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1,194 changes: 1,194 additions & 0 deletions TeamOne/HackThon1.ipynb
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# Ignore everything in this directory
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23 changes: 23 additions & 0 deletions TeamOne/final_submission_template.md
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# I Hacked-LA-2023
- "submit" your work by creating a pull request as detailed in the README.md.

List your group members:
> Your Group Members
Team name: TeamOne
Member: 1.Luke Zhu 2.Annie Zhou 3.Zack Ye 4.Logan Wu

# This Project
> Link to presentation: https://docs.google.com/presentation/d/1CwManzvBTZHfPu2v5853XIFaBGimSVk_Q5kk-1Y4kGk/edit?usp=sharing
> Give a brief description of the final product:
We concluded all our member's events count for each course, and also 1. (plot the bar chart for events distribution by hour of the day) and 2.(plot the pie chart for distribution of working time by weekdays versus weekends) and 3.(plot the bar chart for distribution of events counts by day of the day).
We also investiagte which factor influents most to the final grade of a student.

# Reflection
## Approach
> What was your approach to the dataset? What problem did you want to solve? What technology did you decide to use? How did your team split the work?
We use Python/R to mine the data to find patterns. We investigate te time pattern of University students' time spent on studies and alos explore the most influenting factor to the final grade of a student.
## Wins / Challenges
> Describe some wins / challenges. What did you learn? What would you do differently next time?
The challenges that we faced was the data wrangling, which we did combined and organzied different data frame into one for later analization and interpretation, futhermore, it is quit time comsumming to find a strong corelattion between some of the feature and target--grade.
With our strong fundation on LM model, we found that the mean grades of students where highly related to their participation in the course and understanding shown in discussion board. For next time, we will try the best to fit all features into one data frame, then use multipel model to exam it, in order to get model that is best for the data set, which we will be able to predic new data with high accurcy.

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