Naim Iskandar Zahari

Data Analyst | Game Programmer

Employing understanding of the game development pipeline and data analytics to make data-driven solutions. Come see what I am working on.


A few details about myself. I love mystery thriller movies (Knives Out being at the top of my list). I am also active in outdoor activities. Taking strolls and hiking grant me moments of calmness and a sense of appreciation towards nature. Lastly, I love learning and taking on challenges. I constantly push myself to learn something new, whether that be about machine learning, game development, data analytics or miscellaneous facts in general.

I am a professional game programmer with over 2 years of working experience in developing video games. I hold a Bachelor's degree in Game Development (UOW Malaysia KDU University College, Malaysia). Being part of the development pipeline has allowed me to be immersed in the process of working on a project from its inception to its commercial release. I cherish the moments where I was able to work with other passionate teammates, build friendships, take responsibilities at leadership roles and continuously improve upon myself at a professional capacity.

Currently in pursuit of a career in data analytics, I want to leverage my extensive knowledge of project development and technical skills to offer fresh insights and translate complex questions into actionable information.

Areas of Interest

Game Development

I utilize in-depth knowledge of game development and apply them to the process of developing solutions in the pipeline.

Data Analytics

I enjoy taking on challenges. Exploring big data is a good opportunity to get straight to a problem and solving it is its own reward.

Machine Learning

I am passionate about exploring the depths of what ML can accomplish.


I enjoy working with other people and combining a creative capacity that exceeds my own, and creating greater results.

Web Scraping

Scraping pages allows me to gather data and tackle challenges unique to each website.


Data Analysis
Disneyland Rating

This python project explores the Disneyland reviews dataset from Kaggle. The goal of the project is to identify individual visitor ratings with relations to the country of origin, park location, date of visit and median rating.

Disneyland Reviews with Sentiment Analysis

Python project of an analysis on Disneyland reviews using NLTK, Sentiment Analysis and WordCloud. The dataset was taken from Kaggle.

The project explores importing huge dataset and an image file into a python project using Google Colab. Using the Sentiment Intensity Analyzer from the NLTK library, the analyzer is able to identify the sentiment of the reviews from the park visitors and then, it is sorted from the most positive review to the most negative review.

As an added bonus, the projects also explores the extensive parameters of the WordCloud library for added customizability as demonstrated in the image above.

Data Storytelling
Employee Retention

Data dashboard on employee retention analysis using Google Data Studio.

The project explores an arbitrary dataset of employee retention in an organization and identify the likelihood of employees to stay with the organization based on key features, such as duration of employment, satisfaction level, project scope or level of responsibility and et cetera.

The process of preparing the dashboard in Google Data Studio was rewarding, to say the least. Google Data Studio provides a wide selection of charts and graphs to form the dashboard, however other details were taken into consideration to provide an insightful finding such as hybrid title, annotations or compliance to the rule of thirds that adds to the dashboard quality.

Machine Learning
Telco Churn Analysis

The project explores the process of data cleaning and processing before fitting it into the Decision Tree Classifier. Preprocessing the data remain a crucial step that includes exploring relationship between variables, handling missing values and determining which variables are important in answering the problem statement.

Using the Decision Tree Classifier from the SciKit Learn library, the decision tree is able to predict if the customer will continue to use the service based on the current lifestyle and consumer habits.

Web Scraping
Numbeo with Python and BeautifulSoup

This project is made using Python and the BeautifulSoup package to scrape the living costs data from the Numbeo website.

From the terminal, the project gives the option to choose between getting the living cost data of specific cities or countries. The data on living cost includes median expenditure at different types of restaurants, groceries, transportation, accomodation and miscellaneous consumption.

Once the data is gathered, it is saved into xlsx files in a specified directory.
Machine Learning
Sales Forecasting from Ads Expenditure using Linear Regression

Python project of forecasting sales performance based on social media advertisement activites using Linear Regression.

The project explores relationship between variables and using Linear Regression from SciKit Learn to predict the expected sales performance based on the budget distribution across 3 different social media platforms(Facebook, Instagram and Google) and evaluate the performance of the machine learning model itself.


Petaling Jaya, Malaysia or remote work

Let's be social. We can get in touch via email or LinkedIn.