FinTech Apps
The goal of these projects is to harness the power of data to revolutionize the way businesses and consumers interact with financial services. By combining my expertise in data engineering and my deep understanding of the financial industry, I create intuitive and scalable solutions that streamline processes, mitigate risk, and enhance user experiences.
Want to get in touch? Contact me here.
BNPL Credit Worthiness App
This project focuses on using the Keras Sequential model, a powerful deep learning technique that can accomplish various tasks such as prediction, classification, and regression, to analyze the creditworthiness of potential borrowers.
Check out the project here.
DJIA Analysis using NLP and MACD
This project focuses on applying machine learning techniques to stock market analysis and trading. The goal is to leverage natural language processing (NLP) of financial news combined with quantitative trading algorithms to generate actionable insights for making profitable trades.
Check out the project here.
Trading Agent using Q-Learning
This project focuses on developing a reinforcement learning-based algorithmic trading strategy with the goal of creating a trading agent that learns optimal trading strategies by interacting with historical market data and making buy/sell decisions based on current market conditions.
Check out the project here.
Data Science
These projects are a dive deep into complex datasets, employing advanced statistical techniques and data visualization to extract meaningful insights that inform strategic decision-making. By leveraging my skills in data wrangling, exploratory analysis, and predictive modeling, I can help organizations identify hidden patterns and anticipate trends so they can optimize their strategies.
Want to get in touch? Contact me here.
Stock Market Prediction using LSTM
Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN) architecture. Unlike standard feedforward neural networks, LSTM has feedback connections that make it a "general-purpose computer" for processing sequences of data. This allows it to store information over long sequences and is exceptionally well-suited for various time-series prediction tasks, such as stock market movements over a preiod of time.
Check out the project here.
Churn Prediction using RF Classifier
The primary objective is to predict customer churn for a major telecom company. The dataset contained various features related to customer activity and a churn label indicating whether a customer canceled their subscription. The goal with this excersise is not just to predict churn but also to understand customer value and segmentation for targeted retention strategies.
Check out the project here.
Bankruptcy Prediction using Ensemble ML
The goal is to perform an ensemble machine learning approach to predict bankruptcy. Before diving into modeling, we'll conduct an Exploratory Data Analysis (EDA) to understand the dataset and address any issues such as missing values or class imbalances.
Check out the project here.
Machine Learning
With a passion for pushing the boundaries of what's possible with data, I develop sophisticated machine learning models that enable organizations to automate processes, improve predictive accuracy, and gain a competitive edge. By staying at the forefront of the latest advancements in ML algorithms and techniques, I help businesses harness the power of artificial intelligence to drive innovation and achieve their goals.
Want to get in touch? Contact me here.
Customer Segmentation using Clustering
The goal of this exercise is to better understand the behavior of customer segments to aid in assessing the overall risk profile of the customer base and in making informed decisions on risk mitigation strategies.
Check out the project here.
Dropout Prediction using FF Neural Network
The objective of this notebook is to explore the application of a Keras-based Feed-Forward Neural Network (FFNN), a type of artificial neural network implemented using the Keras framework, to predict the likelihood of college student dropouts.
Check out the project here.
Sentiment Analysis using LDA
This project uses Latent Dirichlet Allocation (LDA) to identify five key topics in news articles for each company in the Dow Jones Industrial Average and then performs a Weighted Sentiment Analysis using these topic-specific sentiment scores.
Check out the project here.
Data Engineering
Comming Soon!
In the meantime, why not check out my other projects?
Generative AI projects can be found here.
FinTech projects can be found here.
Data Science projects can be found here.
Machine Learning projects can be found here.
Generative AI
I explore the transformative potential of Generative AI, developing innovative applications that push the boundaries of what's possible with Large Language Models. By harnessing the power of advanced techniques like Retrieval Augmented Generation, Anomaly Detection, and AI-driven Summarization, I create solutions that tackle real-world challenges and provide valuable insights to users. From empowering individuals with personalized financial literacy tools to streamlining research processes and identifying data anomalies, my projects showcase the immense potential of Generative AI in driving positive change and unlocking new opportunities across various domains.
Want to get in touch? Contact me here.
AI Enabled Financial Literacy Q&A System
By leveraging the power of Retrieval Augmented Generation and locally hosted large language models, this project takes a significant step towards combating financial illiteracy. The AI-powered question-answering system empowers users with the knowledge and insights they need to navigate the complex world of personal finance, while ensuring the privacy and security of their sensitive financial information.
Check out the project here.
LLM Powered Data Anomaly Detection App
This application is designed to identify anomalies or outliers in datasets, leveraging the strengths of advanced Python scripting for data analysis combined with the interpretative power of a locally hosted large language model (LLM). By separating the data analysis and narrative explanation processes, the app effectively addresses the limitations commonly associated with LLMs handling raw tabular data.
Check out the project here.
AI Research Summarizer with LLM Feedback
The AI Research Summarizer is an innovative tool designed to help researchers, students, and AI enthusiasts stay up-to-date with the latest developments in artificial intelligence research. By leveraging the power of the arXiv API and a local language model (LLM), specifically the Llama 3 model, this application fetches the most recent AI research papers and provides concise summaries along with insightful feedback from the LLM.
Check out the project here.
Contact
As a Data & ML Engineer, I specialize in building machine learning models that not only solve complex problems but also drive actionable insights. My work encompasses everything from data preprocessing and feature engineering to model evaluation and deployment. Email me
