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Tag: EDA

Missing Features: A challenge in data preparation

Missing Features: A challenge in data preparation

What is Missing Features Problem? The principle of machine learning and AI based application is “Garbage in-garbage out”. Hence, data cleaning and Exploratory Data Analysis are the key steps of data preparation phase. In the real world, the data comes with noise. The noise may include many things such as incorrect data, unnecessary extra data, missing values, missing attributes/features. Among st them, it is difficult to handle missing features problem. Missing features is one of the most common problems that…

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Exploratory Data Analysis: First milestone of data analysis

Exploratory Data Analysis: First milestone of data analysis

In statistics, exploratory data analysis (EDA) is a technique that analyze data to recapitulate their major features, frequently with visual approaches. Its is an initial step of data anlysis from experiment. Primarily EDA is for sighting what the data can express beyond the formal modeling or hypothesis testing job. EDA is different from initial data analysis (IDA) which emphases on glancing assumptions needed for model fitting and hypothesis testing, and managing missing values and making transformations of variables as required….

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