Real State Price Prediction Using Machine Learning

By Kumari Pritika

This project aimed to develop a comprehensive solution for predicting house prices in Bangalore using a machine learning model.

This project aims to develop a valuable tool that can assist users in estimating house prices in the city. The project involves gathering a comprehensive dataset from reliable sources, implementing effective data preprocessing techniques, exploring feature engineering methods, and evaluating various machine learning algorithms. The project seeks to provide valuable insights into the Bengluru housing market dynamics and support stakeholders in making informed decisions within the real estate sector.

The following methodology is followed to achieve the above mentioned objective:

• Data Collection: Gather a comprehensive dataset containing relevant features for model training.

• Data Preprocessing: Implement effective data preprocessing techniques to handle missing values, outliers, and inconsistencies in the dataset. 

• Feature Engineering: Explore and apply feature engineering techniques to extract meaningful features from the dataset. 

• Model Selection and Training: Evaluate and compare different machine learning algorithms suitable for the task.  Select the best model for the training.

• Model Evaluation and Fine-Tuning: Assess the performance of the trained model using appropriate evaluation metrics.

• Model Deployment: Create an API layer to wrap the model and provide a standard interface for prediction
  

This project aimed to develop a comprehensive solution for predicting house prices in Bangalore using a machine learning model. The process involved creating the model, deploying it as a Flask API, and designing a frontend interface to interact with the model.

In the future, the project can be further enhanced by adding additional features, such as data visualization to display trends and insights, user authentication for secure access, and real-time updates for dynamic predictions. The deployed model and frontend can be scaled and adapted to cater to a larger user base, making it a valuable tool for real estate professionals, home buyers, and sellers.

Overall, this project showcased the potential of machine learning, Flask API, and frontend technologies to solve real-world problems and provide valuable insights to users in the field of real estate and property valuation.