بسم الله الرحمن الرحيم

“In the Name of Allah, the Most Beneficent, the Most Merciful”

Introduction: Machine Learning Project (Price Prediction for 100,000 UK Used Cars)

In this project, I will walk through solving a complete machine learning problem using a real-world dataset, then i will make Model Deployment as web application with flask.

This is a supervised, regression machine learning task: given a set of data with targets (in this case the price).

• Supervised problem: we are given both the features and the target
• Regression problem: the target is a continous variable

Content of data

• Scraped data of used cars listings. 100,000 listings, which have been separated into files corresponding to each car manufacturer (total 13 csv files).

• The cleaned data set contains information of price, transmission, mileage, fuel type, road tax, miles per gallon (mpg), and engine size (in liters).

Workflow

1. Data cleaning and formatting
2. Exploratory data analysis
3. Data visualization
4. Feature engineering and selection
5. Establish a baseline and compare several machine learning models on a performance metric
6. Perform hyperparameter tuning on the best model to optimize it for the problem
7. Evaluate the best model on the testing set
8. Model Deployment as web application with flask
9. Video tutorial on youtube (Link: https://youtu.be/OrtbcW8dS4k)
10. submit kaggle notebook (Link: https://www.kaggle.com/code/mohamedsalama1429/100-000-uk-cars-ml-price-prediction-96-score)