Background
The aim in this project to find a real-world scenario and apply Data Science and Machine Learning concepts to build a model to solve real world problem.
Dataset Link : https://www.kaggle.com/harlfoxem/housesalesprediction?select=kc_house_data.csv
Identifying Problem –: ‘Homelands Constructions’ is the best-known house constructing company in USA. After they constructed the house, based on the area of the land, they need to identify the condition of the house like Single family home, Condo, Townhouse, multifamily house, or co-op. But it is quite difficult to identify the house condition within a less time period. So, they can solve the issues happens when pricing houses. Because the houses with same facilities mat have different prices.
Solution -: We plan to build a clustering model to solve this problem. The goal of clustering is to determine the intrinsic grouping in a set of unlabeled data. And it is very cost effective and easy to build. Also we plan to build a linear regression model to predict the house prices. Condition of the house varying on House prices, no of rooms in the house, no of bathrooms ,square foot of living area ,etc. Through the model which we are designing they can simply enter the details and sort out the condition of house. And by this when customer enter details of a new house, they can predict the house condition. Also, using linear regression by that they can also predict the price range of the houses if they want.
First, we will be applying necessary pre-processes to data set as mentioned in the Activities section. After that model validation will be done. And next we plan to build the front-end part of the application where users can apply the model.