bagalshreerang / real-estate-price-prediction

About The Boston House Price Prediction project utilizes data science methodologies and machine learning algorithms to provide accurate predictions for housing prices in the Boston area.

Boston-House-Price-Prediction-Datascience-Project

The Boston House Price Prediction project utilizes data science methodologies and machine learning algorithms to provide accurate predictions for housing prices in the Boston area.By leveraging the Boston Housing dataset and employing model selection techniques and fitting machine learning algorithms, this project aims to assist individuals in making informed decisions related to buying, selling, or investing in real estate, ultimately benefiting both homebuyers and industry professionals.

Problem Statement:

The problem addressed in the Boston House Price Prediction project is the difficulty faced by potential homebuyers, real estate agents, and investors in accurately determining the prices of houses in the Boston area. The housing market is influenced by various factors, and understanding the relationship between these factors and house prices can be complex and time-consuming. The lack of a reliable and accurate prediction model hinders decision-making processes and can result in suboptimal investments or missed opportunities.The goal of this project is to develop a data-driven solution that can predict housing prices in Boston with a high degree of accuracy.

Solution Approach:

The Boston House Price Prediction project aims to develop a robust and accurate model that can provide reliable predictions for house prices in the Boston area. The approach involves data preprocessing, feature engineering, model selection and training, evaluation, deployment, and continuous improvement to ensure the model remains up-to-date and valuable for stakeholders.

Observations:

The project found that the following factors are most important in prediction the price of house in Boston.

• CRIM - Per Capita Crime Rate by town
• ZN - Proportion of Residential Land Zoned for lots over 25,000 sq.ft.
• INDUS - Proportion of Non-retail Business acres per town
• CHAS - Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
• NOX - Nitric Oxides Concentration (parts per 10 million)
• RM - Average Number of Rooms per dwelling
• AGE - Proportion of Owner-occupied units built prior to 1940
• DIS - Weighted Distances to Five Boston Employment Centres
• RAD - Index of Accessibility to Radial Highways
• TAX - Full-value Property-tax rate per 10,000 dollars
• PTRATIO - Pupil-Teacher Ratio by town\
• B - "1000(Bk - 0.63)^2" where Bk is the proportion of Blacks by town
• LSTAT - Percentage Lower Status of the Population

Insights:

The project aims to provide valuable insights into the factors that impact house prices and enable stakeholders to make well-informed decisions. The Boston House Price Prediction project aims to bridge the gap between available data and actionable insights, empowering stakeholders to navigate the complex Boston housing market more effectively and make informed decisions based on reliable predictions of house prices. This information can be used by both homebuyers and industry professionals for their own advantages.

Findings:

The project found that the machine learning model was able to predict the price of a house in Boston with a high degree of accuracy. The model was able to predict the price of a house in Boston within 96% of the accuracy. The project also found that the model was able to generalize well to new data. The model was able to predict the price of house in Boston if all the required data is given.

Conclusion :

Achieved in developing a predictive model to predict theprice of houses in Boston city based on given data with accuracy of 96.14% (CatBoost Regressor)