Financial Strength Analysis fill the table and the project
Budget: $30 – $250 USD
I need a concise, data-driven report that tells me whether a finance-sector company of my choosing is financially strong and worth investing in. The study must revolve around three core indicators—revenue growth, profit margins and debt levels—and rely on machine-learning models to surface patterns that a straight spreadsheet review might miss.
Here’s how I picture the workflow:
• Collect the firm’s last five-to-ten years of publicly available statements and market data.
• Feed the cleaned dataset into your preferred machine-learning environment (Python with scikit-learn or a comparable toolset is fine).
• Train and test models that highlight trends, anomalies and forward-looking projections for each metric.
• Translate the model output into plain-English insights that clearly state whether the business is financially solid and a sound investment right now.
• Conclude with a short section that explains, step by step, how AI contributed to the findings.
Deliverables
1. A written report (PDF or DOCX) summarising results, visualisations, and your final investment verdict.
2. The annotated notebook or script used to run the machine-learning models so I can reproduce the analysis if needed.
Acceptance criteria: conclusions must reference concrete numbers from the three metrics, show how the model reached them, and link those results directly to an invest-or-avoid recommendation.
Here’s how I picture the workflow:
• Collect the firm’s last five-to-ten years of publicly available statements and market data.
• Feed the cleaned dataset into your preferred machine-learning environment (Python with scikit-learn or a comparable toolset is fine).
• Train and test models that highlight trends, anomalies and forward-looking projections for each metric.
• Translate the model output into plain-English insights that clearly state whether the business is financially solid and a sound investment right now.
• Conclude with a short section that explains, step by step, how AI contributed to the findings.
Deliverables
1. A written report (PDF or DOCX) summarising results, visualisations, and your final investment verdict.
2. The annotated notebook or script used to run the machine-learning models so I can reproduce the analysis if needed.
Acceptance criteria: conclusions must reference concrete numbers from the three metrics, show how the model reached them, and link those results directly to an invest-or-avoid recommendation.