{ "cells": [ { "cell_type": "markdown", "id": "f191f617-72b5-4aa3-a4a1-08bc01ad0681", "metadata": {}, "source": [ "## Car Predict ##\n", "* second hand vehicle prices according to features " ] }, { "cell_type": "code", "execution_count": 11, "id": "e9503d2a-396d-45e3-b59f-45a446b5bbc3", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.metrics import r2_score, mean_squared_error\n", "from sklearn.compose import ColumnTransformer # Sütun Dönüşüm İşlemleri\n", "from sklearn.preprocessing import OneHotEncoder, StandardScaler # kategori - sayısaş dönüşüm ve ölçeklendirme\n", "from sklearn.pipeline import Pipeline # veri işleme hattı" ] }, { "cell_type": "code", "execution_count": 17, "id": "e76a64dd-33b8-40a6-b9f0-3a0a58b5467a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: xlrd in c:\\users\\erayc\\anaconda3\\lib\\site-packages (2.0.1)\n" ] } ], "source": [ "!pip install xlrd" ] }, { "cell_type": "code", "execution_count": 19, "id": "b5b062be-ff68-4ed3-810f-d4c9e92b3653", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", " | Price | \n", "Mileage | \n", "Make | \n", "Model | \n", "Trim | \n", "Type | \n", "Cylinder | \n", "Liter | \n", "Doors | \n", "Cruise | \n", "Sound | \n", "Leather | \n", "
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | \n", "17314.103129 | \n", "8221 | \n", "Buick | \n", "Century | \n", "Sedan 4D | \n", "Sedan | \n", "6 | \n", "3.1 | \n", "4 | \n", "1 | \n", "1 | \n", "1 | \n", "
1 | \n", "17542.036083 | \n", "9135 | \n", "Buick | \n", "Century | \n", "Sedan 4D | \n", "Sedan | \n", "6 | \n", "3.1 | \n", "4 | \n", "1 | \n", "1 | \n", "0 | \n", "
2 | \n", "16218.847862 | \n", "13196 | \n", "Buick | \n", "Century | \n", "Sedan 4D | \n", "Sedan | \n", "6 | \n", "3.1 | \n", "4 | \n", "1 | \n", "1 | \n", "0 | \n", "
3 | \n", "16336.913140 | \n", "16342 | \n", "Buick | \n", "Century | \n", "Sedan 4D | \n", "Sedan | \n", "6 | \n", "3.1 | \n", "4 | \n", "1 | \n", "0 | \n", "0 | \n", "
4 | \n", "16339.170324 | \n", "19832 | \n", "Buick | \n", "Century | \n", "Sedan 4D | \n", "Sedan | \n", "6 | \n", "3.1 | \n", "4 | \n", "1 | \n", "0 | \n", "1 | \n", "
... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
799 | \n", "16507.070267 | \n", "16229 | \n", "Saturn | \n", "L Series | \n", "L300 Sedan 4D | \n", "Sedan | \n", "6 | \n", "3.0 | \n", "4 | \n", "1 | \n", "0 | \n", "0 | \n", "
800 | \n", "16175.957604 | \n", "19095 | \n", "Saturn | \n", "L Series | \n", "L300 Sedan 4D | \n", "Sedan | \n", "6 | \n", "3.0 | \n", "4 | \n", "1 | \n", "1 | \n", "0 | \n", "
801 | \n", "15731.132897 | \n", "20484 | \n", "Saturn | \n", "L Series | \n", "L300 Sedan 4D | \n", "Sedan | \n", "6 | \n", "3.0 | \n", "4 | \n", "1 | \n", "1 | \n", "0 | \n", "
802 | \n", "15118.893228 | \n", "25979 | \n", "Saturn | \n", "L Series | \n", "L300 Sedan 4D | \n", "Sedan | \n", "6 | \n", "3.0 | \n", "4 | \n", "1 | \n", "1 | \n", "0 | \n", "
803 | \n", "13585.636802 | \n", "35662 | \n", "Saturn | \n", "L Series | \n", "L300 Sedan 4D | \n", "Sedan | \n", "6 | \n", "3.0 | \n", "4 | \n", "1 | \n", "0 | \n", "0 | \n", "
804 rows × 12 columns
\n", "Pipeline(steps=[('preprocessor',\n", " ColumnTransformer(transformers=[('num', StandardScaler(),\n", " ['Mileage', 'Cylinder',\n", " 'Liter', 'Doors']),\n", " ('cat', OneHotEncoder(),\n", " ['Make', 'Model', 'Trim',\n", " 'Type'])])),\n", " ('model', LinearRegression())])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
Pipeline(steps=[('preprocessor',\n", " ColumnTransformer(transformers=[('num', StandardScaler(),\n", " ['Mileage', 'Cylinder',\n", " 'Liter', 'Doors']),\n", " ('cat', OneHotEncoder(),\n", " ['Make', 'Model', 'Trim',\n", " 'Type'])])),\n", " ('model', LinearRegression())])
ColumnTransformer(transformers=[('num', StandardScaler(),\n", " ['Mileage', 'Cylinder', 'Liter', 'Doors']),\n", " ('cat', OneHotEncoder(),\n", " ['Make', 'Model', 'Trim', 'Type'])])
['Mileage', 'Cylinder', 'Liter', 'Doors']
StandardScaler()
['Make', 'Model', 'Trim', 'Type']
OneHotEncoder()
LinearRegression()