You will get Bank loan default forecasting
Project details
Developed a logistic regression model to predict customer subscription (yes/no) to a term deposit using the Bank Marketing dataset. The project involved extensive data cleaning, feature engineering, multicollinearity reduction, and evaluation using cutoff optimization techniques. Achieved improved classification by selecting an optimal threshold (cutoff = 0.071) that maximized the KS statistic. Delivered a reproducible pipeline with prediction output on unseen test data.
Machine Learning Tools
RWhat's included
| Service Tiers |
Starter
$20
|
Standard
$35
|
Advanced
$55
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 10 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 2 | 4 | 5 |
Number of Scenarios | 1 | 1 | 1 |
Number of Graphs/Charts | 4 | 4 | 6 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code | - | - |
About Hari
Data cleaning| Data Visualization, Visualization, R, SQL
Seorinarayan, India - 12:06 pm local time
Experienced data analyst with a proven track record of using R, machine learning, and predictive analysis to solve business problems. Seeking a challenging role where I can use my skills to make a significant impact and help further your organization's goals. Able to work independently as well as part of a team, able to produce creative and innovative ideas.
Steps for completing your project
After purchasing the project, send requirements so Hari can start the project.
Delivery time starts when Hari receives requirements from you.
Hari works on your project following the steps below.
Revisions may occur after the delivery date.
Step 1
Project overview & methodology Data cleaning & feature engineering explanation Visuals (Balance Distribution, KS Curve, ROC Curve) Results summary Business impact (how it helped the client)