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Kokorick AI's avatar
Kokorick AI
Houston, USA
AI Agents | LLMs, Computer Vision & Full-Stack Dev
74
Followers
AI Agents | LLMs, Computer Vision & Full-Stack Dev
Cover image for Full Stack Approach
Full Stack Approach
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211
Cover image for Working on AI Face calibration
Working on AI Face calibration
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173
Cover image for AI & Full-Stack Developer | LLMs, Machine Learning
AI & Full-Stack Developer | LLMs, Machine Learning
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10
Cover image for 🚀 Most AI outreach tools
🚀 Most AI outreach tools stop at writing messages. Warmo.ai (http://Warmo.ai) actually helps you find prospects, personalize outreach, and start meaningful conversations—all from one platform. Whether you're a freelancer, agency owner, recruiter, or founder, it's built to save hours of manual work and help you book more meetings. 🔥 Best part? The first users can use it completely FREE. If you've been waiting for an AI tool that actually helps you grow your business instead of just generating text, now's the time to try it. 👉 Check out Warmo.ai (http://Warmo.ai) and claim your free access while it's available. #AI #Sales #LeadGeneration #Outreach #Freelancer #Agency #Startup #BusinessGrowth #Automation #Contra
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pandas(3)
kaze nesia's avatar
kaze nesia
Surabaya, Indonesia
Full-Stack Data Specialist | Automation & Predictive
New to Contra
Full-Stack Data Specialist | Automation & Predictive
Cover image for AI Student Success Intelligence Platform A
AI Student Success Intelligence Platform A twelve‑module analytics platform analyzed 50,000 learners across six countries to predict dropout, model engagement, and simulate interventions, finding that a composite Student Engagement Index (SEI)—built from Time Commitment, Academic Quality, Platform Activity, and Social Learning—is the strongest predictor of dropout (behavior beats demographics), an ensemble of XGBoost/LightGBM/CatBoost achieved 99.72% AUC and F1 = 0.9522, risk tiers were highly precise (Low Risk = 0.0% dropout; Critical Risk = 99.7%), multi‑dimensional “Full Interventions” produced the largest simulated risk reductions, and correcting a data‑leakage issue (attendance proxy) was essential to preserve model integrity.
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Cover image for Advanced Crypto Market Signal Engine with High Precision SELL
Crypto Market Intelligence & Alpha Signal Engine An end-to-end Colab pipeline ingests market feeds, engineers 43 signals, detects regimes (KMeans/HMM/GMM), models 24‑hour alpha with a Random Forest (ROC‑AUC 0.7714), flags anomalies (Isolation Forest/Autoencoders), and backtests strategies; key findings: the SELL signal is highly precise (only 5.14% of SELLs rose next day), anomalies are often bullish (36.87% up vs 25.49% normal), price‑level context and regime probabilities drive predictions, and the model favors low‑volatility, defensive assets during downturns.
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170
Cover image for Global Retail Intelligence System: Product
Global Retail Intelligence System: Product Success Prediction and Strategic Market Analysis A multi-stage ML pipeline analyzed 44,888 Adidas SKUs using XGBoost and Random Forest to predict product success, demand trajectories, and stockout risk, finding that subcategory is the dominant success driver (~6× more explanatory than price, discount, or geography), the Success Classifier reached 94.3% accuracy and the Stockout Risk model 0.99 ROC‑AUC, 42.5% of products carry markdowns with deep discounts (≥30%) often eroding margins, 323 high-performing SKUs are under‑distributed and present near‑term expansion opportunities, the Budget tier outperforms Premium/Luxury in conversion to high performers, and 653 SKUs were flagged as high demand with elevated stockout risk requiring urgent replenishment.
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Cover image for AI-Driven Global Smartphone Sales Strategy
AI-Driven Global Smartphone Sales Strategy Optimizer An end-to-end ML project used four years of global sales data and 132,000+ simulations to optimize pricing across 52 countries, identifying the exact product, channel, and price to maximize profit. Key findings: the “Discount Myth”—discounting has almost no effect on volume but erodes margins; switching from blanket 20% discounts to AI‑optimized pricing yields a 15.1% revenue gain (about $73,993 preserved per simulation). The B2B channel is optimal in 90% of markets. The production XGBoost model achieves 99.73% accuracy, and ultra‑premium products (notably the Samsung Neo QLED 8K) consistently generate the highest revenue.
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119
pandas(6)
Rohit P.'s avatar
Rohit P.
India
Data Scientist, Python, ML Engineer & AI Developer
5.0
Rating
7
Followers
Data Scientist, Python, ML Engineer & AI Developer
Cover image for Iris-Flower Classification | Machine Learning
Iris-Flower Classification | Machine Learning
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13
Cover image for Hand Gesture Video Control | Computer Vision
Hand Gesture Video Control | Computer Vision
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42
Cover image for CIFAR-10 CNN Classifier | Deeplearning
CIFAR-10 CNN Classifier | Deeplearning
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Cover image for Stock Price Visualization and Entry-Points | Machine Learning
Stock Price Visualization and Entry-Points | Machine Learning
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17
pandas(5)
Sarbjot Singh's avatar
Sarbjot Singh
Pimpri-Chinchwad, India
Power BI Expert | Turning Raw Data into Actionable Insights
New to Contra
Power BI Expert | Turning Raw Data into Actionable Insights
Cover image for Data Analytics Project | BlinkIT
Data Analytics Project | BlinkIT Grocery Sales Analysis Excited to share my latest Data Analytics project ,where I analyzed BlinkIT Grocery Sales Data and delivery data to uncover meaningful business insights 📈 📊 What I worked on: Analyzed sales performance across product categories, outlet types, sizes, and locations Identified top-performing item categories and customer preferences Studied the impact of outlet size, establishment year, and visibility on sales Converted raw data into actionable insights and business recommendations 💡 Key Insights: Fruits & Vegetables, Snack Foods, and Frozen Foods drive maximum sales FOR FULL PROJECT VIST GIT HUB –( https://lnkd.in/d34sdAPz )
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Cover image for I’m excited to share my
I’m excited to share my latest Data Analytics project where I analyzed Customer Shopping Behavior to uncover trends in sales, demographics, and purchasing habits.This project was a great exercise in building a complete data pipeline. The Tech Stack: Python (Pandas & NumPy): Used in Jupyter Notebooks for initial data exploration and statistical analysis. PostgreSQL: Utilized for rigorous data cleaning, querying, and structuring the dataset for analysis. Power BI: Built an interactive dashboard to visualize key metrics like seasonal trends, subscription impacts, and category performance. Project Highlights: ✅ Data Cleaning: Leveraged PostgreSQL and Pandas to handle missing values and standardize categorical data.
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Cover image for Venezuela’s Oil Reserves Analysis Dashboard
Venezuela’s Oil Reserves Analysis Dashboard 📊 I am excited to share my latest data analytics project focusing on the energy sector. Using a dataset covering 23 reservoirs, I designed a comprehensive dashboard to track production capacity and resource distribution in Venezuela. Key Features: ✅ Real-time Metrics: Tracking the 390.50 Billion total barrels in reserve. ✅ Granular Analysis: Production capacity breakdown by reservoir name and oil grade (Extra Heavy to Light). ✅ Stakeholder Mapping: Visualizing the production sum by major operators. Tools Used: [pandas(jupyter notebook)/mysql(for analysis and cleaning of data), Power BI / Excel(for building dashboard)] I’d love to hear your thoughts or feedback on the dashboard design! must visit my github for full project -( https://lnkd.in/dYKdZ8J2 )
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Cover image for Data Analytics Project | BlinkIT
Data Analytics Project | BlinkIT Grocery Sales Analysis Excited to share my latest Data Analytics project ,where I analyzed BlinkIT Grocery Sales Data and delivery data to uncover meaningful business insights 📈 📊 What I worked on: Analyzed sales performance across product categories, outlet types, sizes, and locations Identified top-performing item categories and customer preferences Studied the impact of outlet size, establishment year, and visibility on sales Converted raw data into actionable insights and business recommendations 💡 Key Insights: Fruits & Vegetables, Snack Foods, and Frozen Foods drive maximum sales Medium-sized outlets outperform others in overall revenue FOR FULL PROJECT VIST GIT HUB –( https://lnkd.in/d34sdAPz ) Feedback and suggestions are always welcome!
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pandas(3)
Guy G's avatar
Guy G
Oregon, USA
40+ years programming experience including recent AI and ML.
6
Followers
40+ years programming experience including recent AI and ML.
Cover image for Each pulse consists of the changes needed to transform the v...
Each pulse consists of the changes needed to transform the visualization of one subject range to another. I could reduce the pulsing by using inter-prompt interpolation but at the time I made these last year the computational overhead was too expensive. If I had the time to do it now it would be quite a bit more efficient on an A100 for about the same cost including interpolation. This is a visualization of a couple months worth of "Physical Review Letters E" which is very much worth checking out.
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Cover image for Another fun project I started last year is called "CASI" for...
Another fun project I started last year is called "CASI" for Cyclical Adversarial Step-wise Improvement. It leverages the fact that when LLMs engage in self-correcting loops they get better at a task. In this case we are pitting 2 models and 2 system prompts with distinctly different purposes against each other to improve a concept or idea. This is unlike self-play and more like other-play. I would love to include an intermittent training pass or LoRa construction between cycles to make the models focus more completely. CASI is still a work in progress so use at your own frustration. If you encounter any issues please log them with the github repo, you will be helping the entire world :) https://github.com/TheOneTrueGuy/CASI
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Cover image for Language models provide some spectacular new opportunities f...
Language models provide some spectacular new opportunities for discovery. While working on some ideas it occurred to me to explore the relationships between concepts spatially. Interpolating across the latent space between concepts and then mapping to a vectors nearest token predicate. I arranged this formulation as a form of tessellation to cover the n-dimensional volume efficiently. The result is a work-in-progress I call the Tessellator. Since this is an experiment it just made sense to open source it and a new update is due in the next week or two. https://github.com/TheOneTrueGuy/tessellator
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Cover image for This work was from late last year but I've been meaning to r...
This work was from late last year but I've been meaning to revisit it with an Agentic twist. Rebuilt to remain running in real time like a guardian I think it could be truly useful. It got open-sourced in this form after the client decided not to pursue it further. It is a simple tool for analyzing enormous blocks of emails for signs of fraud or deception. It won't take much for me to elevate the performance. https://github.com/TheOneTrueGuy/Fraud-Analysis-Tool and here is the video that was made for the associated Kaggle contest: https://www.youtube.com/watch?v=do9uPzW4AVk
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pandas(5)
Syed Fatik Islam's avatar
Syed Fatik Islam
Gujrat, Pakistan
Data Scientist delivering insights through analytics and ML
Data Scientist delivering insights through analytics and ML
Cover image for Chat Data Analysis and Sales Insights Platform
Chat Data Analysis and Sales Insights Platform
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Cover image for Instagram Chat Analysis
Instagram Chat Analysis
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16
Cover image for Sales Analysis of a Clothing Retail Shop
Sales Analysis of a Clothing Retail Shop
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16
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