Accuracy is something which gives an intuition of model performance. i.e. ratio of number of correct predictions with respect to total sample present. But what in case of unbalanced data.Imagine a case where we need to make a model to predict click through rate over a display rate. The click trough rate used to be […]

Accuracy is something which gives an intuition of model performance. i.e. ratio of number of correct predictions with respect to total sample present. But what in case of unbalanced data.Imagine a case where we need to make a model to predict click through rate over a display rate. The click trough rate used to be […]

Logistic Regression is a method to create Machine Learning model for two class problems. It came out of linear regression but used to generate binary output (0 and 1) for making classifications. For example In Linear Regression we use simple linear equation as follows :- Yh = b0 + b1X1 Where X combines linearly with […]

I am writing this article in the amid of Covid 19 outbreak. Lot many people are suffering of this diseases and lot many lost their life. Social distancing is one of the key that people are using to avoid this disease as much as possible. As well as mask and sanitisers becomes our daily need. […]

Here we will develop a deep learning model using CNN VGG-16 architecture to predict about Pneumonia using any chest x-ray image with more then 95% of accuracy. With continuous growth in the field of AI and Machine Learning It is stepping into almost every section of industry. In healthcares sector a lot of analysis can […]

If we have limited amount of Data, We can diversify it using data augmentation. It is like instead of collecting new data elements we just transform which is already there to increase the sample size along with diversity. We will consider unstructured data i.e. Image data for augmentation process.

Pandas is one of the powerful library used in python for data science and analysis. It has n-number of functions, methods and attributes, which are comparatively easy in syntax and flexible in nature. So a data scientist or any one who wants certain insights from any huge set of data prefers it and let their […]

Decision Tree Classification Algorithm is used for classification based machine learning problems. It is one of the most popular algorithm as the final decision tree is quite easy to interpret and explain. More advanced ensemble methods like random forest, bagging and gradient boosting are having roots in decision tree algorithm. Here we will try to […]

If we figured out our problem needs a regression approach to form a predictive model then generally we adopt linear model to start. Why ? The are easy to interpret, They get trained quickly, Optimization is easy and quite better etc.

KNN or K – Nearest Neighbours is one the powerful algorithm used in classification based problems to successfully make categorical predictions. Scikit-Learn gives us built in library to use and make the process easier for us if we are having data. But here we will write KNN code mathematically without any inbuilt library to figure […]