Download the Dataset https://github.com/Lakshmikiranmai77/Independent_Houses/blob/main/Independent_Houses.csv
Install Numpy via anaconda: conda install numpy Import all the required Libraries
Install OpenCV via anaconda:
The primary aim of this project is to construct robust predictive models capable of estimating rental prices for independent houses. Through the application of machine learning algorithms, we seek to unravel the underlying relationships between different attributes andrental prices, providing stakeholders in the real estate industry with a valuable tool for pricing strategy, market analysis, and investment decisions.
This project focuses on a dataset that includes essential features such as square footage, the number of bedrooms and bathrooms, and the distance from a specific location (WSU).By exploring these attributes, we aim to discern patterns and correlations that contribute to the determination of rental prices. The predictive models developed in this project can serve as valuable assets for property owners, real estate agents, and potential tenants, offering insights into the factors influencing rental costs.
Data Limitations and Predictive Model Refinements Data Preprocessing Handling Missing Values Understanding that machine learning models thrive on numeric input Handling Duplicates Data Summary and Statistics