Steps involved in machine learning projects
1. Defining the problem Assessing the data available (internal and third party sources) or the databases that need to be created, as well as database architecture for optimum storing and processing.
Also discuss costs, ROI, vendors, and timeframe. Decision makers and business analysts are heavily involved, and data scientists and engineers may participate in the discussion.
2. Defining goals Are we going to use the data for segmentation, customer profiling and better targeting, to optimize some processes such as pricing or supply chain, for fraud detection, taxonomy creation, to increase sales, for competitive or marketing intelligence, or to improve user experience for instance via a recommendation engine or better search capacities?
3. Collecting the data. Assessing who has access to the data and in what capacity. Here privacy and security issues are also discussed.
Dashboard design is also discussed, with the purpose of designing good dashboards for end-users such as decision makers, product or marketing team, or customers.
4. Exploratory data analysis Here data scientists are more heavily involved, though this step should be automated as much as possible. You need to detect missing data and how to handle it like to deal with data flows, summarize and visualize the data.
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