Talo

Machine learning model to predicted rent of houses using potential features and LinearRegression, on dataset provided by StreetEasy

Architecture

Data Collection:

The house rent dataset was provided by StreetEasy, collected from the New York cities of manhattan, queens, brooklyn all of these data are stored as comma seprated values

Data Preprocessing

The features with positive correlation were extracted and made as one of the features columns The target feature was extracted from the dataframe and made as the target columns

Data Test Train Split

To Provide unbaised validation, the dataset is split into test and train data with train 80% and test 20%

Modelling

Mutiple LinearRegression was used to train the model The trained model was saved using pickle

Web app Deployment