Hire the Best Logistic Regression Specialists

99 stocks screened. 6 economies mapped. One client's manual reporting, gone overnight. I'm a Quantitative Data Analyst working in Python, R, and SQL. My background spans Mathematics, Data Science, and Industrial Engineering, three lenses most analysts pick one from. That combination means clients don't get a generic report. They get a model that actually explains what the data is doing and a decision they can act on. What I have delivered for clients: ✦ 99-stock portfolio analysis. Logistic regression, risk-adjusted, benchmarked against the Nasdaq. ✦ Black-Scholes options model integrated with decision tree analysis. Delivered on deadline. ✦ Macroeconomic analysis across 6 economies in R. Inflation, GDP, and unemployment mapped as heatmaps and time-series dashboards using Tableau. ✦ Power BI dashboard for an automotive client. 48-hour manual reporting lag, eliminated overnight. ✦ ARIMA-based forecasting model in Excel. Seasonal demand patterns detected, inventory recommendations delivered. ✦ Bank credit risk study replicated in R using panel regression and financial modeling techniques. ROA strongest predictor, R² of 0.91. What I work on: ✦ Statistical analysis and quantitative modeling ✦ Python and R for data analysis and automation ✦ Tableau and Power BI dashboards ✦ Excel models, pivot tables, and reporting automation ✦ SQL data extraction and structuring ✦ Data visualization, heatmaps, time-series, dashboards I don't just find what's broken. I use advanced mathematics and analytics to find WHY it's broken, then I build the system to fix it permanently. I work best with: ✦ Operations teams, ✦ Researchers ✦ Business owners ✦ E-commerce brand founders, ✦ Fintech managers who have data but aren't sure what it's telling them, and need one clean deliverable they can act on immediately. If your analysis is manual, your reporting takes too long, or your data sits in files nobody looks at, that's exactly where I come in. Send me a note about where your data or process is breaking down. I'll take it from there.

Top Rated Economist | Data Analyst | Statistician | Quantitative & Qualitative Researcher | Survey Analyst | Statistician Econometrician | Research Analyst | Survey Specialist | Visualization Expert ✅ Lead Analyst @Suncor Energy. Previously, a Business Analyst @NNPC & @Pan Allen Energy ✅ A Consulting Researcher @Statisda with high level research experience -000's of offers closed ✅ Able to Gather and transform data into publication-ready insight for Businesses & Researchers ✅ Econometrics| Time-series |Panel data |Cross-sectional |Non-parametric |ANOVA | Regression ✅ Adapt to any writing & formatting style; MLA, Harvard, APA, Chicago, IEEE ✅ Tools: SPSS | Stata | EViews |R | Python | Power BI | Tableau | Excel ✅ 100% original, Copyscape-verified work with unlimited revisions ✅ Professional dashboards, reports, visualizations & clear storytelling My Services: Thesis, Dissertation & Research Editing, Proofreading & Guide ⭐ Areas of focus; social sciences; Business, Finance, Economics etc ⭐ Data Collection, Literature reviews, case studies, whitepaper guide ⭐ Assistance on In-depth literature review using peer-reviewed journals ⭐ Guide to Perfect formatting in APA, MLA, Chicago, Harvard or IEEE ⭐ Insight on SEO-optimized where needed, while keeping full scholarly rigor ⭐ Professional structure with tables, figures & references Time Series Analysis ⭐ Unit root testing/ Stationarity ⭐ Cointegration analysis ⭐ Long Run Regression ⭐ Auto-Regressive Distributive Lag ⭐ Error Correction Model ⭐ Vector Error Correction Model ⭐ Simultaneous Equation ⭐ Vector Auto-Regressive model ⭐ Generalized Method of Moment ⭐ 2 Stage Least Squares ⭐ Volatility Analysis ⭐ Instrumental Variables ⭐ GARCH Modelling ⭐ ARCH Modelling ⭐ Variance Decomposition ⭐ Impulse Response Model ⭐ Volatility Check ⭐ Auto Correlation ⭐ Walds Test ⭐ And a lot more Panel Analysis ⭐ Panel Unit Root ⭐ Panel Cointegration ⭐ Fixed Effects OLS ⭐ Pool Ordinary Least Square ⭐ Random Effects ⭐ Random Walk ⭐ Fully Modified OLS ⭐ Canonical Regression ⭐ Graphical Presentation ⭐ Online Consultation ⭐ And a lot more Cross Sectional Analysis ⭐ Binary Models: Tobit ⭐ Probit Model ⭐ Logit Model ⭐ Multinomial Analysis ⭐ Factorial Analysis ⭐ Linear Regression ⭐ Correlation ⭐ Delphi Method of Estimation ⭐ Multivariate analyses ⭐ Simultaneous equations ⭐ And a lot more Non Parametric Analysis ⭐ Kruskal-walis Test ⭐ Mann-Whitney Test ⭐ Friedman Test ⭐ Chi-square ⭐ Kappa Measure of Agreement ⭐ Wilcoxon Signed-rank Test ⭐ And a lot more Group Comparison ⭐ T-Tests ⭐ Effect Size ⭐ One Way ANOVA ⭐ Two Way ANOVA ⭐ Multivariate Analysis of Variance ⭐ Analysis of Covariance ⭐ Generalized Linear Models ⭐ And a lot more Survey & Questionnaire ⭐ Questionnaire Design ⭐ Survey Design ⭐ Survey Analysis ⭐ And a lot more Preliminary & Descriptive Analysis ⭐ Massaging and Cleaning ⭐ Data Visualization Graphs & Tables ⭐ Development of Scale ⭐ Cronbach’s Alpha Coefficient ⭐ Mean, Frequency ⭐ Proportions, Charts, Graphs ⭐ Assessing Normality ⭐ Trend Analysis ⭐ Sampling Distributions ⭐ PowerPoint Presentations for Statistical Results ⭐ And a lot more Interactive Dashboard & Visualization ⭐ Visualize sales performance, ⭐ Performance tracking ⭐ Track project progress ⭐ Analyze market trend ⭐ Powerful insights. Tools ⭐ Microsoft Office Programs ⭐ Proficient with Python Programming language ⭐ Online survey distribution programs; Qualtrics, Survey Monkey, Google Survey ⭐ Econometrics; SPSS, STATA, EVIEWS, MAT-LAB, TABLEAU, RStudio etc

I am a dedicated and skilled Systematic Review, Meta-analysis, Network Meta-Analysis, Machine Learning including a predictive Model (LSTM, Regression, Logistics GB Algorithm), Time series Modelling, Panel Data Analysis, Econometrics expert with five years of enriching experience in the field of research and data analysis. My journey in the realm of systematic reviews, meta-analyses and machine learning has been marked by a fervent passion for unraveling complex scientific queries and synthesizing extensive volumes of data into meaningful insights. In my dynamic career spanning half a decade, I have delved deep into the intricate world of systematic reviews, meta-analyses and Machine learning. My expertise lies in meticulously designing and conducting comprehensive data analysis and report writing, employing rigorous methodologies to collect, assess, and interpret a plethora of research studies. My goal is to unearth patterns, trends, and discrepancies in existing research, contributing significantly to the advancement of various scientific domains. Over the years, I have spearheaded numerous research projects, where I have demonstrated my prowess in synthesizing diverse data sets. My systematic reviews have not only provided a panoramic view of existing research but have also paved the way for groundbreaking meta-analyses, amalgamating quantitative data from multiple studies to draw statistically robust conclusions. One of my significant accomplishments includes the successful completion of a meta-analysis that elucidated the efficacy of a groundbreaking medical intervention. This analysis, widely cited in academic circles, has played a pivotal role in shaping clinical guidelines and has been instrumental in guiding healthcare professionals worldwide. Methodological Proficiency: My expertise encompasses a wide array of systematic review methodologies, ranging from Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to Cochrane Collaboration standards. I am adept at employing statistical tools and software, ensuring the precision and accuracy of my meta-analytical findings. Additionally, I possess a keen eye for detail, enabling me to critically appraise studies, identify biases, and offer nuanced interpretations. Contributions to the Academic Community: Beyond my individual research endeavors, I am passionate about knowledge dissemination. I have actively contributed to the academic community by publishing my findings in reputable journals, thereby facilitating the exchange of ideas and encouraging further exploration within the scientific community. I have also served as a mentor, guiding aspiring researchers in the nuances of systematic reviews and meta-analyses. Future Endeavors: As I look forward, my aim is to continue my scholarly pursuits, delving into cutting-edge research questions and collaborating with experts from diverse fields. I aspire to further refine and expand my methodological toolkit, exploring innovative approaches that push the boundaries of systematic review and meta-analysis methodologies. In essence, my journey as a Systematic Review and Meta-analysis expert is defined by a relentless pursuit of excellence, a commitment to scientific rigor, and a genuine passion for unraveling the mysteries of the research landscape. I am excited about the limitless possibilities that the future holds and am dedicated to making enduring contributions to the world of academia and scientific inquiry. Some of the research i have done include systematic review and about analysis in medical field, business, management, accounting and agriculture. I have full access to WOS, Scopus, Medline, PubMed and Clinical trial website where i obtained the articles for research purpose.

Greetings! I am a PhD candidate in Statistical Machine Learning (ML), and a certified Machine Learning Scientist and Data scientist (Upwork & IBM) with both Master’s and Bachelor’s degrees in Statistics. My strong academic foundation equips me advanced data science and ML workflows. I specialize in developing predictive models for financial markets—particularly in crypto and stock price forecasting—using time series analysis, deep learning, and ensemble methods. My work also involves designing and backtesting algorithmic trading strategies, optimizing model performance under live market conditions, and building interactive dashboards for real-time data visualization and decision support. 🌟 Top Rated on Upwork – Consistent record of client satisfaction and delivery excellence. 🔍 Why Hire Me? ✅ Advanced ML Expertise I leverage cutting-edge techniques to deliver highly accurate and innovative predictive solutions. ✅ Comprehensive Tool Proficiency Programming: Python, R Dashboards & Visualization: Tableau, Power BI, Streamlit Statistical Software: SPSS, STATA, E-Views, Minitab Database & Cloud: SQL, Google Cloud (for scalable, production-ready ML deployments) ✅ Quantitative Research Strength Deep experience in algorithm design and time series forecasting enables trend prediction with exceptional precision. ✅ End-to-End Execution From data ingestion to predictive modeling and visualization, I manage the full pipeline with performance and clarity in mind. ✅ Clear Communication I break down complex ML models and insights into intuitive, actionable formats for stakeholders and non-technical audiences. 💼 What I Deliver: Predictive Modeling Time Series Forecasting & Analysis Interactive Dashboards in Tableau, Power BI, and Streamlit Risk Assessment & Optimization Models Feature Engineering & Model Tuning Visual Analytics & Business Intelligence

DATA SCIENTIST — everything I build, you can verify the math. I deliver transparent, auditable data models where every number can be checked. Specializing in quantitative research, statistical modeling, Data insights and automation. QUANTITATIVE RESEARCH & PORTFOLIO CONSTRUCTION : I build and backtest equities, with live execution and kill switches on Alpaca where needed. Position sizing, risk allocation, rebalancing logic, all of it grounded in the math, not gut feel. Quick example of how I think: a momentum strategy covering 145 stocks was delivering 41% CAGR. Expanding to 245 stocks dropped it to 36%. Counterintuitive. The root cause: New stocks were selected on hot recent momentum, so by entry their cycle was already done. Built a 5-filter Monte Carlo system, tested 700+ parameter combinations, to identify which satellites are early-cycle vs peaked. Result: 43%+ CAGR with lower drawdown than the 145-only baseline. The problem wasn't the strategy. It was the timing of entry. Another example: I built a live MBS (Mortgage-Backed Securities) forecasting and trading system for a fund client, covering 265+ cohort securities across weekly, monthly, and quarterly return horizons. The interesting part wasn't the ML, it was the feature design. Standard technical indicators on bond prices are noise. What actually drives MBS price movements is duration, convexity, prepayment rates, regime variables like curve steepness, VIX, and Fed MBS holdings. Every feature built from first principles, nothing borrowed from a vendor that can't be verified. This is where I spend most of my time. Systematic backtests, portfolio construction, Monte Carlo optimization. No black boxes, no ML hype every output can be checked in a spreadsheet. Not "add more data." Find the actual structure driving returns, and build features that reflect it. DATA SCIENCE & ANALYTICS I've worked across real estate, e-commerce, and financial analytics, usually called in when a team has data but no clear answer yet. I focus on getting to a decision you can act on, not a 40-slide deck. Results worth mentioning: ML automation cut a client's data processing from 286 days to 1 day, after they'd run the same manual process for two years without realizing it was automatable Ad spend attribution model that improved campaign ROI by 25% WHAT MAKES ME DIFFERENT ▸ I show my work. Every model, every analysis, every number comes with the method behind it, so you're never trusting a black box ▸ I'd rather tell you a strategy doesn't hold up than build you something that looks good and fails in real world. ▸ I scope real problems fast: send me what you're working with and I'll tell you within 24 hours if and how I can help

Most business teams I've worked with have the data — it's just buried in SAP exports, manual Excel files, or reports nobody reads. My job is to turn that mess into something people actually open and use to decide. I've spent 12 years doing this inside John Deere and Volvo spare parts operations — not as a consultant who comes in with a framework, but as the person who built the systems from scratch. I architected the SAP ↔ John Deere RPM integration that runs purchase orders automatically. I built the forecasting models (XGBoost, CatBoost, LightGBM) for my PhD. And right now I'm using Claude AI to automate decisions that used to take days of manual work. The way I work is simple: before touching any data, I ask "what questions are we trying to answer?" That one habit saves weeks of building the wrong thing. What I build: Decision tools — Streamlit apps · AI agents · automated reporting Data infrastructure — SAP · SQL · Snowflake · ETL pipelines BI Dashboards — Power BI · Tableau · inventory & commercial KPIs ML & Forecasting — XGBoost · LightGBM · demand modeling · Python Inventory Management and Replenishment Data Driven Systems Industrial Engineer · MBA · PhD in ML-based inventory optimization. Bilingual EN/ES.