Love the Process, Not the Goal

There are 6 evaluations metrics for regression, Those are  Mean Absolute Error Mean Square Error Root Mean Square Error Root Mean Square Log Error R-Square Adjusted R-Square Let us discuss the first four evaluation metrics in this article Before diving into the evaluation metrics, let us discuss what is meant by error  Error:  If you observe the above image, the distance between the actual point and the predicted point on the straight line is called Error Error = Actual Value - Predicted Value  If we observe the above table, Error can be either positive or negative  If we take the average of all the errors in the dataset, we will be getting the mean error, but if we look closely at the above figure, error values have the possibility of getting both positive and negative, so there are high chances of those values being canceled while doing average Hence we will be taking the Absolute of the difference of the values   Mean Absolute Error : MAE is nothing...

What is EDA (Exploratory Data Analysis) In layman terms, EDA is nothing but going through the dataset, identifying the features, how they are related to each other, and how those features will be helpful for you in identifying the target values Now let us try to understand the textbook definition of EDA in Datascience  Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques to maximize insight into a data set. Techniques used in EDA   Let us step back for a moment and try to understand how we are deciding whether the mobile is good or not and whether the features might satisfy your needs we will check for RAM / MEMORY / CAMERA / DISPLAY SIZE and many more. and with each one, again we will deep dive and analyze the specifications and whether those are sufficient for our usage  Similar to that even in EDA we have a lot of parameters and techniques to implement and check on our dataset and see whether our dataset is in...

What is K-means Clustering K-Means is a simple and unsupervised machine learning algorithm, Basically, K-Means clustering is nothing but segregating the given data points of the dataset into multiple groups based on similar characteristics and identifying the underlined patterns, here the cluster refers to a collection of data points aggregated together because of certain similarities. K-Means clustering comes under unsupervised machine learning because, we won't be having the target values here, instead we group the data points which are similar in nature Where is the K-means Clustering algorithm used? There are multiple scenarios in the real world, where K-Mean clustering is being used, some of them are movie recommendations and to identify the type of the customer in any organizations Without any further delay, let's dive in understanding how K-Means clustering works  How is the K-means clustering algorithm works Step-1  K-Means clustering will keep few centroids in a rando...