Used car values dataset 3 of this paper that are required to predict and classify the range of prices of used cars. Check car prices and values when buying and selling new or used vehicles. It contains 26 columns, including both numerical and categorical features. Jul 25, 2023 · The dataset contains 301 rows and 9 columns. For eg. The primary objective of this project is to develop a pricing model that can effectively predict the price of used cars and can help the business devise profitable strategies using differential pricing. In [1], the patent describes a generic engine . It can be used to predict the price of a used car based on these factors and has 4009 rows and 13 columns. OVERVIEW OF THE DATASET The dataset includes a variety of features describing cars listed on Craigslist. Car prices dataset sample Data points may include year, make, model, mileage, condition, location, vehicle identification number (VIN), transmission and drivetrain, fuel economy, options and features, market value, seller information, and more. Prediction of the second-hand vehicle price provides an important benchmark to both private buyer and the seller as well as business professionals such as car dealers, lenders and insurance companies. Key steps include data exploration, extensive correlation analysis, handling missing data, feature engineering, and model tuning. A Dataset for Predicting Used Car PricesSomething went wrong and this page crashed! If the issue persists, it's likely a problem on our side. Step 1: Understanding the Dataset The dataset contains various attributes of used cars, including price, brand, color, horsepower and more. In this article, I analyse the factors staying behind the used car price. Apr 19, 2022 · This project uses machine learning to predict the price of a used car. Inferring the Different model outputs 6. It has shown an excellent performance in such a big dataset and it has performed consistently throughout the Training and Testing process. The dataset can be used for a variety of applications such as price prediction, trend analysis, and market research. So In Apr 25, 2023 · In this analysis, I used a dataset of used vehicles for sale to perform clustering and extract insights about groups of similar vehicles based on their make, model, year, price, and mileage. Evaluating the Model We will look at all these concepts practically using “ Car Price Prediction ” dataset as we go. Access a huge and complete dataset on used cars, with data on make, model, year, mileage, condition, and price. Jul 20, 2025 · It is designed to offer insights into various attributes of cars, making it suitable for analysis and model development. The goal was to clean the dataset, handle missing values, remove duplicates, extract important fields, and visualize pricing trends by model and brand. To Achieve this objective we wanted to do an Exploratory Data Analysis on our dataset to understand the relationship between the price of the car and other Get used car pricing and explore thousands of car listings at Kelley Blue Book. The analysis and model building are conducted in R, while the data were preprocessed in python. 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. By employing advanced algorithms such as XGBoost, this solution outperforms traditional pricing methods by delivering higher accuracy and reliability in price estimation, thus reducing the gap between buyer expectations and seller offerings. 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. states from 2010 to 2019. Our goal is to analyze these factors and determine their impact on selling price. It has 1 4 3 6 records containing details on 3 8 variables, including Price, Age, Kilometers, HP, and other specifications. Download automotive data in CSV/XLS/Excel format for year, make, model, trim, specs & more from 1900 onward About In this competition, I worked on predicting used car prices using a dataset filled with valuable information about vehicle characteristics, condition, and sale prices. Sep 11, 2022 · Nowadays, buying a car is a common practice in many countries. 3 Predicting Prices of Used Cars (Regression Trees) The dataset mlba::ToyotaCorolla contains the data on used cars (Toyota Corolla) on sale during late summer of 2 0 0 4 in the Netherlands. Purchase a full coverage dataset or a subset. Find expert reviews and ratings, explore latest car news, get an Instant Cash Offer, and 5-Year Cost to Own information on The fig. To accomplish this, data mining technology has been employed. Our results show Jul 15, 2023 · The used car market has a high global economic importance, with more than 35 million cars sold yearly. Source: Creator/Donor: Jeffrey C. Contribute to pc1991/India-Used-Cars-Dataset development by creating an account on GitHub. • Key features include the car’s brand, model, manufacturing year, mileage, fuel type, transmission type, number of owners, engine displacement, horsepower, number of seats, and new car price. To use, please go through the user manual [PDF]. The dataset consists of multiple variables such as car make, fuel type, engine size, drive wheels, and more. 5 illustrates a line chart titled Sum of predicted values and Sum of engine(cc) by car name using knn, which uses a dataset of used cars. orgSomething went wrong and this page crashed! If the issue persists, it's likely a problem on our side. (AI-generated) Jun 29, 2024 · This paper explores the predictive modeling of used car prices using regression techniques, focusing on the Indian automotive market. Dec 13, 2022 · Data Information 3. This project uses machine learning to predict the price of a used car. For this analysis, I used the vehicle dataset available on Jul 4, 2020 · Predicting Car Prices with TensorFlow — a case of Multiple Linear Regression (1 of 2) Jul 4, 2020 · 5 min read You’ve got a used car you’d like to sell. Sep 27, 2023 · Figure 2. Introduction This project focuses on predicting used car prices by utilizing machine learning algorithms. Download Open Datasets on 1000s of Projects + Share Projects on One Platform. OUTPUT: MSRP (price) This project can be used by car dealerships to predict used car prices and understand key factors that contribute to used car prices. Jun 4, 2022 · 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. NULL values present in some columns No column present with Car brand name which is created via Excel itself Text present with numerical values in columns are converted to decimal Price Prediction of Used CarsSomething went wrong and this page crashed! If the issue persists, it's likely a problem on our side. Spot trends over time or dig in by body type and brand. cars. Apr 20, 2024 · In this project, I explored the Car Dataset found on Kaggle, a greatly used dataset website in machine learning. 2) Data Utilization (1) Characteristics of the Mar 10, 2024 · This project utilizes Python’s capabilities to explore trends within a car dataset. The project employs Random Forest and Gradient Boosting models with preprocessing, feature engineering, and hyperparameter tuning to achieve accurate predictions. What is the variance for this distribution Sep 16, 2017 · 322 economic data series with tags: Vehicles, Used. This project focuses on analyzing and predicting the selling price of used cars based on various features using Python. After analyzing the dataset, I created a model to estimate car prices based on various factors such as year, make, mileage, and more. cmu. Nov 16, 2023 · In the context of the automotive market, the analysis of used cars is important for understanding pricing, conditions, and specifications. Search for your next used car at KBB. In our dataset, there are 167 duplicate values. There is a huge demand for used cars in the Indian Market today. The dataset encompasses a variety of features such as the car's make, model, year of manufacture, mileage, fuel type, and various other factors that influence This paper aims to build a model to predict used cars' reasonable prices based on multiple aspects, including vehicle mileage, year of manufacturing, fuel consumption, transmission, road tax, fuel type, and engine size. The literature survey provides few papers where researchers have used similar data set or related data-set for such price prediction. The dynamic nature of the automotive market, coupled with various influencing 9. Aug 3, 2020 · Our purpose was to predict the price of the used cars having 25 predictors and 509577 data entries. Jan 2, 2024 · January 02, 2024 Used Car Price Prediction using Machine Learning Models & Techniques. 50, 6. This project aims to leverage data analytics and machine learning to identify key drivers of used car prices and develop a predictive model to forecast these prices accurately. Engineered features from the CarDekho dataset and built a user-friendly frontend to serve accurate price estimates. - naveen-pulivarti/M Nov 17, 2023 · This research paper explores the imperative of employing linear regression models for predicting used car prices. With a large number of rows and many attributes, the dataset provides a comprehensive basis for predicting the price of a car, making it an invaluable resource for car Dec 6, 2018 · We use dataset from Kaggle for used car price prediction. The focus of this project is developing machine learning models that can accurately predict the price of a used car based on its features, in order to make informed purchases. Together with the index for used vehicles, leased cars and trucks, and car and truck rental, it makes up the new and used motor vehicles index. Dataset: The dataset used for this project was retrieved from Kaggle. Click here for a full description of the dataset, or read the description file. Next, with the help of data visualization features were explored deeply. About the Dataset: 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. Jun 8, 2023 · A data dictionary is a collection of names, definitions, and attributes about data elements that are used to explain what all the variable names and values in a dataset mean. Our goal is to understand what factors make a car more or less expensive. 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 status. We then cleaned the data by converting strings to integers where possible and dropping some features that were likely to have little predictive value, including many with missing values. While the cars themselves may seem like relics Welcome to the Used Car Price Prediction project! This repository harnesses the power of data science to predict used car prices with a sleek linear regression model, spiced up with in-depth exploratory data analysis (EDA) and vibrant visualizations. Altogether, this highlights the need for data-driven models that predict a vehicle’s market value. May 14, 2025 · Measuring Price Change in the CPI: New vehicles The new vehicles index, a component of the private transportation index, is included in the transportation group of the Consumer Price Index (CPI). com/nehalbirla 840 economic data series with tags: Price, Vehicles. We will be working on the Used Cars dataset to perform federated learning. com, the site you trust the most. This page includes a chart with historical data for the United States Used Car Prices YoY. What is the mode of the sample?, The values in a dataset are 25, 15, 13, 6, 14, 18, and 11. Mar 26, 2022 · 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. Find expert reviews and ratings, explore latest car news, get an Instant Cash Offer, and 5-Year Cost to Own information on Sep 12, 2023 · “Welcome to a data-driven journey through the world of used car prices! In this exploration, we’re delving into a fascinating dataset from 1985. The inventory dataset contains all U. It includes numerical values representing the population count for each year, allowing for an analysis of population trends over time. This dataset focuses on used cars, and provides insights Used Car Price Analysis : Capstone project from General Assembly Data Science Immersive course Feb 6, 2020 · I used Kaggle’s used car data set because it had a variety of categorical and numerical data and allows you to explore different ways of dealing with missing data. Oct 24, 2019 · 3 main ways of selling a used car. In our study, we examined the effectiveness of the linear regression model for used car price prediction. 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. The sections of this tutorial are presented as follows; Dataset Preparation Node Configurations Create an Experiment to Train a Model Testing Regression of Used Car Prices Overview: This project is part of a Kaggle competition focused on predicting the prices of used cars using machine learning models. Personal Auto Manuals, Insurance Services Office, 160 Water Street, New York, NY 10038 Insurance Collision Report, Insurance Institute for Highway Safety 3 Million US used carsSomething went wrong and this page crashed! If the issue persists, it's likely a problem on our side. This project focuses on analyzing a used car dataset to uncover insights into factors affecting car prices, mileage, and resale value. Get your car's value in real-time from Kelley Blue Book, the most trusted resource on the planet for used car value. Also included are a project report, user guide, and resources like datasets and joblib files in a ZIP. Some notable features include: - year: The manufacturing year of the car. FRED: Download, graph, and track economic data. Oct 14, 2020 · For this project, I have only used a single model in order to predict the price of used cars: the Random Forest Regressor. Feb 6, 2024 · Enter the National Automobile Dealers Association (NADA) and its renowned Used Car Values, serving as the leading source for accurate appraisals across various categories – cars, SUVs, motorcycles, RVs, and boats. The model should take car-related parameters and output a selling price. A comprehensive dataset of 180+ cities featuring used car listings from Cars24. 3: Check Duplicate Values Duplicate values can impact the accuracy of ML models and data analysis results so, it’s a good practice to always check for duplicate values in the dataset. com, the goal of this paper is to identify key predictors of used car prices and develop a robust multiple linear regression model. This dataset obtained from Kaggle Used Car Auction Prices. As sales of new cars have slowed Predict price of used cars based on car features and its current condition Introduction The dataset that I am using in this project was found on Kaggle, the well-known Machine Learning Competition website. Equation for Linear Regression 5. Discover examples and use cases to enhance pricing strategies and consumer insights about car prices. , region This light-duty vehicle inventory dataset provides information on vehicle registrations by vehicle type (car vs. gp. 00. India's Used Cars Prediction Dataset. Although various machine learning techniques have been applied to create robust prediction models, a comprehensive approach has yet to Apr 26, 2025 · This project analyzes a dataset of second-hand cars listed for sale, containing fields like brand, model, price, engine type, mileage, color, and date of advertisement update. Count of missing values for each column of the car’s dataset. It is important to understand how the used car prices are being influenced by various features as the market has changed dramatically. Cars are initially assigned a risk Aug 30, 2023 · Companies across the used-car market’s value chain can use data and analytics to tap into growth opportunities and improve margins. S. A machine learning app for predicting used car prices based on features such as model year, mileage, transmission, and more, using an XGBoost model trained on CarDekho dataset. The dataset contains various features as mentioned in Sect. We have used dataset of used cars containing missing values in few columns to predict the price of car given the details of car and thus comparing the accuracy of the estimated price with different approaches. It features data cleaning, model selection, training, and evaluation in a Jupyter Notebook, along with a Streamlit app for interactive predictions. " Discover datasets around the world!This data set consists of three types of entities: (a) the specification of an auto in terms of various characteristics, (b) its assigned insurance risk rating, (c) its normalized losses in use as compared to other cars. Analyze publicly available used car datasets to identify market trends, pricing patterns, and consumer preferences. This project analyzes a large-scale dataset of 426,880 used car listings scraped from Craigslist, with a focus on identifying key factors that influence car pricing, value retention, and common traits of higher-priced vehicles. The dataset comprises features including the odometer value, the year the car was made, and the engine capacity. This dataset contains information on used cars for sale, including model name, year of manufacturing, Km driven, price, and fuel type. Feb 26, 2023 · The most commonly used method for price prediction is the linear regression model. Flexible Data Ingestion. 75, 7. Introduction This tutorial focuses on how to train a federated regression model on Non-IID dataset using PyTorch framework. About Developed an end-to-end ML application using Python and Flask to predict used car values with a Random Forest model. , 2020), accurately predicting resale values is a challenging task, which often involves large uncertainty for both buyers and sellers. kaggle. The most important headings that affect second-hand car prices are included in this dataset, which is formed by the compilation of used vehicle sales advertisements on the Internet, in line with Subsequently, after completing the dataset cleaning, we will conduct a small business simulation exercise with the remaining cars. If Xbar (sample mean) = sigma X / n, what is the mean for the sample?, The values in a dataset are 7. Dataset Description: The datasets include various attributes of cars listed on CarDekho, such as make, model, year, mileage, and price. Null, redundant, and missing values were removed from the dataset during pre-processing. This dataset has over 426 thousand rows of data that you can use for pricing analysis, market research, or machine learning. Mar 5, 2025 · Given the inherent volatility and opacity of the used car market (Akerlof, 1970, Bauer et al. a used vehicle’s price on the market. The data can be used to observe growth patterns, demographic shifts, and potential correlations with economic factors such as car pricing. This result makes sense, since we have 27 variables in total, and we About In this competition, I worked on predicting used car prices using a dataset filled with valuable information about vehicle characteristics, condition, and sale prices. The features available in this dataset are Mileage, Make, Model, Year, State and City. Vehicle dataset from cardekho : https://www. Data visualization plays a crucial role in extracting insights from complex datasets, and libraries like This project looks into exploratory data analysis performed on the Used Cars dataset, including preprocessing, data interpretations and comparision of various classfication and regression algorithms. python food education r topic nfl pokemongo dataset housing-prices used-cars fake-news analyses environment-detection financial-analysis health-data temperature-data Updated on Nov 13, 2018 Jupyter Notebook Predicting used car prices using Craigslist dataset and XGBoost regression with advanced feature engineering and cross-validation. So we need to build a model to estimate the price of used cars. Jun 13, 2025 · 1) Data Introduction • The Used Car Prices in india dataset contains information on the prices and related characteristics of used cars sold in India. com, used for building and evaluating machine learning models for car price prediction. Used Car Price Prediction Dataset is a comprehensive collection of automotive information extracted from the popular automotive marketplace website, https://www. This repository offers a complete project on predicting used car prices using machine learning. To be able to predict used cars market value can help both buyers and sellers. The dataset contains information on 2000+ used cars including make, model, manufacturer, price, year of production, fuel type, states sold in, and kilometers driven. cars_price_prediction Objective In this project I'm trying to analyse and visualize the used car prices from the dataset availabe at Kaggle in order to predict the most probable car prices with the use of basic linear regression models: Linear Regression, Ridge Regression, Lasso Regression and ElasticNet Regression. We implement and evaluate various learning methods on a dataset consisting of the sale prices of different makes and models across cities in the United States. This dataset provides annual population estimates for U. 25, 7. Study with Quizlet and memorize flashcards containing terms like The values in a dataset are 7. This result makes sense, since we have 27 variables in total, and we Download automotive data in CSV/XLS/Excel format for year, make, model, trim, specs & more from 1900 onward A machine learning project to predict used car prices using a dataset from Kaggle. The final LASSO model is integrated into a shiny app to estimate the price of different car models. The dataset includes various features such as model, year of manufacture, kilometers are Used Car Price Prediction Analysis This project provides a comprehensive analysis and predictive model training for used car prices using a detailed dataset. Using Python and common data analytics libraries, the analysis DVM-Car A Large-Scale Dataset for Automotive Applications This publicly available dataset aims to facilitate business related research and applications in automotive industry such as car appearance design, consumer analytics and sales modelling. In this data science project, I have conducted a comprehensive Exploratory Data Analysis (EDA) on a dataset containing information about used cars. cs. You know the acquired price, but how do you measure how much a fair sale price would be based on how much it has been used? Let’s build a TensorFlow model to help suggest near-perfect used car prices. Dataset using historical data of sold used car in United States moreless 2 years before. We also defined functions for visualizing data with boxplots and histograms. Schlimmer '@' a. Upon completion, it can output a relatively accurate price prediction based on the This project leverages machine learning techniques to build a regression model that predicts used car prices based on a rich dataset of car attributes. Our group has chosen a dataset on Used Cars from Kaggle, that is between the 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). Each row in the dataset contains information about one car. May 25, 2025 · EDA--Car-Sales-Dataset This project explores a used car sales dataset to analyze factors influencing resale value, identify trends in the second-hand car market, and understand buyer behavior. - bala-1409/Price-Prediction-for-Used-Cars-Datascience-Project May 9, 2020 · To summarize, in this post we went over several methods for analyzing the US Cars Dataset. City Average (CUUR0000SETA01) from Mar 1947 to Sep 2025 about vehicles, urban, new, consumer, CPI, inflation, price index, indexes, price, and USA. May 1, 2025 · Our project aims to build a robust machine learning model to predict used car prices in major Indian metro cities, leveraging a rich dataset and advanced analytical techniques. 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]. In this exercise, we will simulate owning all these cars and outline a small business strategy for a rental/leasing company. I hope this post was useful/interesting. The task is to find a way to estimate the value in the “Selling_Price” column using the This project predicts the most probable car prices with the use of basic linear regression models. This dataset comprises 4,009 data points, each representing a unique vehicle listing, and includes nine distinct features providing valuable insights into the world of Jul 12, 2025 · This article will explore how to analyze the selling price of used cars using Python. The objective of this project is to explore a dataset of used car sales in the U. • Cleaned & ready — zero nulls New or used cars from dealerships, auction sites, and private listings – all in one place! Register for our dataset and initiate data retrieval on a monthly, weekly, or daily basis instantly. Get Blue Book resale value, trade-in value, or even a cash offer from a dealer. -based new franchise auto dealerships; independent stores are not included. Feb 25, 2022 · A primary objective of this project is to estimate used car prices by using attributes that are highly correlated with a label (Price). May 14, 2025 · This document provides a comprehensive overview of the Used Cars Dataset, a core dataset utilized throughout the Data Science Bootcamp repository for car price prediction analysis. Dive into a dataset packed with car attributes Used car dataset of 15 thousand used cars The fig. Tools: Python / R Dataset: CarGurus (IL/IA/WI/MI/IN) Analyses performed: Linear/Polynomial regression, Random Forest, KNN Oct 24, 2025 · Graph and download economic data for Consumer Price Index for All Urban Consumers: Used Cars and Trucks in U. - SripathiVR/CarDekho-Insights-Predicting-Used Used car pricing trends. The provided dataset contains information on 426K cars to ensure speed of processing. And also provide clear recommendations to the Used Car Dealership owners as to what consumers value in a used car. In the highly competitive used car market, accurate pricing is crucial for maximizing profits, attracting customers, and managing inventory effectively. Motive: Each day, thousands of pre-owned cars are sold worldwide. Schlimmer (Jeffrey. Oct 7, 2025 · Used Car Prices YoY in the United States decreased to 0 percent in October from 2 percent in September of 2025. The new vehicles index is published at the U. City Average (CUSR0000SETA02) from Jan 1953 to Sep 2025 about used, trucks, vehicles, urban, consumer, CPI, inflation, price index, indexes, price, and USA. The distribution plot of Linear Regression and Multiple Regression technique shows how the model predicts the prices of automobiles based on "horsepower", "curb-weight", "engine-size" and "highway-mpg" Comparing these three models, we conclude that the MLR model is the best model to be able to predict price from our dataset. The dataset is taken from Kaggle and contains details of the used cars in India which are on sold through Cardekho. THE LARGEST USED CAR DATASET ON KAGGLE IN 2025 — 10+ MILLION ROWS! 🚗💨 Perfect for: • Price prediction (regression) • Multi-country modeling • NLP on car titles • Recommendation systems Features: • 10,000,000+ realistic listings (USA 50%, Germany 25%, UK/France) • price_usd, mileage_km, horsepower, condition_score, is_electric, etc. 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 Covering a rolling 2-year period and tracked weekly, this dataset helps automotive professionals, dealers, and analysts see how disruptions, including chip shortages, inflation, and tariffs, may have affected list prices. Companies like Cars24 and Cardekho. com uses Regression analysis to estimate the used car prices. Research new and used car book values, trade-in values, ratings, specs and photos. com. edu) Sources: 1985 Model Import Car and Truck Specifications, 1985 Ward's Automotive Yearbook. truck), fuel type, and model year showing the changes Oct 24, 2025 · Graph and download economic data for Consumer Price Index for All Urban Consumers: New Vehicles in U. The dataset for this competition was generated from a deep learning model trained on the "Used Car Price Prediction Dataset. com The dataset is not clean and hence a lot of data cleaning is carried out through Python. It provides valuable insights into the used car market, including popular models, manufacturer companies, and average prices in different states. Utilizing historical data from CarDekho. 9% this month We track millions of used car prices so you can stay on top of market trends—whether you’re buying, selling, or just researching. - sgassner/used-cars-price-prediction CarDekho Car Price Dataset: This repository contains datasets collected from CarDekho. A Dataset ofCars Across Major Brands in IndiaSomething went wrong and this page crashed! If the issue persists, it's likely a problem on our side. Use this data to make informed decisions about inventory, pricing strategies, and emerging demand for specific car models. Jun 12, 2023 · 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. INPUTS (FEATURES): Make, Model, Type, Origin, Drivetrain, Invoice, EngineSize, Cylinders, Horesepower, MPG_City, MPG_Highway, Weight, Wheelbase and Lenght. The second rating corresponds to the degree to which the auto is more risky than its price indicates. Prices down 0. - odometer: The total mileage of the car. - manufacturer: The car’s manufacturing company. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. This model can benefit sellers, buyers, and car manufacturers in the used cars market. This project focuses on Nov 20, 2020 · Exploring and Analyzing Used Car Data Set This blog post is a component of our undergraduate course of Data Science. This post is a step by step Mar 22, 2021 · 4. The analysis includes data cleaning, univariate and bivariate Jul 11, 2023 · Statistical Analysis: Used Car Price Statistics for business project Introduction This report dives into used car prices, exploring how rapidly changing technology and consumer preferences have … Current Used-Car Monthly Market Report on Weekly Values & Residual Values For New Cars & Used Cars And Consumer Shopping Trends At KBB. The dataset includes key information such as car name, manufacturing year, selling price, kilometres driven, fuel type, seller type, transmission, and the number of previous owners. Lasso Regression applied to a PCA-transformed dataset (Model #3) exhibited very high MSE on both the training and testing sets, making it unsuitable for predicting used car prices. Vehicles listings from Craigslist. __ This included defining functions for generating summary statistics like the mean, standard deviation, and counts for categorical values. This dataset consist of data From 1985 Ward's Automotive Yearbook. , analyze factors affecting car prices, and build a predictive model using linear regression to estimate car prices based on specific features. The x-axis lists various car models and their corresponding years, with labels rotated for better readability. Machine Learning has become a tool used in almost every task that requires estimation. Provided By Kelley Blue Book, The Trusted Resource. Initially, data cleaning is performed to remove the null values and outliers from the dataset then ML models are implemented to predict the price of cars. The project involves data cleaning, preprocessing, visualization, and the application of several analytical techniques to explore key factors influencing car prices. Accurately predicting prices is a crucial task for both buyers and sellers to facilitate informed decisions in terms of opportunities or potential problems. 50, and 6. Explore car price data, its role in automotive market analysis, and where to buy datasets. The data types of the columns vary, including both numeric and object (string) types.