In this video, I explain a complete project applying Machine Learning to car data, starting from collecting data from the QatarSale website using Web Scraping, then transforming the data into an organized Dataset, conducting Data Understanding and Data Preprocessing, and finally training a Linear Regression model to predict car prices.
The project includes extracting car data such as manufacturing year, number of cylinders, mileage, and price, then converting textual data into numerical data that can be used in the machine learning model.
After that, the data is cleaned and unnecessary columns are removed, then the data is split into Training and Validation sets, applying Min-Max Normalization and Standardization, analyzing Correlation between variables, and then building a Linear Regression model and evaluating it using Mean Squared Error (MSE).
Data Visualization is also presented before and after processing, along with a comparison of actual prices to the prices predicted by the model.
The project illustrates a complete workflow in Machine Learning starting from:
Web Scraping → Dataset Creation → Data Understanding → Data Preprocessing → Data Visualization → Feature Selection → Data Splitting → Normalization → Standardization → Correlation Analysis → Linear Regression → Model Evaluation.
Dataset: 200 Car Records
Model: Linear Regression
Target Variable: Car Price
Features: Year, Cylinders, Mileage
Evaluation Metric: Mean Squared Error (MSE)