You will get Get a Machine Learning Model Predicting Customer Outcomes
Project details
Most predictive modeling gigs deliver a model and an accuracy number — I deliver a model you can actually trust and explain.
Every project starts with visible data cleaning: I document what was filtered, how missing values were handled, and confirm class balance before training anything. The model itself uses a fixed random seed, disclosed in the report, so results are reproducible if you need a re-run.
What sets this apart is honesty about performance. If precision or recall for your target class is weak — which happens often with imbalanced real-world data — I flag it clearly with a confusion matrix and plain explanation, instead of only showing a polished ROC curve. You'll know exactly how reliable the model is before you use it for a business decision.
Feature importance results come with plain-language interpretation, not just a ranked bar chart — I explain what the top predictors actually mean for your specific data.
My background is in bioinformatics and clinical data analysis, where reproducibility and honest reporting of statistical limitations are non-negotiable. I apply that same standard here, whether the data is customer records, transactions, or survey responses.
Every project starts with visible data cleaning: I document what was filtered, how missing values were handled, and confirm class balance before training anything. The model itself uses a fixed random seed, disclosed in the report, so results are reproducible if you need a re-run.
What sets this apart is honesty about performance. If precision or recall for your target class is weak — which happens often with imbalanced real-world data — I flag it clearly with a confusion matrix and plain explanation, instead of only showing a polished ROC curve. You'll know exactly how reliable the model is before you use it for a business decision.
Feature importance results come with plain-language interpretation, not just a ranked bar chart — I explain what the top predictors actually mean for your specific data.
My background is in bioinformatics and clinical data analysis, where reproducibility and honest reporting of statistical limitations are non-negotiable. I apply that same standard here, whether the data is customer records, transactions, or survey responses.
Machine Learning Tools
NLTK, pandas, Python Scikit-Learn, R, Scrapy, XGBoostWhat's included
| Service Tiers |
Starter
$75
|
Standard
$150
|
Advanced
$200
|
|---|---|---|---|
| Delivery Time | 3 days | 4 days | 5 days |
Number of Revisions | Unlimited | Unlimited | Unlimited |
Number of Model Variations | 1 | 1 | 3 |
Number of Scenarios | 1 | 1 | 1 |
Number of Graphs/Charts | 1 | 2 | 3 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | - | - |
Source Code | - | - |
Frequently asked questions
About Nuzhnenko
Bioinformatics Specialist | RNA-Seq, Molecular Docking & Data Visualiz
Poznan, Poland - 11:45 pm local time
What I can help with:
🧬 Transcriptomics & Functional Genomics
Bulk RNA-Seq differential expression analysis (DESeq2), volcano plots, heatmaps, PPI network hub gene identification (STRING + Cytoscape), and single-cell RNA-seq analysis (Scanpy) — QC, clustering, UMAP, marker gene identification.
🦠 Microbiome Analysis
ASV/OTU taxonomic profiling, alpha/beta diversity (phyloseq), and environmental correlation analysis (CCA) for 16S/metagenomic datasets.
💊 Drug Discovery & Molecular Dynamics
Protein-ligand molecular docking (AutoDock Vina), binding pose visualization with hydrogen bonds (ChimeraX/PyMOL), and MD trajectory analysis (RMSD, RMSF, radius of gyration via MDAnalysis).
🧪 Clinical Genomics
VCF variant annotation (Ensembl VEP, ClinVar filtering), IGV visualization, and machine learning biomarker discovery (Random Forest, ROC curves, feature importance).
📊 Publication-Ready Scientific Figures
Volcano plots, PCA, heatmaps, Kaplan-Meier survival curves, Circos plots — all delivered at 300dpi with colorblind-safe palettes and journal-ready formatting.
Every analysis comes with transparent methodology: tool versions, parameters, random seeds, and honest notes on QC filtering and any borderline findings — because reproducibility and accuracy matter more than a pretty picture when it's going into your paper or thesis.
Tools: R (DESeq2, phyloseq, vegan, ggplot2), Python (Scanpy, MDAnalysis, RDKit, scikit-learn), Cytoscape, ChimeraX/PyMOL, IGV.
📈 Statistical Analysis Beyond Biology
The same rigor I apply to genomic data — hypothesis testing, survival analysis, predictive modeling — works just as well outside biology. I take on general data analysis projects: A/B test evaluation, churn/time-to-event modeling, classification with transparent performance metrics (ROC/AUC, feature importance), and publication-quality data visualization for any domain. If your data has a clear question and a way to test it, I can help — regardless of field.
Message me with your dataset and goal — happy to advise on the right approach before you commit to a project.
Steps for completing your project
After purchasing the project, send requirements so Nuzhnenko can start the project.
Delivery time starts when Nuzhnenko receives requirements from you.
Nuzhnenko works on your project following the steps below.
Revisions may occur after the delivery date.
Data cleaning and preprocessing
I clean your dataset (handle missing values, encode categorical features, verify class balance) and confirm the target variable before any modeling begins.
Model training with fixed random seed
I train a Random Forest classifier on a stratified train/test split, using a documented random seed so results are fully reproducible if you request a re-run.