Prediction on house price evaluation By applying ML models.

Machine learning Model

predictive modeling

to solve imbalanced data using smote

it is dashboard developed by rstudio data taken by online retail item sales.

IT IS Modeling project done on taking telecom domain data.

The data set HMEQ reports characteristics and delinquency information for 5,960 home equity loans. A home equity loan is a loan where the obligor uses the equity of his or her home as the underlying collateral. The data set has the following characteristics: ◾ BAD: 1 = applicant defaulted on loan or seriously delinquent; 0 = applicant paid loan ◾ LOAN: Amount of the loan request ◾ MORTDUE: Amount due on existing mortgage ◾ VALUE: Value of current property ◾ REASON: DebtCon = debt consolidation; HomeImp = home improvement ◾ JOB: Occupational categories ◾ YOJ: Years at present job ◾ DEROG: Number of major derogatory reports ◾ DELINQ: Number of delinquent credit lines ◾ CLAGE: Age of oldest credit line in months ◾ NINQ: Number of recent credit inquiries ◾ CLNO: Number of credit lines ◾ DEBTINC: Debt-to-income ratio

TAKEN SAMPLE DATA FROM THE LINK https://www.ibm.com/communities/analytics/watson-analytics-blog/sales-products-sample-data/ . And did analysis