You will get I will build a machine learning model to predict loan default risk
Project details
Get a machine learning-based loan prediction system that evaluates key financial inputs income, credit score, loan amount, and more to predict loan default risk or approval likelihood with real, data-driven accuracy.
Built using Python, Scikit-learn, and XGBoost, this project combines data preprocessing, model training and evaluation, and a clean, interactive Streamlit web application into one practical, end-to-end solution designed for real fintech use cases not just a theoretical exercise.
Depending on your needs, I offer everything from a focused risk model to a fully validated, documented solution with source code included so you only pay for the depth you actually need.
My focus is on delivering models that are accurate, explainable, and genuinely usable for real business decisions not just a notebook with a good score.
Built using Python, Scikit-learn, and XGBoost, this project combines data preprocessing, model training and evaluation, and a clean, interactive Streamlit web application into one practical, end-to-end solution designed for real fintech use cases not just a theoretical exercise.
Depending on your needs, I offer everything from a focused risk model to a fully validated, documented solution with source code included so you only pay for the depth you actually need.
My focus is on delivering models that are accurate, explainable, and genuinely usable for real business decisions not just a notebook with a good score.
Machine Learning Tools
Microsoft Excel, NumPy, pandas, Python, scikit-learn, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$40
|
Standard
$100
|
Advanced
$180
|
|---|---|---|---|
| Delivery Time | 2 days | 4 days | 6 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 1 | 2 |
Number of Graphs/Charts | 2 | 4 | 6 |
Model Validation/Testing | - | ||
Model Documentation | - | ||
Data Source Connectivity | |||
Source Code | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$10
Additional Revision
+$10
Additional Model Variation
(+ 1 Day)
+$10
Additional Scenario
(+ 1 Day)
+$10
Additional Graph/Chart
(+ 1 Day)
+$10
Model Validation/Testing
(+ 2 Days)
+$15
Model Documentation
(+ 1 Day)
+$10
Source Code
(+ 1 Day)
+$10Frequently asked questions
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EM
Eric M.
Aug 15, 2025
Social Media Data Analyst (YouTube, Facebook, TikTok, Instagram)
Extremly skilled guy and clients satifaction is his priority! He did the job and even when beyond. Thank you Muza and thanks Upwork.
About Muzamil
Data Scientist | ML, EDA & Data Cleaning | Python Expert
Muzaffargarh, Pakistan - 4:33 pm local time
I help businesses turn raw data into decisions — through clean analysis, predictive modeling, and machine learning.
My core work:
Data cleaning, EDA, and analysis in Python
Supervised learning: classification, regression, churn prediction, demand forecasting
Unsupervised learning: clustering, segmentation, anomaly detection
Gradient boosting models (XGBoost, LightGBM, CatBoost)
Deployment via Streamlit and FastAPI, so results are usable, not just notebooks
I also build LLM-powered workflows and AI agents to automate parts of the analysis pipeline — useful when a project needs automated reporting or repetitive analysis handled reliably.
Featured project — AutoDS AI: a multi-agent platform (Python + Google Gemini) that automates data cleaning, feature engineering, EDA, model selection, and reporting.
I focus on validated, production-ready solutions — not just model outputs. If you have data and need it turned into something actionable, let's talk.
Steps for completing your project
After purchasing the project, send requirements so Muzamil can start the project.
Delivery time starts when Muzamil receives requirements from you.
Muzamil works on your project following the steps below.
Revisions may occur after the delivery date.
Preprocessing
Data cleaning and preprocessing (handling missing values, encoding, scaling).
EDA
Exploratory Data Analysis (EDA) to understand patterns in loan data