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:
- Car_name: which consist of various major indian car and have 98 unique value
- 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
- selling_price: the current average market value of the car
- Present_price : the present market price of the car
- km_driven: overall km driven by the car, it has the highest unique values
- fuel: it is categorical data which is petrol, diesel, cng
- seller_type:it is categorical data which is petrol, diesel, cng
