iter_no_change': None,\n",
       " 'random_state': 42,\n",
       " 'subsample': 1.0,\n",
       " 'tol': 0.0001,\n",
       " 'validation_fraction': 0.1,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gbm hyper params search : gbm hyper-params \n",
    "gbm.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "6a76b307",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/m22/conda/envs/env_pylab/lib/python3.9/site-packages/sklearn/model_selection/_search.py:961: RuntimeWarning: overflow encountered in square\n",
      "  (array - array_means[:, np.newaxis]) ** 2, axis=1, weights=weights\n"
     ]
    }
   ],
   "source": [
    "# search strategy : halving random search\n",
    "#from sklearn.experimental import enable_halving_search_cv  # noqa\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "#from scipy.stats import uniform\n",
    "\n",
    "# hyper-params search\n",
    "distributions = dict(alpha=[0.1,0.2,0.3,0.4,0.5],\n",
    "                      learning_rate=[0.001,0.1,1,10],\n",
    "                      max_depth = [1,2,3,4])\n",
    "\n",
    "gbm_hp = RandomizedSearchCV(gbm, distributions,\n",
    "                            scoring = [\"max_error\",\"r2\"],\n",
    "                            random_state=0,\n",
    "                           refit = \"max_error\")\n",
    "search_gbm = gbm_hp.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "a9fcedfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-29 {color: black;background-color: white;}#sk-container-id-29 pre{padding: 0;}#sk-container-id-29 div.sk-toggleable {background-color: white;}#sk-container-id-29 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-29 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-29 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-29 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-29 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-29 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-29 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-29 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-29 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-29 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-29 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-29 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-29 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-29 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-29 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-29 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-29 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-29 div.sk-item {position: relative;z-index: 1;}#sk-container-id-29 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-29 div.sk-item::before, #sk-container-id-29 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-29 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-29 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-29 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-29 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-29 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-29 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-29 div.sk-label-container {text-align: center;}#sk-container-id-29 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-29 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-29\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor(alpha=0.5, max_depth=1, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" checked><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor(alpha=0.5, max_depth=1, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.5, max_depth=1, random_state=42)"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_gbm.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "0cb5d0b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#gbm evaluation\n",
    "gbm_hp2 = search_gbm.best_estimator_\n",
    "                   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "51d07063",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 0.5,\n",
       " 'copy_X': True,\n",
       " 'fit_intercept': True,\n",
       " 'max_iter': 1000,\n",
       " 'positive': False,\n",
       " 'precompute': False,\n",
       " 'random_state': None,\n",
       " 'selection': 'cyclic',\n",
       " 'tol': 0.0001,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lasso hyper-params  list\n",
    "reg_lasso.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "ce23f39e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/m22/conda/envs/env_pylab/lib/python3.9/site-packages/sklearn/model_selection/_search.py:305: UserWarning: The total space of parameters 5 is smaller than n_iter=10. Running 5 iterations. For exhaustive searches, use GridSearchCV.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# lasso evaluation\n",
    "distributions = dict(alpha=[0.1,0.2,0.3,0.4,0.5])\n",
    "\n",
    "# hyper-params search \n",
    "lasso_hp = RandomizedSearchCV(reg_lasso, \n",
    "                              distributions, \n",
    "                              scoring = [\"max_error\",\"r2\"],\n",
    "                              refit = \"max_error\",\n",
    "                              random_state=0)\n",
    "search_lasso = lasso_hp.fit(X_train, y_train)\n",
    "lasso_hp2 = search_lasso.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "6fae047d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-30 {color: black;background-color: white;}#sk-container-id-30 pre{padding: 0;}#sk-container-id-30 div.sk-toggleable {background-color: white;}#sk-container-id-30 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-30 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-30 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-30 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-30 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-30 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-30 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-30 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-30 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-30 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-30 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-30 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-30 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-30 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-30 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-30 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-30 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-30 div.sk-item {position: relative;z-index: 1;}#sk-container-id-30 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-30 div.sk-item::before, #sk-container-id-30 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-30 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-30 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-30 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-30 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-30 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-30 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-30 div.sk-label-container {text-align: center;}#sk-container-id-30 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-30 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-30\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso(alpha=0.1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" checked><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso(alpha=0.1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Lasso(alpha=0.1)"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# best lasso estimator \n",
    "lasso_hp2 "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5d69f62c",
   "metadata": {},
   "source": [
    "This lasso model/estimator  alpha is not different from the initial one. We hypothesize no amelioration in performance with respect to gbm estimator. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "b2ee09ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           rmse\n",
      "gbm    0.021864\n",
      "lasso  0.287826\n"
     ]
    }
   ],
   "source": [
    "#  model evaluation\n",
    "# models dictionary\n",
    "models_hp = {\"gbm\" : gbm_hp2, \n",
    "             \"lasso\" :lasso_hp2}\n",
    "\n",
    "# models evaluation \n",
    "model_cross_val(models_hp,train0,y_train0,cv = 10)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "6cbdf10d",
   "metadata": {},
   "source": [
    "Results :\n",
    "We evaluated our models based on rmse. \n",
    "gbm initial rmse was 0.08 and lasso 0.85. After CV in model evaluation, we choose gbm and lasso. In validation step , they present a rmse , 0.021 and 0.34 respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "1215cba3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         rmse\n",
      "gbm  0.028194\n"
     ]
    }
   ],
   "source": [
    "# model validation : use test set\n",
    "model_cross_val({\"gbm\":gbm_hp2},test,y_test,cv = 2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3184af7c",
   "metadata": {},
   "source": [
    "gbm presents a rmse of 0.028 with test set."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "822a8c09",
   "metadata": {},
   "source": [
    "Conclusion :  \n",
    "Following, hyper-parameters search, gbm shows a far better rmse of 0.021 and lasso a rmse of 0.29.\n",
    "gbm performance improved with hyper-parameters search, while lasso did not. Therefore for this application and in a regression setting, we would recommend gbm as final model.\n",
    "\n",
    "In test step (model evaluation) gbm yields 0.028, which is almost similar to the validation rmse. However, we would recommend further, hyper-parameters search and investigation to fine tune this model again with a large random parameters set or grid search strategy instead."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b633ec29",
   "metadata": {},
   "source": [
    "Suggestion :\n",
    "We would suggest to further perform more hyper-params search to control any overfitting issue with gbm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "1a4a42ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": "\n  var msg = new SpeechSynthesisUtterance();\n  msg.text = \"End of file...\";\n  window.speechSynthesis.speak(msg);\n",
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#vocal message \n",
    "display(Javascript(\"\"\"\n",
    "  var msg = new SpeechSynthesisUtterance();\n",
    "  msg.text = \"End of file...\";\n",
    "  window.speechSynthesis.speak(msg);\n",
    "\"\"\"))"
   ]
  }
 ],
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  "celltoolbar": "Tags",
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   "display_name": "env_pylab",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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  "vscode": {
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