What is Random Forest Classification? Random Forest is an ensemble learning technique combining multiple decision trees to improve the model's accuracy. It's called "Random" because each tree in the forest is trained on a random subset of the training data and a random subset of the features. This helps to reduce overfitting and improve the generalization of the model. In Random Forest, the output of each tree is combined to make the final prediction. The majority vote of all the trees is taken to make the final prediction. This approach is called bagging or bootstrap aggregating. Real-world Example Let's consider a real-world example to understand how Random Forest Classification works. We will use the famous Iris dataset, which consists of 150 samples of iris flowers. Each sample has four features: sepal length, sepal width, petal length, and petal width. The task is to classify the flowers into one of the three species: setosa, versicolor, or virginica. Impleme...
COVID-19 - B e a u t y a t a P r i c e The global beauty industry (comprising skin care, color cosmetics, hair care, fragrances, and personal care) has been shocked by the COVID-19 crisis. First-quarter sales have been weak, and there have been widespread store closures. But the industry has quickly adapted to the change by changing its product line to hand sanitizers and house cleaning products also offering free beauty services to front line workers to gain positive brand positioning. The global beauty industry generated $50 billion in sale a year and accounted to millions of jobs, directly and indirectly giving people in these tough times financial capabilities. Let’s be clear we are talking about an industry which even recession couldn’t kick to the ground. In 2008 financial crises, the spending fell slightly but it was regained by 2010. Figure 1: Even though recession didn’t had stronger economic impact compared to COVID-19....