Date of Publication :12th August 2021

Abstract: Increased digitalization has influenced real estate sector dramatically. Internet has enabled us to search, rent and buy property online. But there is hard to find the suitable property to purchase as purchasing property include lot of factor like finance, location, accessibility , etc. User purchase preferences, their likes and dislikes are a very tricky task even for humans. Many website are available for real estate but they just have standard filter feature to filter out the available property they cannot understand the full user requirement. The main help of our system to recommend suitable home to user based on user financial condition, sentiment, family, locality etc. Your system uses the sentiment analysis and considers the various factor of the property and recommend most suitable property match. First our system takes the user input which includes user details, financial condition, etc. The system also performs the sentiment analysis using various algorithms like fast Text. After the sentiment analysis user classification is done and lastly personalized recommendation is done. The input to system is the user data and the output of the system is the best home recommendation to user based on user preference. Your system also include to home price predication which will help user to buy home. Ultimate aim of the system is to recommend the best possible home to the user which perfectly matches the various requirement of the user. Currently the content based recommendation and collaborative recommendation are used in recommendation system. We are using the hybrid recommendation algorithm which combines the content based recommendation and collaborative filtering. First we are going to apply the collaborative filtering and the output of collaborative filtering will be taken as input to content based recommendation model. Hybrid recommendation algorithm will increase the accuracy of recommendation system.

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