Waqar Ul W. looks like a good fit?

We can organize an interview with Aldin or any of our 25,000 available candidates within 48 hours. How would you like to proceed?

Schedule Interview Now

Waqar Ul W. Backend, AI and Data Platforms

My name is Waqar Ul W. and I have over 2 years of experience in the tech industry. I specialize in the following technologies: Python, Computer Vision, Machine Learning, OpenCV, Machine Learning Model, etc.. I hold a degree in , , Bachelor of Science in Information Technology. Some of the notable projects I’ve worked on include: Portfolio WordPress Website, Heart Disease ML Model, Smoker Analysis Through Visualization. I am based in Islamabad, Pakistan. I've successfully completed 3 projects while developing at Softaims.

I approach every technical challenge with a mindset geared toward engineering excellence and robust solution architecture. I thrive on translating complex business requirements into elegant, efficient, and maintainable outputs. My expertise lies in diagnosing and optimizing system performance, ensuring that the deliverables are fast, reliable, and future-proof.

The core of my work involves adopting best practices and a disciplined methodology, focusing on meticulous planning and thorough verification. I believe that sustainable solution development requires discipline and a deep commitment to quality from inception to deployment. At Softaims, I leverage these skills daily to build resilient systems that stand the test of time.

I am dedicated to making a tangible difference in client success. I prioritize clear communication and transparency throughout the development lifecycle to ensure every deliverable exceeds expectations.

Main technologies

  • Backend, AI and Data Platforms

    2 years

  • Python

    1 Year

  • Computer Vision

    1 Year

  • Machine Learning

    1 Year

Additional skills

Direct hire

Potentially possible

Previous Company

Techlogix

Ready to get matched with vetted developers fast?

Let's get started today!

Hire Remote Developer

Experience Highlights

Portfolio WordPress Website

Portfolio Website

Heart Disease ML Model

There is data on heart disease that can be used to train any model that will produce high-accuracy predictions.

Smoker Analysis Through Visualization

Title: Predictive Model for Gender-based Smoking Patterns and Analysis Through Visualization Abstract: This project aims to develop a predictive model for identifying gender-based smoking patterns using a dataset containing various demographic and lifestyle attributes. The model employs machine learning techniques to classify individuals as male or female smokers based on the provided features. The results are then analyzed and visualized to gain insights into the gender-specific tendencies towards smoking. 1. Introduction: Smoking behavior varies across genders due to social, cultural, and biological factors. This project focuses on building a predictive model that can effectively classify individuals into male and female smokers based on relevant attributes. The dataset used for this analysis includes variables such as age, income, education level, marital status, and more. 2. Data Preprocessing: The dataset undergoes preprocessing steps including handling missing values, encoding categorical variables, and normalizing numerical features. This ensures that the data is suitable for training machine learning models. 3. Feature Selection: Feature selection techniques, such as correlation analysis and recursive feature elimination, are applied to identify the most influential attributes for predicting gender-specific smoking patterns. 4. Model Selection and Training: Several machine learning algorithms, such as logistic regression, decision trees, random forests, and support vector machines, are evaluated to determine the best-performing model. Cross-validation techniques are used to prevent overfitting and ensure robustness. 5. Model Evaluation: The selected model's performance is assessed using metrics such as accuracy, precision, recall, and F1-score. A confusion matrix is generated to provide a comprehensive view of the model's classification outcomes. 6. Visualization: Some visualization techniques are employed to analyze the model's predictions and gain insights into gender-based smoking patterns:

Education

  • Saint Paul High School , Wah Cantt

    in Computer science

    2017-01-01-2019-01-01

  • BASE College Wah Cantt

    in Computer science

    2019-01-01-2021-01-01

  • University of Haripur

    Bachelor of Science in Information Technology in Software Engineering

    2021-01-01-2025-01-01

Languages

  • English
  • Pashto
  • Urdu

Personal Accounts