## Inspiration

As a team of undergrad computer scientists we will soon face the crazy world or resumes, interviews, work, etc... It stresses everyone out whether they will be able to get that dream job at Google or just to for the sake of competition.

## What it does

Our machine learning system uses supervised learning algorithms to classify resumes of top software engineers in various companies and label their skills accordingly. Next, the user uploads their resume and it is tested against the predicted hypothesis by the ML algorithms which in hand return what tier(s) you belong to. As for tiers, we divided them into two parts. Top tier, such as Google, Facebook, Amazon, IBM, etc..., and bottom tier such as local Houston companies. Unfortunately, due to time constraints we cannot provide company specific statistics according to ones resume. (We plan to offer location based ranking and statistics in the future). Another factor we included was education and improvement. After viewing the results, users can follow recommended links provided to websites where they can practice on algorithms or take online certifications that can help them stand out even further!

## How we built it

We used scikit-learn modules such as Bernoulli Naive Bayes algorithm, Passive Aggressive algorithm, Logistic Regression, ADA Boost, and Decision Trees. After comparing the accuracy results, Passive Aggressive algorithm outputted 96.875% accuracy thus becoming our main model classifier. Next we se tup the backend using Flask and published it on PythonAnyWhere. The frontend is built using HTML5/CSS3.

## Challenges we ran into

Finding resumes! In order for our system to work accurately we must provide as much training data (resumes) as possible! And not only that but quality resumes! Such things are hard to come across. Luckily, we managed to come up with 1000 resumes that we used in training and testing the model. The following are the results outputted by various classifiers used:

Algorithm Accuracy Scores:

1. Bernoulli Naive Bayes: 83.984%
2. Logistic Regression: 92.092%
3. Passive Aggressive: 96.875%
4. Decision Tree: 95.102%

## Accomplishments that we're proud of

We are really proud with the idea that we came up with because many students are worried about their future and would like to know what the future holds for them. Unfortunately, we are no fortune tellers but nevertheless we are skillful machine learning engineers who with some help of statistics, machine learning tools and Python can help anyone discover their real potential!

## What we learned

Nothing is easy! Heck, writing this story isn't as easy as it looks. But we didn't give up and here we are presenting our final product (not the final idea) and open to suggestions and contributions!

## What's next for MLRP

We would like to dive deeper into machine learning algorithms to provide more descriptive and personalized results to every single user for a full user friendly experience.