Our project is a machine learning app, based on certain specifications of your future home it will try to guess the most accurate price. Specification-->Information such as Bedrooms, Sea View, Sqft living, grades.
Tool Used: Language Used PYTHON Library Used Numpy , pandas , Sklearn. Data Storing Pickel. IDE Vs code. Data Extraction pandas,excel. Data visualization Seaborn,Matplot Data Modelling Random Forest Regressor(Used because it has high accuracy score). Frontent Streamlit. Deployement Heroku. Steps i have taken make this model: COLLECTING DATA : FIRST STEP WAS TO COLLECT DATA WE COLLECTED DATA FROM DIFFERENT SOURCES & MERGED THEM TOGETHER TO FORM OUR TRAINING DATA SET. THEN WE TRAINED THE MODEL USING MACHINE LEARNIG ALGORITHM WHICH IN THIS CASE IS Random Forest Regressor. BASED ON THE GENERATED GRAPHS WE PREDICT THE COST OF THE HOUSE. Then I deployed my model.