This data can be used for a lot of purposes such as price prediction to exemplify the use of linear regression in Machine Learning.

The columns in the given dataset are as follows:

1. name
2. year
3. selling_price
4. km_driven
5. fuel
6. seller_type
7. transmission
8. Owner

This dataset contains information about used cars listed on Kaggle.

This project is completely from scratch to deployment, the data set can be downloaded from the above Kaggle link.

For every new project, we should make a new environment and install particular libraries in it. You can search on google for how to make a new environment or you can write the command below:

conda create -n carprediction python=3.7

#to activate the environemnt
activate carprediction

Open the python3 in a jupyter notebook. First, we need to import the panda library and read the CSV dataset.

import pandas as pd
df=pd.read_csv('car data.csv')
df.shape

#output:
(301, 9)

To see the view of the dataset. In the output above, we see that there are 301 rows and 9 column features.

df.head()

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