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Can behavioral patterns predict human decision making? I scraped 450+ Reddit posts, built an NLP pipeline with 85% predictive accuracy, and prototyped a bias awareness feature to surface unconscious patterns just to find out.
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Crop Yield Prediction System
Developed a machine learning-based Crop Yield Prediction System to estimate agricultural yield from key environmental and farming factors.
• Cleaned and preprocessed real-world agricultural data
• Performed exploratory data analysis and feature engineering
• Trained and compared Linear Regression and Random Forest models
• Built an end-to-end Scikit-learn ML pipeline for reliable predictions
• Achieved strong predictive performance and deployed the model through an interactive Streamlit application
Tools: Python | Pandas | NumPy | Scikit-learn | Streamlit | Machine Learning | Data Analysis
#MachineLearning #Python #DataScience #CropYieldPrediction #AgricultureAnalytics #PredictiveAnalytics #DataAnalysis #Pandas #ScikitLearn #RandomForest #LinearRegression #FeatureEngineering #DataCleaning #MLModel #Streamlit #AI #Agritech #MachineLearningProject #DataScientist #FreelanceDataAnalyst
What makes an A/B test readout useful to a product team?
My preferred first page answers four questions:
What changed, and by how much?
How uncertain is the estimate?
Did an important guardrail get worse?
What decision does the evidence support, and what remains unresolved?
A result can be statistically significant and still too small to matter. An inconclusive result can still leave a meaningful gain or loss plausible. The decision needs more than a green badge.
I built an independent A/B analyzer to make these checks easier to inspect. The project includes a live demo, downloadable reports and documented limitations:
https://contra.com/p/mgoXb3qY-ab-test-analyzer-correct-p-values-power-and-fdr
If you work on product or CRO, what do you wish analysts put on the first page more often?
This is a strong framing of experiment readouts: separating signal, uncertainty, guardrails, and the actual decision keeps the team honest. The reminder that significance is not the same as usefulness is especially important.
YOUR DASHBOARD MIGHT BE LYING TO YOU.
Not because Power BI is wrong.
Because the data underneath it is.
A beautiful dashboard built on messy data doesn't create better decisions.
It creates confident mistakes.
Duplicates.
Missing values.
Inconsistent names.
Broken formats.
Multiple Excel files.
Numbers that don't agree.
Then someone adds a few colorful charts, calls it "analytics," and moves on.
I don't work that way.
I take the mess first.
RAW DATA → CLEAN → TRANSFORM → MODEL → ANALYZE → POWER BI → INSIGHTS
I work with Excel, CSV and business datasets to:
→ Clean and validate messy data
→ Transform data using Power Query
→ Combine multiple files and sources
→ Build data models and DAX measures
→ Create interactive Power BI dashboards
→ Identify trends, KPIs and business insights
→ Build reporting workflows that are easier to refresh and maintain
Because a dashboard shouldn't just look impressive.
Every KPI should answer a question.
Every visual should have a purpose.
And every number should be trustworthy.
That's the service I'm offering.
If your business data is sitting across messy Excel files, CSVs or scattered spreadsheets, I can turn it into:
Clean Data → Clear Analysis → Interactive Power BI → Better Decisions
You bring the data.
I'll find what it's trying to say.
📊 Power BI Dashboard & Data Analytics
Now available for freelance projects on Contra.
I actually 99.99% agree,
Furthermore Outliers removal is part of cleaning datasets we choose to begin a project, Descriptive statistics is a key for detection
