A model to predict the price of a used car should be developed in order to assess its value based on a variety of characteristics. Several factors affect the price of a used car, such as company, model, year, transmission, distance driven, fuel type, seller type, and owner type. As a result, it is crucial to know the car's actual market value before purchasing or selling it..

1. Creating a new Conda env.
2. Training.
3. Execution
4. Testing of the Model.
5. Dataset

Its always better to implement a project in a new environment as you can know the exact requirements needed for the project to run. When we create a new environmen, we are starting with no pre-installed packages or tools. So to create a new environment in anaconda prompt use the command : conda create -n carprice python=3.6

and now activate and switch to this environment using the command: conda activate carprice

Move to the location where you have cloned this repo, and now open the jupyter notebook from this directory. now run every cell in the notebook.

After every cell is done running, we see a new file with ".pkl" extension has been created in the same src folder. This file contains our model. A web interface has been developed using flask and HTML and can be used for prediction.

I recommend to install the following packages in the "carprice" environment

- "flask" package : `pip install flask` command.

- "jsonify" package : `pip install jsonify` command.

- "requests" package : `pip install requests` command.

- "numpy" package : `pip install numpy` command.

- "sklearn" package : `pip install sklearn` command

Run the app.py in the anaconda prompt using python3 app.py then open the web-address displayed below.

A html page opens and enter the values according to the HTML file and the price is predicted.

The data we have used in this project was downloaded from Kaggle. It was uploaded from Cardekho.com . The dataset consists of 301 rows and 9 columns with no null values. Column data consist of independent Features. The independent features contain both categorical and numeric values.