Data Science Lifecycle revolves around the use of machine learning and different analytical strategies to produce insights and predictions from information in order to acquire a commercial enterprise objective. The complete method includes a number of steps like data cleaning, preparation, modelling, model evaluation, etc. It is a lengthy procedure and may additionally take quite a few months to complete. So, it is very essential to have a generic structure to observe for each and every hassle at hand. The globally mentioned structure in fixing any analytical problem is referred to as a Cross Industry Standard Process for Data Mining or CRISP-DM framework....

The three ways to add a column to Pandas DataFrame with Default Value....

Numpy in Python is a general-purpose array-processing package. It provides a high-performance multidimensional array object and tools for working with these arrays. It is the fundamental package for scientific computing with Python. Numpy provides very easy methods to calculate the average, variance, and standard deviation....

R Programming Language is an open-source programming language that is widely used as a statistical software and data analysis tool. R generally comes with the Command-line interface. R is available across widely used platforms like Windows, Linux, and macOS. R language provides very easy methods to calculate the average, variance, and standard deviation....

Collaborative Filtering is a technique or a method to predict a user’s taste and find the items that a user might prefer on the basis of information collected from various other users having similar tastes or preferences. It takes into consideration the basic fact that if person X and person Y have a certain reaction for some items then they might have the same opinion for other items too....

User-Based Collaborative Filtering is a technique used to predict the items that a user might like on the basis of ratings given to that item by other users who have similar taste with that of the target user. Many websites use collaborative filtering for building their recommendation system....

Data is widely considered a crucial resource in different organizations across every industry. Data Science can be described in simple terms as a separate field of work that deals with the management and processing of data using statistical methods, artificial intelligence, and other tools in partnership with domain specialists. Pursuing Data Science encompasses concepts and epochs derived from different fields including Mathematics and Computer Science and Information Theory to interpret large data....

R language is mostly used for statistics and data analytics purposes to represent the data graphically in the software. To represent those data graphically, charts and graphs are used in R....

In a world of data space where organizations deal with petabytes and exabytes of data, the era of Big Data emerged, and the essence of its storage also grew. It was a great challenge and concern for industries for the storage of data until 2010. Now when frameworks like Hadoop and others solved the problem of storage, the focus shifted to the processing of data. Data Science plays a big role here. All those fancy Sci-fi movies you love to watch around can be turned into reality by Data Science. Nowadays its growth has been increased in multiple ways and thus one should be ready for our future by learning what it is and how can we add value to it....

Hypothesis testing involves formulating assumptions about population parameters based on sample statistics and rigorously evaluating these assumptions against empirical evidence. This article sheds light on the significance of hypothesis testing and the critical steps involved in the process....

Estimations like mean, median, standard deviation, and variance are very much useful in case of the univariate data analysis. But in the case of bivariate analysis(comparing two variables) correlation comes into play....

TensorFlow is a machine learning framework that has offered flexibility, scalability and performance for deep learning tasks. tf.function helps to optimize and accelerate computation by leveraging graph-based execution. In the article, we will cover the concept of tf.function in TensorFlow....