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

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

• 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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