Predicting the stock market is one of the most important applications of Machine Learning in finance. In this article, I will take you through a simple Data Science project on Stock Price Prediction using Machine Learning Python. At the end of this article, you will learn how to predict stock prices by using the Linear Regression model by implementing the Python programming language. Predicting the stock market has been the bane and goal of investors since its inception. Every day billions of dollars are traded on the stock exchange, and behind every dollar is an investor hoping to make a profit in one way or another.

Optimal control under uncertainty is a prevailing challenge for many reasons. One of the critical difficulties lies in producing tractable solutions for the underlying stochastic optimization problem. We show how advanced approximate inference techniques can be used to handle the statistical approximations principled and practically by framing the control problem as a problem of input estimation. Analyzing the Gaussian setting, we present an inference-based solver that is effective in stochastic and deterministic settings and was found to be superior to popular baselines on nonlinear simulated tasks. We draw connections that relate this inference formulation to previous approaches for stochastic optimal control and outline several advantages that this inference view brings due to its statistical nature.

py-irt is a Python library for fitting Bayesian Item Response Theory (IRT) models. py-irt estimates latent traits of subjects and items, making it appropriate for use in IRT tasks as well as ideal-point models. py-irt is built on top of the Pyro and PyTorch frameworks and uses GPU-accelerated training to scale to large data sets. Code, documentation, and examples can be found at https://github.com/nd-ball/py-irt. py-irt can be installed from the GitHub page or the Python Package Index (PyPI).

Abstract--We study cluster assignment in multi-agent networks. The process of reaching an agreement between agents is In this work we focus on homogeneous networks, that is one of the fundamental tasks for a multi-agent system (MAS). The problem we aim to solve is how to design graphs computation [1], robotics [2], biochemical systems [3], and that ensure the networked system will converge to a prescribed sensor networks [4]. A natural extension to the agreement cluster configuration, i.e., specifying the number of clusters problem is the cluster agreement problem, which seeks to and the number of agents within each cluster. Employing tools drive agents into groups. All the agents within the same group from group theory, we show that it is possible to design an should then reach an agreement.

Set-valued prediction is a well-known concept in multi-class classification. When a classifier is uncertain about the class label for a test instance, it can predict a set of classes instead of a single class. In this paper, we focus on hierarchical multi-class classification problems, where valid sets (typically) correspond to internal nodes of the hierarchy. We argue that this is a very strong restriction, and we propose a relaxation by introducing the notion of representation complexity for a predicted set. In combination with probabilistic classifiers, this leads to a challenging inference problem for which specific combinatorial optimization algorithms are needed. We propose three methods and evaluate them on benchmark datasets: a na\"ive approach that is based on matrix-vector multiplication, a reformulation as a knapsack problem with conflict graph, and a recursive tree search method. Experimental results demonstrate that the last method is computationally more efficient than the other two approaches, due to a hierarchical factorization of the conditional class distribution.

Most representative decision tree ensemble methods have been used to examine the variable importance of Treasury term spreads to predict US economic recessions with a balance of generating rules for US economic recession detection. A strategy is proposed for training the classifiers with Treasury term spreads data and the results are compared in order to select the best model for interpretability. We also discuss the use of SHapley Additive exPlanations (SHAP) framework to understand US recession forecasts by analyzing feature importance. Consistently with the existing literature we find the most relevant Treasury term spreads for predicting US economic recession and a methodology for detecting relevant rules for economic recession detection. In this case, the most relevant term spread found is 3 month to 6 month, which is proposed to be monitored by economic authorities. Finally, the methodology detected rules with high lift on predicting economic recession that can be used by these entities for this propose. This latter result stands in contrast to a growing body of literature demonstrating that machine learning methods are useful for interpretation comparing many alternative algorithms and we discuss the interpretation for our result and propose further research lines aligned with this work.

The Ordinary Least Squares model (OLS) is a central building block in Machine Learning (ML). OLS is also used everywhere in Social Sciences. I come from an Economics background and I was initially a bit puzzled by the way the ML textbooks solve OLS. In this blog post, I explain the Economics way versus the ML way and why both make sense. TL;DR: In a high-dimensional setting, do not inverse a huge matrix, use gradient descent.

When training deep neural networks for classification tasks, an intriguing empirical phenomenon has been widely observed in the last-layer classifiers and features, where (i) the class means and the last-layer classifiers all collapse to the vertices of a Simplex Equiangular Tight Frame (ETF) up to scaling, and (ii) cross-example within-class variability of last-layer activations collapses to zero. This phenomenon is called Neural Collapse (NC), which seems to take place regardless of the choice of loss functions. In this work, we justify NC under the mean squared error (MSE) loss, where recent empirical evidence shows that it performs comparably or even better than the de-facto cross-entropy loss. Under a simplified unconstrained feature model, we provide the first global landscape analysis for vanilla nonconvex MSE loss and show that the (only!) global minimizers are neural collapse solutions, while all other critical points are strict saddles whose Hessian exhibit negative curvature directions. Furthermore, we justify the usage of rescaled MSE loss by probing the optimization landscape around the NC solutions, showing that the landscape can be improved by tuning the rescaling hyperparameters. Finally, our theoretical findings are experimentally verified on practical network architectures.

The Machine Learning Crash Course with TensorFlow APIs is a self-study guide for aspiring machine learning practitioners. Learn how to build Machine Learning projects in this TensorFlow Course created by The Click Reader. In this course, you will be learning about Scalar as well as Tensors and how to create them using TensorFlow. You will also be learning how to perform various kinds of Tensor operations for manipulating and changing tensor values. You will be learning how to create a Linear Regression model from scratch using TensorFlow.

Added: Object-Oriented Programming (OOP) for complete Beginners: with real-world examples and in a way that everyone understands OOP! This is the first-ever comprehensive Python Course for Business and Finance Professionals. You will learn and master Python from Zero and the full Python Data Science Stack with real Examples and Projects taken from the Business and Finance world. You will understand and master all required theoretical concepts behind the projects and the code from scratch. Important: the quality Benchmark for the theory part is the CFA (Chartered Financial Analyst) Curriculum.