As a tech leader, the possibilities of Machine Learning intrigue me. Most recently, we implemented a Linear Regression model to predict our resource allocation. Its intuition went beyond, pointing out that our investment in R&D is leading to a surge in product success. This insight latterly popped up as an unexpected blessing. We, therewith, revised our strategy, investing more in innovation, which resulted in improved product performance. Once again, machine learning shed light where we weren't even looking.
We used an unsupervised clustering model to better understand patient uptake on a mental health product. Mental health stigma has a big impact on patients decisions to seek care, so we used publicly available social determinants of health in our clustering to try and identify groups of patients facing different types of stigma. While these factors did impact patient treatment decisions, some groups that faced high stigma still sought treatment at high rates. It turned out that these groups seemed to have an easier time finding a doctor, while other groups were getting stuck at this stage. It really underscored the importance of easy access to healthcare in patient journeys, and we're hoping to take some steps to make that process even easier.
One transformative model that delivered unforeseen insights into our data was the implementation of a deep neural network for anomaly detection. Implementing a deep neural network for anomaly detection revolutionized our understanding of complex datasets, revealing hidden patterns previously elusive to traditional methods. Our methodical deployment began with careful dataset curation, covering diverse features and anomalies. Leveraging cutting-edge deep learning frameworks, we fine-tuned the model's parameters, ensuring optimal performance. Crucially, we prioritized proactive model interpretation using techniques like layer-wise relevance propagation and activation maximization. This approach unveiled the rationale behind predictions, enabling us to identify anomalies with unprecedented accuracy. The results were groundbreaking, as the model not only detected known anomalies but also unearthed previously undiscovered irregularities, guiding further investigation and optimization. In essence, by harnessing deep learning and adopting a rigorous interpretation approach, we unlocked unexpected insights that drove innovation and enhanced our understanding of our data ecosystem.
At Carepatron, we've seen firsthand how machine learning can be a game-changer in healthcare. It not only boosts patient engagement and streamlines operations, but also holds immense potential to personalize and improve patient care. However, machine learning in healthcare shouldn't be solely about the technology. We believe it should empower medical professionals. That's why we focus on developing machine learning solutions that equip doctors, nurses, and other caregivers to deliver the best possible care, with both efficiency and a human touch.
In one of my Projects, I applied a machine learning model for financial forecasting for a retail shop based on weekly sales revenue. Financial forecasting, particularly for a retail store's weekly sales income, uses a time-series forecasting model. Random Forest and XGBoost are sophisticated machine learning algorithms that can be used to forecast financial data, such as a retail store's weekly sales income. Here's a approach to build the model: Data Preparation: • Collect weekly sales revenue data as historical data. • Preprocess data to remove missing values, outliers, and ensure uniform time intervals (weekly). • Organize data by features (e.g., time, prior sales) and target variables (weekly sales revenue). Feature Engineering: Add features that affect sales, such as seasonality indicators, promotional events, holidays, economic data, and more. Model Selection and Training: Random Forest: • Random Forest is an ensemble method that constructs numerous decision trees during training and outputs their average forecast. • Utilize the ready data to train a Random Forest regressor. Use methods like grid search or random search to fine-tune hyperparameters like the minimum amount of samples per leaf, maximum depth, and number of trees. XGBoost: • XGBoost (Extreme Gradient Boosting) builds trees successively and optimizes residuals at each phase. • Train an XGBoost regressor on the data by setting hyperparameters like learning rate, maximum depth, number of estimators, and regularization parameters. Forecasting: • Use the best-performing model (Random Forest and XGBoost) to anticipate weekly sales revenue using new data. • Adjust the forecasting horizon as necessary (for example, forecasting for the next few weeks or months). Model Evaluation: • Analyze the Random Forest and XGBoost models on a validation/test set using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), or Root Mean Squared Error (RMSE). The accuracy was only displayed for values of 65% and 72%. • Evaluate both models' performances to see which offers the best generalization and accuracy. To increase the performance of the model, I moved to Fuzzy Logic methodology. There was no such pattern or trend in the historical data. Even clients also ask for a Daily basis instead of weekly. The membership value lies between 0 to 1. K-fold cross-validation was applied to avoid the biasness of the model. The accuracy was increased range from 88% to 92%.
At Zibtek, one impactful machine learning model we implemented was a Random Forest algorithm aimed at predicting customer churn. The unexpected insight it provided was not just in identifying the likelihood of churn but in uncovering underlying patterns that contributed to customer dissatisfaction which we hadn’t pinpointed before. Approach: Data Collection and Preparation: We gathered a comprehensive dataset from our CRM and user interaction logs that included both numerical and categorical data types. This data was then cleaned and pre-processed to handle missing values, outliers, and normalization. Feature Engineering: We created new features that we hypothesized could influence churn, such as customer engagement scores based on interaction frequency and types of service issues reported. Model Building: We chose the Random Forest model for its robustness and ability to handle large data sets with a complex mixture of features. It’s also beneficial for its feature importance capabilities, which we leveraged to understand which variables most strongly predicted churn. Training and Testing: The model was trained on a split of historical data, with careful cross-validation to avoid overfitting. We continuously adjusted parameters and features to optimize the model's accuracy and recall. Implementation and Monitoring: Once validated, we implemented the model into our customer service workflow, allowing real-time predictions to inform our support team’s engagement strategies. Unexpected Insights: The model revealed that customers who experienced specific types of service disruptions were significantly more likely to churn, an insight that traditional analysis had not uncovered. This led to a targeted improvement in our service delivery in those areas, which had a direct and measurable decrease in churn. Impact: This approach not only enhanced our predictive capabilities but also drove a deeper understanding of our customer base, leading to more tailored and effective retention strategies. It demonstrated the power of integrating machine learning into business processes to transform data into actionable business intelligence. This model has become a cornerstone of our strategy to enhance customer satisfaction and retention at Zibtek.