Noisy data in datasets impact the prediction of meaningful information. Studies stand evidence that noise in datasets leads to poor prediction results and decreased classification accuracy. Noise impacts algorithms in missing out patterns in any dataset. To be precise, noisy data is equivalent to meaningless data.
When you learn data mining, you get to know about data cleaning. Removing noisy data is an integral part of data cleaning as noise hampers data analysis significantly. Improper data collection processes often lead to low-level data errors. Also, irrelevant or partially relevant data objects might hinder data analysis. For enhancing data analysis, all such sources are considered noise.
In data science training, you will learn the skills of removing noise from datasets. One such method is data visualisation with tableau. Neural networks are also quite efficient in handling noisy data.
Effective ways of managing and removing noisy data from datasets
You must have heard the term ‘data smoothing’. It implies managing and removing noise from datasets. Let us look at some effective ways of managing and removing noisy data from datasets:
There are innumerable instances where the dataset contains a huge volume of unnecessary data. Regression helps in handling such data and smoothens it to quite an extent. For the purpose of analysis, regression helps in deciding the suitable variable. There are two variables in regression, which are as follows:
Linear regression deals with finding the best line for fitting between two variables so that one is used for predicting the other.
Multiple Linear Regression
There is the involvement of two or more variables in multiple linear regression. By using regression, you can easily find a mathematical equation for fitting into the data. This helps in smoothing out the noise successfully to quite an extent.
When you learn data mining, you will surely learn about binning. It is one of the best and most effective ways of handling noisy data in datasets. In binning, you can sort the data. You can then partition this data into bins of equal frequency. You can replace the sorted noisy data with bin boundary, bin mean or bin median methods.
Let us look at the three popular methods of binning for smoothing data:
Bin median method for data smoothing
In this data smoothing method, the median value replaces the existing values that are taken in the bin.
Bin mean method for data smoothing
The mean value of the values in the bin replaces the actual value in the bin in this data smoothing process.
Bin boundary method data smoothing
In this data smoothing method, the maximum and minimum values in the bin values are then replaced by the boundary value that is closest.
Outliers are detected by clustering. It is evident from the name that close or similar values are organised in clusters or in the same groups. The values which do not fit into the cluster or fall apart are considered outliers or noise.
However, outliers provide important information and should not be neglected. They are extreme values which deviate from other data observations. They might be indicative of novelty, experimental errors or even measurement variability.
To be precise, an outlier is considered an observation which diverges from a sample’s overall pattern. Outliers are of different kinds. Some of the most common kinds are as follows:
These are single data points, which rest away quite far from the rest of the distribution.
These outliers are found when you look at value distributions in a single feature space.
These outliers are found in an n-dimensional space containing n-features. The human brain finds it very difficult to decipher the various distributions in n-dimensional spaces. To understand these outliers, we have to train a model to do the work for us.
Collective outliers might be subsets of various novelties in data. For instance, it can be a signal indicating the discovery of any new or unique phenomena.
Contextual outliers are strong noises in datasets. Examples to illustrate this include punctuation symbols in text analysis or background noise signals while handling speech recognition.
Clustering is one of the most commonly used ways for noise removal from datasets. In data science training, you will learn how to find outliers and also the skills of grouping data effectively. This way of noise removal is mainly used in unsupervised learning.
Using neural networks
Another effective way of removing noise from datasets is by using neural networks. A neural network is an integral part of Artificial Intelligence (AI) and a subset of Machine Learning, in which computers are taught to process data inspired by the human brain. It is a kind of Machine Learning process known as Deep Learning where interconnected nodes are used in a layered structure for analysing data.
Data visualisation with tableau
Tableau is a data processing programme which creates dynamic charts and graphs for visualising data in a professional, clean and organised manner. While removing noise from datasets, this programme proves to be truly effective. Clear identification of data is possible with data visualisation with tableau.
Almost all industries are implementing Artificial Intelligence (AI), Machine Learning (ML) and Data Science tools and techniques in their works. All these technologies work with huge volumes of data, using the most valuable ones for improved decision-making and forecasting trends. Noise removal techniques help in removing unimportant and useless data from datasets to make them more valuable.
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