-4d90550d17f4602e0891c06 20220702060016936 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 07 30 2022 11 8 Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore (Tamil Nadu), India. J. Gowri R. Kamini PG Scholar, Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore (Tamil Nadu), India. G. Vaishnavi PG Scholar, Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore (Tamil Nadu), India. S. Thasvin PG Scholar, Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore (Tamil Nadu), India. C. Vaishna PG Scholar, Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore (Tamil Nadu), India. Heart is one most important organ in our body. The prediction of heart disease is most complicated task in today world. There are number of instruments available in today’s worlds. These instruments are so expensive some of them can afford that instrumentals some of them cannot afford the instruments. Early prediction of heart disease will reduce the death rate. we can tell the patients before the hand. In todays world we all have the good amount of data using that good amount of data we can predict the heart disease using various machine learning techniques. The proposed method will tell to patients probabilities of heart diseases. In this paper using the UCI dataset performed various machine learning techniques like Logistic Regression, Decision tree, KNN, Naïve Bayes, Random Forest, XGBoost, Support vector machine . In this paper we used proposed methodology from PHASE I to PHASE VII Using the evaluation metrics we can check the performance of the machine learning which gives more accuracy from the above seven machine learning algorithm. 07 30 2022 29 32 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.H9148.0711822 https://www.ijitee.org/portfolio-item/h91480711822/ Master of Technology in Structural Engineering, Department of Civil Engineering, Brindavan Institute of Technology and Science, Kurnool, Affiliated to Jawaharlal Nehru Technological University (JNTU), Anantapur and approved by AICTE Andhra Pradesh, India. Shaik Umar Faruq Yalamanjula Venkata Archan Head of the Department (H.O.D.) & Assistant Professor in Department of Civil Engineering, Brindavan Institute of Technology and Science, Kurnool, affiliated to Jawaharlal Nehru Technological University (JNTU), Anantapur and approved by AICTE Andhra Pradesh, India. Maddikera Lokanath Reddy PG Coordinator & Assistant Professor in Department of Civil Engineering, Brindavan Institute of Technology and Science, Kurnool, affiliated to Jawaharlal Nehru Technological University (JNTU), Anantapur and approved by AICTE Andhra Pradesh, India. Construction projects are characterized as very complex projects, where uncertainties are part of it. Risk is an uncertain event or condition that, if it occurs, has a positive or a negative impact on one or more project objectives, such as time, cost, scope or quality. Risk management includes the process concerned with the conducting the risk identification, analysis, responses and management planning and control on a project The study aims at carrying out the risk assessment and management process in the construction of Bangalore Underground Metro project. At first, the risks associated with the project and also with the similar projects in past are identified and listed based on the historic reviews, interviews and literature review. A questionnaire survey is prepared for the risks that are listed and probability and impact of these risks on the projects are found out, and risks are prioritized based on the risk index score which forms the probability-impact matrix and risk register is formed. A schedule is prepared in Primavera P6, and then integrated with Primavera Risk Analysis (PERT Master) software which analyze the schedule for the risk events assigned to the activities and for the defined probability distributions and then schedule of the project is simulated using the Monte Carlo simulation for the both pre mitigated and the post mitigated analysis, and then results are compared. Response strategies are suggested for the moderate to higher priorities risks. 07 30 2022 33 42 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.H9155.0711822 https://www.ijitee.org/portfolio-item/h91550711822/