Võ Chơn Chánh

DATA ANALYST INTERN

078*******

ad4lp4@r.postjobfree.com

Thu Duc District, Ho Chi Minh City

OBJECTIVE

I am a student with a strong work ethic and a creative mind who is always looking for new and innovative ways to solve problems As a student interested in Machine Learning, Data Analysis, Learning and working in AI & Data field is an exciting experience EDUCATION

2020 - 2024

BACHELOR OF SCIENCE, MAJOR: INFORMATION TECHNOLOGY

(JAPANESE - ORIENTED)

University of Information Technology (VNU-HCM)

GPA:8.09/10

HONORS & AWARDS

2019-2020

Consolation prize for excellent students in Mathematics at Dong Thap province.

2022-2023

UIT Excellent Academic Scholarship in 1st semester 2022-2023 2022-2023

UIT Good Academic Scholarship in 2nd semester 2022-2023 SKILLS

Programing language:

Python, Java Script, C/C++, R.

Framework

PyTorch, Tensorflow, Sklearn, Matplotlib,..

Database

SQL Sever, PostgreSQL

Tool:

• Canvas, Drawio, Figma

• Power BI

• QGIS

• GG Colab, Visual Studio code

Langguage:

• Japanese - Intermediate ( ~N3)

• English - Elementary

ACTIVITIES

05/2022 - 06/2022

VOLUNTEERED AT THU DUC SOS

VILLAGE

Volunteer

Give gifts and participate in group

performing arts activities for children.

Listen and give positive motivation to

children

CERTIFICATIONS

2023

Technical Support Fundamentals

2023-2024

Google Data Analytics Professional

Certificate

INTERESTS

I like a music, cooking, reading.

I am passionate about studying

mathematics, especially logical

mathematics.

PROJECTS

Comments Data Analysis

(Natural Language Processing)

2022 - 2023

Size of team : 1

Position: Fullstack

GITHUB: HTTPS://GITHUB.COM/LEMON1810/COMMENTS-ANALYSIS

• Description: Find the causes affecting negative and positive comments. Build a machine learning model to predict negative and positive labels.Evaluate and optimize model parameters, visualize and solve problems.

• Tool: GG colab, excel.

• Framework and Library: Pandas, sklearn, pyvi, Transform.

• Method: Feature extraction using vectorizer (Countvector, TF-IDF) combined with tokenize. Tuning model parameters with GridSearchCV.

• Models: SVM, Logistic Regression.

Malware Data Analysis

2022 - 2023

Size of team : 3

Position: Leader

GITHUB: HTTPS://GITHUB.COM/LEMON1810/MALWARE-CLASSIFICATION

• Description: Malware Classification using extracting significant MIPS features from the malware Portable Executable header. Build machine learning models to predict and visualize data to find malware.

• Tool: GG colab, excel, PE extract, visual studio code.

• Framework and Library : Sklearn, pandas, seabon

• Method : Extract attributes and principal component analysis(PCA).

• Models: XGBoost, MLPC, Extra Tree, Logistic Regression Amazon Data Analysis

2023 - 2024

Size of team : 3

Position: Leader

GITHUB: HTTPS://GITHUB.COM/LEMON1810/AMAZON-DATA-ANALYSIS

• Description: Collect data from laptop products and then analyze it quantitatively. Find the causes that affect laptop prices then build an appropriate model from selected attributes.

• Method: Crawl data Amazon, clean data, drop and fill data gaps. Analysis(EDA) and build a machine learning model to train data.

• Tool: GG colab, Power BI, Visual studio code.

• Framework and library: Pandas, seabon, scipy, plotly

• Models: Logistic Regression, Random Forest, SVM, AdaBoost. Predicting landslides and

sedimentation in Mekong River

using machine learning models

(Geographi Information System)

2023 - 2024

Size of team : 3

Position: Leader

GITHUB:

HTTPS://GITHUB.COM/LEMON1810/PREDICTING-LANDSLIDES-AND-SEDIMENTATION-IN- MEKONG-RIVER

• Description: Collect cadastral maps, geological maps, floods, rivers, etc. Then extract attributes that affect landsline. Calculate and rasterize maps to retrieve information. Use machine learning models to predict missed charging points.

• Method: Calculate distances in euclidean and haversine spaces. Qualitative analysis and quantitative analysis. Build a machine learning model to train data. Create a prediction map using raster

• Tool: QGIS, GG Colab, Drawio.

• Framework and library: Pandas, seabon, scipy, plotly.

• Models: Logistic Regression, Random Forest, KNN.

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