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AI / ML offers tools to give a competitive edge in predictiveanalytics, business intelligence, and performance metrics. In the link above, you will find great detail in data visualization, script explanation, use of neural networks, and several different iterations of predictiveanalytics for each category of NFL player.
This is part 2, and you will learn how to do sales prediction using Time Series. Please refer to Part 1– to understand what is Sales Prediction/Forecasting, the Basic concepts of Time series modeling, and EDA I’m working on Part 3 where I will be implementing Deep Learning and Part 4 where I will be implementing a supervised ML model.
Summary: AI in Time Series Forecasting revolutionizes predictiveanalytics by leveraging advanced algorithms to identify patterns and trends in temporal data. This is due to the growing adoption of AI technologies for predictiveanalytics. billion in 2024 and is projected to reach a mark of USD 1339.1 billion by 2030.
Exploratory Data Analysis (EDA) EDA is a crucial preliminary step in understanding the characteristics of the dataset. EDA guides subsequent preprocessing steps and informs the selection of appropriate AI algorithms based on data insights. Feature Engineering : Creating or transforming new features to enhance model performance.
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