Asma Nadeem
I turn complex data into insights and intelligent solutions
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I conducted an end-to-end statistical analysis in R to investigate how everyday factors such as sleep, screen time, weather, temperature, and work schedules relate to productivity and daily behaviour.
I built and analysed a self-collected dataset using exploratory data analysis, statistical visualisation, hypothesis testing, regression, chi-square and Fisher’s exact tests, alongside a Bayesian Beta–Bernoulli analysis.
The analysis uncovered several patterns, including a positive relationship between sleep and productivity, while weather showed little meaningful relationship with sleep or work attendance.
The complete analysis was developed as a reproducible R Markdown report, combining statistical methods with clear, non-technical explanations of the results.
Key skills: R · Statistical Analysis · Data Analysis · Data Visualization · Regression · Hypothesis Testing · Bayesian Statistics · R Markdown
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I developed a deep learning image segmentation system to automatically identify lung regions in chest X-ray images using a UNETR-inspired architecture, combining Vision Transformers with CNN-based decoding.
The model processes X-ray images as patches through a Transformer encoder, using self-attention to learn global image features. A U-Net-style CNN decoder then reconstructs these representations into pixel-level lung segmentation masks using upsampling and skip connections.
I built the complete pipeline including image preprocessing, model architecture, training, validation, evaluation and inference visualization. The model achieved approximately 92% validation accuracy, with segmentation performance evaluated using Intersection over Union (IoU).
This project demonstrates my ability to develop end-to-end computer vision and deep learning solutions, particularly for medical image analysis.
Key skills: Python · Deep Learning · Computer Vision · UNETR · Vision Transformers · CNNs · TensorFlow/Keras · Medical Image Segmentation
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I analyzed a large retail dataset to uncover “Hidden Gem” products items receiving highly positive customer feedback despite having relatively low visibility.
Using Python and Pandas, I cleaned and combined product, customer review, and metadata from multiple CSV and JSON sources. I then applied VADER sentiment analysis to thousands of customer reviews to measure customer sentiment beyond traditional star ratings.
To identify overlooked products, I developed a custom Hidden Gem Score combining average rating, customer sentiment, and review volume. The analysis identified approximately 2.8% of products as strong hidden gems, highlighting products with strong customer satisfaction but comparatively limited engagement.
Key skills: Python · Pandas · Data Cleaning · Sentiment Analysis · NLP · Data Visualization · Feature Engineering · Business Analytics
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