ML-DS-Portfolio

Data Science Portfolio - Martins Nnamchi

Hello there!

Welcome to my data science playground! I’m a data enthusiast with a knack for turning numbers into stories and patterns into insights. With a flair for machine learning and a love for all things data, I’m here to explore, innovate, and, most importantly, have fun with data! Dive in to see how I blend analytical rigor with a dash of creativity to solve real-world problems!

• Email: mnnamchi@gmail.com
• LinkedIn: linkedin.com/mnnamchi

Projects

Link to All ML-DS Project GitHub Repo

Accurate Celebrity Identification Using Prompt Engineering and Google Gemini 2.0 LLM API

This project demonstrates the application of Large Language Models, specifically Google’s Gemini 2.0 Flash, to perform celebrity identification in images using Python. An upload interface is provided using IPyWidgets to test the functionality.

Predictive Modeling of Wind-Energy Generation with FLASK deployment: Time Series and Regression Analyses

In this project, I explored time series and regression for renewable-energy forcasting. I developed XGBOOST-trained ML models to predict the amount of wind energy that can be generated over a period. I deployed the model using FLASK, creating an interactive web app that delivers real-time energy predictions. This project kickstarts my learning in time series analysis and end-to-end model development/deployment.

SpaceX Launch Analysis and Landing Predictions

In this project, I predict if the Falcon 9 first stage will land successfully. The predictions will help determine launch costs and aid operational planning. I implement Dash/Plotly Interactive Dashboards, REST APIs, Web scraping, SQL queries, Data Wrangling/Preprocessing, EDA, and ML pipeline development. Full PDF Report

Credit Card Fraud Detection

In this project, I built models that predict if a financial transaction is fraudulent or not, aiming to enhance credit card security. I model the task as a binary classification problem and implement SVM and DT models using both Scikit-Learn and Snap ML. Linkedin Report Article

Rainfall Prediction in Australia

In this project, I employ supervised classification models to predict rainfall in Australia. Four different classification models were implemented: K Nearest Neighbors, Decision Tree, Logistic Regression, and Support Vector Machine. The Logistic Regression model exhibited the best performance, with a prediction accuracy of 84%.

Micro Projects

• Data Collection & Vizualization

  • Stock Data Extraction and Visualization Using REST APIs and Webscraping: In this project, I extract and vizualize stock data with the yfinance API and Web scraping.
  • Car Dealership’s Inventory Management System: This Python program simulates a car dealership’s inventory management system. The system aims to model cars and their attributes accurately.
  • Chicago Dataset SQL Querying: In this file, I attempt to comprehensively understand three Chicago datasets using SQL queries and %sql magic.

Core Competencies

• Methodologies: Machine Learning, Deep Learning, Time Series Analysis, Natural Language Processing, Statistics and Probability, Explainable AI, A/B Testing and Experimentation Design, Big Data Analytics
• Languages: Python (Pandas, Numpy, Scikit-Learn, Snap ML, Scipy, Keras, Matplotlib), R (Dplyr, Tidyr, Caret, Ggplot2), SQL, Javascript, HTML5, CSS, LaTex.

Certificates

• ChatGPT Prompt Engineering for Developers by DeepLearning.AI

• IBM Data Science Professional Certificate
  • Generative AI: Elevate Your Data Science Career
  • Tools for Data Science
  • Databases and SQL for Data Science with Python
  • Python for Data Science, AI & Development
  • Machine Learning with Python
  • Data Analysis with Python
  • Data Visualization with Python
  • Data Science Methodology
  • Applied Data Science Capstone
  • Python Project for Data Science
  • What is Data Science?
  • Data Scientist Career Guide and Interview Preparation
• Foundations of Data Structures and Algorithms Specialization, University of Colorado Boulder
  • Algorithms for Searching, Sorting, and Indexing
  • Trees and Graphs
  • Dynamic Programming, Greedy Algorithms (Ongoing)
  • Approximation Algorithms and Linear Programming
  • Advanced Data Structures, RSA, and Quantum Algorithms

Informal Learning and Books

• Foundational and Advanced Math (Brilliant.org)
• 3Blue1brown (Youtube)
• StatQuest with Josh Starmer (Youtube)
• Simplilearn (Youtube)
• Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning (Book by Alex J. Gutman and Jordan Goldmeier)
• Getting Started with Data Science: Making Sense of Data with Analytics (IBM Press; book by Murtaza Haider)
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (Book by Aurélien Géron) -Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics (Book by Thomas Nield)

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