Project Evaluation Portal using Machine Learning

Abstract

This project develops an intelligent project evaluation portal leveraging machine learning to automate and enhance the assessment process. The system aims to improve the accuracy and efficiency of project evaluation by analyzing various project parameters, including timelines, budgets, risks, and deliverables. Through the implementation of a robust machine learning model, the system predicts project success probability and identifies potential risks early on. The outcome is a streamlined and data-driven evaluation process leading to better informed decision-making and improved project outcomes. The project successfully demonstrated improved accuracy compared to traditional manual evaluation methods.

Introduction

Project evaluation is crucial for organizational success. Traditional methods are often time-consuming, subjective, and lack the ability to predict future outcomes accurately. This project addresses these limitations by developing a machine learning-based project evaluation portal. The increasing volume and complexity of projects necessitate a more efficient and objective evaluation system. Current methods frequently rely heavily on human judgment, leading to inconsistencies and biases. The gap lies in the lack of automated, data-driven systems that can provide reliable predictions and insightful analysis to inform decision-making.

Objectives

• Develop a machine learning model capable of accurately predicting project success probability.
• Create a user-friendly portal for efficient project evaluation and data management.
• Integrate risk assessment capabilities into the evaluation process.