I am Muhammad Anas Abbas, a Machine Learning Engineer and Data Scientist with a Bachelor's in Computer Engineering. With a strong background in developing and optimizing machine learning models, I have hands-on experience in exploratory data analysis, predictive modeling, and real-time data processing. My professional experience includes an internship at Glowing Soft Technologies, where I conducted data analysis, developed customer churn prediction models, and performed sentiment analysis using social media data. I have worked on several notable projects, including a final year project leveraging IoT for air quality data collection and forecasting using AWS DynamoDB, React, and Django. Additionally, I developed a collaborative music-playing system with real-time features using Python, Django, and the Spotify API. My technical skills encompass programming languages like Python and JavaScript, frameworks such as Django and React, and expertise in database management and web development. With certifications in front-end and back-end development and data analytics on AWS, I am well-equipped to deliver robust and scalable data-driven solutions

I am Muhammad Anas Abbas, a Machine Learning Engineer and Data Scientist with a Bachelor's in Computer Engineering. With a strong background in developing and optimizing machine learning models, I have hands-on experience in exploratory data analysis, predictive modeling, and real-time data processing. My professional experience includes an internship at Glowing Soft Technologies, where I conducted data analysis, developed customer churn prediction models, and performed sentiment analysis using social media data. I have worked on several notable projects, including a final year project leveraging IoT for air quality data collection and forecasting using AWS DynamoDB, React, and Django. Additionally, I developed a collaborative music-playing system with real-time features using Python, Django, and the Spotify API. My technical skills encompass programming languages like Python and JavaScript, frameworks such as Django and React, and expertise in database management and web development. With certifications in front-end and back-end development and data analytics on AWS, I am well-equipped to deliver robust and scalable data-driven solutions

Available to hire

I am Muhammad Anas Abbas, a Machine Learning Engineer and Data Scientist with a Bachelor’s in Computer Engineering. With a strong background in developing and optimizing machine learning models, I have hands-on experience in exploratory data analysis, predictive modeling, and real-time data processing. My professional experience includes an internship at Glowing Soft Technologies, where I conducted data analysis, developed customer churn prediction models, and performed sentiment analysis using social media data.

I have worked on several notable projects, including a final year project leveraging IoT for air quality data collection and forecasting using AWS DynamoDB, React, and Django. Additionally, I developed a collaborative music-playing system with real-time features using Python, Django, and the Spotify API. My technical skills encompass programming languages like Python and JavaScript, frameworks such as Django and React, and expertise in database management and web development. With certifications in front-end and back-end development and data analytics on AWS, I am well-equipped to deliver robust and scalable data-driven solutions

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Skills

Da
Data Visualization
HTML5
Python
We
Web Design
Da
Data Science
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Experience Level

Data Visualization
Expert
HTML5
Expert
Python
Expert
Web Design
Expert
Data Science
Expert
UI
Expert
DynamoDB
Expert
AWS
Expert

Language

English
Fluent
Urdu
Fluent

Education

Bachelors of Science In Computer Engineering at COMSATS
June 1, 2024

Qualifications

Data Scientist and ML
January 20, 2023 - June 20, 2024

Industry Experience

Software & Internet
    uniE621 Prediction for PM2.5 Using LSTM
    i have predicted the pm2.5 from the given AQI dataset the prediction was for next 5 hours MachineLearning LSTMDeeplearning datasciencetimeseriesforecasting dataanalysis predictivemodels
    uniE613 Real-Time Train Delay Prediction System
    Developed a robust real-time train delay prediction system utilizing machine learning and Flask. The project aimed to provide accurate delay predictions based on historical data and real-time inputs, helping train operators and passengers manage schedules more efficiently. Details: Data Preparation: Handled preprocessing of historical train data, including cleaning, normalizing, and encoding. Created a pipeline for feature engineering and selection to ensure high-quality input data for the model. Machine Learning Model: Flask API Development Frontend Integration:

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