Milestone two $(20\%)$: Data Collection and Preprocessing $--$ Feb. $18$

Acquire and prepare the data required for your course project.

Tasks:

identify relevant data sources and collect your project dataset

understand the dataset

clean and preprocess the collected dataset

analyze the dataset to gain insights into the data

write your project draft with a simple workflow$/$prototype$.$ The project draft should be uploaded to Canvas in a PDF format at least one page long by one group member and should include:

Names of group members;

An introductory paragraph to describe your project task, and show the motivation and goal of your project

Project plan: the main steps you will take to accomplish this project, the workflow or framework of your project.

Dataset analysis section: illustrating your work related to data collection, data preprocessing, and data analysis

Key references, including links or explanations to any resources you plan to use $($especially code and data$).--$topic is is The goal of the project is to develop a machine learning model that can classify and predict the price of a used car based on various features such as make, model, year, mileage, condition, and other relevant factors.

Title

Author$($s$)$:

Briefly summarize your problem, your work, and the most interesting$/$surprising findings.

$1.$ Introduction

Define and motivate the problem, discuss some background information, and briefly summarize your

approach.

$2.$ Dataset Analysis

Details of the dataset you used in this project. Include the overview of the dataset including the size,

format, and structure, the features $($columns$)$ and their data types, the target variable, some statistical

summary, data visualization, preprocessing, etc.

$3.$ Machine Learning Algorithm$($s$)$ Description

Details of the machine learning you used in this project. Include any formulas, machine learning

algorithm$($s$),$ diagrams $--$ anything that is necessary to clearly explain your project and what you have

done. If possible, illustrate the intermediate stages of your approach with results images.

$4.$ Results Analysis

Clearly describe your experimental process and identify any external code and datasets used. Present your

quantitative evaluation $($if any$)$ and show your program outputs.

$4.$ Discussion and Conclusion

Summarize the main insights drawn from your analysis and experiments. You can get a good project

grade with mostly negative results, as long as you show evidence of extensive exploration, thoughtfully

analyze the causes of your negative results, and discuss potential solutions.

$5.$ Statement of individual contribution

Required if there is more than one group member.Title

Author$($s$)$:

Briefly summarize your problem, your work, and the most interesting$/$surprising findings.

$1.$ Introduction

Define and motivate the problem, discuss some background information, and briefly summarize your

approach.

$2.$ Dataset Analysis

Details of the dataset you used in this project. Include the overview of the dataset including the size,

format, and structure, the features $($columns$)$ and their data types, the target variable, some statistical

summary, data visualization, preprocessing, etc.

$3.$ Machine Learning Algorithm$($s$)$ Description

Details of the machine learning you used in this project. Include any formulas, machine learning

algorithm$($s$),$ diagrams $--$ anything that is necessary to clearly explain your project and what you have

done. If possible, illustrate the intermediate stages of your approach with results images.

$4.$ Results Analysis

Clearly describe your experimental process and identify any external code and datasets used. Present your

quantitative evaluation $($if any$)$ and show your program outputs.

$4.$ Discussion and Conclusion

Summarize the main insights drawn from your analysis and experiments. You can get a good project

grade with mostly negative results, as long as you show evidence of extensive exploration, thoughtfully

analyze the causes of your negative results, and discuss potential solutions.

$5.$ Statement of individual contribution

Required if there is more than one group member.

Reference

Including URLs for any external code or data used here $--$Inthis way