Profile Matcher

Introduction

This simple application will score each row defined in the src/data/profile.csv which represents the candidate data with their job title, industries they worked in, and their residence.

The scoring will be performed based on the data match between the project data provided in the src/data/project.json and the ideal candidates that matches those data with the relevant fields such as industries, job title, and the distance between the project location and candidates location.

Description

Term Frequency–Inverse Document Frequency for industrial experience scoring

We will be leveraging the TFIDF algorithm to evaluate the weight of the industries that represents the expected industrial experience an ideal candidate should possesses to the industrials experience a candidate actually possess. We will measure the average frequency of each expected project industry in the candidate's profile data to get the final matching score.

Fuzzy Match for Job Title Scoring

We will be using a fuzzy match to score the possible match to match the expected job titles with the title a candidate possesses.

Scoring Distance

We will be using the Haversine formula to measure the distance between two geo coordinates. Once we measure the distance, we want to score it and scale it to match the other scores so we can get some accurate results. To do this, we will subtract the closest distance with 1 so we get a number close to 1. We are also filtering distance that is more than 100KM as per the requirements.

Final Score

The final scoring will be nothing but the average of all three scores.

Developer Productivity

We are using Docker and docker-compose to ease the application packaging and deploying process.

Testing

We are using Mocha and Chai for performing unit testing and assertion. To measure and generate the coverage report, we are using Istanbul.

Implementation Details

The engineering of the application leverages the TypeScript's Object Oriented functionalities. We are using concepts such as Inheritance, and Encapsulation to make the application strictly typed, more resilient, and secure.

The engineered application project structure looks like the following tree:

.
├── app.ts
├── data
│   ├── project.json
│   └── profile.csv
├── lib
│   ├── helper
│   │   └── helper.ts
│   ├── nlp
│   │   ├── fuzzy.ts
│   │   ├── normalizer.ts
│   │   └── tfidf.ts
│   └── profiler.ts
├── model
│   ├── city.ts
│   ├── location.ts
│   ├── person.ts
│   └── profile.ts
└── tests
   ├── fixtures
   │   └── project.ts
   └── unit
       ├── helper
       │   ├── helper.spec.ts
       │   └── profiler.spec.ts
       ├── model
       │   ├── person.spec.ts
       │   └── profile.spec.ts
       └── nlp
           ├── fuzzy.spec.ts
           ├── normalizer.spec.ts
           └── tfidf.spec.ts

11 directories, 21 files

The following block will describe what each directory in the project structure represents.

• The file app.ts holds the main function which will drive the full flow from data ingestion to scoring and result printing.

• The lib directory will hold all the classes that are required to perform candidate profiling.

  • The Helper class in src/lib/helper/helper.ts holds utilities for all the basic tasks required to be shared by multiple classes such as loading CSV and JSON files, measuring distance and radius.

  • The FuzzyMatch class in src/lib/nlp/fuzzy.ts will provide the fuzzy matching related functionalities.

  • The Normalizer class in src/lib/nlp/normalizer.ts will provide the text normalization functionality.

  • The NlpTfIdf class in src/lib/nlp/tfidf.ts will provide the functionality to perform tfidf on any given document.

  • The Profiler class in src/lib/profiler.ts will provide the ability to perform profiling on any given candidate profile and calculate the final score.

• The model directory will hold all the data models for modeling city, location, person, and profile data.

• The tests directory has all the unit tests and it's required fixtures to perform testing.

Running the app locally

# Clone the Git Repo
> git clone https://github.com/shreyaspatel7/profile-matcher.git

> cd profile-matcher/

# Via NPM
> npm install -D
> env PROFILE_DATA=src/data/profile.csv PROJECT_DATA=src/data/project.json  npm start

# Via Docker Compose
> docker-compose run profiler

Running the tests

Running the unit tests CLI

> PROFILE_DATA=src/data/profile.csv PROJECT_DATA=src/data/project.json npm test

Running the unit tests via Docker compose

> docker-compose run test

Running the unit tests with coverage

PROFILE_DATA=src/data/profile.csv PROJECT_DATA=src/data/project.json npm run coverage

Running the unit tests with coverage via Docker compose

> docker-compose run coverage

We will be able to get the coverage report in both HTML and CLI format. If we run the coverage via docker compose, the results will be available in the results directory.