jmarihawkins
QA Professional | ISTQB | Aspiring Machine Learning & AI Developer | Bridging the Gap Between Quality and Innovation
Brooklyn, NY
Pinned Repositories
ai-case-study
Discover how Simply Homes is using AI to revolutionize affordable housing, raising $22m in funding to address the pressing issue of homelessness. With a focus on underserved communities, they're reshaping real estate with innovative technology, empowering tenants towards homeownership and making meaningful changes one family at a time.
athletic_sales_analysis
The purpose of this project is to analyze athletic sales data from 2020 and 2021 using Python within Jupyter Notebook. By combining, cleaning, and visualizing the data, we aim to uncover insights into regional sales patterns, retailer performance, and product trends. This analysis can be applied to retail businesses seeking to optimize sales.
classification-challenge
The purpose of this project is to develop and compare two machine learning models to detect spam emails. Spam detection is a crucial task in email filtering systems to protect users from unwanted and potentially harmful emails. The project involves using a dataset containing various features extracted from email content.
CryptoClustering
This project aims to cluster various cryptocurrencies based on their market performance using machine learning techniques. The analysis involves several key steps: normalizing the data, reducing its dimensionality with Principal Component Analysis (PCA), and using K-Means clustering to identify distinct groups.
customer_banking
This project is a Python-based banking system for users to manage their savings and Certificate of Deposit (CD) accounts easily. It simplifies entering account details like balances and interest rates, then calculates interest earned and updates balances automatically.
neural-network-challenge-1
The purpose of this project is to predict student loan repayment success using a neural network. Neural networks are computational models inspired by the human brain's structure and function, consisting of layers of interconnected nodes or "neurons" that can learn to recognize patterns in data.
neural-network-challenge-2
The purpose of this project is to develop a machine learning model that predicts employee attrition (whether an employee will leave the company) and department assignment (which department an employee belongs to) based on various factors. These factors include age, travel frequency, education level, job satisfaction, marital status, and more.
pandas-challenge-1
The purpose of this project was to analyze wholesale data using Python and pandas to gain insights into total revenue, total profit, and other key metrics for various clients. This project involved reading and processing data, performing calculations, and validating results to ensure accuracy.
prophet-challenge
The purpose of this project is to analyze and forecast MercadoLibre's search trends using Python. It showcases time series analysis and Prophet forecasting, providing insights into user behavior and potential market influences.
python-challenge-1
This Python script offers a user-friendly experience, allowing customers to effortlessly navigate the menu, select their desired items, and specify the quantity for each. Once the ordering process is complete, the script generates a comprehensive receipt displaying all ordered items, their respective prices, quantities and total cost.
jmarihawkins's Repositories
jmarihawkins/ai-case-study
Discover how Simply Homes is using AI to revolutionize affordable housing, raising $22m in funding to address the pressing issue of homelessness. With a focus on underserved communities, they're reshaping real estate with innovative technology, empowering tenants towards homeownership and making meaningful changes one family at a time.
jmarihawkins/athletic_sales_analysis
The purpose of this project is to analyze athletic sales data from 2020 and 2021 using Python within Jupyter Notebook. By combining, cleaning, and visualizing the data, we aim to uncover insights into regional sales patterns, retailer performance, and product trends. This analysis can be applied to retail businesses seeking to optimize sales.
jmarihawkins/classification-challenge
The purpose of this project is to develop and compare two machine learning models to detect spam emails. Spam detection is a crucial task in email filtering systems to protect users from unwanted and potentially harmful emails. The project involves using a dataset containing various features extracted from email content.
jmarihawkins/CryptoClustering
This project aims to cluster various cryptocurrencies based on their market performance using machine learning techniques. The analysis involves several key steps: normalizing the data, reducing its dimensionality with Principal Component Analysis (PCA), and using K-Means clustering to identify distinct groups.
jmarihawkins/customer_banking
This project is a Python-based banking system for users to manage their savings and Certificate of Deposit (CD) accounts easily. It simplifies entering account details like balances and interest rates, then calculates interest earned and updates balances automatically.
jmarihawkins/neural-network-challenge-1
The purpose of this project is to predict student loan repayment success using a neural network. Neural networks are computational models inspired by the human brain's structure and function, consisting of layers of interconnected nodes or "neurons" that can learn to recognize patterns in data.
jmarihawkins/neural-network-challenge-2
The purpose of this project is to develop a machine learning model that predicts employee attrition (whether an employee will leave the company) and department assignment (which department an employee belongs to) based on various factors. These factors include age, travel frequency, education level, job satisfaction, marital status, and more.
jmarihawkins/pandas-challenge-1
The purpose of this project was to analyze wholesale data using Python and pandas to gain insights into total revenue, total profit, and other key metrics for various clients. This project involved reading and processing data, performing calculations, and validating results to ensure accuracy.
jmarihawkins/prophet-challenge
The purpose of this project is to analyze and forecast MercadoLibre's search trends using Python. It showcases time series analysis and Prophet forecasting, providing insights into user behavior and potential market influences.
jmarihawkins/python-challenge-1
This Python script offers a user-friendly experience, allowing customers to effortlessly navigate the menu, select their desired items, and specify the quantity for each. Once the ordering process is complete, the script generates a comprehensive receipt displaying all ordered items, their respective prices, quantities and total cost.
jmarihawkins/sms_spam_detector
The purpose of this project is to build a machine learning model to classify SMS messages as either "spam" or "ham" (not spam). Using TF-IDF vectorization and LinearSVC, it reads an SMS dataset, transforms text data into numerical features, and trains a model to distinguish between spam and ham. The "SMSSpamCollection" dataset has labeled messages.
jmarihawkins/testplan_example_bookhaven
The test plan outlines a comprehensive strategy for testing an online bookstore web application to ensure its functionality, performance, security, accessibility, and compliance. The plan covers various scenarios, including user authentication, book browsing, cart functionality, checkout process, recommendation system, and error handling.