This portfolio is a compilation of notebooks which I created for data analysis or for exploration of machine learning algorithms. A separate category is for separate projects. If you want more information about me or want to get in touch, feel free to message me on Linkedin.
This is a hybrid engine combining two deep learning models; ANN and SOM (Self-Organizing Map) to detect fraud. Used SOM to locate the fraudulent customers and feed it in to ANN to identify the predicted probability ranking of fraud customers. My goal was to determine the highest predicted probability of fraud. Dataset: Statlog(Aus. credit approval)
Motivation: To build an engine composed with supervised and unsupervised deep-learning models
This dataset is well studied and is a good problem for practicing on neural networks because all of the 4 input variables are numeric and have the same scale in centimeters. Each instance describes the properties of an observed flower measurements and the output variable is specific iris species.
Therefore, it's a multi-class problem, meaning that there are more than two classes to be predicted, in fact there are three flower species. This is an important type of problem on which to practice with ANN because the three class values require specialized handling.
This notebook demonstrate the way of using KNeighbors classifier on sales data.
Bag of Words Meets Bags of Popcorn is a sentimental analysis problem. Based on texts of reviews we predict whether they are positive or negative. General description and data are available on Kaggle. The data provided consists of raw reviews and class (1 or 2), so the main part is cleaning the texts.
Problem: Analysis of Customer purchasing Patterns, a clustering approach to unsupervised machine learning. Clustering with KMeans is one of algorithms of clustering. in this notebook I'll demonstrate how it works.
Canberra,AU based and results driven data analyst with strong commercial skills and business acumen. Willing to work with data science in business environments to solve real-life problems. A professional with a background in Software engineering and a track record of delivering projects to budget within deadlines and effective use of resources.
- Python, R, Java programming
- SQL
- Practical Machine Learning and Deep Learning
- Data Cleaning and Data Pre-processing