This project is an example of performing data cleaning and visualization on a restaurant and tips dataset. The data contains information about customers'sbills, including the total bills, tips amount and various attributes about the restaurant and working day.

The goals of this project is provide insights into the data and visualiz the relationships between different varibles in the dataset.The data is cleaned by removing missing values handle outliers, and ensure that data is in correct format. Then use visualization tools to create chart and graphs that show the distribution of data and relationship between different varibles.

The project python and Scikit-lern libary to preprocess the data, split the data set into training and testing set, and train by machine leaning models to estimate the tip amount of the restaurant.

By performing data cleaning and visualization techiques, the audience can gain a better understanding of restaurant and tip dataset and proide into insight tip prediction and trends.