Project Abstract | Project Introduction | Installation | Motivation |
The primary purpose of hotels is to provide guests with a place to stay, food to eat, beverages to drink, and other such services and goods. They achieve this by commercially offering products that are frequently supplied in homes but unavailable to folks who are travelling away from home. Hotels are an essential component for any person or tourist that is travelling from one location to another. When the hotel's services are subpar, reservations are low, and when they are fantastic, reservations are high. Every traveller has their own idea of what makes a good hotel, but the majority concur that there are six qualities that characterise a really memorable stay. These include the hotel's ambiance, management, prices, location, and services.
A predicted hotel price system that considers factors like city, distance, cost, the availability of rooms, and user reviews is what I have presented in this github repository. As a result, it might be difficult to anticipate the price of a hotel at some points. To make prediction easier, I've utilised a few machine learning algorithms to look at hotel pricing which assisted me in making a Hotel Price Prediction System.
Keywords:- Machine Learning, Precitive-Model, Hotel Real-time Datasets, Predictive Hotel Price System
Sometimes the finest part of a trip is lying on the bed in your hotel room watching television while wearing different funky clothes. Every latitude experiences a similar level of hotel life. Hotels are raising the bar on luxury. In space, ultra-luxury hotels are being constructed. People think being a fan is so glamorous, yet we sometimes live out of suitcases in weird rooms. In this project, I plan to make predictions about hotels with favourable reviews and location which is also near to user's present location.
A Python predictive model predicts a specific future result based on trends discovered through historical data. In essence, we train a model that recognises specific patterns to predict outcomes, such as future sales, disease contraction, fraud, and so forth, by gathering and analysing historical data. Here I took the dataset from Kaggle.com and used many popular Machine Learning Algorithms like linear regression model on the data sets in order to train the data. Further which we try to predict the values for the untrained data. This is when the predict() function comes into the picture., imported some importand libraries like pandas, numpy, matplotlib, sklearn, pathlib, etc. This Dataset contains the details of Hotel name , id , ratings , rooms availability, price, distance, city.
By ‘HOTEL PRICE PREDICTION’ user can know about the price of Hotels in very early time. With the help of these predictions of Hotel price user can take decision about the booking of hotel in lesser time. The user will choose their current location and look for the nearest hotel from their current location considering price and ratings.
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Clonning the repository into your local machine
git clone https://github.com/impeccable16/BINGO-Hotel-Price-Prediction.git -
Moving inside the cloned directory
cd BINGO-Hotel-Price-Prediction -
Installing the required dependencies
python3 -m pip install -r requirements.txt -
Install Anaconda Navigator with Jupyter Notebook
*“Money doesn’t mean anything to me. I’ve made a lot of money, but I want to enjoy life and not stress myself building my bank account. I give lots away and live simply, mostly out of a suitcase in hotels. We all know that good health is much more important.” ~ Keanu Reeves *
Back then, when it was festival season, my friends and I went on a night trip without making hotel reservations in advance. We mistakenly believed that we would do it once we arrived at our location. We literally didn't get a room at any hotels and were just wandering everywhere.
From that moment, I instantaneously had the idea to create a model that would make it simple for us to locate hotels in our local surroundings.