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Prescriptive Analytics Projects: Prescriptive analytics takes predictive analysis a step further by recommending actions to optimize future outcomes. NLP techniques help extract insights, sentiment analysis, and topic modeling from text data. Create machine learning models to quickly identify and stop fraudulent transactions.
Machine Learning Algorithms: These algorithms can identify patterns in data and make predictions based on historical trends. NaturalLanguageProcessing (NLP): NLP techniques analyse textual data from sources like customer reviews or social media posts to derive sentiment analysis or topic modelling.
Machine Learning Algorithms: These algorithms can identify patterns in data and make predictions based on historical trends. NaturalLanguageProcessing (NLP): NLP techniques analyse textual data from sources like customer reviews or social media posts to derive sentiment analysis or topic modelling.
1 Data Ingestion (e.g., ApacheKafka, Amazon Kinesis) 2 Data Preprocessing (e.g., In the case of ride-hailing apps, each activity outcome contributes to completing the ride-hailing process. Model Training : Embeddings enable neural networks to consume training data in formats that extract features from the data.
This explosive growth is driven by the increasing volume of data generated daily, with estimates suggesting that by 2025, there will be around 181 zettabytes of data created globally. Real-Time DataProcessing The demand for real-time analytics is growing as businesses seek immediate insights to drive decision-making.
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