I am Ansa Baby
I love Data Storytelling & Machine Learning. I'm excited to present
my portfolio filled with data-driven discoveries. Have fun exploring!
I am a data science enthusiast who is passionate about helping people learn and grow.
with Master's degree in Data Science from Manchester Metropolitan University and 7 years of industry experise,
I bring a wealth of experience to the world of data science and software development.
Feel free to explore my skills and experience, and don't hesitate to reach out for mentorship, advice, or opportunities in the
dynamic realm of data science and Machine Learning.
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Latest Blog Posts
- Storytelling with Iris Dataset — Multi-class Classification using Machine Learning
- Communicate with a Native App from Chrome Extension using C# Blazor to save a file to a specific location
- How do I create web extensions using C#?
- How & Where to start your Data Science Journey?
- A Roadmap to Python Programming
- An introduction to Python Basics
- Advanced Python
- Machine Learning & its Types
- Data Cleaning Techniques
Pandas
Numpy
Matplot
Scikit-Learn
Excel
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Google-Colab
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Education
Address
Manchester Metropolitan University, UKM.Sc. Data Science, 2022-2023
Passed with Distinction (80%)
Address
Easa College of Engg & Tech, IndiaB.E. Electronics & Communication Engineering, 2015-2019
Passed with Distinction (80%)
Story telling with Iris Dataset
The Iris dataset is one of the most explained and easy-to-use datasets available for beginners to explore machine learning. We know that to draw in recruiters, we must find solutions to new challenges. Then why is it important to solve that again, knowing it’s a very small dataset with already clean data? It's because the important aspect is not always how you solve a problem; it's how you narrate the solution, i.e., the art of storytelling with the data and conclusions obtained.So, in this article, I am going to dive into the Iris dataset again. But this time I’m going to focus on creating a compelling story with Iris Flowers instead of just predicting the results.
Netflix Movies and Guest Stars Analysis
Investigated Netflix movies and guest stars using Python on DataCamp. Explored relationships and trends within the dataset to derive meaningful insights. Conducted sentiment analysis on user reviews. Applied natural language processing techniques to extract valuable information from textual data.
Fitness Dataset Analysis
Conducted a machine learning analysis on gym attendance data over several years. Explored trends and patterns to understand factors influencing attendance. Applied time-series analysis to predict future attendance. Provided actionable insights for optimizing gym operations.
Car Prize Prediction - Academic Mini Project
Developed an academic mini project to predict car prices for both used and new cars. Explored various regression models and assessed their predictive accuracy. Conducted feature importance analysis to understand the factors influencing car prices. Applied cross-validation techniques to ensure model reliability.
Big Data Hypothesis Analysis using Apache Spark
Utilized Apache Spark for hypothesis analysis on two large datasets. Investigated correlations and relationships between variables to derive meaningful insights. Implemented distributed computing techniques to handle big data efficiently. Presented findings through interactive visualizations.
Brain Tumor Classification
Developed a deep learning model for classifying brain tumor images. Explored hyperparameter tuning techniques to enhance model accuracy and performance. Implemented transfer learning with pre-trained models for feature extraction. Evaluated model robustness and generalization on diverse datasets.
Auto Trader - Advanced Machine Learning Analysis
Implemented advanced machine learning algorithms to predict car prices. Explored various models, including ensemble methods, and fine-tuned parameters for optimal performance. Conducted thorough feature engineering to enhance model accuracy.