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": 553,
   "id": "4f6b1fb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!conda env list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "id": "42ab7ab5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# activate environment \n",
    "#!conda init bash\n",
    "#!conda activate Project1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "id": "ea7c92fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "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": 557,
   "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": 558,
   "id": "54080887",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\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>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": 558,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "id": "79cd1ae4",
   "metadata": {},
   "outputs": [
    {
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       "    .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>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": 559,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "id": "9f3eb044",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11127, 12)"
      ]
     },
     "execution_count": 560,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df shape \n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 561,
   "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": 561,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# columns names\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "id": "91609d11",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <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": 562,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describe numeric variables\n",
    "df.describe(exclude=\"object\").T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 563,
   "id": "e0043404",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 563,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describe non-numeric  variables\n",
    "df.describe(include=\"object\").T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f38cba68",
   "metadata": {},
   "source": [
    "There is no duplicate ID in data as isbn is 11127 the datat 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": 564,
   "id": "5a58c05c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/dr/qtbb25gd2pl7ssnx51bqxv6r0000gp/T/ipykernel_1136/1680810485.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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no date issue\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": 565,
   "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": 565,
     "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": 566,
   "id": "cb646068",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 566,
     "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": 567,
   "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": 568,
   "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": 568,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# numerical summary : average_rating \n",
    "df.average_rating.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 569,
   "id": "5a1b9a43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 569,
     "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": 570,
   "id": "662fe9af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 570,
     "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": 571,
   "id": "2a247bb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 571,
     "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": 572,
   "id": "5a8159b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 572,
     "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": 573,
   "id": "4e73733d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(104, 13)"
      ]
     },
     "execution_count": 573,
     "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": 574,
   "id": "f8da9b2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31, 13)"
      ]
     },
     "execution_count": 574,
     "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": 575,
   "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": 575,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# average ratings sumary statistics \n",
    "df.average_rating.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 576,
   "id": "dc497d64",
   "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",
       "    </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": 576,
     "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"
   ]
  },
  {
   "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": 577,
   "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": 577,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#numerical summary num_pages\n",
    "df.num_pages.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 578,
   "id": "b27c9e61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 578,
     "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": 579,
   "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": 579,
     "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": 580,
   "id": "b49a1477",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10340    Twilight (Twilight  #1)\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 580,
     "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": 581,
   "id": "d9129cfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 581,
     "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": 582,
   "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": 582,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# numerical summary publication Date\n",
    "df.publication_Date.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 583,
   "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": 583,
     "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": 584,
   "id": "e2c09838",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 584,
     "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)"
   ]
  },
  {
   "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": 585,
   "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": 585,
     "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": 586,
   "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": 586,
     "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": 587,
   "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": 587,
     "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": 588,
   "id": "2b2ec4c3",
   "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",
       "    </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": 588,
     "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. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac548d06",
   "metadata": {},
   "source": [
    "We are not going to do EDA on bookID, isbn , isbn13. \n",
    "The first 3 are identifier 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": 589,
   "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": 590,
   "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": 590,
     "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": 591,
   "id": "73705dac",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mpaga/opt/anaconda3/lib/python3.9/site-packages/pandas/plotting/_matplotlib/core.py:1114: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  scatter = ax.scatter(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='num_pages', ylabel='average_rating'>"
      ]
     },
     "execution_count": 591,
     "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": 592,
   "id": "814c2a4b",
   "metadata": {
    "tags": [
     "correlation_coef"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15"
      ]
     },
     "execution_count": 592,
     "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": 593,
   "id": "0324b724",
   "metadata": {
    "tags": [
     "2D_plot"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='text_reviews_count', ylabel='average_rating'>"
      ]
     },
     "execution_count": 593,
     "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": 594,
   "id": "34dd7701",
   "metadata": {
    "tags": [
     "corr_coef"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03"
      ]
     },
     "execution_count": 594,
     "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": 595,
   "id": "44c1d079",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='ratings_count', ylabel='average_rating'>"
      ]
     },
     "execution_count": 595,
     "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": 596,
   "id": "112b2b91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04"
      ]
     },
     "execution_count": 596,
     "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. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47d91f8d",
   "metadata": {},
   "source": [
    "what is the average rating of books written in english ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 597,
   "id": "0cfb0ce8",
   "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",
       "    </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": 597,
     "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": 598,
   "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": 598,
     "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": 599,
   "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": 599,
     "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": 600,
   "id": "0edc2361",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/dr/qtbb25gd2pl7ssnx51bqxv6r0000gp/T/ipykernel_1136/744164174.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": 601,
   "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": 601,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# head average rating \n",
    "df_qualitative.av_cat.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 602,
   "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": 602,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tail av rating \n",
    "df_qualitative.av_cat.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 603,
   "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": 603,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# proportion of classes in the data \n",
    "round(df_qualitative.av_cat.value_counts()/ len(df1.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": 604,
   "id": "7d582e0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 604,
     "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": 605,
   "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": 606,
   "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": 607,
   "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": 608,
   "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": 609,
   "id": "01aefb2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 609,
     "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": 610,
   "id": "55e71c99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=0.01)"
      ]
     },
     "execution_count": 610,
     "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": 611,
   "id": "0d2dd4dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RidgeCV(alphas=array([0.001, 0.01 ]), cv=5)"
      ]
     },
     "execution_count": 611,
     "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": 612,
   "id": "06e11c43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=0.5)"
      ]
     },
     "execution_count": 612,
     "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": 613,
   "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": 614,
   "id": "f63624c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(random_state=42)"
      ]
     },
     "execution_count": 614,
     "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": 615,
   "id": "c974678b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('linearsvr',\n",
       "                 LinearSVR(max_iter=100000, random_state=0, tol=1e-05))])"
      ]
     },
     "execution_count": 615,
     "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": 616,
   "id": "7b2602a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(random_state=0)"
      ]
     },
     "execution_count": 616,
     "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": 617,
   "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": 618,
   "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": 619,
   "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": 620,
   "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": 621,
   "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": 622,
   "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": 622,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mdl.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 623,
   "id": "d0f3d73e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 623,
     "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": 624,
   "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": 624,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ocrqaZcuW+ZyYp7a2VklJScrIyAjoa8Yul0vp6elB+bXL9qD38Os92PtOdW5v87q2CEP3pzXrnt0Rcjd37omkvmuvs/sPT5/pef/iiy9UW1ur6OhoxcTEdPqJtrqKYRg6evSoevXqFTI9+ctsvTc3N+vo0aNKSEjQiBEjWo2p5QiIPwIKKP/v//0/LV26VP/xH/8h6fgFjA4cOKCVK1dq9uzZ3gsmVVZWasCAAd71qqqqvLMqdrtdjY2Nqq6u9plFqaqq0tixY0+6XZvN5j3F7ndZrdaAd75tWSdU0Hv49R6sfQd6/pKTPkazpUMex19m+nc+1fM+cOBARUZG+pyuPRQYhqGGhoaTXk8n1Jmx94iICA0cOLDVBQ6lwN4nAQWU+vr6VieCiYyM9E7hJCcny263y+VyaeTIkZKkxsZGFRcX6+GHH5YkjRo1SlarVS6XS5mZmZKkiooK7d27V7m5uYEMBwAQgJbDPP379w/6z9t8l8fj0c6dO3XllVeaKih2BTP23qNHjw45aVxAAWX69Ol68MEHNXjwYF1wwQV65513tHr1at1yyy2Sjr/4s7OzlZOTo5SUFKWkpCgnJ0cxMTHeayPEx8dr7ty5WrRokRISEtS3b18tXrxYw4cP936rBwDQeSIjI0PqlA6RkZE6duyYoqOjTfNHuquEcu8BBZT8/Hzdc889mj9/vqqqquRwODRv3jzde++93polS5aooaFB8+fPV3V1tUaPHq2ioiKfD2WtWbNGUVFRyszMVENDgyZMmKCNGzeG1BsGAAC0XUABJTY2Vnl5ed6vFZ+MxWKR0+mU0+k8ZU10dLTy8/OVn58fyOYBAECY6N6rKgEAAJwEAQUAAJgOAQUAAJgOAQUAAJhOQB+SBRBehiz9U5vX/eyhqR04EgDhhhkUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOlHdPQAAnWvI0j919xAAIGABzaAMGTJEFoul1c/tt98uSTIMQ06nUw6HQz179tT48eO1b98+n8dwu91asGCB+vXrp169emnGjBk6dOhQx3UEAACCXkABpbS0VBUVFd4fl8slSbr++uslSbm5uVq9erXWrl2r0tJS2e12paenq66uzvsY2dnZ2rJliwoLC7Vr1y4dOXJE06ZNU1NTUwe2BQAAgllAAeXss8+W3W73/rz88ss677zzNG7cOBmGoby8PC1fvlwzZ85UamqqNm3apPr6ehUUFEiSampqtH79eq1atUoTJ07UyJEjtXnzZu3Zs0c7duzolAYBAEDwafNnUBobG7V582YtXLhQFotFn376qSorK5WRkeGtsdlsGjdunEpKSjRv3jyVlZXJ4/H41DgcDqWmpqqkpESTJk066bbcbrfcbrf3dm1trSTJ4/HI4/H4Nd6WOn/rQwm9h1/v3+3bFml06xjaoj1jtkUYPr+7ihleY7zew6tvKfh6D2ScFsMw2vQu/t///V9lZWXp4MGDcjgcKikp0WWXXabDhw/L4XB462677TYdOHBA27dvV0FBgebMmeMTNiQpIyNDycnJevzxx0+6LafTqRUrVrRaXlBQoJiYmLYMHwAAdLH6+nplZWWppqZGcXFxp61t8wzK+vXrNXnyZJ8wIkkWi8XntmEYrZad6Ew1y5Yt08KFC723a2trlZSUpIyMjDM22MLj8cjlcik9PV1Wq9WvdUIFvYdf79/te+SDr3bLGPY6Tz4j6o9U5/Y2r2uLMHR/WrPu2R0hd/Pp9z0dqT39dhRe7+HVtxR8vbccAfFHmwLKgQMHtGPHDr3wwgveZXa7XZJUWVmpAQMGeJdXVVUpMTHRW9PY2Kjq6mr16dPHp2bs2LGn3J7NZpPNZmu13Gq1BvyEtGWdUEHv4de71WqVu6nr/kifuO226ogxu5stXdq7mV5f4fx6D8e+peDpPZAxtulEbRs2bFD//v01depU77Lk5GTZ7XbvN3uk459TKS4u9oaPUaNGyWq1+tRUVFRo7969pw0oAAAgvAQ8g9Lc3KwNGzZo9uzZior6v9UtFouys7OVk5OjlJQUpaSkKCcnRzExMcrKypIkxcfHa+7cuVq0aJESEhLUt29fLV68WMOHD9fEiRM7risAABDUAg4oO3bs0MGDB3XLLbe0um/JkiVqaGjQ/PnzVV1drdGjR6uoqEixsbHemjVr1igqKkqZmZlqaGjQhAkTtHHjRkVGRravEwAAEDICDigZGRk61Rd/LBaLnE6nnE7nKdePjo5Wfn6+8vPzA900AJhWey4p8NlDU89cBIQZLhYIAABMh4ACAABMh4ACAABMh4ACAABMh4ACAABMh4ACAABMh4ACAABMh4ACAABMh4ACAABMp01XMwaAM2nPmVUBgBkUAABgOgQUAABgOhziAYJAoIdLbJGGci+RUp3bJVk6Z1AA0ImYQQEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKYTcEA5fPiwfvKTnyghIUExMTG66KKLVFZW5r3fMAw5nU45HA717NlT48eP1759+3wew+12a8GCBerXr5969eqlGTNm6NChQ+3vBgAAhISAAkp1dbUuu+wyWa1WvfLKK3r//fe1atUqnXXWWd6a3NxcrV69WmvXrlVpaansdrvS09NVV1fnrcnOztaWLVtUWFioXbt26ciRI5o2bZqampo6rDEAABC8ogIpfvjhh5WUlKQNGzZ4lw0ZMsT734ZhKC8vT8uXL9fMmTMlSZs2bVJiYqIKCgo0b9481dTUaP369XryySc1ceJESdLmzZuVlJSkHTt2aNKkSR3QFgAACGYBBZSXXnpJkyZN0vXXX6/i4mINHDhQ8+fP109/+lNJUnl5uSorK5WRkeFdx2azady4cSopKdG8efNUVlYmj8fjU+NwOJSamqqSkpKTBhS32y232+29XVtbK0nyeDzyeDx+jb2lzt/6UELvwd+7LdIIrD7C8PkdToKx9456fYbK6z1Q4dq3FHy9BzJOi2EYfr+Lo6OjJUkLFy7U9ddfr7/97W/Kzs7W448/rlmzZqmkpESXXXaZDh8+LIfD4V3vtttu04EDB7R9+3YVFBRozpw5PoFDkjIyMpScnKzHH3+81XadTqdWrFjRanlBQYFiYmL8bhYAAHSf+vp6ZWVlqaamRnFxcaetDWgGpbm5WWlpacrJyZEkjRw5Uvv27dNjjz2mWbNmeessFovPeoZhtFp2otPVLFu2TAsXLvTerq2tVVJSkjIyMs7YYAuPxyOXy6X09HRZrVa/1gkV9B78vac6twdUb4swdH9as+7ZHSF38+nfe6EmGHvf6+yYQ9uh8noPVLj2LQVf7y1HQPwRUEAZMGCAzj//fJ9lP/jBD/T8889Lkux2uySpsrJSAwYM8NZUVVUpMTHRW9PY2Kjq6mr16dPHp2bs2LEn3a7NZpPNZmu13Gq1BvyEtGWdUEHvwdu7u6ltf2jdzZY2rxvsgqn3jn5tBvvrva3CtW8peHoPZIwBfYvnsssu0/79+32WffjhhzrnnHMkScnJybLb7XK5XN77GxsbVVxc7A0fo0aNktVq9ampqKjQ3r17TxlQAABAeAloBuXOO+/U2LFjlZOTo8zMTP3tb3/TunXrtG7dOknHD+1kZ2crJydHKSkpSklJUU5OjmJiYpSVlSVJio+P19y5c7Vo0SIlJCSob9++Wrx4sYYPH+79Vg8AAAhvAQWUiy++WFu2bNGyZcv0y1/+UsnJycrLy9ONN97orVmyZIkaGho0f/58VVdXa/To0SoqKlJsbKy3Zs2aNYqKilJmZqYaGho0YcIEbdy4UZGRkR3XGQAACFoBBRRJmjZtmqZNm3bK+y0Wi5xOp5xO5ylroqOjlZ+fr/z8/EA3DwAAwgDX4gEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKYT1d0DAIBwN2Tpn9q87mcPTe3AkQDmwQwKAAAwHQIKAAAwHQIKAAAwHQIKAAAwHQIKAAAwHQIKAAAwHQIKAAAwnYDOg+J0OrVixQqfZYmJiaqsrJQkGYahFStWaN26daqurtbo0aP161//WhdccIG33u12a/HixXr66afV0NCgCRMm6NFHH9WgQYM6oB0ACC/fPYeKLdJQ7iVSqnO73E0Wv9bnPCowq4BnUC644AJVVFR4f/bs2eO9Lzc3V6tXr9batWtVWloqu92u9PR01dXVeWuys7O1ZcsWFRYWateuXTpy5IimTZumpqamjukIAAAEvYDPJBsVFSW73d5quWEYysvL0/LlyzVz5kxJ0qZNm5SYmKiCggLNmzdPNTU1Wr9+vZ588klNnDhRkrR582YlJSVpx44dmjRpUjvbAQAAoSDggPLRRx/J4XDIZrNp9OjRysnJ0bnnnqvy8nJVVlYqIyPDW2uz2TRu3DiVlJRo3rx5Kisrk8fj8alxOBxKTU1VSUnJKQOK2+2W2+323q6trZUkeTweeTwev8bdUudvfSih9+Dv3RZpBFYfYfj8Dif0Hljvwf7ekELnfd4WwdZ7IOO0GIbh9yv5lVdeUX19vYYOHaovvvhCDzzwgP7xj39o37592r9/vy677DIdPnxYDofDu85tt92mAwcOaPv27SooKNCcOXN8woYkZWRkKDk5WY8//vhJt3uyz75IUkFBgWJiYvwdPgAA6Eb19fXKyspSTU2N4uLiTlsb0AzK5MmTvf89fPhwjRkzRuedd542bdqkSy+9VJJksfh+MMswjFbLTnSmmmXLlmnhwoXe27W1tUpKSlJGRsYZG2zh8XjkcrmUnp4uq9Xq1zqhgt6Dv/dU5/aA6m0Rhu5Pa9Y9uyPkbvbvw5Khgt4D632vM/gPrYfK+7wtgq33liMg/mjX1Yx79eql4cOH66OPPtJ1110nSaqsrNSAAQO8NVVVVUpMTJQk2e12NTY2qrq6Wn369PGpGTt27Cm3Y7PZZLPZWi23Wq0BPyFtWSdU0Hvw9u7vNzJarddsafO6wY7e/es9mN8XJwr293l7BEvvgYyxXedBcbvd+uCDDzRgwAAlJyfLbrfL5XJ5729sbFRxcbE3fIwaNUpWq9WnpqKiQnv37j1tQAEAAOEloBmUxYsXa/r06Ro8eLCqqqr0wAMPqLa2VrNnz5bFYlF2drZycnKUkpKilJQU5eTkKCYmRllZWZKk+Ph4zZ07V4sWLVJCQoL69u2rxYsXa/jw4d5v9QAAAAQUUA4dOqQf//jH+uqrr3T22Wfr0ksv1VtvvaVzzjlHkrRkyRI1NDRo/vz53hO1FRUVKTY21vsYa9asUVRUlDIzM70natu4caMiIyM7tjPAZL57Qi0AwOkFFFAKCwtPe7/FYpHT6ZTT6TxlTXR0tPLz85Wfnx/IpgEAQBjhWjwAAMB0CCgAAMB0CCgAAMB02nUeFABAcGvPh7e5EjI6EzMoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdNoVUFauXCmLxaLs7GzvMsMw5HQ65XA41LNnT40fP1779u3zWc/tdmvBggXq16+fevXqpRkzZujQoUPtGQoAAAghbQ4opaWlWrdunUaMGOGzPDc3V6tXr9batWtVWloqu92u9PR01dXVeWuys7O1ZcsWFRYWateuXTpy5IimTZumpqamtncCAABCRpsCypEjR3TjjTfqt7/9rfr06eNdbhiG8vLytHz5cs2cOVOpqanatGmT6uvrVVBQIEmqqanR+vXrtWrVKk2cOFEjR47U5s2btWfPHu3YsaNjugIAAEGtTQHl9ttv19SpUzVx4kSf5eXl5aqsrFRGRoZ3mc1m07hx41RSUiJJKisrk8fj8alxOBxKTU311gAAgPAWFegKhYWFevvtt1VaWtrqvsrKSklSYmKiz/LExEQdOHDAW9OjRw+fmZeWmpb1T+R2u+V2u723a2trJUkej0cej8evcbfU+VsfSujdHL3bIo2u21aE4fM7nNB71/VuhveVZK73eVcLtt4DGWdAAeXzzz/XHXfcoaKiIkVHR5+yzmKx+Nw2DKPVshOdrmblypVasWJFq+VFRUWKiYnxY+T/x+VyBVQfSui9e+Ve0vXbvD+tues3ahL03vm2bt3aJdvxlxne590lWHqvr6/3uzaggFJWVqaqqiqNGjXKu6ypqUk7d+7U2rVrtX//fknHZ0kGDBjgramqqvLOqtjtdjU2Nqq6utpnFqWqqkpjx4496XaXLVumhQsXem/X1tYqKSlJGRkZiouL82vsHo9HLpdL6enpslqt/jcdAujdHL2nOrd32bZsEYbuT2vWPbsj5G4+/f8chBp677re9zondfo2/GGm93lXC7beW46A+COggDJhwgTt2bPHZ9mcOXM0bNgw3XXXXTr33HNlt9vlcrk0cuRISVJjY6OKi4v18MMPS5JGjRolq9Uql8ulzMxMSVJFRYX27t2r3Nzck27XZrPJZrO1Wm61WgN+QtqyTqig9+7t3d3U9X8s3c2WbtmuGdB75/fe3e+pE5nhfd5dgqX3QMYYUECJjY1Vamqqz7JevXopISHBuzw7O1s5OTlKSUlRSkqKcnJyFBMTo6ysLElSfHy85s6dq0WLFikhIUF9+/bV4sWLNXz48FYfugUAAOEp4A/JnsmSJUvU0NCg+fPnq7q6WqNHj1ZRUZFiY2O9NWvWrFFUVJQyMzPV0NCgCRMmaOPGjYqMjOzo4QAAgCDU7oDy2muv+dy2WCxyOp1yOp2nXCc6Olr5+fnKz89v7+YBAEAI4lo8AADAdAgoAADAdDr8MyhAKBuy9E/dPQQACAvMoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANOJ6u4BAACC05Clf2rzup89NLUDR4JQxAwKAAAwHWZQEHba8399AICuwQwKAAAwHQIKAAAwHQIKAAAwHQIKAAAwHQIKAAAwHQIKAAAwnYACymOPPaYRI0YoLi5OcXFxGjNmjF555RXv/YZhyOl0yuFwqGfPnho/frz27dvn8xhut1sLFixQv3791KtXL82YMUOHDh3qmG4AAEBICCigDBo0SA899JB2796t3bt36+qrr9a1117rDSG5ublavXq11q5dq9LSUtntdqWnp6uurs77GNnZ2dqyZYsKCwu1a9cuHTlyRNOmTVNTU1PHdgYAAIJWQAFl+vTpmjJlioYOHaqhQ4fqwQcfVO/evfXWW2/JMAzl5eVp+fLlmjlzplJTU7Vp0ybV19eroKBAklRTU6P169dr1apVmjhxokaOHKnNmzdrz5492rFjR6c0CAAAgk+bzyTb1NSkZ599VkePHtWYMWNUXl6uyspKZWRkeGtsNpvGjRunkpISzZs3T2VlZfJ4PD41DodDqampKikp0aRJk066LbfbLbfb7b1dW1srSfJ4PPJ4PH6Nt6XO3/pQQu++vdsije4aTpexRRg+v8MJvQdH7x25P2IfFzy9BzLOgAPKnj17NGbMGH377bfq3bu3tmzZovPPP18lJSWSpMTERJ/6xMREHThwQJJUWVmpHj16qE+fPq1qKisrT7nNlStXasWKFa2WFxUVKSYmJqDxu1yugOpDCb0fl3tJNw6ki92f1tzdQ+g29G5uW7du7fDHZB9nfvX19X7XBhxQvv/97+vdd9/Vv/71Lz3//POaPXu2iouLvfdbLBafesMwWi070Zlqli1bpoULF3pv19bWKikpSRkZGYqLi/Nr3B6PRy6XS+np6bJarX6tEyro3bf3VOf2bh5V57NFGLo/rVn37I6Qu/n0779QQ+/B0fte58lnzNuCfVzw9N5yBMQfAQeUHj166Hvf+54kKS0tTaWlpfqf//kf3XXXXZKOz5IMGDDAW19VVeWdVbHb7WpsbFR1dbXPLEpVVZXGjh17ym3abDbZbLZWy61Wa8BPSFvWCRX0frx3d5O5d9wdyd1sCat+v4vezd17Z+yL2MeZv/dAxtjuqxkbhiG3263k5GTZ7Xa5XC6NHDlSktTY2Kji4mI9/PDDkqRRo0bJarXK5XIpMzNTklRRUaG9e/cqNze3vUNBGPH3isS2SEO5lxyfNTH7DhsA8H8CCih33323Jk+erKSkJNXV1amwsFCvvfaatm3bJovFouzsbOXk5CglJUUpKSnKyclRTEyMsrKyJEnx8fGaO3euFi1apISEBPXt21eLFy/W8OHDNXHixE5pEAAABJ+AAsoXX3yhm266SRUVFYqPj9eIESO0bds2paenS5KWLFmihoYGzZ8/X9XV1Ro9erSKiooUGxvrfYw1a9YoKipKmZmZamho0IQJE7Rx40ZFRkZ2bGcAACBoBRRQ1q9ff9r7LRaLnE6nnE7nKWuio6OVn5+v/Pz8QDYNAADCCNfiAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAAphPQ1YwBAOgIQ5b+qc3rfvbQ1A4cCcyKGRQAAGA6BBQAAGA6BBQAAGA6BBQAAGA6BBQAAGA6BBQAAGA6BBQAAGA6BBQAAGA6BBQAAGA6BBQAAGA6BBQAAGA6BBQAAGA6BBQAAGA6XM0Y3aY9VzMFAIQ2ZlAAAIDpEFAAAIDpEFAAAIDpBBRQVq5cqYsvvlixsbHq37+/rrvuOu3fv9+nxjAMOZ1OORwO9ezZU+PHj9e+fft8atxutxYsWKB+/fqpV69emjFjhg4dOtT+bgAAQEgIKKAUFxfr9ttv11tvvSWXy6Vjx44pIyNDR48e9dbk5uZq9erVWrt2rUpLS2W325Wenq66ujpvTXZ2trZs2aLCwkLt2rVLR44c0bRp09TU1NRxnQEAgKAV0Ld4tm3b5nN7w4YN6t+/v8rKynTllVfKMAzl5eVp+fLlmjlzpiRp06ZNSkxMVEFBgebNm6eamhqtX79eTz75pCZOnChJ2rx5s5KSkrRjxw5NmjSpg1oDAADBql1fM66pqZEk9e3bV5JUXl6uyspKZWRkeGtsNpvGjRunkpISzZs3T2VlZfJ4PD41DodDqampKikpOWlAcbvdcrvd3tu1tbWSJI/HI4/H49dYW+r8rQ8lZu3dFml0/jYiDJ/f4SJc+5bo/bu/Q9WJ+zKz7uO6QrD1Hsg42xxQDMPQwoULdfnllys1NVWSVFlZKUlKTEz0qU1MTNSBAwe8NT169FCfPn1a1bSsf6KVK1dqxYoVrZYXFRUpJiYmoHG7XK6A6kOJ2XrPvaTrtnV/WnPXbcxEwrVvid5D2datW0+63Gz7uK4ULL3X19f7XdvmgPLzn/9c7733nnbt2tXqPovF4nPbMIxWy050upply5Zp4cKF3tu1tbVKSkpSRkaG4uLi/Bqvx+ORy+VSenq6rFarX+uECrP2nurc3unbsEUYuj+tWffsjpC7+fSvwVASrn1L9B4Ove91+s60m3Uf1xWCrfeWIyD+aFNAWbBggV566SXt3LlTgwYN8i632+2Sjs+SDBgwwLu8qqrKO6tit9vV2Nio6upqn1mUqqoqjR079qTbs9lsstlsrZZbrdaAn5C2rBMqzNa7u6nrdqDuZkuXbs8swrVvid5DufdT7cfMto/rSsHSeyBjDOhbPIZh6Oc//7leeOEFvfrqq0pOTva5Pzk5WXa73WeqqbGxUcXFxd7wMWrUKFmtVp+aiooK7d2795QBBQAAhJeAZlBuv/12FRQU6A9/+INiY2O9nxmJj49Xz549ZbFYlJ2drZycHKWkpCglJUU5OTmKiYlRVlaWt3bu3LlatGiREhIS1LdvXy1evFjDhw/3fqsHAACEt4ACymOPPSZJGj9+vM/yDRs26Oabb5YkLVmyRA0NDZo/f76qq6s1evRoFRUVKTY21lu/Zs0aRUVFKTMzUw0NDZowYYI2btyoyMjI9nUDAABCQkABxTDO/NU1i8Uip9Mpp9N5ypro6Gjl5+crPz8/kM0DAIAw0a7zoABDlv6pu4cAAAhBXCwQAACYDgEFAACYDgEFAACYDgEFAACYDgEFAACYDgEFAACYDgEFAACYDgEFAACYDgEFAACYDgEFAACYDgEFAACYDgEFAACYDgEFAACYDgEFAACYTlR3DwAAgK4yZOmf2rzuZw9N7cCR4EyYQQEAAKZDQAEAAKZDQAEAAKZDQAEAAKZDQAEAAKbDt3gAAEHlxG/i2CIN5V4ipTq3y91k6aZRoaMxgwIAAEyHgAIAAEyHQzwh4nQnH2L6EwAQbJhBAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAAphNwQNm5c6emT58uh8Mhi8WiF1980ed+wzDkdDrlcDjUs2dPjR8/Xvv27fOpcbvdWrBggfr166devXppxowZOnToULsaAQAAoSPggHL06FFdeOGFWrt27Unvz83N1erVq7V27VqVlpbKbrcrPT1ddXV13prs7Gxt2bJFhYWF2rVrl44cOaJp06apqamp7Z0AAICQEfCZZCdPnqzJkyef9D7DMJSXl6fly5dr5syZkqRNmzYpMTFRBQUFmjdvnmpqarR+/Xo9+eSTmjhxoiRp8+bNSkpK0o4dOzRp0qR2tAMAAEJBh57qvry8XJWVlcrIyPAus9lsGjdunEpKSjRv3jyVlZXJ4/H41DgcDqWmpqqkpOSkAcXtdsvtdntv19bWSpI8Ho88Ho9fY2up87c+2NgijVPfF2H4/A4n4dp7uPYt0ft3f4eLrurbjH8/gu1vWyDj7NCAUllZKUlKTEz0WZ6YmKgDBw54a3r06KE+ffq0qmlZ/0QrV67UihUrWi0vKipSTExMQGN0uVwB1QeL3EvOXHN/WnPnD8SkwrX3cO1bovdw1Nl9b926tVMfvz2C5W9bfX2937WdcrFAi8X3gnSGYbRadqLT1SxbtkwLFy703q6trVVSUpIyMjIUFxfn15g8Ho9cLpfS09NltVr9WieYpDq3n/I+W4Sh+9Oadc/uCLmbw+tigeHae7j2LdF7OPbeVX3vdZrvIwjB9ret5QiIPzo0oNjtdknHZ0kGDBjgXV5VVeWdVbHb7WpsbFR1dbXPLEpVVZXGjh170se12Wyy2Wytllut1oCfkLasEwz8uUqxu9kStlczDtfew7Vvid7DsffO7tvMfzuC5W9bIGPs0POgJCcny263+0w1NTY2qri42Bs+Ro0aJavV6lNTUVGhvXv3njKgAACA8BLwDMqRI0f08ccfe2+Xl5fr3XffVd++fTV48GBlZ2crJydHKSkpSklJUU5OjmJiYpSVlSVJio+P19y5c7Vo0SIlJCSob9++Wrx4sYYPH+79Vg8AAAhvAQeU3bt366qrrvLebvlsyOzZs7Vx40YtWbJEDQ0Nmj9/vqqrqzV69GgVFRUpNjbWu86aNWsUFRWlzMxMNTQ0aMKECdq4caMiIyM7oCUAADrekKV/avO6nz00tQNHEh4CDijjx4+XYZz6q1wWi0VOp1NOp/OUNdHR0crPz1d+fn6gmwcAAGGAa/EAAADTIaAAAADT6ZTzoKBt2nN8EwCAUMIMCgAAMB0CCgAAMB0CCgAAMB0CCgAAMB0+JNvB+KArAADtR0ABAKCTcRbawHGIBwAAmA4BBQAAmA4BBQAAmA4BBQAAmA4BBQAAmA4BBQAAmA4BBQAAmA4BBQAAmA4BBQAAmA5nkj0JTlcPADCL0/1NskUayr1ESnVul7vJ0ur+YD4LLTMoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdAgoAADAdDiTLAAAIao9Z0bv7rPQMoMCAABMh4ACAABMp1sDyqOPPqrk5GRFR0dr1KhRev3117tzOAAAwCS6LaA888wzys7O1vLly/XOO+/oiiuu0OTJk3Xw4MHuGhIAADCJbgsoq1ev1ty5c3XrrbfqBz/4gfLy8pSUlKTHHnusu4YEAABMolu+xdPY2KiysjItXbrUZ3lGRoZKSkpa1bvdbrndbu/tmpoaSdI333wjj8fj1zY9Ho/q6+v19ddfy2q1nrY26thRvx4zWEQ1G6qvb1aUJ0JNzZbuHk6XCtfew7Vvid7Dsfdw7Vvq3N6//vrrDn08Saqrq5MkGYZx5mKjGxw+fNiQZLzxxhs+yx988EFj6NChrervu+8+QxI//PDDDz/88BMCP59//vkZs0K3ngfFYvFNe4ZhtFomScuWLdPChQu9t5ubm/XNN98oISHhpPUnU1tbq6SkJH3++eeKi4tr38CDDL2HX+/h2rdE7+HYe7j2LQVf74ZhqK6uTg6H44y13RJQ+vXrp8jISFVWVvosr6qqUmJiYqt6m80mm83ms+yss85q07bj4uKC4knsDPQefr2Ha98SvYdj7+HatxRcvcfHx/tV1y0fku3Ro4dGjRoll8vls9zlcmns2LHdMSQAAGAi3XaIZ+HChbrpppuUlpamMWPGaN26dTp48KD+8z//s7uGBAAATKLbAsoNN9ygr7/+Wr/85S9VUVGh1NRUbd26Veecc06nbM9ms+m+++5rdagoHNB7+PUern1L9B6OvYdr31Jo924xDH++6wMAANB1uBYPAAAwHQIKAAAwHQIKAAAwHQIKAAAwnZAIKI899phGjBjhPVHNmDFj9Morr5yy/oUXXlB6errOPvtsb/327du7cMQdJ9Dev+uNN95QVFSULrroos4dZCdpS+9ut1vLly/XOeecI5vNpvPOO0+/+93vumjEHaMtfT/11FO68MILFRMTowEDBmjOnDmdcp2NrrZy5UpZLBZlZ2eftq64uFijRo1SdHS0zj33XP3mN7/pmgF2En/6DqX93Hf5+5y3CPb93Hf523so7OekEAkogwYN0kMPPaTdu3dr9+7duvrqq3Xttddq3759J63fuXOn0tPTtXXrVpWVlemqq67S9OnT9c4773TxyNsv0N5b1NTUaNasWZowYUIXjbTjtaX3zMxM/fnPf9b69eu1f/9+Pf300xo2bFgXjrr9Au17165dmjVrlubOnat9+/bp2WefVWlpqW699dYuHnnHKi0t1bp16zRixIjT1pWXl2vKlCm64oor9M477+juu+/WL37xCz3//PNdNNKO5W/fobSfa+Fv7y1CYT/XIpDeQ2E/J0ndcrHArtCnTx/jiSee8Lv+/PPPN1asWNGJI+o6/vR+ww03GP/1X/9l3HfffcaFF17YNQPrAqfr/ZVXXjHi4+ONr7/+uotH1flO1/evfvUr49xzz/VZ9sgjjxiDBg3qiqF1irq6OiMlJcVwuVzGuHHjjDvuuOOUtUuWLDGGDRvms2zevHnGpZde2smj7HiB9H0ywbyfa0vvobKfC6T3UNrPhcQMync1NTWpsLBQR48e1ZgxY/xap7m5WXV1derbt28nj65z+dv7hg0b9Mknn+i+++7rwtF1Ln96f+mll5SWlqbc3FwNHDhQQ4cO1eLFi9XQ0NDFo+04/vQ9duxYHTp0SFu3bpVhGPriiy/03HPPaerUqV082o5z++23a+rUqZo4ceIZa998801lZGT4LJs0aZJ2794tj8fTWUPsFIH0faJg388F2nso7ecC6T2U9nPdejXjjrRnzx6NGTNG3377rXr37q0tW7bo/PPP92vdVatW6ejRo8rMzOzkUXaOQHr/6KOPtHTpUr3++uuKigr+pz+Q3j/99FPt2rVL0dHR2rJli7766ivNnz9f33zzTdAdnw2k77Fjx+qpp57SDTfcoG+//VbHjh3TjBkzlJ+f38Wj7hiFhYV6++23VVpa6ld9ZWVlq4uQJiYm6tixY/rqq680YMCAzhhmhwu07xMF834u0N5DaT8XaO+htJ8LmUM8brfb+Oijj4zS0lJj6dKlRr9+/Yx9+/adcb2CggIjJibGcLlcXTDKzuFv78eOHTPS0tKMxx57zLss2Kc+A3ne09PTjejoaONf//qXd9nzzz9vWCwWo76+vquG3CEC6Xvfvn3GgAEDjNzcXOPvf/+7sW3bNmP48OHGLbfc0sWjbr+DBw8a/fv3N959913vsjNNeaekpBg5OTk+y3bt2mVIMioqKjprqB2qLX1/VzDv5wLtPZT2c2153kNpPxcyAeVEEyZMMG677bbT1hQWFho9e/Y0Xn755S4aVdc4Ve/V1dWGJCMyMtL7Y7FYvMv+/Oc/d8NoO9bpnvdZs2YZ5513ns+y999/35BkfPjhh10xvE5zur5/8pOfGD/60Y98lr3++uuGJOOf//xnVwyvw2zZsqXVa1iSYbFYjMjISOPYsWOt1rniiiuMX/ziFz7LXnjhBSMqKspobGzsqqG3S1v6bhHs+7lAew+l/VxbnvdQ2s8F99zXaRiGIbfbfcr7n376ad1yyy16+umng/pY/Mmcqve4uDjt2bPHZ9mjjz6qV199Vc8995ySk5O7aoid5nTP+2WXXaZnn31WR44cUe/evSVJH374oSIiIjRo0KCuHGaHO13f9fX1raa5IyMjvesFkwkTJrR6Dc+ZM0fDhg3TXXfd5e3ru8aMGaM//vGPPsuKioqUlpYmq9XaqePtKG3pWwqN/VygvYfSfq4tz3tI7ee6NR51kGXLlhk7d+40ysvLjffee8+4++67jYiICKOoqMgwDMNYunSpcdNNN3nrCwoKjKioKOPXv/61UVFR4f357pRYsAi09xMF69SnYQTee11dnTFo0CDjRz/6kbFv3z6juLjYSElJMW699dbuaqFNAu17w4YNRlRUlPHoo48an3zyibFr1y4jLS3NuOSSS7qrhQ514pT3if1/+umnRkxMjHHnnXca77//vrF+/XrDarUazz33XDeMtuOcqe9Q2s+d6Ey9nyiY93MnOlPvobKfM4wQmUH54osvdNNNN6miokLx8fEaMWKEtm3bpvT0dElSRUWFDh486K1//PHHdezYMd1+++26/fbbvctnz56tjRs3dvXw2yXQ3kNJoL337t1bLpdLCxYsUFpamhISEpSZmakHHnigu1pok0D7vvnmm1VXV6e1a9dq0aJFOuuss3T11Vfr4Ycf7q4WOtWJ/ScnJ2vr1q2688479etf/1oOh0OPPPKIfvjDH3bjKDteKO/nziSU93NnEqr7OUmyGEaQzfECAICQF3LnQQEAAMGPgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEzn/wPhAU/aaKIj0AAAAABJRU5ErkJggg==",
      "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": 625,
   "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": 625,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remove_outliers(df_mdl.average_rating, plot = False).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 626,
   "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": 626,
     "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": [
    "# 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": 627,
   "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": 628,
   "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": 628,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 629,
   "id": "58f9db08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 629,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# linear model \n",
    "reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 630,
   "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:                Sat, 25 Mar 2023   Prob (F-statistic):                        0.00\n",
      "Time:                        18:28:37   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": 631,
   "id": "d1338305",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=0.5)"
      ]
     },
     "execution_count": 631,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lasso\n",
    "reg_lasso.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 632,
   "id": "48740eb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=0.01)"
      ]
     },
     "execution_count": 632,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ridge\n",
    "reg_ridge.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 633,
   "id": "639fda74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(random_state=42)"
      ]
     },
     "execution_count": 633,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# random forest\n",
    "rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 634,
   "id": "efbab8d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('linearsvr',\n",
       "                 LinearSVR(max_iter=100000, random_state=0, tol=1e-05))])"
      ]
     },
     "execution_count": 634,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# svm\n",
    "svm_regr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 635,
   "id": "973c1d0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(random_state=0)"
      ]
     },
     "execution_count": 635,
     "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": 636,
   "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)"
   ]
  },
  {
   "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": 637,
   "id": "62b16dc7",
   "metadata": {},
   "outputs": [],
   "source": [
    " # IMPORT validation curve \n",
    "from sklearn.model_selection import validation_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 638,
   "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": 638,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gbm hyper params search : gbm hyper-params \n",
    "gbm.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 639,
   "id": "6a76b307",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mpaga/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_search.py:978: 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": 640,
   "id": "a9fcedfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.5, max_depth=1, random_state=42)"
      ]
     },
     "execution_count": 640,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_gbm.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 641,
   "id": "0cb5d0b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#gbm evaluation\n",
    "gbm_hp2 = search_gbm.best_estimator_\n",
    "                   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 642,
   "id": "51d07063",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 0.5,\n",
       " 'copy_X': True,\n",
       " 'fit_intercept': True,\n",
       " 'max_iter': 1000,\n",
       " 'normalize': 'deprecated',\n",
       " 'positive': False,\n",
       " 'precompute': False,\n",
       " 'random_state': None,\n",
       " 'selection': 'cyclic',\n",
       " 'tol': 0.0001,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 642,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lasso hyper-params  list\n",
    "reg_lasso.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 643,
   "id": "ce23f39e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mpaga/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_search.py:292: 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": 644,
   "id": "6fae047d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=0.1)"
      ]
     },
     "execution_count": 644,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# best lasso estimator \n",
    "lasso_hp2 "
   ]
  },
  {
   "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": 645,
   "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": 646,
   "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)"
   ]
  },
  {
   "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. Therfore for tis application and in aregression setting, we would recommend gbm as final model in regression application. \n",
    "\n",
    "In test step (model evaluation)  gbm yields 0.028, wich is almost similar to the validation rmse. Howevr, we would recommend further , hyper-parameters serach and investigation to fine tune this model again with a large grid or grid 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": 647,
   "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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