First off, what each part means:
– “AirBnB Rental Price Prediction” This is pretty self-explanatory. We’re trying to predict the price that an AirBnB rental will fetch based on certain factors.
– “Machine Learning With MongoDB And Tensorflow Keras” Here, we have a few different tools being used together:
Machine learning refers to the process of training algorithms to make predictions or decisions based on data. In this case, we’re using machine learning techniques to predict rental prices.
MongoDB is a database system that allows us to store and manage large amounts of data. We can use it to collect information about AirBnB rentals from various sources (like the website itself or other databases) and then analyze that data to make predictions.
Tensorflow Keras is a popular machine learning library for Python that makes it easy to build and train models using deep learning techniques. We’ll be using this tool to create our rental price prediction model.

So, how does all of this work in practice? Let’s say we have a dataset containing information about AirBnB rentals in New York City. This data might include things like the address of each property, the number of bedrooms and bathrooms, whether or not it has Wi-Fi, and so on.

To create our rental price prediction model, we’ll first load this dataset into MongoDB using a script that connects to the database and inserts the data. Then, we can use Tensorflow Keras to build a machine learning model that takes as input various features (like location, number of bedrooms, etc.) and outputs a predicted rental price.

To train our model, we’ll feed it lots of examples from our dataset each example representing an AirBnB property with its associated rental price. The model will learn to identify patterns in the data that are correlated with higher or lower prices, and then use this knowledge to make predictions for new properties that haven’t been seen before.

Once we have a trained model, we can use it to predict rental prices for any given property based on its features. This could be useful for AirBnB hosts who want to set competitive prices for their rentals, or for investors looking to identify profitable opportunities in the market.

It combines machine learning techniques with MongoDB and Tensorflow Keras to create a powerful tool for predicting rental prices based on various factors like location, number of bedrooms, and amenities.