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Dropping those rows or columns or completely replacing them with another value is one of the simplest ways to deal with missing or corrupted data.
In Pandas, there are two practical methods:
IsNull() and dropna() will help to find the columns/rows with missing data and drop them
Fillna() will replace the wrong values with a placeholder value
Clustering is a method for unsupervised learning which includes putting data points in groups. With a set of data points, clustering can be used. You can use this method to group all the data pieces into their appropriate categories. While data points belonging to different groups have different features and properties, those belonging to the same classification have similar characteristics and traits. This technique can be used to analyse statistical data. We will examine three of the most commonly used and effective clustering techniques.
This algorithm is frequently used when data does not fit into any particular category or group. Using K-means clustering, you can identify the data's hidden patterns, which can then be used to divide the data into different groups. The number of groups into which the data is divided is represented by the variable k, and the data points are clustered based on how similar their features are. Here, new data is labelled using the centroids of the clusters.
Mean - Shift Clustering
The main goal of this algorithm is to find the centres of all groups by updating the centre-point candidates to be mean. The potential number of clusters can be automatically determined by the mean shift in mean-shift clustering, unlike k-means clustering.
Density-based spatial clustering of applications with noise (DBSCAN)
The number of clusters does not need to be predetermined, but unlike mean-shift clustering, DBSCAN recognises outliers and treats them as noise. Additionally, it can easily locate clusters of any size or shape.
The supervised Machine Learning algorithm is known as Linear Regression. Predictive analysis establishes the linear connection between the dependent and independent variables.
Linear Regression Equation where:
X is the input or independent variable
The result, or dependent variable, is Y.
The intercept is a, and the X coefficient is b.
The data of weight, Y, the dependent variable, and height, X, the independent variable, of 21-year-old applicants are plotted below the best-fit line. The straight line represents the best linear relation for estimating candidate weight from height. The ideal values of a and b must be determined to obtain this best-fit line. The errors in the prediction of Y can be decreased by modifying the values of a and b. In this way, linear regression aids in identifying the linear relationship and predicting the result.
The steps that must be taken to achieve a specific result can be visualised in the form of a decision tree. The actions are depicted in a hierarchical diagram. The set hierarchy of actions can be the foundation for a decision tree algorithm. For example, the preceding decision tree depicts a series of actions for operating a vehicle with or without a licence.
The machine is trained using labelled data in supervised machine learning. The learning model is then fed a fresh dataset, hoping to produce a successful result based on its examination of the labelled data. For instance, to train the model for classification, we must first label the data. Unsupervised machine learning is a method in which the machine is not taught with labelled data and is left to make decisions based solely on input variables.