Solving Data Challenges at Boston Technology Corporation

The BTC Team

At Boston Technology Corporation, we firmly believe in leveraging the power of technology to solve Data Challenges. Recently, we undertook a project that involved overcoming some of the most common and significant challenges in data science, such as overfitting, sub setting, splitting, and classification problems. This blog post will outline how our team effectively addressed these issues using innovative techniques and algorithms.

Data Challenges

Overcoming Overfitting with SMOTE Technique

The first challenge we faced was the overfitting problem. Overfitting occurs when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. It is an issue because it means that the model will have poor predictive performance.

To tackle this, we utilized the Synthetic Minority Over-sampling Technique (SMOTE). SMOTE is an oversampling method that creates synthetic samples from the minority class instead of creating copies. This method helped us to balance class distribution and improve the performance of our models. By leveraging SMOTE, we were able to generalize the model better to unseen data, significantly reducing the overfitting problem.

Fine-Tuning Algorithms for Subsetting and Splitting

Next, we had to address the subsetting and splitting problem. Subsetting involves selecting a subset of relevant features for model building, while splitting is about dividing data into training and testing sets.

Our approach to solving this problem was fine-tuning three popular algorithms—Naive Bayes, k-Nearest Neighbours, and Random Forest. By adjusting the parameters of these algorithms, we were able to achieve optimal subsets and splits, which significantly improved the efficiency and accuracy of our models.

Solving Classification Problem with Naive Bayes, k-Nearest Neighbours, and Random Forest

Finally, we tackled the classification problem. Classification involves predicting the category or class of given data points. It’s one of the most commonly encountered problems in machine learning and data science.

We used three algorithms—Naive Bayes, k-Nearest Neighbours, and Random Forest—to solve this problem. Each of these algorithms has unique strengths that make them suitable for different types of classification problems. Naive Bayes, for instance, is simple yet powerful and works well with high-dimensional datasets. K-Nearest Neighbours is a non-parametric method that is useful when the decision boundary is very irregular. Random Forest, on the other hand, is a versatile algorithm that can handle both categorical and numerical features.

By leveraging these algorithms, we were able to build robust models to predict the likelihood of a transport request being accepted by the non-emergency medical transportation providers for our client  Read more

Conclusion

Our journey at Boston Technology Corporation is all about solving complex data challenges using innovative solutions. 

Don’t just face your data challenges, conquer them!

Boston Technology Corporation is here to help your business towards unprecedented growth and innovation with cutting-edge data science solutions. Schedule a Consultation

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