Behigu Gizachew profile site
Behigu Gizachew
Addis Ababa, Ethiopia
Bahir Dar University (2014-2019)
BSc. (Software Engineering)
tele: +251918216560
Email: Be.gizachew@gmail.com
Python programming
Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together.
machine learning and data science
Machine learning method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
Website Development
Web development is the work involved in developing a Web site for the Internet (World Wide Web) or an intranet (a private network). Web development can range from developing a simple single static page of plain text to complex web applications, electronic businesses, and social network services.
Software Engineering
Software engineering is the application of engineering concepts to software development. Its main goal is the creation, improvement, and maintenance of software. Software engineering takes into account engineering aspects like the hardware and software environment when working on a program.
About me
A Web-App developer with 2 years of experience in Laravel and CodeIgniter. I also have worked in Flutter and React native.
A data scientist who takes pride in building models that translate data points into business insights with experience in executing data-driven solutions to increase efficiency, accuracy, and utility of internal data processing.
Looking to use my Software Engineering skills to manage statistical machine learning and data-related solutions at organizations. Self-driven, hardworking and charismatic individual looking to make an impact using technology.
Education
- Bahir Dar University ( 2014-2019 )
BSc.(Software Engineering)
10Academy
Training on Machine learning, data science, working remotely, updating one self constantly and being on the top of the market.
Work Experience
I spend my days with my hands in many different areas of web development from back end programming (PHP, Django/Python, Ruby on Rails) to front end engineering (HTML, CSS, and jQuery/JavaScript), digital accessibility, user experience and visual design.
I also prepared software requirement documentations and API documentations for the company and the clients of the company.
Done the sentiment analysis task to gain insights on Twitter data to get perspectives on debts by scraping data from different sources (news articles, social media) using python libraries(BeautifulSoup and Selenium).
Spearheaded the analysis of the school loan debt impact on different groups (based on ethnicity, age, gender) which uncovered a new approach to the problem.
Projects
SmartAd A/B testing
Using A/B testing to test if the ads that the advertising company ran resulted in a significant lift in brand awareness.Comparing machine learning models vs A/B testing gave me insights on what to use in which particular problem.
Amharic language speech to text model training
using deep learning, recurrent neural network and conventional neural network to train the data we have to detect the speech and get the transcription(text ) from the audio in real time.
Pharmacy sales prediction
Sales forecasting is the process of estimating future revenue by predicting the amount of product or services a sales unit (which can be an individual salesperson, a sales team, or a company) will sell in the next week, month, quarter, or year.
Causal graph and causal analysis
An attempt to draw dependable inferences about cause-and-effect relationships from research data. Encompassing a variety of methods (e.g., path analysis, structural equation modeling), such analyses differ in the degree to which they are statistically complex and the degree to which causal inferences from them are, in fact, justified.
In this project I tried to track the cause for breast cancer in the given data and extracting new features in the data. I also tried to see if the causal inference I made is correct using other statistical machine learning models.