Machine Learning Data Science

University of Science, VNU-HCM

GPA: *.*/**

NGUYỄN THỊ CẨM LAI

D A T A A N A L Y S T

Programming Languages: C/C++, Python, Julia, SQL

Technology/Framework: Github, Jupyter Notebook,

Google Colab, VS Code

Well-trained: pandas, numpy, sklearn, markdown,

data visualization libraries in python

Data visualization tool: Tableau, Python

Soft skills: problem-solving, analytical thinking, self- study, teamwork, time management

Languages: Vietnamese (Native), English

Others: Office (word, excel, powerpoint), statistical S K I L L S

087*******

Ho Chi Minh city, Viet Nam

***************@*****.***

A B O U T M E

C E R T I F I C A T E

GRANDMASTER KAGGLE

ONLINE CERTIFICATE

E D U C A T I O N

MAJOR IN DATA SCIENCE

linkedin.com/in/ntclaii/

Full name: Nguyễn Thị Cẩm Lai

Date of birth: 13/03/2002

Gender: Female

Hi, call me Lai. I'm here to affirm my enthusiasm for the field of Data Science. I'm a final year students, majoring in Data Science at the University of Sciences - VNU. I’m addicted to learning and growing every day. With my enthusiasm and willingness to learn, I hope to be able to become a member and contribute to the development of your company.

C O N T A C T

ENGLISH

Google Data Analytics (Coursera)

Data Analysis Using Python (Coursera)

Data Analysis with Python: Zero to Pandas (Jovian) Machine Learning with Python: Zero to GBMs (Jovian) Three-level SQL: Basic, Intermediate, Advanced (HackerRank) TOEIC 815 (2021)

I N V O L V E D P R O J E C T

ANALYZE DATA AND BUILD MACHINE LEARNING

MODELS PYTHON

BUILD DASHBOARD TABLEAU

Kaggle: kaggle.com/nguyenthicamlai

Github: github.com/ntclai

E X P E R I E N C E

Explore data properties and conduct data preprocessing Visualize data with a variety of chart types, show relationships between attributes

Make comments on the current human resource situation of the enterprise and propose solutions to reduce the employee attrition rate

Building a Logistic Regression model to predict employee resignation decisions (get accuracy score: 90%)

Use the pineline technique to build a sequence of data preprocessing steps before feeding into the machine learning model

Train data through various types of machine learning models

(Decision Tree Regressor, Linear Regression, Logistic Regression, Gradient Boostring ) to find the model that gives the best prediction results.

Conclusion Gradient Boostring model is the best when it gives RMSLE score: 0.13683

Explore the properties of the data and preprocess it before training the machine learning model

Implement and optimize the Decision Tree model to predict whether that person will live or die on the Titanic (accuracy score: 96%)

Based on students' test results, analyze and visualize the data to find the factors that influence those results

Proposing solutions to help improve learning outcomes for students

Employee Attrition and Factory

House Prices

Titanic - Machine Learning from Disaster

Students Performance in Exams

KAGGLE GRANDMASTER

Notebooks Grandmaster (achieved 16 gold medals, 2 silver medal) Datasets Master

Building personal projects

Participating in machine learning competitions

Contribute self-collected data from websites to

the community

Highest rank: 43 of 290.638

One year of participation and contribution:

Become Notebooks Grandmaster:

SEE MORE PERSONAL PROJECTS

Superstore in US

Build a dashboard showing the business situation (sales, profit, shopping trends, ...) in December 2021 of a supermarket in the US

Contact this candidate