![]() ![]() This is an end-to-end project, and like all Machine Learning projects, we'll start out with - with Exploratory Data Analysis, followed by Data Preprocessing and finally Building Shallow and Deep Learning Models to fit the data we've explored and cleaned previously. Additionally - we'll explore creating ensembles of models through Scikit-Learn via techniques such as bagging and voting. Our baseline performance will be based on a Random Forest Regression algorithm. Using Keras, the deep learning API built on top of Tensorflow, we'll experiment with architectures, build an ensemble of stacked models and train a meta-learner neural network (level-1 model) to figure out the pricing of a house.ĭeep learning is amazing - but before resorting to it, it's advised to also attempt solving the problem with simpler techniques, such as with shallow learning algorithms. In this guided project - you'll learn how to build powerful traditional machine learning models as well as deep learning models, utilize Ensemble Learning and traing meta-learners to predict house prices from a bag of Scikit-Learn and Keras models. ![]() Mathematically, if ( σXY) is the covariance between X and Y, and ( σX) is the standard deviation of X, then the Pearson's correlation coefficient ρ is given by: It is a measure of the linear relationship between two random variables - X and Y. The Pearson's Correlation Coefficient is also known as the Pearson Product-Moment Correlation Coefficient. What is The Pearson Correlation Coefficient? Non-linearly related variables may have correlation coefficients close to zero. In this article, we'll also show that zero correlation does not always mean zero associations. We'll illustrate how the correlation coefficient varies with different types of associations. The Pearson correlation coefficient measures the linear association between variables. This article is an introduction to the Pearson Correlation Coefficient, its manual calculation and its computation via Python's numpy module. ![]()
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