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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...

Prophet of The Future .             Prophet is open source software released by Facebook’s Core Data Science team. It is available for download on CRAN and PyPI. Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.       Accurate and fast.       Fully automatic.       Tunable forecasts.       Available in R or Python. Let’s explore this with an example.   Here we are using Air Passenger dataset and our jupyter workbook. (you can get the link to this dataset at the end) import warnings warnings . filterwarnings( "ignore" ) import numpy as np from d...