Arfa Khalid
Data Scientist
"Where there is Uncertainty, there is Statistics."
Arfa Khalid is a data professional with over four years of experience in business analytics, specializing in transforming data into strategies that drive business growth. She is adept at predictive analytics, reporting, and problem-solving in high-precision environments. Her career spans the Research, Technology, Finance, and Retail sectors, where she has used machine learning and predictive modeling to enhance decision-making, improving project efficiency by 20% and reducing turnaround time by 15%.
With expertise in Python, R, and SQL, Arfa excels in data analysis, model development, and effectively communicating insights to bridge technical and non-technical audiences. She is passionate about continuous learning and stays updated with the latest trends and tools in her field. Her journey in statistics began unexpectedly during her undergraduate studies, leading her to pursue a master's degree abroad, which has enriched both her personal and professional development.
Beyond her career, Arfa enjoys painting, playing the piano, hiking, and learning new languages, including French. She is open to connecting with like-minded professionals who share her commitment to data excellence and problem-solving.
Projects
Welcome to my data science portfolio, where I embark on a journey through captivating projects that showcase my prowess in unravelling insights from intricate datasets. Beyond mere exercises, these projects are tales of real-world problem-solving, driven by a passion for deciphering the narratives hidden within the numbers, employing cutting-edge machine-learning techniques.
I derive joy from transforming data into actionable outcomes, be it forecasting future trends, unveiling hidden patterns, or implementing innovative algorithms for task automation.
Let my work be the voice: from predicting the future to uncovering hidden patterns, witness the power of pairing data science with practical applications. Explore my portfolio, and let the magic of data unfold before you.
Climate Change Analysis
Explored NASA's 30 Years datasets for climate change analysis, employing the Seaborn library in Python to forecast temperature trends in the coming years.
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Project Summary
In this project, I leveraged my skills in data analysis. I utilized the Python programming language, particularly the Seaborn library, to conduct an in-depth analysis of NASA's 30 Years datasets for climate change. By applying data visualization and predictive modeling techniques, I aimed to understand and predict temperature trends in the upcoming years.
Skills and Technologies Used
- Data collection and cleaning
- Data visualization with Seaborn (Python)
- Predictive modeling
Deep Learning with Keras
Implemented a deep learning model using Keras for Natural Language Processing. The model achieved an accuracy of 90% on custom text inputs for sentiment analysis on IDMB Movies Reviews.
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Project Summary
Leveraging deep learning with TensorFlow and Keras, I built a model that predicts the sentiment (positive or negative) of movie reviews from the IMDB dataset, achieving an impressive 86.97% accuracy on unseen data.
Skills and Technologies Used
- Deep learning with TensorFlow and Keras
- NLP principles and techniques
- Sentiment Aanalysis Function
- Model evaluation and optimization
Impact
- Automated Sentiment Analysis: Eliminates the need for manual review analysis, saving time and resources.
- Enhanced Customer Understanding: Provides businesses with valuable insights into customer opinions, driving informed product development and marketing strategies.
- Improved Recommendation Systems: Recommends products or content tailored to user preferences based on sentiment analysis of past reviews.
- Public Opinion Monitoring: Tracks sentiment trends towards specific topics or events, enabling proactive response and strategy adjustment.
Predicting Vaccination Status for Analyzing COVID-19 Vaccine Effectiveness
Designed and developed a Multinomial Logistic Regression Model using pandas, scikit-learn, and Matplotlib. Implemented features like vaccination status, age groups, and Covid-19 cases.
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Project Summary
Developed a multinomial logistic regression model, the project predicts an individual's vaccination status (Fully Vaccinated, Partially Vaccinated, Unvaccinated) based on age group and the number of Covid-19 cases. This allows for a data-driven assessment of vaccine effectiveness against case rates across various age demographics.This project demonstrates my proficiency in building and evaluating machine learning models for real-world impact.
Skills and Technologies Used
- Web Scrabbing
- Multinomial logistic regression model using scikit-learn
- Data Analysis
- Model Evaluation & Visualization
Impact
- Targeted Vaccine Interventions: By predicting vaccination status, the model can inform targeted vaccine campaigns and interventions for specific age groups with lower vaccination rates.
- Data-Driven Decision Making: The project demonstrates the power of machine learning in analyzing complex public health issues, supporting data-driven decision-making for effective COVID-19 response strategies.
- Enhanced Case Management: Understanding the impact of vaccination status on case rates can help healthcare systems prioritize resources and interventions for different age groups.
- Enhancing Research and Analysis: The project contributes to the ongoing research on COVID-19 vaccines, providing a replicable methodology for analyzing effectiveness across various populations.
Enhancing Air Travel Efficiency with Classification Model
In this project, I worked on US major airline datasets. I successfully applied a classification model to predict flight arrival delays, providing insights into model performance and areas for improvement.
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Project Summary
This project tackled the challenge of predicting on-time flight arrivals using a classification model. Harnessing a major U.S. airline dataset, I built a robust pipeline that cleans, manipulates, and prepares data for optimal model training. Employing a Random Forest classifier from scikit-learn, I achieved an impressive 86.43% accuracy in predicting on-time arrivals.
Skills and Technologies Used
- Data manipulation and cleaning
- Classification Model Building
- Model evaluation & Visualization
- Prediction & Visualization
Impact
- Reduced flight delays: This model has the potential to significantly decrease flight delays, saving airlines millions of dollars in operational costs and passenger compensation.
- Improved passenger experience: Accurate arrival predictions can empower passengers to make informed decisions about travel plans, reducing stress and inconvenience.
- Enhanced airline efficiency: By optimizing resource allocation and crew scheduling based on predicted delays, airlines can improve operational efficiency and profitability.
Emergency Medical Response Assessment
Analyzed New York City's collision data to assess hospital response, revealing patterns through interactive maps. Proposed strategic locations for new hospitals to minimize distant collision occurrences, emphasizing the proactive role of spatial analysis in optimizing emergency medical services.
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Project Summary
Leveraged spatial data analysis and visualization to pinpoint high-collision areas, assess hospital coverage, and propose strategic hospital locations for improved emergency response.
Skills and Technologies Used
- Spatial Data Analysis
- Geovisualization
- Statistical Analysis
- Problem-Solving
- Decision-Making
Impact
- Pinpointed high-collision areas: Informed targeted interventions to reduce accidents.
- Optimized hospital coverage: Improved accessibility and resource allocation for emergency response.
- Proposed strategic hospital locations: Data-driven decisions for faster response times and improved patient outcomes.
- Enhanced decision-making: Empowered stakeholders with spatial insights for informed choices.
- Increased resource efficiency: Maximized impact of emergency response efforts.
Database Management using PostgreSQL
This project implemented database triggers in PostgreSQL to automatically track and record changes in an employee database.
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Project Summary
This project showcases my expertise in database management and trigger design, delivering a robust and automated solution for employee data auditing with significant benefits for data integrity, transparency, and efficiency.
Skills and Technologies Used
- PostgreSQL
- SQL
- Trigger Design
- Database Design
- Testing and Validation
Impact
- Enhanced Data Integrity: Automated audit trails guarantee complete and reliable records of all changes, improving data trust and accountability.
- Reduced Manual Effort: Eliminates the need for manual change tracking, freeing up valuable time for other tasks.
- Improved Compliance: Provides documented evidence of data changes, aiding compliance with regulations and internal governance requirements.
- Simplified Data Analysis: Centralized log tables facilitate easy analysis of change trends, employee activity, and potential anomalies.
- Increased Transparency: Provides a clear audit trail for employee data changes, promoting trust and accountability within the organization.