Used car price dataset. 87% missing values fuel_type: 4.

Used car price dataset This dataset has over 426 thousand rows of data that you can use for pricing Used Car Dataset. Several regression techniques have Car Price Prediction Dataset. price adjustment, and car sale nationwide. txt: List of This is the second project for the Applied Statistics I1030 graduate course at City College of New York, Fall 2024. g. Dataset and Pre-Processing For this project, we are using the dataset on used car sales from all over the United States, available on Kaggle[1]. This article uses a total of three data processing methods to find the most Saved searches Use saved searches to filter your results more quickly This project can be used by car dealerships to predict used car prices and understand key factors that contribute to used car prices. Transmission Type: Automatic or manual, among others. Something went wrong and this page crashed! In this post I will be working with a dataset of used vehicles for sale, obtained from Kaggle. A used car dataset is a collection of information related to previously owned vehicles, including their make, model, year, mileage, condition, sales price, and vehicle history. [ ] spark Gemini keyboard_arrow_down Libraries/dataset import [ ] spark Gemini [ ] Run cell (Ctrl+Enter) Split the dataset into Train and Test parts [ ] spark Gemini One-hot encoding [ ] spark Gemini [ ] Run Predicting Used Car Prices with Heuristic Algorithms and Creating a New Dataset. The Dataset contains 7906 Feature values and 18 feature labels of Used Cars in some state in USA. Figure 3. Correlation of attributes . The "Indian IT Cities Used Car Dataset 2023" is a comprehensive collection of data that provides valuable insights into the used car market across major metro cities in India. Hyundai and Honda have the best resale value as they are selling their used cars on 50% of their original price. Click here for a full description of the dataset, or read the description file. 8%; JavaScript 3. 14M records available. Quarterly, Seasonally Adjusted Q1 1947 to Q4 2024 (Feb 27) Annual, Not 3. Access a huge and complete dataset on used cars, with data on make, model, year, mileage, condition, and price. 本数据集来源于汽车市场网站 Cars. This project serves as a valuable resource for those looking to understand how regression Used Car Price Prediction (Machine Learning & Flask) - pvunofar/UsedCarPricePredictionWeb. Readme Activity. Build your own Algo for cars 24 !! Predict the price of an unknown car. - nileshely/Second-Hand-Car-Price-Prediction This dataset can be leveraged to gain insights into the factors influencing used car prices and to develop In this application, we explored an used cars dataset from kaggle. The used car market has seen a dramatic increase in price volatility over recent years. This repository contains a Python script (car_price_prediction. G. No. Dataset Description: The datasets include various attributes of cars listed on CarDekho, such as make, model, year, mileage, and price. However, a predefined model can easily predict the price based on recorded past data. It was created in 2015 and it is not live data. About. A sample dataset with four features is display in Table 11. In this dataset, I am exploring to draw some insight which I wil be using for my explanatory part to communicate my findings. For this analysis, I used the vehicle dataset available on The dataset used in this project comes from a Kaggle competition. It is a regression problem and predictions are carried out on dataset of used car sales in the Indian car market Cardheko website. fuel_type: Type of fuel (e. used-car-price-prediction-dataset. The dataset was pre-processed to remove missing values and extraneous characters using a Python script. COMPETITIONS. 1. 📄 requirements. 1 watching Forks. com in April 2023. Used Car Price Prediction (Machine Learning & Flask) - pvunofar/UsedCarPricePredictionWeb The dataset used in this project is available: Cardetails. Python. The most comprehensive view available for used car market across the United States and Canada. Understand the Used Car Market in India. Each row in the dataset contains information about one car. csv: Contains the dataset used for this project. We fed the model with data points, the features of cars like year, odometer reading, and colour, and asked The given project is a car price prediction project using a linear regression model of a known company Car Dekho which is in the business of selling second-hand cars, it is India's leading car search venture. Price table: entry-level (i. This repository offers a complete project on predicting used car prices using machine learning. Da ta s e t For this project, we are using the dataset on used car sales from all over the United States, available on Kaggle [1]. Explore the code and data for insights into car pricing trends and predictions. Figure 1. The provided dataset contains information on 426K cars to ensure speed of processing. The primary objective is to identify factors influencing car prices and develop an accurate This project analyzes and visualizes the Used Car Prices from the Automobile dataset in order to predict the most probable car price - sanithps98/Automobile-Dataset-Analysis The aim of this project is to accurately predict car prices based on various features such as brand, model, mileage, engine type, year, and more. Step 1: Data Collection – The dataset is collected from the Kaggle. Stage 2 - EDA: We start to reach the dataset to gain the characteristic from the data. In this project, we use a Kaggle dataset on used car sales to analyze factors influencing car prices and develop a predictive model using linear regression. - udhaya2823/CarDheko The dataset, sourced from Kaggle, comprises a comprehensive collection of data related to 762,091 used cars. transmission: Type of transmission (automatic/manual). Accurate data on vehicle make, model, year, mileage, pricing, location, and more. The objective of this This project provides a comprehensive analysis and predictive model training for used car prices using a detailed dataset. Amount of attributes in the dataset . You switched accounts on another tab or window. It has India's Used Cars Prediction Dataset (Courtesy: Vijayaadithyan V. They evaluate their model using a manually-collected dataset comprising 192 used car sales. The main aim of this project is to predict the price of used cars using the various Machine Learning (ML) models. INTRODUCTION . the type of fuel it uses, the size of its engine . 1 fork Report repository Releases No releases published. 24% missing values accident: 2. 1. Find the right Car Price Datasets: Explore 100s of datasets and databases. Mileage: The distance the car has been driven. 6(1), 29-43, 2021 e-ISSN: 2564-6095 Predicting Used Car Prices with Heuristic Algorithms and Creating a New Dataset – Bilen Figure 1. I took a sample of 6,000 observations after data cleaning and feature engineering. The model is trained on a dataset of historical car sales data, and it can then be used to predict the price of a car based on its features. Updated Sep 22, 2022; Jupyter Notebook; iyashk / Car-Price-Prediction. Experimental results reached a training accuracy of 95. 1 Data Exploration. The dataset consists of various features related to used cars, including: brand: The brand of the car. used random forest model on a Kaggle dataset to predict the used car prices. The business objective is to identify key features for used car prices based on the dataset provided so that Car Dealers and their Sales Team can use these key features to understand the cars that they need to have in their inventory to increase sales. The dataset used for this project is taken from Kaggle. This extensive dataset was meticulously scraped from cars. Car Age Calculation: Derives a new column CarAge by subtracting model_year from 2024. The predictions are based on historical data collected from In this project, we divide into 4 stages:. DATASETS. Engine Size: The capacity of the car's engine. Based on existing data, the aim is to use machine learning algorithms to develop models for predicting used car prices. csv: Contains car features like name, year, selling price, kilometers driven, fuel type, seller type, Humans can mistake to predict the price because so many factors are involved to predict price. org Buy a car prices dataset. Leveraging a diverse dataset encompassing essential features like car model, number of owners, age, mileage, fuel type, kilometers driven, additional features, and location, this project aspires to build a powerful machine - Thangam-11/Used-car This file contains analysis performed upon used car dataset - Used_Car_Analysis. The dataset comprises a range of features such as the car’s model, manufacturing year, kilometers are driven, fuel type, seller type, transmission Explore and run machine learning code with Kaggle Notebooks | Using data from CarDekho Used Car Dataset. I worked in the automotive industry for 12 years and I remain a devoted pistonhead, so getting a better understanding of the used car market was very appealing. As first step we removed them from the dataset and stayed with 79. The goal of this project is to develop a robust regression model that accurately The purpose of this dataset is to facilitate the development and evaluation of machine learning models for car price prediction. 4%; Jupyter Notebook 1. It is important to understand how the used car prices are being influenced by various features as the market has changed dramatically. Because of the affordability of used cars in developing countries, people tend more purchase used cars. Something went wrong and this page crashed! If the issue persists, it's likely a problem on our side. Preview data samples for free. Predict price of used cars based on car features and its current condition Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Competition Notebook. The dataset of PakWheels used in this research is taken from Kaggle. - Car_price_prediction/Used Car Dataset. The dat aset is chosen from the Kaggle website. Learn more. Due to the unprecedented number of cars being purchased and sold, used car price prediction is a topic of high interest. Python 91. Predict the price of an unknown car. Sümeyra MUTİ, Kazım YILDIZ/IJCESEN 9-1(2023)11-16. By employing various techniques, including data analysis and predictive modeling, we aim to provide sellers with insights to set competitive prices and assist buyers in making informed decisions. 0 stars Watchers. There are no missing values in the dataset and also all the columns in the Machine Learning model to predict used car prices based on Kaggle dataset and deployed using Django Resources. This project aims to solve the problem of predicting the price of a used car, using Sklearn's supervised machine learning techniques. com This data can be used for a lot of purposes such as price prediction to exemplify the use of linear regression in Used-Car-Price-Prediction-Dataset. This study analyzed the selling prices of used cars in India from 2017–2018 using a dataset from Kaggle with 892 rows and 6 columns. This is a subset from the original dataset that contained information on 3 million used cars. flask machine-learning car-price-prediction. 1- Importing necessary Libraries (Pandas, NumPy, SciPy, Matplotlib, Seaborn, Scikit-learn, Statsmodels) Introduction. This dataset contains information about used cars listed on CarDekho Analysis I used two models - Random Forrest Regressor and Decision Tree Regressor and these are the evaluations. Identify market trends, optimize pricing strategies. Updated Jan 19, 2021; R; shrutisbhosale14 3 Million US used cars . You signed in with another tab or window. Table The used car dataset is divided into a training dataset and a test dataset according to the ratio of 83% and 17%. Using a multi-city dataset, we perform data cleaning, feature engineering, and model optimization. Get data oused & new cars. python machine-learning sklearn data-visualization used-cars data-preprocessing json-schemas used-cars-price Data manipulation of the Cars data set found on Kaggle. describe() again, we can see that the range of price seems much more realistic than it initially was, but year and odometer seem a bit off (for example the max value for year is 2021). About the dataset The dataset contains car auction sales prices, which were scraped from Pal et al. 82% missing values For the data Training dataset contains the prices of used cars which help us to train our model and then implement on our test dataset where price of used cars are to be calculated. - kb22/Used-Car-Price-Prediction In this competition, I worked on predicting used car prices using a dataset filled with valuable information about vehicle characteristics, condition, and sale prices. This project uses machine learning to predict the price of a used car. Something went wrong and this page crashed! Introduction In our project, we have decided to work on the "Used Car Auction Prices" dataset. Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. Year: The year of manufacture. price: The listed price of the car in dollars. play_arrow. 4s. The project aims to train and calibrate a car price prediction model based on a kaggle data set consisting of 3 million observations of US cars. Used car price dataset | Kaggle Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Tools: Python / R; Dataset: CarGurus (IL/IA/WI/MI/IN) Analyses performed: Linear/Polynomial regression, Random Forest, KNN Analyzing selling price of used cars using Python Now-a-days, with the technological advancement, Techniques like Machine Learning, etc are being used on a large scale in many organisations. You signed out in another tab or window. Track active vehicle retail Predict the price of a used vehicle Used Car Price Prediction Dataset | Kaggle Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. int_col: Interior color of the car. Dataset was scraped with Scrapy from German eBay and there were some mistakes, duplications and outliers. Our group has chosen a dataset on Used Cars from Kaggle, that is between the years of 1923–2020 and contains the data on To estimate the prices of used cars in our dataset, we initially used Linear Regression. Vehicles listings from Craigslist. Used Car Dataset includes. Percent, Quarterly, Not Seasonally Adjusted Q1 2008 to Q3 2024 (Dec 20) Net purchases of used autos (chain-type price index) Index 2017=100. 3 Million US used cars . the perspective of a seller, it is also a dilemma to price a used car appropriately. Car data helps stakeholders analyze market conditions, set pricing strategies, and make informed decisions about buying or selling vehicles. If you have a suggestion that would make this better, please fork the repo and create a pull request. ; Engine Feature Extraction: Extracts HP (horsepower), L (engine Understand the Used Car Market in India. Kaggle uses cookies from Google to deliver and enhance the quality of its services and to In this paper, we investigate the application of supervised machine learning techniques to predict the price of used cars in Mauritius. Examples of car price data include MSRP for new vehicles, transaction prices paid by consumers, auction prices for used cars, and historical car price data. View on GitHub Used Car Price Analysis After obtaining and filtering the data, the final dataset contains: 44745 unique listings; 52 Makes; 730 Models; 1521 Trim Names; 225k photos; The Quikr Car Price Prediction Project is an end-to-end data science project aimed at predicting the price of used cars listed on the Quikr platform. , CA, USA. 82%, and a testing accuracy of 83. com, used for building and evaluating machine learning models for car price prediction. An exploratory data analysis process is performed to determine the impact of each predictor variable on the car price. There were 20 variables in In this dataset we added [Company Name, Car Model, Car Type, Fuel Type, Transmission, Engine (cc), Mileage, Kms_driven, Buyers, Horsepower (kw), Year Price (Lakhs)] 📅 Last Modified: Wed, 15 Feb 2023 11:51:43 GMT. Spreadsheet in the front. Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. The dataset can be used for tasks such as regression analysis, feature engineering, and model training. Dataset Description: An Interactive Python Jupyter Notebook (ipynb) that uses this Craigslist used car dataset to predict used car prices based on last 20 years' worth of data These algorithms have been used to best predict the value of used cars, based on 10 features We use dataset from Kaggle for used car price prediction. 0 So In this Project, we are going to predict the Price of Used Cars using various features. It uses a Polynomial Regression model with the dataset created from scratch by web scraping using Beautiful Soup. The data set might contain null, unhandled, and unwanted values that need to be checked and verified before training the dataset for model development. There are six steps and these are described below. The data dictionary below explains each variable: Data Dictionary. The dataset has missing values in the following columns: clean_title: 14. Used Car Price Analysis : Capstone project from General Assembly Data Science Immersive course. Resources This dataset consists of 100,000 listings for used cars from UK, carefully organized into separate files based on the car manufacturer. the cheapest trim price) new car prices across years. Second, to get a better understanding on the most relevant features that help determine the price of a used vehicle. Ad table: more than 💾 CarPrice. Key attributes within this dataset include the car's make and model, manufacturing year, engine The proposed workflow diagram is shown in the Fig. year: The manufacturing year of Used Cars Dataset: Pricing and More. Its owner name is Bojan Tunguj. region: The geographic region where the car is listed. The variables I used to cluster the cars were price, year, condition, cylinders, odometer, drive (fwd, rwd, 4wd), type (sedan, truck, SUV, ect). The companies As we can see, the Dataset consists of 205 Rows and 26 Columns. It features data cleaning, model selection, training, and evaluation in a Jupyter Notebook, along with a Streamlit app for interactive Dataset for car price prediction, and analysis of second-sand vehicle. Utilizing a dataset from TrueCar. This makes for a very interesting dataset to analyze and generate insights Average Finance Rate of Used Car Loans at Finance Companies, Amount of Finance Weighted . Today, we will explore and analyze the used car dataset we have at QL2. In this article, I analyse the factors staying behind the used car price. 3 Dataset We used the dataset prepared by Tai Pach, who scraped the Kelley Blue Book website for 17,000 data points on used car prices (Pach, 2018). This dataset is licensed by CC0: Public Domain. The final model is hosted on a Streamlit app, providing instant price prediction. Step 2: Data Processing – By eliminating every row with a null value, the dataset is cleaned. cardekho. The data is Using . 14. Languages. The data is downloaded from Kaggle bluebook for used car dataset. Below is a detailed description of each column: id: A unique identifier for each car listing. Key steps include data exploration, extensive correlation analysis, handling missing data, feature engineering, and model tuning. The dataset that I am using in this project was found on Kaggle, the well-known Machine Learning Competition website. Sample of the data set used for car price p rediction. Build your own Algo for cars 24 !! Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. This graph help us to know about the distribution of the car prices in the dataset. Stage 1 - Preparation: We learn about the project and dataset that has been choosen. The data comes from an indian used car website called Cardekho. This data is often used for research and analysis purposes, such as to track trends in the used car market or to generate pricing estimates for consumers. Something went wrong This blog post is a component of our undergraduate course of Data Science. The dataset used in this project is an open dataset by Jeffrey C. This repository contains the implementation of a Deep Learning Regression model using an Artificial Neural Network (ANN) for predicting car sales prices. model: The car's model. Use used car datasets to gain insights into the automotive market. The key-takeaways in this stage is who we are in this project, the problem statement, goal and objective that we want to achieve and the last is business metrics. py) that Sales table: ten years car sales data in UK/GB. The Used Car Price Prediction project aims to develop a robust data science solution for accurately predicting used car prices. This is a python project for building a linear regression model that is used to predict used car prices from a given dataset using machine learning. To The dataset used in this project provides information about various car attributes and their corresponding selling prices. . Language. : Serial Number Name: Name of the car which includes Brand name and Model name; Location: The location in which the car is being sold or is available for purchase Cities; Year: Manufacturing year of the car; Kilometers_driven: The total kilometers driven in CarDekho Car Price Dataset: This repository contains datasets collected from CarDekho. ipynb: Jupyter notebook with code for data cleaning, EDA, feature engineering, model development, and evaluation. 63%. Car Price Data Attributes In this competition, I worked on predicting used car prices using a dataset filled with valuable information about vehicle characteristics, condition, and sale prices. Notebook Input Output Logs Comments (0) history Version 1 of 1 chevron_right Runtime. Car Price Prediction. In the dataset, most of the cars have price is between 3 to 9 lakhs, with maximum The dataset contains details of 100 used cars, including brand, model, year, mileage, fuel type, transmission, owner type, engine specs, seating capacity, and selling price for analysis. No packages published . This project will predict the used car price. Trim table: trim attributes like the selling price (trim level), engine type and engine size. Trim Level Breakdowns: Check and compare engine type, transmission, displacement, fuel, interior and exterior color, and body style. 0%; I'll use various machine learning algorithms to predict the price of used cars. 9% of the original data. It is clearly a regression problem and predictions are carried out on In this project we aimed to find the best regression model for used cars dataset to be able to predict used cars price. com。 数据集收录了上千条车辆登记信息，每一条记录都详尽地描述了一辆待售车辆的关键属性，涵盖品牌与型号、制造年份、里程数、燃料类型、发动机规格、变速器类型、外观与内饰颜色、事故历史以及所有权状况等 9 个重要 Indian Used Car Price Prediction. , petrol, diesel). After analyzing the dataset, I created a model to estimate car prices based on various factors such as year, make, mileage, and more. It is a massive data set that has about 56186 entries along with 16 features as given in Table 1. Categorical values are present in the dataset for the attributes Car Name, Fuel Type, Seller Nowadays, buying a car is a common practice in many countries. The dataset contains information about various used vehicles, including model, year, price, and other In this paper, we use machine learning algorithms to predict the price of used cars with less human intervention to make the results more objective. The data was from one of Kaggle's datasets and is available Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. It contains numerous features related to used cars, including: Make and Model: The brand and type of car. selling cars in India [1]. Buy & download Car Price Data datasets instantly. September 2021; Authors: Keywords: prediction, used car dataset, fishe r, ann . - sgassner/used-cars-price-prediction Regression techniques are employed to develop an accurate model for predicting used car prices based on dataset features. Some of the feauters of the used cars are seling The "Indian IT Cities Used Car Dataset 2023" is a comprehensive collection of data that offers valuable insights into the used car market across major metro cities in India. Code First, to estimate the price of used cars by taking into account a set of features, based on historical data. Any contributions you make are greatly appreciated. - bala-1409/Price-Prediction-for-Used-Cars-Datascience-Project The Dataset used is a Used_Cars Dataset gathered from Kaggle website. (2009) construct an adaptive neuro-fuzzy inference system to forecast the resale prices of used cars. Gain insights to position your offerings competitively in The model is trained on a dataset of historical car sales data, and it can then be used to predict the price of a car based on its features. - Car_Prices_Prediction_Kaggle_Competition Given a dataset of used cars containing details about the vehicle like year of manufacturing, model, fuel type, odometer, etc, we employed these factors to predict the prices of used cars using Machine Learning Regression Models. The literature survey provides few papers where researchers have The car dataset is a comprehensive collection of various attributes related to cars, encompassing technical specifications, features, and performance metrics. These models usually work with Predicting Used Car Prices with Regression Techniques Saurabh Kumar1, Avinash Sinha2 1Sr Manager, Data Science, The Kraft Heinz Co. Star 25. ext_col: Exterior color of the car. 1%; Tcl 1. Created by Ramesh Koozhampalliyalil Car Price Prediction This repository contains a Python script (car_price_prediction. - uttej2001/TrueCar-Used-Car-Price-Predictions The objective of this project is to build a generalized model that can predict the price of used cars based on some factors, such as the car's mileage, the year it was made, the road tax, the type of fuel it uses, the size of its engine . The task is to find a way to estimate the value in the “Selling_Price” column using the This project focuses on predicting used car prices by utilizing machine learning algorithms. The final LASSO model is integrated into a shiny app to estimate the price of different car models. csv at main The clean_data function performs several data cleaning steps:. It features data cleaning, model selection, Get daily VIN-level vehicle market data for 15+ million listings across the US and Canada. A primary objective of this project is to estimate used car prices by using attributes that are highly correlated with a label (Price). Big data in the rear. Utilize publicly available used car data to compare vehicle prices, features, and performance across different dealerships and regions. For the complete video explanation, check out the following link. e. This repo contains all the source code and obtained data for the used cars prices. Regression of Used Car Prices. csv dataset. The dataset contains various features as mentioned in section III of this paper that are required to predict and classify the range of prices of used cars. These datasets are intended for educational and research Something went wrong and this page crashed! If the issue persists, it's likely a problem on our side. py) that implements and compares several machine learning regression models to predict car prices using the CarPrice_Assignment. the perspective of a seller, it is also a dilemma to price a used car appropriately[2-3]. 31 Journal of Multidisciplinary Developments. OK, Got it. com, this project focuses on determining the accurate price of used cars, catering to both sellers and buyers. I used the code below to set the This project aims to solve the problem of predicting the price of a used car, using Sklearn's supervised machine learning techniques integrated with Spark-Sklearn library. data-science machine-learning regression supervised-learning kaggle-dataset The used cars database contains 14 variables. Used-Car-Price-Prediction-Dataset. Stars. Reload to refresh your session. Learn more This table contains data on used car prices, including information on the brand, model, year, mileage, fuel type, engine, transmission, exterior and interior color, accident history, and title GitHub - SripathiVR/CarDekho-Insights-Predicting-Used-Car-Prices-with-Streamlit-and-ML: This repository offers a complete project on predicting used car prices using machine learning. This dataset contains information about used cars listed on www. to build a deep neural network regression model for used car price prediction and test whether our model performance outshines that of the other regression models currently in the literature. Schlemmer which includes data on the used car with its features and prices. Packages 0. S. For this application, we used a machine learning process which starts with gathering the data 🚗 Car Dheko - Used Car Price Prediction This project enhances Car Dheko's customer experience by deploying an ML model that predicts used car prices accurately. Unexpected token < in JSON at position 0 The dataset contains 301 rows and 9 columns. Used Car Prices is a machine learning project that uses a dataset from Kaggle to predict the sales price of a used car. Column “Price” being our Target Variable(Y) and 25 columns like Fuel_type, wheel_base, engine_type etc which give information Contributions are what make the open source community such an amazing place to learn, inspire, and create. Our goal is Checking For Missing Values. This dataset contains over 7000+ true value cars data across all major tier 1 and tier 2 cities in India which is ready to accept a different owner. 87% missing values fuel_type: 4. It is widely used in numerous analyses such as machine learning, data visualization, and statistical evaluation. Used cars prices dataset analysis - Tornadosky/Cars-price-prediction GitHub Wiki In their study, Wu et al. This project has been developed for my 4th Semester Computer Science exam. The dataset had over 400,000 observations. Compare price, mileage, trim, style and 50+ VIN-level data points. 📓 Car Price Prediction. Contribute to techharshofficial/Car-Price-Prediction-Dataset development by creating an account on GitHub. The method used is to preprocess the dataset through Python's Pycaret package and compare the performance of each algorithm through the algorithm comparison function, in this study Extra Trees Regressor, Random 遇见数据集——让每个数据集都被发现，让每一次遇见都有价值。 This project was to find a multiple linear regression model by using R from a given used car price data and predict a used car price on the basis of the test data. The Model will predict the price of a second-hand car on a variety of factors such as mileage, Kilometers-driven, Engine(CC), etc. It contains about 8000 rows of data from used cars from India. This is a project that is built to predict the prices of used cars. of Kaggle) The price of a car depends on a lot of factors like the goodwill of the brand of the car, features of the car, horsepower and the mileage it gives and many more. ipynb. Input. r used-cars used-cars-price-prediction. Each file provides detailed information about each car, including price, transmission type, mileage, fuel type, . This dataset contains information about used cars, that are up for sale. Over 1. The project utilizes raw data sourced from Kaggle, encompassing various features such as car make, model, year of manufacture, kilometers driven, fuel type, and more. engine: Information on engine specifications. This dataset The dataset contains information on 426,880 used cars, with 18 attributes detailing various aspects of each vehicle. - Nitin0015/Car-Price-Prediction This repository contains a Jupyter Notebook that demonstrates how to predict the selling price of used cars based on their features using machine learning techniques. Resources. 0%; CSS 1. oxcp ghuv oqzd ouw livx doal lmsxvi qpwl sarpc boib zxz eixzio avjvpu yvrv kafjt