Machine Learning Pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "pending-deadline",
   "metadata": {},
   "source": [
    "# Python ile uçtan uca makine öğrenmesi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "instrumental-animation",
   "metadata": {},
   "source": [
    "Bu çalışmada Python kullanarak en otomatik fonksiyonlar ile uçtan uca bir makine öğrenmesi yapmaya çalışacağız. scikit-learn\n",
    "kütphanesinin sağladığı bütün fonksiyonellikten yararlanıp en az müdahale ile ve en hızlı şekilde nasıl bir prototip model \n",
    "yaratabiliriz bunu görmeye çalışacağız.\n",
    "\n",
    "Öncelikle kullanacağız kütüphaneleri çağıralım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "electric-return",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.experimental import enable_halving_search_cv\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from scipy.stats import loguniform\n",
    "from sklearn.ensemble import  GradientBoostingRegressor\n",
    "from sklearn.model_selection import HalvingRandomSearchCV\n",
    "import xgboost as xb\n",
    "\n",
    "import warnings\n",
    "\n",
    "pd.set_option ('display.float_format', lambda x: '%.3f' % x)\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tender-studio",
   "metadata": {},
   "source": [
    "model olarak  burada popüler olan olan xgboost ve GradientBoostingRegressor kullanmayı seçtim, regresyonda çok sık kullanılan bu modeller böyle bir çalışma için uygun olacaktır."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "victorian-pioneer",
   "metadata": {},
   "source": [
    "Veri setimiz github ta bulduğum 6k satırlı ve ikinci el araba fiyatlarını içeriyor. Burada amacımız\n",
    "ikinci el araç fiyatını verilen girdiler ile tahminlemeye çalışmak olacak.\n",
    "\n",
    "Burada en iyi skoru bulmak gibi bir amaca çok girmeyeceğiz çünkü göstermek istediğim hızlıca nasıl bir makine öğrenmesi pipeline ı kurabiliriz.\n",
    "\n",
    "Veriyi github'ta herhangi bir çalışma arayarak hızlıca buldum, siz de farklı veriler üstünden kodlara fazla müdahale etmeden\n",
    "çalışma yapabilirsiniz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "increased-prime",
   "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>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.6 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>58.16 bhp</td>\n",
       "      <td>5.000</td>\n",
       "      <td>1.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67 kmpl</td>\n",
       "      <td>1582 CC</td>\n",
       "      <td>126.2 bhp</td>\n",
       "      <td>5.000</td>\n",
       "      <td>12.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda Jazz V</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2011</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.2 kmpl</td>\n",
       "      <td>1199 CC</td>\n",
       "      <td>88.7 bhp</td>\n",
       "      <td>5.000</td>\n",
       "      <td>4.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>88.76 bhp</td>\n",
       "      <td>7.000</td>\n",
       "      <td>6.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>15.2 kmpl</td>\n",
       "      <td>1968 CC</td>\n",
       "      <td>140.8 bhp</td>\n",
       "      <td>5.000</td>\n",
       "      <td>17.740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name    Location  Year  Kilometers_Driven  \\\n",
       "0            Maruti Wagon R LXI CNG      Mumbai  2010              72000   \n",
       "1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015              41000   \n",
       "2                      Honda Jazz V     Chennai  2011              46000   \n",
       "3                 Maruti Ertiga VDI     Chennai  2012              87000   \n",
       "4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013              40670   \n",
       "\n",
       "  Fuel_Type Transmission Owner_Type     Mileage   Engine      Power  Seats  \\\n",
       "0       CNG       Manual      First  26.6 km/kg   998 CC  58.16 bhp  5.000   \n",
       "1    Diesel       Manual      First  19.67 kmpl  1582 CC  126.2 bhp  5.000   \n",
       "2    Petrol       Manual      First   18.2 kmpl  1199 CC   88.7 bhp  5.000   \n",
       "3    Diesel       Manual      First  20.77 kmpl  1248 CC  88.76 bhp  7.000   \n",
       "4    Diesel    Automatic     Second   15.2 kmpl  1968 CC  140.8 bhp  5.000   \n",
       "\n",
       "   Price  \n",
       "0  1.750  \n",
       "1 12.500  \n",
       "2  4.500  \n",
       "3  6.000  \n",
       "4 17.740  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"https://raw.githubusercontent.com/sagnikghoshcr7/Car-Price-Prediction/master/data/dataset.csv\")\n",
    "data=data.drop([\"Unnamed: 0\", \"New_Price\"], axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "synthetic-camping",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6019 entries, 0 to 6018\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Name               6019 non-null   object \n",
      " 1   Location           6019 non-null   object \n",
      " 2   Year               6019 non-null   int64  \n",
      " 3   Kilometers_Driven  6019 non-null   int64  \n",
      " 4   Fuel_Type          6019 non-null   object \n",
      " 5   Transmission       6019 non-null   object \n",
      " 6   Owner_Type         6019 non-null   object \n",
      " 7   Mileage            6017 non-null   object \n",
      " 8   Engine             5983 non-null   object \n",
      " 9   Power              5983 non-null   object \n",
      " 10  Seats              5977 non-null   float64\n",
      " 11  Price              6019 non-null   float64\n",
      "dtypes: float64(2), int64(2), object(8)\n",
      "memory usage: 564.4+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "working-uncertainty",
   "metadata": {},
   "source": [
    "Fonksiyonlarımıza bakmadan önce hızlıca bazı sayısal değerlerin dağılımına bakabiliriz. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "tropical-entertainment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data, x=\"Price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "streaming-glance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data, x=\"Year\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "systematic-barbados",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data, x=\"Kilometers_Driven\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fundamental-pepper",
   "metadata": {},
   "source": [
    "Şimdi Makine öğrenmesi fonksiyonları oluşturmaya başlayabilirz.\n",
    "\n",
    "- Modelimiz için doğru parametrelere seçmek iyi çalışan bir model için önemli bir adım.\n",
    "\n",
    "- Manuel şekilde testler yapıp en iyisini bulmak büyük bir zaman kaybı olabilir bu yüzden bu işi\n",
    "daha hızlı ve otomatik yapan algoritmalar ortaya çıktı.\n",
    "Scikit-learn kütüphanesi bu konuda oldukça yardımcı ve çeşitli fonksiyonlar barındırıyor.\n",
    "Ben bu çalışmada hızlı olduğu için HalvingRandomSearchCV 'yi tercih ettim ancak bu deneysel aşamada olduğu\n",
    "için sklearn dökümantasyonları bu riski alarak kullanmamızı öneriyor.\n",
    "\n",
    "- HalvingRandomSearchCV succesive halving metoduyla çalışır, bu metod bir multi-armed bandit metodur aslında \n",
    "çalışma prensibi verideki bir kısım kaynağı kullanarak kötü performans gösteren parametre değerlerini\n",
    "hızlıca eliyor böylece parametrelerin optimal değerine diğer gridsearch algoritmalara göre daha hızlı\n",
    "varmış oluyor.\n",
    "\n",
    "- Gireceğimiz veride bazı kategorik veriler de mevcut olabilir, bu yüzden feature_engineering isimli bir fonksiyon yaratıp,\n",
    "burada girdi verisetindeki sütunları tiplerine göre ayırıp StandardScaler ve OneHotEncoder ile tranform ettim.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "harmful-subscription",
   "metadata": {},
   "outputs": [],
   "source": [
    "def grid_seach_algo (data, model, params):\n",
    "    model = model\n",
    "    params = params\n",
    "    preprocessor = feature_engineering(data)\n",
    "    X = data.drop ('Price', axis=1)\n",
    "    y = data[['Price']]\n",
    "    data_prepared = preprocessor.fit_transform (X)\n",
    "\n",
    "    clf = HalvingRandomSearchCV (model, \n",
    "                                 params,\n",
    "                                 scoring='neg_mean_absolute_percentage_error',\n",
    "                                 verbose=-1,\n",
    "                                 n_candidates=\"exhaust\",\n",
    "                                 n_jobs=-1)\n",
    "    clf.fit (data_prepared, y.values.ravel ())\n",
    "    \n",
    "    return clf "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "automated-sugar",
   "metadata": {},
   "outputs": [],
   "source": [
    "def feature_engineering (data):\n",
    "    \n",
    "    #sayısal değerler içeren sütunları bulma ve pipeline oluşturma\n",
    "    numeric_features = [col for col in data.columns if data[col].dtype != \"O\"]\n",
    "    numeric_features.remove ('Price')#target değerimiz burada olmamalı\n",
    "    numeric_transformer = Pipeline (\n",
    "              steps=[(\"imputer\", SimpleImputer (strategy=\"mean\")), (\"scaler\", StandardScaler ())])\n",
    "    \n",
    "    #kategorik değerler içeren sütunları bulma ve pipeline oluşturma\n",
    "    categorical_features = [col for col in data.columns if data[col].dtype == \"O\"]\n",
    "    categorical_transformer = OneHotEncoder (handle_unknown=\"ignore\")\n",
    "\n",
    "    preprocessor = ColumnTransformer ( transformers=[\n",
    "                                                     (\"num\", numeric_transformer, numeric_features),\n",
    "                                                     (\"cat\", categorical_transformer, categorical_features),\n",
    "                                                     ]\n",
    "                                     )\n",
    "    return preprocessor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "compound-windows",
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_scores (data):\n",
    "    preprocessor = feature_engineering (data)\n",
    "    \n",
    "    #kullanacağımız modeller için parametre havuzu oluşturma\n",
    "    param_grid_xgboost = {'max_depth': [3, 6, 10],\n",
    "                          'learning_rate': [0.01, 0.05, 0.1],\n",
    "                          'n_estimators': [100, 500, 1000],\n",
    "                          'colsample_bytree': [0.3, 0.7]}\n",
    "    \n",
    "    param_grid_gbr = {\"n_estimators\": [1, 2, 5, 10, 20, 50, 100, 200, 500],\n",
    "                      \"max_leaf_nodes\": [2, 5, 10, 20, 50, 100],\n",
    "                      \"learning_rate\": loguniform (0.01, 1)}\n",
    "\n",
    "            \n",
    "\n",
    "    X = data.drop ('Price', axis=1)\n",
    "    y = data[['Price']]\n",
    "\n",
    "    X_train, X_test, y_train, y_test = train_test_split (X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "    #en iyi modelin parametrelerini bulma ve bunu pipeline a ekleme\n",
    "    grid_result_xgboost = grid_seach_algo (data, xb.XGBRegressor (), param_grid_xgboost)\n",
    "    besties_xgboost = grid_result_xgboost.best_params_\n",
    "    clf_xgboost = Pipeline (\n",
    "                    steps=[(\"preprocessor\", preprocessor), (\"regressor\", xb.XGBRegressor (**besties_xgboost))])\n",
    "\n",
    "               \n",
    "\n",
    "    grid_result_gbr = grid_seach_algo (data, GradientBoostingRegressor (), param_grid_gbr)\n",
    "    besties_gbr = grid_result_gbr.best_params_\n",
    "    clf_gbr = Pipeline (steps=[(\"preprocessor\", preprocessor), (\"regressor\", GradientBoostingRegressor (**besties_gbr))])\n",
    "            \n",
    "\n",
    "    clf_xgboost.fit (X_train, y_train)\n",
    "    clf_gbr.fit (X_train, y_train)\n",
    "\n",
    "    predictions_xgboost = clf_xgboost.predict (X_test)\n",
    "    predictions_gbr = clf_gbr.predict (X_test)\n",
    "         \n",
    "\n",
    "    results = {'xgboost_accuracy_score': clf_xgboost.score (X_test, y_test),\n",
    "                'gbr_accuracy_score': clf_gbr.score (X_test, y_test),\n",
    "\n",
    "                'xgboost_mea_score': str (mean_absolute_error (predictions_xgboost, y_test)),\n",
    "                'gbr_mea_score': str (mean_absolute_error (predictions_gbr, y_test)),\n",
    "\n",
    "                'xgboost_mape_score': str (mean_absolute_percentage_error (predictions_xgboost, y_test)),\n",
    "                'gbr_mape_score': str (mean_absolute_percentage_error (predictions_gbr, y_test)),\n",
    "\n",
    "                'xgboost_best_params': besties_xgboost,\n",
    "                'gbr_best_params': besties_gbr\n",
    "              }\n",
    "\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "subtle-index",
   "metadata": {},
   "source": [
    "İlk çalıştırmamızda veriye hiç müdehale etmeden nasıl skorlar alacağımıza bakalım. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fossil-architecture",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_iterations: 4\n",
      "n_required_iterations: 4\n",
      "n_possible_iterations: 6\n",
      "min_resources_: 10\n",
      "max_resources_: 6019\n",
      "aggressive_elimination: False\n",
      "factor: 3\n",
      "----------\n",
      "iter: 0\n",
      "n_candidates: 54\n",
      "n_resources: 10\n",
      "----------\n",
      "iter: 1\n",
      "n_candidates: 18\n",
      "n_resources: 30\n",
      "----------\n",
      "iter: 2\n",
      "n_candidates: 6\n",
      "n_resources: 90\n",
      "----------\n",
      "iter: 3\n",
      "n_candidates: 2\n",
      "n_resources: 270\n",
      "n_iterations: 6\n",
      "n_required_iterations: 6\n",
      "n_possible_iterations: 6\n",
      "min_resources_: 10\n",
      "max_resources_: 6019\n",
      "aggressive_elimination: False\n",
      "factor: 3\n",
      "----------\n",
      "iter: 0\n",
      "n_candidates: 601\n",
      "n_resources: 10\n",
      "----------\n",
      "iter: 1\n",
      "n_candidates: 201\n",
      "n_resources: 30\n",
      "----------\n",
      "iter: 2\n",
      "n_candidates: 67\n",
      "n_resources: 90\n",
      "----------\n",
      "iter: 3\n",
      "n_candidates: 23\n",
      "n_resources: 270\n",
      "----------\n",
      "iter: 4\n",
      "n_candidates: 8\n",
      "n_resources: 810\n",
      "----------\n",
      "iter: 5\n",
      "n_candidates: 3\n",
      "n_resources: 2430\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'xgboost_accuracy_score': 0.4105195535658379,\n",
       " 'gbr_accuracy_score': 0.8413734976004645,\n",
       " 'xgboost_mea_score': '4.370583965205406',\n",
       " 'gbr_mea_score': '1.7311315139641363',\n",
       " 'xgboost_mape_score': '0.6042636708505607',\n",
       " 'gbr_mape_score': '0.317835659818613',\n",
       " 'xgboost_best_params': {'n_estimators': 100,\n",
       "  'max_depth': 3,\n",
       "  'learning_rate': 0.01,\n",
       "  'colsample_bytree': 0.7},\n",
       " 'gbr_best_params': {'learning_rate': 0.5447993210956236,\n",
       "  'max_leaf_nodes': 10,\n",
       "  'n_estimators': 500}}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "besties = model_scores(data)\n",
    "besties"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bibliographic-nerve",
   "metadata": {},
   "source": [
    "Skorlara baktığımızda müdahale olmadan en iyi modelimiz GrdaientBoostingRegressor olmuş gibi, tabi skorlama yöntemi de önemli olduğu\n",
    "için kendi probleminize en uygun skoru siz de seçebilirsiniz. \n",
    "Ben burada **mean square error**, **negative mean absolute percentage error** ve  **mean absolute percentage** error kullandım.  \n",
    "Ayrıca size bnu skorları sağlayacak parametreleri de görebiliyorsunuz."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "athletic-process",
   "metadata": {},
   "source": [
    "şimdi km ve fiyat değerlerinde anomalik değerleri yakınlaştırma metodu ile %5 ve %95 değerlerine yakınlaştırıp tekrar\n",
    "deneyip skora bakabiliriz.\n",
    "\n",
    "Bunu yaparken stackoverflow dan bulduğum bir çözüm ile yaptım, linkini de bırakıyorum. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ecological-dietary",
   "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>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000.000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.6 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>58.16 bhp</td>\n",
       "      <td>5.000</td>\n",
       "      <td>1.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000.000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67 kmpl</td>\n",
       "      <td>1582 CC</td>\n",
       "      <td>126.2 bhp</td>\n",
       "      <td>5.000</td>\n",
       "      <td>12.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda Jazz V</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2011</td>\n",
       "      <td>46000.000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.2 kmpl</td>\n",
       "      <td>1199 CC</td>\n",
       "      <td>88.7 bhp</td>\n",
       "      <td>5.000</td>\n",
       "      <td>4.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000.000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>88.76 bhp</td>\n",
       "      <td>7.000</td>\n",
       "      <td>6.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670.000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>15.2 kmpl</td>\n",
       "      <td>1968 CC</td>\n",
       "      <td>140.8 bhp</td>\n",
       "      <td>5.000</td>\n",
       "      <td>17.740</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6014</th>\n",
       "      <td>Maruti Swift VDI</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2014</td>\n",
       "      <td>27365.000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>28.4 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>74 bhp</td>\n",
       "      <td>5.000</td>\n",
       "      <td>4.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6015</th>\n",
       "      <td>Hyundai Xcent 1.1 CRDi S</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>2015</td>\n",
       "      <td>100000.000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>24.4 kmpl</td>\n",
       "      <td>1120 CC</td>\n",
       "      <td>71 bhp</td>\n",
       "      <td>5.000</td>\n",
       "      <td>4.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6016</th>\n",
       "      <td>Mahindra Xylo D4 BSIV</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>2012</td>\n",
       "      <td>55000.000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second</td>\n",
       "      <td>14.0 kmpl</td>\n",
       "      <td>2498 CC</td>\n",
       "      <td>112 bhp</td>\n",
       "      <td>7.000</td>\n",
       "      <td>2.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6017</th>\n",
       "      <td>Maruti Wagon R VXI</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>2013</td>\n",
       "      <td>46000.000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.9 kmpl</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>67.1 bhp</td>\n",
       "      <td>5.000</td>\n",
       "      <td>2.650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6018</th>\n",
       "      <td>Chevrolet Beat Diesel</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2011</td>\n",
       "      <td>47000.000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>25.44 kmpl</td>\n",
       "      <td>936 CC</td>\n",
       "      <td>57.6 bhp</td>\n",
       "      <td>5.000</td>\n",
       "      <td>2.500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6019 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Name    Location  Year  Kilometers_Driven  \\\n",
       "0               Maruti Wagon R LXI CNG      Mumbai  2010          72000.000   \n",
       "1     Hyundai Creta 1.6 CRDi SX Option        Pune  2015          41000.000   \n",
       "2                         Honda Jazz V     Chennai  2011          46000.000   \n",
       "3                    Maruti Ertiga VDI     Chennai  2012          87000.000   \n",
       "4      Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013          40670.000   \n",
       "...                                ...         ...   ...                ...   \n",
       "6014                  Maruti Swift VDI       Delhi  2014          27365.000   \n",
       "6015          Hyundai Xcent 1.1 CRDi S      Jaipur  2015         100000.000   \n",
       "6016             Mahindra Xylo D4 BSIV      Jaipur  2012          55000.000   \n",
       "6017                Maruti Wagon R VXI     Kolkata  2013          46000.000   \n",
       "6018             Chevrolet Beat Diesel   Hyderabad  2011          47000.000   \n",
       "\n",
       "     Fuel_Type Transmission Owner_Type     Mileage   Engine      Power  Seats  \\\n",
       "0          CNG       Manual      First  26.6 km/kg   998 CC  58.16 bhp  5.000   \n",
       "1       Diesel       Manual      First  19.67 kmpl  1582 CC  126.2 bhp  5.000   \n",
       "2       Petrol       Manual      First   18.2 kmpl  1199 CC   88.7 bhp  5.000   \n",
       "3       Diesel       Manual      First  20.77 kmpl  1248 CC  88.76 bhp  7.000   \n",
       "4       Diesel    Automatic     Second   15.2 kmpl  1968 CC  140.8 bhp  5.000   \n",
       "...        ...          ...        ...         ...      ...        ...    ...   \n",
       "6014    Diesel       Manual      First   28.4 kmpl  1248 CC     74 bhp  5.000   \n",
       "6015    Diesel       Manual      First   24.4 kmpl  1120 CC     71 bhp  5.000   \n",
       "6016    Diesel       Manual     Second   14.0 kmpl  2498 CC    112 bhp  7.000   \n",
       "6017    Petrol       Manual      First   18.9 kmpl   998 CC   67.1 bhp  5.000   \n",
       "6018    Diesel       Manual      First  25.44 kmpl   936 CC   57.6 bhp  5.000   \n",
       "\n",
       "      Price  \n",
       "0     1.750  \n",
       "1    12.500  \n",
       "2     4.500  \n",
       "3     6.000  \n",
       "4    17.740  \n",
       "...     ...  \n",
       "6014  4.750  \n",
       "6015  4.000  \n",
       "6016  2.900  \n",
       "6017  2.650  \n",
       "6018  2.500  \n",
       "\n",
       "[6019 rows x 12 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#https://stackoverflow.com/a/55471980\n",
    "def cap_data(df):\n",
    "    for col in df.columns:\n",
    "        if (((df[col].dtype)=='float64') | ((df[col].dtype)=='int64')):\n",
    "            percentiles = df[col].quantile([0.05,0.95]).values\n",
    "            df[col][df[col] <= percentiles[0]] = percentiles[0]\n",
    "            df[col][df[col] >= percentiles[1]] = percentiles[1]\n",
    "        else:\n",
    "            df[col]=df[col]\n",
    "    return df\n",
    "\n",
    "data=cap_data(data)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "clear-glucose",
   "metadata": {},
   "source": [
    "normalizasyon sonucu km ve price değerlerimize tekrar bakabiliriz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "marked-scott",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data, x=\"Kilometers_Driven\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "meaning-federal",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data, x=\"Price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "accredited-preserve",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_iterations: 4\n",
      "n_required_iterations: 4\n",
      "n_possible_iterations: 6\n",
      "min_resources_: 10\n",
      "max_resources_: 6019\n",
      "aggressive_elimination: False\n",
      "factor: 3\n",
      "----------\n",
      "iter: 0\n",
      "n_candidates: 54\n",
      "n_resources: 10\n",
      "----------\n",
      "iter: 1\n",
      "n_candidates: 18\n",
      "n_resources: 30\n",
      "----------\n",
      "iter: 2\n",
      "n_candidates: 6\n",
      "n_resources: 90\n",
      "----------\n",
      "iter: 3\n",
      "n_candidates: 2\n",
      "n_resources: 270\n",
      "n_iterations: 6\n",
      "n_required_iterations: 6\n",
      "n_possible_iterations: 6\n",
      "min_resources_: 10\n",
      "max_resources_: 6019\n",
      "aggressive_elimination: False\n",
      "factor: 3\n",
      "----------\n",
      "iter: 0\n",
      "n_candidates: 601\n",
      "n_resources: 10\n",
      "----------\n",
      "iter: 1\n",
      "n_candidates: 201\n",
      "n_resources: 30\n",
      "----------\n",
      "iter: 2\n",
      "n_candidates: 67\n",
      "n_resources: 90\n",
      "----------\n",
      "iter: 3\n",
      "n_candidates: 23\n",
      "n_resources: 270\n",
      "----------\n",
      "iter: 4\n",
      "n_candidates: 8\n",
      "n_resources: 810\n",
      "----------\n",
      "iter: 5\n",
      "n_candidates: 3\n",
      "n_resources: 2430\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'xgboost_accuracy_score': 0.8345172866572028,\n",
       " 'gbr_accuracy_score': 0.9063989465122364,\n",
       " 'xgboost_mea_score': '2.1188986550222317',\n",
       " 'gbr_mea_score': '1.4640366399311902',\n",
       " 'xgboost_mape_score': '0.2528434334726373',\n",
       " 'gbr_mape_score': '0.19198461532532085',\n",
       " 'xgboost_best_params': {'n_estimators': 500,\n",
       "  'max_depth': 3,\n",
       "  'learning_rate': 0.05,\n",
       "  'colsample_bytree': 0.7},\n",
       " 'gbr_best_params': {'learning_rate': 0.2068843637202841,\n",
       "  'max_leaf_nodes': 100,\n",
       "  'n_estimators': 500}}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "besties = model_scores(data)\n",
    "besties"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "modified-elite",
   "metadata": {},
   "source": [
    "Normalizasyon sonrası sonuçlara baktığımızda bazı skorlarımızın iyileştiğini ve parametrelerin de değiştiğini görebiliyoruz, \n",
    "doğal olarak beklenen bir şeydi çünkü bir makine öğrenmesi algoritması anomalik değerlere karşı hassas olabiliyor. \n",
    "\n",
    "Bu çalışmada umarım basitçe nasıl bir fonksiyonel makine öğrenmesi mimarisi yaratbileceğinize dair güzel bir örnek olmuştur.\n",
    "Siz de kendi verileriniz ve farklı yaklaşımlarınız da yeni testler denemeyi ihmal etmeyin.\n",
    "\n",
    "Okuduğunuz için teşekkürler."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}