Neural Network Regression Implementation and Visualization in Python

Neural network regression is a machine learning technique used for solving regression problems. In regression tasks, the goal is to predict a continuous numeric value (e.g., a price, a temperature, a score) based on input data. Neural networks, a type of deep learning model, can be used for regression by learning a mapping from input features to the target output. Here are the key steps and concepts involved in neural network regression: 1. Data Preparation: — Collect and preprocess your dataset, which should include input features and corresponding target values (the continuous variable you want to predict). — Divide the data into training and testing sets to evaluate the model’s performance. <a href="https://medium.com/@nandiniverma78988/neural-network-regression-implementation-and-visualization-in-python-d5893713ed79">Read More</a>