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I developed a Sentiment Analysis project using Natural Language Processing (NLP) to classify customer messages based on their sentiment. I worked with customer text data, performed text preprocessing, and implemented both a lexicon-based approach and a DistilBERT transformer model for sentiment classification.
The project helped identify positive, negative, and neutral customer feedback and demonstrated how NLP and machine learning can be used to understand customer opinions from text data.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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Customer Churn Prediction project using Machine Learning. I analyzed customer data, compared Logistic Regression, Decision Tree, and Random Forest models, and evaluated their performance using accuracy, precision, recall, and ROC-AUC. I also performed threshold analysis to identify high-risk customers for better retention strategies.
Built a Capacity & Bottleneck Analysis tool in Excel to help operations teams identify overloaded work centers, understand capacity gaps, and make better production planning decisions.
The analysis compares available capacity against required hours across each resource, highlights constraints before they impact output, and provides a clear view of where overtime, workload balancing, or schedule changes may be needed.
Key features:
Available vs. required capacity analysis
Work center utilization tracking
Capacity gap calculations
Bottleneck identification
Overtime requirement visibility
Capacity vs. demand trends
Utilization trend analysis
Risk-based alerts
Actionable planning recommendations
Management-ready summary dashboard
Designed to help planners quickly identify where capacity is constrained, understand the size of the gap, and determine where resources or schedules may need to be adjusted.
Excited to share a new milestone!
I’m thrilled to announce that I have successfully completed the AI & Machine Learning track at AMIT Learning with a grade of 99%! ✨️
Throughout this journey, I’ve focused on bridging the gap between theoretical knowledge and real-world application, applying these concepts directly to hands-on projects like my current Traffic Congestion Analysis using Python, Pandas, and advanced data cleaning techniques.
A big thank you to Maria Roshdy and the entire team at AMIT Learning for the valuable guidance and great learning experience. Looking forward to the next steps into predictive modeling and AI solutions!
#ArtificialIntelligence #MachineLearning #DataScience #AMIT #Python #DataAnalytics
