car price 369

A second hand car price prediction has been a high-interest research area, as it requires noticeable effort and knowledge of the field expert. Considerable numbers of distinct attributes are examined for reliable and accurate prediction. The data used for the prediction was collected from the kaggle.com In our project we will build a model for predicting the price of used cars in India, by applying several machine learning techniques and Respective performances of different algorithms were then compared to find one that best suits the available data set, the final prediction model will be integrated with a flask app where the user can input attributes like their budget, model of the car, age of the car etc., in a responsive layout and hosting it in heroku.com. Furthermore, the model will evaluate using test data and shows the output as the amount of money required to purchase the used car

Introduction

Deciding whether a used car is worth the posted price when you see listings online can be difficult. Several factors, including mileage, make, model, year, etc. can influence the actual worth of a car. From the perspective of a seller, it is also a dilemma to price a used car appropriately Different websites have different algorithms to generate the retail price of the used cars, and hence there isn't a unified algorithm for determining the price. By training statistical models for predicting the prices, one can easily get a rough estimate of the price without actually entering the details into the desired website. The aim is to use a flask app which work on a machine learning algorithms to predict the price of used car and use three different prediction models to predict the retail price of a used car and compare their levels of accuracy

Data Analysis

The Dataset used for the prediction models was created by nehla birla. This dataset contains information about used cars, from cardekho and indian used car selling websites. This data can be used for a lot of purposes, our main goal is to make price prediction to exemplify the use of various regression models in Machine Learning. data have 301 rows and 9 columns. The columns in the given dataset are as follows:

1. Car_name: which consist of various major indian car and have 98 unique value
2. year: The year when the car is purchased, the year consist of car registered from the past 10 years and it has 16 unique values
3. selling_price: the current average market value of the car
4. Present_price : the present market price of the car
5. km_driven: overall km driven by the car, it has the highest unique values
6. fuel: it is categorical data which is petrol, diesel, cng
7. seller_type:it is categorical data which is petrol, diesel, cng

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