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lab_5/lab5.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         ID  Price  Levy Manufacturer    Model  Prod. year   Category  \\\n",
+      "0  45654403  13328  1399        LEXUS   RX 450        2010       Jeep   \n",
+      "1  44731507  16621  1018    CHEVROLET  Equinox        2011       Jeep   \n",
+      "2  45774419   8467     -        HONDA      FIT        2006  Hatchback   \n",
+      "3  45769185   3607   862         FORD   Escape        2011       Jeep   \n",
+      "4  45809263  11726   446        HONDA      FIT        2014  Hatchback   \n",
+      "\n",
+      "  Leather interior Fuel type Engine volume    Mileage  Cylinders  \\\n",
+      "0              Yes    Hybrid           3.5  186005 km        6.0   \n",
+      "1               No    Petrol             3  192000 km        6.0   \n",
+      "2               No    Petrol           1.3  200000 km        4.0   \n",
+      "3              Yes    Hybrid           2.5  168966 km        4.0   \n",
+      "4              Yes    Petrol           1.3   91901 km        4.0   \n",
+      "\n",
+      "  Gear box type Drive wheels   Doors             Wheel   Color  Airbags  \n",
+      "0     Automatic          4x4  04-May        Left wheel  Silver       12  \n",
+      "1     Tiptronic          4x4  04-May        Left wheel   Black        8  \n",
+      "2      Variator        Front  04-May  Right-hand drive   Black        2  \n",
+      "3     Automatic          4x4  04-May        Left wheel   White        0  \n",
+      "4     Automatic        Front  04-May        Left wheel  Silver        4  \n",
+      "Index(['ID', 'Price', 'Levy', 'Manufacturer', 'Model', 'Prod. year',\n",
+      "       'Category', 'Leather interior', 'Fuel type', 'Engine volume', 'Mileage',\n",
+      "       'Cylinders', 'Gear box type', 'Drive wheels', 'Doors', 'Wheel', 'Color',\n",
+      "       'Airbags'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import sklearn\n",
+    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
+    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
+    "from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier\n",
+    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
+    "from sklearn.metrics import mean_squared_error, f1_score, accuracy_score, roc_auc_score, confusion_matrix, classification_report\n",
+    "df = pd.read_csv(\"./static/csv/car_price_prediction.csv\")\n",
+    "print(df.head())\n",
+    "print(df.columns)"
+   ]
+  }
+ ],
+ "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": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}