Cat boost final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "import catboost\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "from sklearn.metrics import r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import re\n",
    "import string\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "import datetime as dt\n",
    "from matplotlib import cm\n",
    "from matplotlib import colors\n",
    "import scipy.stats as sts\n",
    "from wordcloud import WordCloud\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('dataset/prep.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Change data to use CatBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_from_dict(dct, column):\n",
    "    return list(map(lambda x: dct.get(x) if x in dct else x, data[column]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cuzov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_cuzov = {'седан':'sedan', \n",
    "               'хэтчбек':'hatchback',\n",
    "               'внедорожник':'SUV',\n",
    "               'универсал':'universal',\n",
    "               'минивэн':'minivan',\n",
    "               'лифтбек':'liftback',\n",
    "               'микроавтобус':'minibus',\n",
    "               'купе':'coupe',\n",
    "               'легковой':'passenger',\n",
    "               'пикап':'pickup',\n",
    "               'кабриолет':'cabriolet',\n",
    "               'другой':'other',\n",
    "               'лимузин':'limousine',\n",
    "               'родстер':'roadster'               \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.cuzov = replace_from_dict(dict_cuzov, 'cuzov')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fuel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_fuel = {'бензин':'petrol', \n",
    "             'дизель':'diesel'               \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.fuel = replace_from_dict(dict_fuel, 'fuel')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Brand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_brand = {'ГАЗ':'GAZ', \n",
    "             'УАЗ':'UAZ',\n",
    "             'Москвич':'Moskvich', \n",
    "             'ЗАЗ':'ZAZ', \n",
    "             'Эксклюзив':'Exclusive',\n",
    "             'ЛуАЗ':'LuAZ'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.brand = replace_from_dict(dict_brand, 'brand')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_region = {'Минск':'Minsk', \n",
    "               'Брестская обл.':'Brest reg.',\n",
    "               'Гродненская обл.':'Grodno reg.', \n",
    "               'Гомельская обл.':'Gomel reg.', \n",
    "               'Могилевская обл.':'Mogilev reg.',\n",
    "               'Витебская обл.':'Vitebsk reg.',\n",
    "               'Минская обл.':'Minsk reg.'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.region = replace_from_dict(dict_region, 'region')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model - drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('model', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['черный', 'серебристый', 'зеленый', 'фиолетовый', 'синий',\n",
       "       'бордовый', 'серый', 'белый', 'другой', 'красный', 'коричневый',\n",
       "       'оранжевый', 'желтый'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.color.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "russian_colors = ['черный', 'серебристый', 'зеленый', 'фиолетовый', 'синий',\n",
    "                  'бордовый', 'серый', 'белый', 'другой', 'красный', \n",
    "                  'коричневый', 'оранжевый', 'желтый']\n",
    "english_colors = ['black', 'silver', 'green', 'purple', 'blue',\n",
    "                  'burgundy', 'gray', 'white', 'other', 'red',\n",
    "                  'brown', 'orange', 'yellow']\n",
    "dict_colors = dict(zip(russian_colors, english_colors))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.color = replace_from_dict(dict_colors, 'color')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Drive Unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_drive_unit = {'Передний':'front-wheel', \n",
    "                   'Постоянный полный':'all-wheel', \n",
    "                   'Задний':'rear', \n",
    "                   'Подключаемый полный':'four-wheel'               \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['drive-unit'] = replace_from_dict(dict_drive_unit, 'drive-unit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['drive-unit'].fillna('Unknown', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_state = {'с пробегом':'with mileage', \n",
    "              'с повреждениями':'with damage', \n",
    "              'на запчасти':'for parts'               \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.state = replace_from_dict(dict_state, 'state')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Transmission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_transmission = {'Автомат':'automatic', \n",
    "                     'Механика':'mechanical'               \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.transmission = replace_from_dict(dict_transmission, 'transmission')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Drop views"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['all_views', 'today_views', 'up', 'days_ago'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('modified', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.cost = data.cost.str.replace(' ', '').astype('int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data.drop('cost', axis=1), \n",
    "                                                    data.cost, random_state=42,\n",
    "                                                    test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Remove Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_brand = X_train.brand.value_counts()\n",
    "count_cuzov = X_train.cuzov.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[(list(count_brand[data.brand]>15))]\n",
    "data = data[list(count_cuzov[data.cuzov]>20)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Try CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "ctbst = catboost.CatBoostRegressor(cat_features=['cuzov', 'fuel', 'brand', 'region',\n",
    "                                                 'color', 'drive-unit', 'state', 'transmission'],\n",
    "                                   od_pval=1e-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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      "994:\tlearn: 2952.8697623\ttotal: 46.7s\tremaining: 235ms\n",
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      "996:\tlearn: 2952.8693343\ttotal: 46.8s\tremaining: 141ms\n",
      "997:\tlearn: 2952.8689318\ttotal: 46.8s\tremaining: 93.8ms\n",
      "998:\tlearn: 2952.8687765\ttotal: 46.9s\tremaining: 46.9ms\n",
      "999:\tlearn: 2952.8687589\ttotal: 46.9s\tremaining: 0us\n"
     ]
    }
   ],
   "source": [
    "score_cv = cross_val_score(ctbst, X_train, y=y_train, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average R2-score in cv: 0.882778\n"
     ]
    }
   ],
   "source": [
    "print(f'Average R2-score in cv: {np.mean(score_cv):.6}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Try on Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
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     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostRegressor at 0x1de15c43048>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctbst.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 metric on test data: 0.90855\n"
     ]
    }
   ],
   "source": [
    "y_pred = ctbst.predict(X_test)\n",
    "print(f'R2 metric on test data: {r2_score(y_test, y_pred):.6}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "ctbst.save_model('model_final.cbm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('age', 48.571533981406226),\n",
       " ('volume', 21.271085219494648),\n",
       " ('drive-unit', 8.818975345516932),\n",
       " ('run', 6.31400023759299),\n",
       " ('brand', 3.1322499704037265),\n",
       " ('Материал салона - натуральная кожа', 2.320150868342343),\n",
       " ('cuzov', 2.120925365610398),\n",
       " ('fuel', 1.278864069607278),\n",
       " ('transmission', 1.1511345233988044),\n",
       " ('region', 0.8342446477591213),\n",
       " ('Камера заднего вида', 0.763333612598413),\n",
       " ('Контроль мертвых зон на зеркалах', 0.63558378801931),\n",
       " ('color', 0.5560213165016196),\n",
       " ('Мультимедийный экран', 0.3147293877051294),\n",
       " ('Климат-контроль', 0.3133630095198925),\n",
       " ('Парктроники', 0.26315622795274335),\n",
       " ('is_restyle', 0.2484334410334558),\n",
       " ('Светодиодные фары', 0.24200112672537422),\n",
       " ('Рейлинги на крыше', 0.13875474934642396),\n",
       " ('Датчик дождя', 0.13363856837316213),\n",
       " ('ESP (система поддержания динамической стабильности)', 0.11140439638099164),\n",
       " ('state', 0.0925740158575948),\n",
       " ('Штатная навигация', 0.08463667213889652),\n",
       " ('Панорамная крыша', 0.06065543852131846),\n",
       " ('Ксеноновые фары', 0.05042394607364415),\n",
       " ('Круиз-контроль', 0.043390770557997595),\n",
       " ('Электрорегулировка сидений', 0.03582486574538988),\n",
       " ('Фаркоп', 0.030441719483607337),\n",
       " ('Обогрев сидений', 0.021446226594757683),\n",
       " ('CD/MP3 проигрыватель', 0.00960872051226392),\n",
       " ('Системы помощи', 0.008053527030594929),\n",
       " ('Подушки безопасности боковые', 0.006647121846442783),\n",
       " ('Кондиционер', 0.006301592367940656),\n",
       " ('Иммобилайзер', 0.005070396887319215),\n",
       " ('Электростеклоподъемники передние', 0.004667851378508347),\n",
       " ('ABS (антиблокировочная система)', 0.003958725319088539),\n",
       " ('Легкосплавные диски', 0.0027145563957578275),\n",
       " ('Противотуманные фары', 0.0),\n",
       " ('Обогрев зеркал', 0.0),\n",
       " ('Обогрев руля', 0.0),\n",
       " ('Управление мультимедиа с руля', 0.0),\n",
       " ('Обогрев лобового стекла', 0.0),\n",
       " ('AUX/iPod', 0.0),\n",
       " ('USB', 0.0),\n",
       " ('Подушки безопасности задние', 0.0),\n",
       " ('Автозапуск двигателя', 0.0),\n",
       " ('Сигнализация', 0.0),\n",
       " ('Подушки безопасности передние', 0.0),\n",
       " ('Электростеклоподъемники задние', 0.0),\n",
       " ('Bluetooth', 0.0),\n",
       " ('Антипробуксовочная система', 0.0),\n",
       " ('Люк', 0.0)]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst_zip = list(zip(ctbst.feature_names_, ctbst.feature_importances_))\n",
    "lst_zip = sorted(lst_zip, key=lambda tup: tup[1], reverse=True)\n",
    "lst_zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Камера заднего вида',\n",
       " 'region',\n",
       " 'transmission',\n",
       " 'fuel',\n",
       " 'cuzov',\n",
       " 'Материал салона - натуральная кожа',\n",
       " 'brand',\n",
       " 'run',\n",
       " 'drive-unit',\n",
       " 'volume',\n",
       " 'age']"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "impot_features = []\n",
    "for tup in lst_zip[:11]:\n",
    "    impot_features.append(tup[0])\n",
    "impot_features.reverse()\n",
    "impot_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Reorder it following the values:\n",
    "my_range=range(1,12)\n",
    "\n",
    "# Vertical version.\n",
    "plt.hlines(y=my_range, xmin=0, xmax=sorted(ctbst.feature_importances_)[-11:], color='skyblue')\n",
    "plt.plot(sorted(ctbst.feature_importances_)[-11:], my_range, \"o\", lw=33)\n",
    "plt.yticks(my_range, impot_features)\n",
    "plt.show()\n"
   ]
  }
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