natural-language-processing-project

• Data Science Team Members: Gabby Broussard

Project Summary:

• The purpose of this project is to scape data from Github repository README files covering different Animal Crossing topics. The goal is to build a machine learning model that will predict what programming language the repository is based off of the given text in the README.

Project Goals:

• The purpose of this project is to scape data from Github repository README files covering different Animal Crossing topics. The goal is to build a machine learning model that will predict what programming language the repository is based off of the given text in the README.
• All files referenced in this presentation are available in the github repository for this project: https://github.com/GabbyBarajasBroussard/natural-language-processing-project

Conclusion and Takeaways

• The most common language used is Javascript followed by Python
• All models beat baseline but linear regression beat baseline the most with a 0.571 accuracy on train and 0.5 accuracy on validate.
• One of the most common phrases is rryywwss suggesting that many of the animal crossing repos cover how to get hybrid flowers in the game
• Important to note that even if there are close to 1,000 instances of having a repo, many may not have a readme. The larger the sample will yield better results.

Progression through the Data Science Pipeline: PLAN -> ACQUIRE -> PREPARE -> EXPLORE -> MODEL -> DELIVER

Each step in my process is recorded and staged on a Trello board at: https://trello.com/b/CoF5AaWI/nlp-project

Plan:

• Plan a website or a topic to scrape data on
• Create GitHub organization and set up GitHub repo, to include readme.md and .gitignore.
• Brainstorm a list of questions and form hypotheses about how variables might impact one another.
• Create Trello Board to organize time

Acquire:

• Have an env.py file created with credentials to access data
• Read data using acquire.py functions.
• Write data into a csv to use later.

Prepare:

• Carefully reviewed data, identifying any missing, erroneous, or invalid values.
• Explored value counts of the dataframe
• Created and called a function, prepare, as a reproducible component that cleans/prepares data for analysis by: handling missing values, adjusts data types, handles any data integrity
• Split the data into train, validate and test sets.

Explore:

• Visualized all combination of variables to explore relationships.
• Summarized takeaways and conclusions.
• Vectored data to create numerical values for alphabetical values.

Model:

• Developed a baseline model.
• Modeled train and validate data on Linear Regression, K-Nearest Neighbors, and Random Forest Clasffier.
• Modeled test on Linear Regression.

Deliver:

• Clearly document all code in a reproducible Jupyter notebook called Walkthrough.
• A well-documented jupyter notebook that contains your analysis called Walkthrough.
• One or two google slides suitable for a general audience that summarize your findings.
  • Include a well-labelled visualization in your slides.

Instructions for Reproducing My Findings:

• Start by cloning the github repository on your From your terminal command line, type: [email protected]:GabbyBarajasBroussard/natural-language-processing-project.git

• Download the following files from https://github.com/GabbyBarajasBroussard/natural-language-processing-project to your working directory:

• acquire.py

• prepare.py

• wrangle.py

Sources:

• Data Obtained From Github Repository Search Scrape
• Image From: https://www.nintendo.co.uk/Games/Nintendo-Switch/Animal-Crossing-New-Horizons-1438623.html