Pavan Kumar Dharmoju

Introduction: Greetings, esteemed readers! Today, we delve into the realm of tourism-related hotels, where an industry worth billions continues to flourish. Our purpose is to introduce a sophisticated system that harnesses the power of Natural Language Processing (NLP) and Machine Learning (ML) to rank hotels based on comprehensive customer reviews. By analyzing the attributes contributing to revenue, we aim to identify the most esteemed hotel industries globally. Join us as we unravel the intricacies of this groundbreaking approach and discover how it can shape the future of the hospitality sector. Unveiling the Methodology: In an era where online customer reviews hold significant sway, understanding and extracting valuable insights from user-generated content have become paramount. Our pioneering system combines the strength of the Bidirectional Encoder Representations from Transformers (BERT) model with the principles of sentiment analysis and recommender systems. By leveraging this...

Introduction: Accurately predicting electricity consumption at the national level is crucial for efficient power system planning. The residential sector, which is the main source of peak demand, poses challenges due to its rapidly fluctuating energy consumption. In recent years, deep learning methods, such as Long Short-Term Memory (LSTM), have shown success in various time series studies. However, their effectiveness in forecasting monthly household energy demand has not been thoroughly investigated. This blog post explores a research paper that introduces an LSTM-based forecasting model and compares its performance to three benchmark models. The proposed model aims to enhance power system planning and improve grid efficiency by anticipating future energy demands in the residential sector. Understanding the Challenge: The residential sector accounts for a significant portion of global energy consumption and is expected to increase further in the coming years. Predicting monthly energy...

If you are a beginner in the machine learning field and have worked on a unique project, you will know how tedious it can be to curate a dataset. If this problem still bothers you, don’t worry here are three of the most popular and important ways data can be gathered for your next machine learning project. 1. Downloading the Dataset This is the obvious and the easiest option. But finding that download button is often very hard; numerous websites provide readily available datasets. I am listing a few of them below. i) Kaggle ii) Data World iii) IMDb iv) UCI Machine Learning Repository v) Awesome-Public-Datasets on Github vi) Data.Gov 2. Scraping Data I)Scraping with Excel: Yes, you read that right, you can scrape data using Excel. As this is extremely unpopular I am giving a step by step guide on how to download data from a web source. To extract data from a webpage on timeanddate.com without images and save it in a format like CSV, you can follow these steps using Microsoft Excel: Copy...