This is an ML-based application that predicts the house prices based upon the features provided (such as area, num of bedrooms, etc.).
For testing using sample inputs, click here
Model Used | Accuracy |
---|---|
Linear Regressor | 81.705317 (Cost) |
Ordinary Least Squares | 92.9 (R-squared value) |
File Description:
- Delhi_load.ipynb : Application interface for users.
- CSV File/ Delhi.csv : dataset
- Codes/ Delhi.ipynb : Main file with all processing stuff
- Codes/ ols_results_delhi.pickle : Pickled OLS model after training on dataset
Given a set of features, predict the price of any given house in the Delhi region.
Libraries Used
Outliers
- Price
- Area
- Price per sq. foot
New Features
All the features provided can be reframed to the format : {Area, AttributeScore, Resale, LogPremium, Bedrooms}
- Area = Floor area of the property
- AttributeScore = An integer based on features like num of bedrooms, gym facility, etc
- Resale = A binary value denoting if the propery is first hand usage (0) or a resale (1)
- LogPremium = An integer value depending on the Price per sq. foot value
- Bedrooms = Number of bedrooms in the property
Two models were cosidered as most optimum ones, whose predictions are depicted below:
Ordinary Least Squares | Linear Regressor |
---|---|
92.9 (R-squared value) | 81.705317 (Score) |
The trained OLS model gives an accuracy of 92.9 (R-squared value). The model summary for the same is provided below