In this project, we will be predicting car prices using a dataset of car features and prices. We will use a variety of techniques, including data cleaning, exploratory data analysis, feature engineering, and machine learning modeling.

The first step in our analysis is to clean the data. This involves checking for missing values, outliers, and errors in the data. We will also perform some basic data transformations, such as converting categorical variables to numerical variables, and scaling numerical variables.

Next, we will perform exploratory data analysis to understand the relationships between the variables and the target variable (car price). We will use visualization techniques such as scatterplots, histograms, and box plots to explore the data and identify any trends or patterns.

Based on our exploratory data analysis, we will perform feature engineering to create new variables that may be useful in predicting car prices. This can include combining existing variables, creating interaction terms, and transforming variables to better capture their relationship with the target variable.

Finally, we will build a machine learning model to predict car prices. We will use a variety of models, such as linear regression, decision trees, and random forests, and evaluate their performance using metrics such as mean squared error and R-squared.

Overall, this project will provide a comprehensive overview of the process of predicting car prices, from data cleaning to machine learning modeling. By following this project, data scientists can gain a better understanding of how to approach similar prediction problems in their own work.

• Download Anaconda from their website and follow the installation instructions for your operating system. https://www.anaconda.com/download/

• Download Visual Studio Code from their website and follow the installation instructions for your operating system. https://code.visualstudio.com/Download

• Install the Python extension from within Visual Studio Code by searching for "Python" in the extensions marketplace.

• Install the jupyter extension from within Visual Studio Code by searching for "jupyter" in the extensions marketplace.

• Create a virtual environment using the Anaconda prompt by running the command conda create --name dsworkshopenv

• Activate the virtual environment by running conda activate dsworkshopenv

• Clone this repo using the command git clone https://github.com/karthikeyanVK/CarPreditionWorkshopTemplate.git

• Open Visual studio code, open the folder where the repo is cloned and open CarPricePrediction.ipynb notebook for the workshop

• Press ctrl+shitf+p and type interpreter and select python:select interpreter and again select dsworkshopenv