You will get AI-Powered Credit Risk Prediction Model with Dashboard & Full Report
Rising Talent

Project details
I will deliver a high-quality machine learning project using logistic regression and random forest to predict credit default. You’ll receive clean, well-commented code, evaluation metrics (Accuracy, Precision, Recall, F1), visualizations in Power BI, and a polished PDF report. Perfect for business use, academic purposes, or to strengthen your portfolio.
Machine Learning Tools
Apache Spark, ChatGPT, Google Sheets, GPT-3, Microsoft Excel, Microsoft Power BI, NLTK, NumPy, pandas, Python, Python Scikit-Learn, SQL, Tableau, XGBoostWhat's included $75
These options are included with the project scope.
$75
- Delivery Time 5 days
- Number of Revisions 1
- Number of Model Variations 1
- Number of Scenarios 1
- Number of Graphs/Charts 2
- Model Validation/Testing
- Model Documentation
- Source Code
Optional add-ons
You can add these on the next page.
Fast 3 Days Delivery
+$25
Additional Revision
+$10Frequently asked questions
About Iliana
Data Analyst | Power BI & Machine Learning | Bilingual
Rockville, United States - 6:03 pm local time
I help businesses clean messy data, organize it in Excel, and turn it into simple, clear reports and dashboards.
If you have:
âś” messy spreadsheets
âś” duplicate or missing data
✔ data you don’t understand
I can help you turn it into something clear and useful.
What I can do for you:
• Data cleaning (Excel / Python)
• Data organization & formatting
• Simple dashboards (Power BI / Excel)
• Basic data analysis & reports
I focus on clear communication, fast delivery, and simple solutions that actually help you.
Let’s make your data easy to understand 📊
Steps for completing your project
After purchasing the project, send requirements so Iliana can start the project.
Delivery time starts when Iliana receives requirements from you.
Iliana works on your project following the steps below.
Revisions may occur after the delivery date.
Step 1: Data Understanding and Cleaning
I will explore your dataset, identify issues, and clean the data to ensure high model quality.
Step 2: Model Training and Evaluation
I will train and evaluate logistic regression and random forest models, providing clear metrics and interpretation.



