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What is Data Preprocessing ?
Data preprocessing is the method of manipulating or dropping the data before it is used in order to ensure or enhance performance. It is an important process in data mining. The phrase "garbage in, garbage out" is particularly applicable to data mining and machine learning projects. During the data gathering, it is loosely controlled, hence the outcome is in the dataset out of range values, missing values, impossible data combination etc. This type of data can produce misleading results. Data preprocessing is the most important step in a machine learning project. If there is more relevant or redundant information present or noisy or imbalanced data then identifying the useful information is more difficult. Data preparation and filtering steps can take a considerable amount of processing time. Examples of data preprocessing include cleaning, instance selection, normalization, one hot encoding, transformation, feature extraction and selection, etc. Preprocessing data can have an impact on how the results of the final data processing are understood.
Task of Data preprocessing
Data cleansing
Data integration
Data reduction
Data transformation
Data cleansing : Data cleansing or data cleaning is the process of detecting and correcting or remove corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data. Data cleansing may be performed interactively with data wrangling tools, or as batch processing through scripting.
Data Integration : in this process the combining multiple sources data into a single dataset. It is also important to process data preprocessing.
Data reduction : In this process reduce the volume of data which makes the analysis easier compared to original data and produce the same or almost same result.
Dimensionality reduction
Data compression
Numerosity reduction
Data transformation : Changing the format of data structure or format for Data analysis or ML project is called Data transformation. There some technique to data transformation
Smoothing
Aggregation
Discretization
Normalization
Data Preprocessing Homework Help
Data science is a popular course that has been introduced in educational institutions and universities. Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning and Data preprocessing is the process of transforming raw data into an understandable format
Students who are pursuing the Data science course face difficulty to complete the homework on time due to a busy schedule. Students need to deal with a lot of complex terminologies to solve Data science Homework Assignments. They look for help. Programming often gives a tough time to students struggling to meet their deadline for the homework or assignments.
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