Hello there! Welcome to my repository showcasing the projects I completed as part of the Google Advanced Data Analytics Professional Certificate on Coursera. This certificate program consists of six courses, each offering hands-on projects to apply the skills I learned.
In the 'Foundations of Data Science' course, I explored core data science principles and their real-world applications. Gaining insights into diverse industries relying on advanced analytics, I understood data analysis' crucial role in decision-making. Moreover, I learned about data privacy and ethics, ensuring confidentiality and integrity.
🟡 Project: Automatidata Project Proposal
In this project, I showcased an effective data science workflow by creating a project proposal. The proposal communicated essential project tasks and milestones to my team. Utilizing the PACE strategy document as my guide, I ensured efficient project planning and development for successful data science project execution.
In the "Get Started with Python" course, I explored essential Python programming fundamentals for data tasks, covering syntax, semantics, loops, control statements, string manipulation, and data structures.
In this project, I analyzed the New York City Taxi and Limousine Commission (TLC) data. The goal was to identify key variables, ensure data readiness, and derive clear insights for data-based solutions.
During the this course, I honed the art of translating data into meaningful insights. I learned the EDA process for valuable information extraction, the benefits of data structuring and cleaning, and gained hands-on experience in Python-based data exploration. Furthermore, I became proficient in data visualization with Tableau, crafting impactful visualizations to effectively convey insights.
The goal of the project was to uncover valuable insights into NYC taxi ridership through EDA and data visualization. A Python notebook with structured data, engaging matplotlib/seaborn visuals, and an inclusive Tableau dashboard was created for NYC TLC's informed decisions and data-driven solutions.
In "The Power of Statistics" course, I explored practical statistical analysis, extracting valuable insights from datasets. Understanding probability distributions enabled accurate data modeling, making informed predictions. Highlights include conducting hypothesis tests and performing statistical analyses with Python, uncovering hidden patterns and trends.
The purpose of this project was to predict taxi cab fares before each ride. At this point, this project’s focus was to find ways to generate more revenue for New York City taxi cab drivers. This project examines the relationship between total fare amount and payment type.
This course equipped me with skills to investigate relationships within datasets and identify regression model assumptions. I became proficient in linear and logistic regression in Python, and learned to evaluate and interpret models which empowered me to make informed decisions based on statistical evidence.
🔵 Project: New York City TLC - Taxi Fare Prediction
Built a multiple linear regression model to accurately predict taxi fares for the New York City Taxi and Limousine Commission (New York City TLC) using existing data collected over a year to deliver valuable insights and enhance data-driven decision-making for both taxi drivers and passengers.
In this course, I explored the foundations of machine learning, learned to prepare data for models, and built and evaluated supervised and unsupervised learning models. Additionally, I gained valuable insights into proper model and metric selection, enabling me to apply machine learning techniques effectively to real-world datasets.
🟣 Project: New York City TLC - Generous Tip Predictor
The goal of this project was to build a machine learning model that predicts the most generous customers—those who will tip 20% or more—based on the data provided by the New York City Taxi & Limousine Commission (New York City TLC). By identifying these generous customers, the aim was to assist taxi drivers in increasing their earnings from tips and improving their overall customer service.
⛳ CAPSTONE: Salifort Motors Employee Attrition Prediction
Project goal: To develop a predictive model that can be used to identify employees who are at risk of leaving Salifort Motors, with the goal of reducing employee attrition.