I will help you implement machine learning algorithm using R and write report
- 4.4
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Project Details
Why Hire Me?
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Combining strong machine learning knowledge with R programming expertise, I provide guidance in not just implementing algorithms but also in understanding their underlying mechanics. My focus is on empowering you to build robust models and present your findings in clear, detailed reports.
Key Strengths:
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Proficient in supervised and unsupervised algorithms using R
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Skilled in
caret,randomForest,ggplot2, and other essential R packages -
Practical understanding of real-world data problems and model selection
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Clarity in report writing for academic, business, or research use
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Support tailored to beginner, intermediate, or advanced learners
What I Need to Start Your Work
To deliver the most suitable solution, please share the following before we begin:
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Project Details
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Overview of the problem statement and intended outcomes
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Specific machine learning approaches or models to be considered
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Data & Description
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Dataset(s) in .csv, .txt, or R-readable formats
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Details on source, variable types, and prior preprocessing
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Exact Requirements
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Goals such as prediction, pattern recognition, or clustering
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Any constraints (e.g., need for model interpretability or speed)
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Reporting Preferences
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Expected level of explanation and preferred visualizations
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Academic or organizational formatting expectations (if any)
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Communication Preferences
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Preferred contact channel (email, phone, etc.)
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Deadlines or expected timeline for delivery
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Portfolio
Predicting Patient Readmissions with Machine Learning in R at a U.S. Urban Hospital
Built a classification model in R using logistic regression and random forest to predict 30-day patient readmission. Helped a hospital target high-risk discharges and reduce avoidable readmissions.
Customer Credit Risk Scoring in R Using Classification Algorithms for a U.S. Lending Startup
Implemented decision trees and logistic regression in R to classify customers into low, medium, and high credit risk tiers. Enabled better loan approval decisions and reduced default rates for a fintech company.
Employee Attrition Segmentation Using Clustering in R for a U.S.-Based Tech Firm
Applied K-means clustering in R to group employees by attrition risk based on workload, role type, and tenure. Insights informed retention programs and improved HR forecasting.
Process
Customer Reviews
5 reviews for this Gig ★★★★☆ 4.4
caret package setup was confusing at first but he guided me and even gave extra learning tips beyond the project really supportive
clustering results were visually great but the report missed a small section on assumptions once i pointed it out he fixed it same day
used my finance data for regression and he chose the right variables to avoid overfitting helped me understand the logic step by step
classification accuracy was decent but would have liked a bit more comparison between models still the code was clean and well commented
very helpful in explaining random forest model i had no clue about confusion matrix but now i can explain it in my own words






