Predictive Analytics on Business License Data Using Deep Learning
This project teaches Deep Neural Networks (DNNs) using a dataset of 86,000 businesses. Participants will learn key concepts and use Python libraries like pandas, numpy, and TensorFlow for data analysis, cleaning, model building, and tuning.
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$15 USD
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Project Outcomes
- Developed a predictive model to predict business license status.
- Had 78+% accuracy in predicting license outcomes.
- Tried to find important factors affecting the approval of the license.
- Have converted categorical data to a format that is usable by the model.
- Filled in the gaps and made indicators of missing data effectively.
- Tweaked the dataset by creating better predictions using feature engineering.
- Created a model that could predict multiple license statuses.
- We analyzed what business trends there were according to license type and status.
- Established a process of how to evaluate the model's performance.
- The data was cleaned and prepared well for machine learning tasks.
- Predictive analytics help organizations in forecasting the approval or denial of business licenses. This helps in decision-making.
- The model can detect anomalies and patterns in business organizations. This prevents fraudulence.
- Businesses benefit from more accurate license approval predictions. This reduces delays in starting operations.
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