Raw data is useless until we use it. Finding and separating information is not that easy where data mining plays its role. Data structures are only for calculating time complexity. Mining is the process of finding some useful. If you really want to extract information from rough collections and figures then preprocessing very first step.
How to deal when having a dataset having some missing values, which is not good for training, the simple answer is data mining. The very Initial step is data preprocessing which plays a role like building blocks. How can you make a strong building when the base is not preprocessed?
Why Preprocessing Is Necessary In Data Mining
Inconsistent Values Leads Drastic
Priority of choosing ML model is second, datasets win very first but how do you manage with missing attributes? Although python programming language is the best tool for dealing with what is not done properly.
Poor preprocessing lead to poor results, as you know data mining process is very critical and would create small mistakes. It is a drastic situation when you are unable to provide a rich dataset. Therefore, it is important to understand the concept of preprocessing in data mining.
It is one of the biggest advantages of preprocessing, as data gathered from distinct resources but improving is crucial. Resources might be interent, mobile devices, website scraping, physical survey etc. Due to incomplete knowledge of data, human mistakes, noisy data, the resultant would affect the training of ML model. Expect imbalanced data, there is a probabilty of occuring unknown or irrelevant data. For example when you are predicting the stock exchange marker price, age of person doest not matter here.
Drop Out Grbage Data
It is important model cannot think like a human does, so grabage data returns garbage output. World is full of data, however, it is said that data is initial access to predict what is going on future. Machine Learning Engineer almost spend 80% of their time for selecting, sorting the dataset that is helpful for novel model training. Extract the unusual data from the dataset is as important asn you deep understand.
Healthy dataset means healthy model, a bad dataset means poor model, therefore, preprocessing is important part of data mining. Collecting data from all possible sectors is not important, collect out useful is. Preprocessing is one of the critical steps for data mining / data analysis / machine learning.