Exam of January 2023
📊 "Election Predictor" - A Machine Learning Project 🗳️
I embarked on an exciting journey into the world of machine learning with my project "Election Predictor." 📈
🔸 Overview: "Election Predictor" is a binary classification project that aimed to predict whether Bill Clinton won a county during the 1992 U.S presidential election based on county-level demographics. This project allowed me to delve into the fascinating world of supervised machine learning, where I harnessed the power of data to make predictions and gain insights.
🔹 Dataset: The project revolved around a dataset containing county-level demographics and the election outcome of each county during the 1992 U.S presidential election. Despite the challenge of having only one election cycle to predict from and limited features in the form of county-level demographics, I relished the opportunity to tackle this tough problem.
🔸 Binary Classification Challenge: The primary objective of "Election Predictor" was to successfully predict whether Bill Clinton won a county using the demographic variables. With nearly perfectly balanced data, accuracy emerged as the most appropriate metric to evaluate the model's performance. This project is suitable for both novice and experienced ML students, making it a valuable resource for those looking to hone their machine learning skills.
🔹 The Learning Journey: This experience of tackling a real-world problem through machine learning left an indelible mark on my journey. It was a testament to the power of data and predictive modeling in understanding historical election outcomes.