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Project 1 — Data Analysis

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CSE4/510: Applied Deep Learning

Project 1 — Data Analysis

Description
Our first project is focused on preprocessing, analysing and visualizing real-world
datasets. Applying the basic statistical methods and extracting valuable summary about
it. During the third part of the project you are expected to employ multiple datasets and
extract insights from them.
Dataset
Requirements to the dataset for Part I and II:
● Represent the real-world data
● Contain at least 50k entries

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Description

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CSE4/510: Applied Deep Learning

Project 1 — Data Analysis

Description
Our first project is focused on preprocessing, analysing and visualizing real-world
datasets. Applying the basic statistical methods and extracting valuable summary about
it. During the third part of the project you are expected to employ multiple datasets and
extract insights from them.
Dataset
Requirements to the dataset for Part I and II:
● Represent the real-world data
● Contain at least 50k entries
● Should be different from the one used in class
Possible resources includes:
● Open Data Buffalo – https://data.buffalony.gov/
● Google Dataset – https://datasetsearch.research.google.com/
● US Government’s Data https://www.data.gov/
Tasks
Part I: Perform data analysis of the dataset [20 points]
1. How many entries and variables does the data set comprise?
2. What types of data is included?
3. Are there any data missing?
4. Provide the main statistics about the entries of the dataset (mean, std, etc.)
5. Visualize the data (min 3 graphs), e.g. correlation between different variables.
Are there any interesting patterns?
Part II: Apply ML analysis [40 points]
1. Choose the features and targets in the dataset.
2. Preprocess the dataset for training (e.g. cleaning and filling the missing variables,
split between training/testing/validation).
3. Apply ML algorithms (min 3 algorithms) to model the target variable. This can be
either classification or regression task. You can use any of the libraries with
inbuilt ML functions.
4. Provide the comparison of the results of different ML models you have used. This
can be in the form of graph representation and your reasoning about the results.
Part III: Employ multiple datasets and extract insights [40 points]
1. Choose any related dataset to your current one. Combine the two into one
dataset. The combined dataset doesn’t have size requirements.
2. Choose the correlated variables.
3. Perform statistical analysis on finding the correlation between selected features
from both datasets. Examples:
a. Find the correlation between the crime and the number of schools in the
area.
b. Find the correlation between the traffic and the population in the area
4. Analyse the results and any interesting patterns.
Submit the Project
● Submit at UBLearns > Assignments
● The code of your implementations should be written in Python. You can submit
multiple files, but they all need to have a clear name.
● All project files should be packed in a ZIP file named YOUR_UBIT_project1.zip
(e.g. avereshc_project1.zip).
● Your Jupyter notebook should be saved with the results. If you are submitting
python scripts, after extracting the ZIP file and executing command python
main.py in the first level directory, all the generated results and plots you used in
your report should appear printed out in a clear manner.
● In your report include the answers to questions for each part. You can complete
the report in a separate pdf file or in Jupyter notebook along with your code.
● Include all the references that have been used to complete the project.
Late Days Policy
Up to 5 free late days can be used throughout the course. They can be applied towards
any project. No need to inform the instructor, late submission will be tracked at
UBlearns.
Important Information
This project should be done individually. The standing policy of the Department is that
all students involved in an academic integrity violation (e.g. plagiarism in any way,
shape, or form) will receive an F grade for the course. Refer to the Academic Integrity
website for more information.
Important Dates
July 14, Tuesday, 11:59pm – Project 1 is Due