You will get scientific machine learning (ML) model details and approach details in pdf
Project details
The delivery will ensure details of variable treatment, variable derivations, model parameters, variable selection, model characteristics, power of model, coefficient stability check, test of model's power on new data, data stability check between train and test, characteristic analysis of variables to know, if any variable is changing a lot between train / test data.
I will try my best to deliver the project ASAP along with all those details. The documentation will be very clear. Step by step details will be provided in PDF.
I will try my best to deliver the project ASAP along with all those details. The documentation will be very clear. Step by step details will be provided in PDF.
Machine Learning Tools
Microsoft Excel, NLTK, NumPy, Python, R, RapidMiner, SAS, scikit-learn, SciPy, SQL, XGBoostWhat's included $4,000
These options are included with the project scope.
$4,000
- Delivery Time 15 days
- Number of Revisions 3
- Number of Model Variations 2
- Number of Graphs/Charts 6
- Model Validation/Testing
- Model Documentation
Optional add-ons
You can add these on the next page.
Fast 10 Days Delivery
+$1,000
Additional Revision
+$400
Additional Model Variation
(+ 1 Day)
+$400Frequently asked questions
About Gopal
Machine Learning Model developer| 23 Years on Credit Card Analytics
Thane, India - 5:41 pm local time
• Visionary, Innovative, Motivational and Decisive for business solutions.
• Established Analytical practices (data science & Data strategy) in SBI Card (data and analytics
driven decision making, propensity model usage, campaign effectiveness measure, forecasting,
planning, product design etc.)
• Created the culture for self-service on analytics across the organization after establishing user
friendly analytical infrastructure in place.
• Ability to drive cross functional high impact projects (matrix team) (like for enterprise wide BI
platform – understand requirement, documentation, development, use case demonstration,
business glossary etc.)
• In depth business knowledge of credit card industry and its different functions (namely risk,
marketing, collections, operations, customer services).
• 23+ years of experience in Statistical modeling (Credit Scoring as well as times series/ loss
forecasting), risk based strategy development (based on segmentation), optimization model
development, machine learning, Artificial Intelligence, Neural Network, Deep Learning,
Campaign management
• Established best practices (theoretical procedure, guidelines and tools) on Model development,
validation, documentation, implementation and usage
• Extensive work on automation & database solutions for business problem
• Good training (2.08 Millions click on youtube videos on statistical topics) , Project Management
and change management skills
• Have published 18 digital books (Audio-Visual Presentation with data workout) so far on Udemy
and have 116K+ students. Some links are
o Logistic Regression (Credit Scoring) Modeling using SAS
o Cluster Analysis –Motivation, Theory & Practical Application
o Statistics for Management (MBA) - Foundation of Analytics
o Decision Tree - Theory, Application and Modeling using R
o Artificial Neural Network – theory and application
Work Experience (23+ years):
ProData Science AI Consultancy Pvt Ltd – MD & CEO
Duration – Feb 2024 onwards
Responsibility:
1. Industry recognition of patented product Extreme-ML n Insta Visualizer
2. Enterprise growth strategy
3. Train and coach Data Science Professionals
SBI Cards (GE Capital) – SVP Marketing Analytics, Campaign management, audit, project
management
Duration – Mar-2010 till Jan 2024
Designation – VP Marketing Analytics (Mar10-Feb14, LPB Band), Feb14-Jul16 (SPB Band), SVP
Jul16 onwards
Responsibility:
1. Ownership of revenue Targets (spend volume, balance, interest, fee etc)
2. Enterprise wide data strategy (data storage, integration & consumption)
3. Responsible for measuring, maintaining and enhancing revenue lines.
4. Track industry metrics and suggest changes / improvements in internal process
5. Establish analytics practices (for credit scoring as well as planning/ forecasting)
6. Enable analytics, business intelligence, campaign management infrastructure
7. Enterprise wide project management
8. Responsible for revenue audit management, Marketing complaint management
Citi Bank (TCS Eserve) – Head of Modelling (Risk modeling for all business of North America)
Duration – Jul 2006-Mar 2010
Designation – SM Modelling (July 2006 to Mar 2008), AVP Modelling (Apr 2008 till Mar 2010)
Responsibility:
1. Ensure delivery of model development projects for entire Citibank NA
2. Responsible for establishing modelling best practices and tools
3. Ensure technical proficiency and continuity of business knowledge of the team
Zenta – Manager Collections analytics
Duration – Feb 2005 to July 2006
Responsibility:
1. Establish collections analytics practices
2. Enhance customer services / collection / sales processes
3. Establish call quality and compliance measurement process
Neural Technologies – Sr. Risk Consultant
Duration – April 2004 to Feb 2005
Responsibility:
1. Develop score card
2. Provide analytics solutions approach for various clients and verticals
3. Provide training to new team members
GE Capital (ACOE) – Assistant Manager – collections analytics
Duration – May 2001 to April 2004
Responsibility:
1. Develop score card
2. Work on modelling / audit / analytics process efficiencies
3. Develop solutions for business problems
Technical Skills
Programming Language C, HTML, SQL, Java, python
Operating Systems
Windows, UNIX, ISPF (Main Frame)
RDBMS
Oracle, Microsoft SQL Server
Tools
SAS (base/ graph/ macro programming), Microsoft office, VBA (MS
Excel & MS-Access), E-Miner, Knowledge Seeker, Minitab, SPSS, Answer
tree, Spectrum Miner / Explorer, Portrait Dialogue, Tableau, R
programming, Python (panda / scikit-sklearn/ numpy / matplotlib /
streamlit), SAS VA (Visual Analytics) & VS (Visual Statistics)
Analytical Skills
Statistics / machine learning:
Supervised Machine learning Objective (CHAID / CART analysis) , Regression, Logistic regression, ANOVA,
CART, Discriminant Analysis, Artificial Neural Network
Steps for completing your project
After purchasing the project, send requirements so Gopal can start the project.
Delivery time starts when Gopal receives requirements from you.
Gopal works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Understanding
Understand project objective Understand what can be used as dependent variable Ask for basic statistics of the data and dependent variables
Document requirement and get client confirmation
Must be clear on dependent variable Independent variable Also provide basic distribution of dependent variable Agree on population exclusions If resampling is needed at final model development stage. Agree on model development method