Before you run this notebook !

I wasn't able to run this notebook on binder and collab. This is probably due to the large size if the dataset. Kaggle was the only option.

Used cars market facts

As an aspiring Data Analyst, (or scientist ? or machine learning engineer ? work in progress ...) I am fascinated by the data. When I had to make a project, as a part of my training with Jovian [Data Analysis with Python: Zero to Pandas], I tried to combine my passion for data and my all-time favorite topic : Cars !

As a car-enthousiast, I like working on cars. I'm often in the market for used cars and parts. The buying process involves lots of research and comparison. The ultimate goal is to get the best deal, using all the available tools.

But what is a good deal ?

While many parameters influence a car buyer, the decision always involves emotion. The first thing to consider before taking any decision is to have as much good quality data as possible. We have then to perform analysis and extract informations.

In this notebook, I study a public dataset available on KAGGLE. The dataset is scraped from Craiglist, a very popular classified ads in the US, and north America. It contains informations from around 450k car classified ads. It is updated every 18 months and contains a fair amount of properties for each transaction (26 columns). More details can be found on this link.

How to run the code

This is an executable Jupyter notebook hosted on Jovian.ml, a platform for sharing data science projects. You can run and experiment with the code in a couple of ways: using free online resources (recommended) or on your own computer.

Option 1: Running using free online resources (1-click, recommended)

The easiest way to start executing this notebook is to click the "Run" button at the top of this page, and select "Run on Binder". This will run the notebook on mybinder.org, a free online service for running Jupyter notebooks. You can also select "Run on Colab" or "Run on Kaggle".

Option 2: Running on your computer locally

1. Install Conda by following these instructions. Add Conda binaries to your system PATH, so you can use the conda command on your terminal.

2. Create a Conda environment and install the required libraries by running these commands on the terminal:

conda create -n zerotopandas -y python=3.8 
conda activate zerotopandas
pip install jovian jupyter numpy pandas matplotlib seaborn opendatasets --upgrade

3. Press the "Clone" button above to copy the command for downloading the notebook, and run it on the terminal. This will create a new directory and download the notebook. The command will look something like this:

jovian clone notebook-owner/notebook-id

4. Enter the newly created directory using cd directory-name and start the Jupyter notebook.

jupyter notebook

You can now access Jupyter's web interface by clicking the link that shows up on the terminal or by visiting http://localhost:8888 on your browser. Click on the notebook file (it has a .ipynb extension) to open it.

Downloading the Dataset

The dataset can be downloaded from kaggle, using this link. We need to download and install some python libraries as well.