AL/ML Mastery: Unlock the Power of Artificial Intelligence and Machine Learning

1. Predictive Maintenance for Manufacturing:
   • Scenario: Imagine you are working with a manufacturing company that operates a large fleet of machinery. The company wants to minimize unexpected downtime and optimize maintenance operations by predicting equipment failures in advance.
   • Real-world Dataset: Utilize a dataset that includes sensor readings, equipment usage patterns, and maintenance records from the manufacturing company’s machinery fleet.
   • Tasks: Perform feature engineering on the dataset to extract relevant features such as temperature, vibration, and usage patterns. Explore various ML algorithms such as Random Forest, Support Vector Machines, or Recurrent Neural Networks to build a predictive maintenance model. Fine-tune the chosen model to maximize accuracy and develop a system that alerts maintenance teams in advance of potential failures.
   • Deliverables: Present findings on the most accurate model, showcase the predictive maintenance system prototype, and discuss the potential cost savings and efficiency improvements that can be achieved by implementing the model in real-time operations.
2. Customer Churn Prediction for Telecom:
   • Scenario: Consider a telecom company that wants to reduce customer churn and improve customer retention strategies. The company is interested in identifying customers who are likely to churn, allowing them to proactively take measures to retain those customers.
   • Real-world Dataset: Utilize a dataset provided by the telecom company, which includes customer demographic information, call logs, service usage, and churn labels indicating whether a customer has churned or not.
   • Tasks: Preprocess and analyze the dataset, exploring factors such as customer demographics, service usage patterns, and call behavior. Implement and compare various ML algorithms like Logistic Regression, Decision Trees, or Gradient Boosting to develop a customer churn prediction model. Fine-tune the selected model to achieve high accuracy and identify potential churn indicators.
   • Deliverables: Present insights on churn indicators, showcase the predictive model, and propose strategies for customer retention based on the model’s predictions. Highlight the potential impact on customer retention rates and the associated revenue gains for the telecom company.
3. Sentiment Analysis for Social Media:
   • Scenario: Imagine working for a marketing agency that wants to understand the sentiment of social media posts related to a specific brand or product. The agency aims to gain insights into customer sentiment and identify areas for improvement in their marketing campaigns.
   • Real-world Dataset: Utilize a dataset of tweets or online reviews related to the brand or product of interest, labeled with sentiment categories such as positive, negative, or neutral.
   • Tasks: Preprocess the text data, including tokenization, removing stopwords, and handling punctuation and special characters. Experiment with various NLP techniques such as Bag-of-Words, TF-IDF, or Word Embeddings to develop an accurate sentiment analysis model. Fine-tune the model to achieve high accuracy in sentiment classification.
   • Deliverables: Present insights on sentiment trends related to the brand or product, showcase the model’s performance in classifying sentiments accurately, and discuss potential applications for sentiment analysis, such as improving marketing strategies or identifying customer feedback for product improvements.
4. Image Classification for Medical Diagnostics:
   • Scenario: Consider a healthcare organization that wants to improve the efficiency and accuracy of medical diagnostics by automatically classifying medical images such as X-rays or histopathology slides.
   • Real-world Dataset: Utilize a dataset of medical images, such as X-rays or histopathology slides, with corresponding labels indicating the presence or absence of specific conditions or diseases.
   • Tasks: Preprocess the medical images, including resizing, normalization, and augmentation techniques. Fine-tune pre-trained CNN models like VGG, ResNet, or InceptionNet to develop an image classification model that can accurately identify various medical conditions or diseases. Evaluate the model’s performance and interpret its predictions.
   • Deliverables: Present findings on the model’s accuracy in medical image classification, showcase example predictions on test images, and discuss the potential impact on medical diagnosis, such as improving the speed and accuracy of radiologists’ assessments or assisting in early disease detection.
5. Fraud Detection in Financial Transactions:
   • Scenario: Imagine working for a financial institution that wants to detect fraudulent transactions in real-time, ensuring the security of their customers’ accounts and preventing financial losses.
   • Real-world Dataset: Utilize a dataset of financial transactions, including transaction details (e.g., amount, location, time) and labels indicating whether a transaction is fraudulent or legitimate.
   • Tasks: Perform feature engineering to extract relevant features from transaction data, such as transaction frequency, amounts, and historical patterns. Experiment with different ML algorithms, including Logistic Regression, Random Forest, or Gradient Boosting, to develop a fraud detection model. Fine-tune the chosen model to achieve high accuracy in identifying fraudulent transactions while minimizing false positives.
   • Deliverables: Present insights on fraudulent transaction patterns, showcase the model’s performance in accurately detecting fraud, and discuss potential fraud prevention strategies that can be implemented by the financial institution to enhance security and protect customer accounts.

In our capstone projects we provide students with realistic scenarios and examples of real-world datasets, that will allow them to apply their knowledge and skills in solving industry-relevant problems. The projects encourage students to tackle significant challenges, develop practical solutions, and present their findings effectively, preparing them for real-world applications of machine learning and deep learning.