99 lines
4.5 KiB
Plaintext
99 lines
4.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Выбор бизнес-целей\n",
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"### Задача регрессии:\n",
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"\n",
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"Цель: Предсказать цену автомобиля (Price) на основе других характеристик.\n",
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"\n",
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"Применение: Это может быть полезно для автосалонов, онлайн-площадок по продаже автомобилей, а также для частных лиц, которые хотят оценить рыночную стоимость своего автомобиля.\n",
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"\n",
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"Задача классификации:\n",
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"\n",
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"Цель: Классифицировать автомобили по категориям (например, \"Эконом\", \"Средний\", \"Премиум\") на основе цены и других характеристик.\n",
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"\n",
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"Применение: Это может быть полезно для маркетинговых кампаний, определения целевой аудитории, а также для анализа рынка автомобилей."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" ID Price Levy Manufacturer Model Prod. year Category \\\n",
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"0 45654403 13328 1399 LEXUS RX 450 2010 Jeep \n",
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"1 44731507 16621 1018 CHEVROLET Equinox 2011 Jeep \n",
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"2 45774419 8467 - HONDA FIT 2006 Hatchback \n",
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"3 45769185 3607 862 FORD Escape 2011 Jeep \n",
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"4 45809263 11726 446 HONDA FIT 2014 Hatchback \n",
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"\n",
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" Leather interior Fuel type Engine volume Mileage Cylinders \\\n",
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"0 Yes Hybrid 3.5 186005 km 6.0 \n",
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"1 No Petrol 3 192000 km 6.0 \n",
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"2 No Petrol 1.3 200000 km 4.0 \n",
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"3 Yes Hybrid 2.5 168966 km 4.0 \n",
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"4 Yes Petrol 1.3 91901 km 4.0 \n",
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"\n",
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" Gear box type Drive wheels Doors Wheel Color Airbags \n",
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"0 Automatic 4x4 04-May Left wheel Silver 12 \n",
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"1 Tiptronic 4x4 04-May Left wheel Black 8 \n",
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"2 Variator Front 04-May Right-hand drive Black 2 \n",
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"3 Automatic 4x4 04-May Left wheel White 0 \n",
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"4 Automatic Front 04-May Left wheel Silver 4 \n",
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"Index(['ID', 'Price', 'Levy', 'Manufacturer', 'Model', 'Prod. year',\n",
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" 'Category', 'Leather interior', 'Fuel type', 'Engine volume', 'Mileage',\n",
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" 'Cylinders', 'Gear box type', 'Drive wheels', 'Doors', 'Wheel', 'Color',\n",
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" 'Airbags'],\n",
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" dtype='object')\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
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"from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
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"from sklearn.compose import ColumnTransformer\n",
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"from sklearn.pipeline import Pipeline\n",
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"from sklearn.linear_model import LinearRegression, LogisticRegression\n",
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"from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier\n",
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"from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
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"from sklearn.metrics import mean_squared_error, f1_score, accuracy_score, roc_auc_score, confusion_matrix, classification_report\n",
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"df = pd.read_csv(\"./static/csv/car_price_prediction.csv\")\n",
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"print(df.head())\n",
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"print(df.columns)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.5"
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}
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"nbformat": 4,
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"nbformat_minor": 2
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}
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