The best way to enhance one’s skill in a particular field is by practicing that particular skill by using that skill in a real world scenario. I have tried to use my skill by aiming to create a web application which gives an estimate of the rent prices in a…

Natural language processing is a relatively new field which involves interaction between a computer and a natural language like English where the machine can learn the features of a language and can use the knowledge to perform some tasks like spam classification, fake news detection, sentiment analysis etc For the…

Doing end to end machine learning projects on a local machine might be a great achievement but sharing it with others and hosting it on the cloud is also equally important, here I will be showing you how to set up the components which are mostly required for an end-to-end…

Introduction to Neural Networks A neural network is a network which closely simulates the learning of a human brain. This is done by using connection weights and biases for each neuron in every layer. In a neural network, the information is passed to the layer, the layer then computes the weighed sum of the…

Gradient descent is an algorithm which aims to minimize the error or the loss metric in order to obtain the best possible set of parameters for your model. This technique is very flexible and can have many hyperparameters which can be tuned for better optimization Gradient descent technique can be…

Introduction to Skewness Skewness is the measure of asymmetry of data distribution. If the data is positively skewed, then we can interpret that there are more values which are greater than the mean than the values that are lesser than the mean If the data is negatively skewed, then we can interpret…

Introduction to Multivariate Linear Regression In this kind of regression, we have multiple features to predict a single outcome or in other words, a single dependent variable can be explained by multiple independent variables. In this regression, we will use the Gauss Markov setup which has the following assumptions Errors follow the normal distribution with…

What is structural break? Structural break is an unexpected change in the pattern of data that we are given to work with. Types of structural breaks Cyclic/seasonal — type of structural break where there are repeated patterns in the structural breaks Non Cyclic — type of structural break where there are no repeated patterns in the structural breaks

What is multicollinearity? Multicollinearity is defined as a condition where two or more explanatory variables are related amongst themselves which may cause misleading predictions. When is multicollinearity an issue? Multicollinearity is an issue when the correlations between the columns may change with change in the conditions. For example let us take the scenario of the stock market before…