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Build a Stocks Price Prediction App powered by Snowflake, AWS, Python and Streamlit?—?Part 2 of 3

Mlearning.ai

Build a Stocks Price Prediction App powered by Snowflake, AWS, Python and Streamlit — Part 2 of 3 A comprehensive guide to develop machine learning applications from start to finish. Introduction Welcome Back, Let's continue with our Data Science journey to create the Stock Price Prediction web application.

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Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

Data Normalization and Standardization: Scaling numerical data to a standard range to ensure fairness in model training. Exploratory Data Analysis (EDA) EDA is a crucial preliminary step in understanding the characteristics of the dataset.

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Top 50+ Data Analyst Interview Questions & Answers

Pickl AI

Overfitting occurs when a model learns the training data too well, including noise and irrelevant patterns, leading to poor performance on unseen data. Techniques such as cross-validation, regularisation , and feature selection can prevent overfitting. In my previous role, we had a project with a tight deadline.

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Large Language Models: A Complete Guide

Heartbeat

It is therefore important to carefully plan and execute data preparation tasks to ensure the best possible performance of the machine learning model. It is also essential to evaluate the quality of the dataset by conducting exploratory data analysis (EDA), which involves analyzing the dataset’s distribution, frequency, and diversity of text.