There are websites that help you acomplish this task, such as Resume Worded and Jobscan, but these are paid services. Therefore, I created this free web application to help job seekers.

This web app works by selecting the most frequent words in the job description. Then the application parses your resume to see which keywords are included. For example, if "SQL" gets mentioned numerous times, we may assume it is important, so we check if your resume contains the word "SQL".

Finally, a percentage score is generated. 0% means that no keywords are included, whereas 100% means that all the keywords are included. The user can see which keywords are included or missing and modify their resume accordingly.

For this project, I used the Twitter API with the Tweepy library to collect 200 tweets from 10 profiles in 5 different languages. This would result in 10,000 tweets in total (in reality, this number came out lower because some of the accounts did not have 200 tweets on their profile or some tweets were only emojis).

After cleaning the data, I calculated the word frequencies and created a word cloud for each language. Using the data collected, I published a web app with Streamlit where the user can view the top x number of words in both tabular form and as a word cloud for each languages.

There are two dashboards included in this project. The first dashboard shows:

• 72% of users logged sleep data, but only 24% of users logged bodyweight data.
• 61% of users who logged their bodyweight did so manually.
• Most users slept on average between 6 to 9 hours per night.
• The histogram for calories burned is bimodal, meaning that there are two groups within the dataset. Most burn around 2,000 calories per day while some burn closer to 2,750 calories per day.

The second dashboard can be found on my Tableau profile.

From the dashboard, we learn that:

• Wine sales account for 50% of all purchases.
• Almost half of the purchases are made in-store.
• The second marketing campaign performed poorly compared to the other campaigns. This should be further looked into.

We also used the data to train a K Means clustering algorithm. The resulting dataset is split into three clusters.

The MIT Press Essential Knowledge series is a collection of books that discusses several topics for a non-specialist audience. The topics include neuroplasticity, data science, post-truth, recycling, GPS, and many more. The beginning of every book in the series contains a short foreward, a section from this foreword reads,

“In today's era of instant gratification, we have ready access to opinions, rationalizations, and superficial descriptions. Much harder to come by is the foundational knowledge that informs a principled understanding of the world. Essential Knowledge books fill that need.”
— Bruce Tidor, MIT Professor

I highly value education and learning; I'm constantly working on learning new skills and gaining an understanding of topics I'm not very familiar with. In my opinion, every book from this series is an amazing resource to get a high-level understanding of various topics.

My goal for this project was to create a file that contains data on the books in the series. I accomplished this by web scraping text from the MIT Press public website. After finishing the project, I can now easily refer to this spreadsheet to find the next book to read.

For this project, I created a model using the Random Forest classification algorithm to predict the language of typed text. The data for this project came from the Wikipedia Language Identification Database.

The Random Forest algorithm outperformed the Naive Bayes and K Nearest Neighbors algorithms. After training the algorithm, I created a web app using Streamlit to allow users to try it out for themselves

The steps I used to take when creating a new repository were: create the repository on GitHub with the correct account, clone the repository, configure the correct username and email, and finally open an IDE to begin working.

As previously stated, this does not take too much time, but it would be nice to have a function that would do all these steps for me, so that's what I created. For this project, I used the GitHub API with Python to set up a repository with the correct account. Each GitHub repository is cloned into the correct folder to make it easier to manage all the projects. Now, instead of writing multiple lines of code to set up the repo, I can just type create work new-project and everything else gets taken care of.