INTL
Freelancer
어려움
외주
원격 가능
AI Hospital Investment Analyzer
예산
$1,500~$12,500 INR
예상 기간
3~5개월
난이도
어려움
기술 스택
Python
Pandas
Scikit-learn
Financial Analysis
Statistical Analysis
Data Visualization
Plotly
ETL
Machine Learning
Linear Regression
ML Explainability (XAI)
Streamlit
Dash
Web Development
Cloud Deployment
AI 분석 요약
이 프로젝트는 병원 투자 분석을 위한 의사결정 지원 도구를 구축하는 것으로, 원시 재무 데이터를 자동으로 정제하고 핵심 금융 지표를 계산한 후, 머신러닝 모델을 통해 병원의 '투자 준비 점수'를 도출하고 해석을 제공합니다. 파이썬 기반의 데이터 엔지니어링, 머신러닝 모델 개발 및 설명 가능 AI(XAI) 구현, 그리고 Streamlit 또는 Dash를 사용한 인터랙티브 웹 대시보드 구축 역량이 필요합니다.
프로젝트 원문 설명
I am building a decision-support tool that acts like an investment banker for hospital deals. The workflow starts with raw historical statements—revenue, profit, cash flow, debt and related line items—pulled in from spreadsheets or a database. I need this data to be cleaned and validated automatically so that outliers, missing values and inconsistent formats are handled without manual intervention.
Once the dataset is in shape, the system must calculate core ratios—operating and EBITDA margins, liquidity and leverage indicators, year-over-year growth rates, cash conversion, risk flags—and feed them into a machine-learning pipeline. I have no fixed allegiance to any one algorithm, but I would like to begin with a solid linear regression benchmark and keep the door open for more sophisticated models if they outperform.
The model should output a single “investment readiness” score for each hospital, accompanied by clear explanations that highlight drivers such as high debt, robust profitability or rapid growth. These explanations have to be both human-readable and suitable for display alongside the score.
Everything culminates in an interactive dashboard where users can:
• Upload or connect fresh financial files
• Inspect the cleaned dataset and calculated ratios
• View the readiness score with drill-down explanations
• Toggle assumptions, refresh predictions in real time and export a concise report
Deliverables:
1. End-to-end Python (or comparable) codebase covering automated ETL, feature engineering, model training and inference.
2. A lightweight web dashboard—Streamlit, Dash or similar—deployed locally or on a small cloud instance.
3. Brief technical documentation and a walkthrough so new users can reproduce results and retrain the model with updated data.
Acceptance will be based on:
• Successful automation of data cleaning for a sample of multi-year hospital statements.
• Generation of ratios matching standard finance formulas within ±1% tolerance.
• A regression-based model that outputs a score and text explanations in the dashboard.
• Smooth, error-free user flow from data upload to report export.
Once the dataset is in shape, the system must calculate core ratios—operating and EBITDA margins, liquidity and leverage indicators, year-over-year growth rates, cash conversion, risk flags—and feed them into a machine-learning pipeline. I have no fixed allegiance to any one algorithm, but I would like to begin with a solid linear regression benchmark and keep the door open for more sophisticated models if they outperform.
The model should output a single “investment readiness” score for each hospital, accompanied by clear explanations that highlight drivers such as high debt, robust profitability or rapid growth. These explanations have to be both human-readable and suitable for display alongside the score.
Everything culminates in an interactive dashboard where users can:
• Upload or connect fresh financial files
• Inspect the cleaned dataset and calculated ratios
• View the readiness score with drill-down explanations
• Toggle assumptions, refresh predictions in real time and export a concise report
Deliverables:
1. End-to-end Python (or comparable) codebase covering automated ETL, feature engineering, model training and inference.
2. A lightweight web dashboard—Streamlit, Dash or similar—deployed locally or on a small cloud instance.
3. Brief technical documentation and a walkthrough so new users can reproduce results and retrain the model with updated data.
Acceptance will be based on:
• Successful automation of data cleaning for a sample of multi-year hospital statements.
• Generation of ratios matching standard finance formulas within ±1% tolerance.
• A regression-based model that outputs a score and text explanations in the dashboard.
• Smooth, error-free user flow from data upload to report export.
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