This section contains projects that focus on Clustering (Unsupervised ML).
Projects
- Customer Segmentation
- Gender By Voice (Clustering)
Customer Segmentation
The report analyzes customer segmentation using clustering techniques, focusing on purchasing behavior. It uses a dataset with 3900 entries and 18 columns, revealing customer demographics ranging from 18 to 70. The K-Means clustering algorithm is used, and the Elbow Method determines the optimal number of clusters. The report provides insights into customer characteristics and offers visualizations for targeted business strategies.
Gender By Voice (Clustering)
The Voice Gender Clustering project uses unsupervised machine learning to identify the gender of a voice based on its acoustic properties. By clustering voice samples with similar characteristics, the project distinguishes between male and female voices. It provides a comprehensive report and insights into the effectiveness of different clustering algorithms for gender identification.
About Me
Hello, I'm Emuejevoke Eshemitan, a passionate data professional specializing in Machine
Learning Engineering and Data Science. My portfolio showcases projects reflecting my
dedication to leveraging data for insightful and innovative solutions.
As a Machine Learning Engineer, I excel in designing cutting-edge models using TensorFlow and
scikit-learn, building scalable data pipelines, and deploying them in production
environments.
As a Data Scientist, I analyze valuable patterns, develop predictive
models, and communicate insights through impactful visualizations.
Let's collaborate on a data-driven journey! Explore my portfolio for more.