Data Science

  A Guide To Successful Data science Projects for Beginners Completing highly-praised Data science projects have become the motto of every working professionals as well as freshers. But Why! The high demand for  data-driven business decisions  has changed the global job market scenario in the last 5 years. The monstrous volume of information has effectively gone past the controlling power of the human cerebrum. So, competitive and successful business decisions now require joint effort with statistically determined technological force. The above scenario develops daily the rising  demand for data science professionals  and lowering the need for non-data science recruitments. As a result, to make the future recession-proof, everyone is dying for a  data science career switch . And what’s the foremost criterion to grab your first promising  data science job role? It’s the presence of a creditable  real-time data science projects  on your resume. Here I have come up with a list of trending

  The supervised learning algorithm is widely used in industries to predict the business outcome, and forecasting the result on the basis of historical data. The output of any supervised learning depends on the target variables. It allows the numerical, categorical, discrete, linear datasets to build a machine learning model. The target variable is known for building the model and that model predicts the outcome on the basis of the given target variable if any new data point comes to the dataset. The supervised learning model is used to teach the machine to predict the result for the unseen input. It contains a known dataset to train the machine and its performance during the training time of a model. And then the model predicts the response of testing data when it is fed to the trained model. There are different machine learning models that are suitable for different kinds of datasets. The supervised algorithm uses regression and classification techniques for building predictive model

  Machine learning has become the sexiest and very trendy technology in this world of technologies, Machine learning is used every day in our life such as Virtual assistance, for making future predictions, Videos surveillance, Social media services, spam mail detection, online customer support, search engine resulting prediction, fraud detection, recommendation systems, etc. In machine learning, Regression is the most important topic that needed to be learned. There are different types of Regression techniques in machine learning, which we will know in this article. Introduction: Regression algorithms such as Linear regression and Logistic regression are the most important algorithms that people learn while they study  Machine learning algorithms . There are numerous forms of regression that are used to perform regression and each has its own specific features, that are applied accordingly. The regression techniques are used to find out the relationship between the dependent and indepe