Benchmark for Burk et al. (2024)
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Updated
Aug 12, 2024 - R
Benchmark for Burk et al. (2024)
By integrating geographical data analysis and statistical modeling, CTCA aims to inform strategies for reducing crash rates and enhancing road safety. This initiative combines innovative data processing techniques with advanced analytics to offer actionable recommendations for policymakers, urban planners, and public safety organizations.
Python programming labs done throughout the course CSC406 - Artificial Intelligence
Analyzing housing complaints in New-York-City and develop a forecasting model
Analyzing and classifying French tweets related to climate change and drought using NLP and Machine Learning.
Marketing strategies on the sales volume and average retail price (ARP) of Good Belly products. Using a dataset encompassing sales, promotions, and demographic information across multiple regions, this project employs causal analysis and multiple linear regression to provide insights into the effectiveness of marketing activities.
client subsection to a term deposit
Text Processing RNN leverages RNN and LSTM models for advanced text processing. It features deep learning techniques for NLP tasks, utilizing GloVe for word embeddings, aimed at both educational and practical applications.
Prediction of happy Customers based on Happiness Survey Data
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