Hi, I'm Daniel Cisneros!
Computer Scientist and Data Scientist (in progress), passionate about using machine learning and statistics to uncover meaningful insights from complex datasets. Whether it's building significant models, predicting trends, uncovering hidden patterns, or solving complex problems, every challenge feels like a puzzle waiting to be solved. I'm always looking for ways to make an impact through innovative solutions.
Skills & Analysis
Analytics
Equipped with strong analytical skills, I specialize in applying advanced analytics to extract meaningful insights, driving strategic decisions that lead to positive outcomes. Committed to offering data-driven solutions that empower organizations to make informed, actionable choices for their success.
Machine Learning
I’ve developed expertise in creating machine learning models that provide accurate predictions. These models enhance decision-making processes and operational efficiency. My academic journey has equipped me with a deep understanding of both the technical aspects of machine learning and their practical applications, enabling me to tackle complex problems with innovative solutions.
Predictive Modeling
Skilled in constructing predictive models using historical data to forecast future trends, I support businesses in developing proactive strategies. These models provide actionable insights, enabling organizations to make informed decisions and achieve better outcomes.
Data Visualization
Proficient in creating visually appealing and insightful data representations using Python libraries such as matplotlib and seaborn, along with R's ggplot2, Julia, SAS, and Tableau. These visuals enhance team understanding and support data-driven decision-making for effective business outcomes.
Frameworks
Through my coursework and projects, I utilize Keras within TensorFlow and PyTorch Lightning for streamlined training processes, alongside scikit-learn and pandas, to ensure robust model development and effective data manipulation. Collaboration is key to driving impactful machine learning applications in my academic and hands-on experiences.
Libraries
Expert in leveraging Pandas and NumPy for efficient dataset processing and analysis. I use these tools for data cleaning, manipulation, and generating actionable insights, enhancing my academic projects while developing proficiency in data-driven decision-making.
Technical Expertise
Languages
Tools & Frameworks
Data Tools
Featured Projects
Paper Helicopter (Experimental Design)
A paper helicopter with different dimensions and factors that can affect the flight time such as the wing length, body, paper clip, tape, wind. The purpose is to peform different experiments and analyze them using real world data.
The AI Job Salary Prediction (2020 - 2025 March)
Prediction of the AI/ML/Data Science Salaries positions with real time data that keeps updating (weekly) based on market, experience, employement type, salary, year, and remote ratio (home, hybrid, fully remote).
Titanic Survival Prediction – Kaggle Competition
Developed a survival prediction model for the Titanic dataset using Decision Tree and Random Forest algorithms in Python. Achieved a score of 85% after data cleaning, feature engineering, and exploratory analysis with pandas, scikit-learn, and visualization libraries.
Handgun Object Detection
Created a Python program that was trained with a dataset provided by the University of Granada research group that can detect and recognize handguns firearms in real-time video using one of the latest algorithms and state-of-the-art for object detection YOLOv8 obtaining an impressive 88% (mAP50) while a 71% in mAP50-95.
Simulated Swarm - Sponsored by Lockheed Martin
Contributed to create a swarm of drones that fly autonomously in a Gazebo virtual environment that recreates real world adversities with YOLOv5 and SLAM to performsearch and object detection for a designated target with the help of artificial intelligence, machine learning, and computer vision
Recommender System
A system that uses data in a frequency matrix to compute similarities and tokenization to suggest users’ books and movies according to ratings and personal suggestions
Branch Prediction with Neural Networks
Improving branch predictors to maximize parallelism and improve processor performance with different types of neural networks like CNN, RNN, MLP, and Perceptron
Contact Me
School Email
da703145@ucf.eduPersonal Email
danielcisneros26@gmail.comFeel free to reach out for collaboration opportunities, AI/ML project discussions, or potential roles in Data Science, Analysis, Machine Learning Engineering, or AI Research.