Empowering Businesses with Predictive Insights: Solving License Compliance and Regulatory Challenges
During the development of the Business License Status Prediction project, I encountered several challenges, including:
Data Quality Issues: One of the main hurdles was the quality of the data used for training and testing the predictive model. There were missing values, inaccuracies, and inconsistencies in the dataset, which affected the model's performance.
How I overcame it: I implemented a data preprocessing pipeline that involved data cleaning, imputation of missing values, and outlier detection. I also collaborated with domain experts to validate and refine the dataset, which significantly improved the model's accuracy.
Model Overfitting: Initially, the predictive model suffered from overfitting, which meant it performed well on the training data but poorly on new, unseen data.
How I overcame it: I employed techniques such as cross-validation, regularization, and hyperparameter tuning to address overfitting. This helped the model generalize better to new data and improve its predictive capabilities.
Scalability: As the project gained popularity, there was a need to scale the system to handle a larger volume of license data and prediction requests.
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