Freelancers using pandas in Surabaya
Freelancers using pandas in Surabaya
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kaze nesia
Surabaya, Indonesia
Full-Stack Data Specialist | Automation & Predictive
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Full-Stack Data Specialist | Automation & Predictive
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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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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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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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Implementing Dynamic Pricing for an E-commerce Platform Developed a dynamic pricing system to adjust prices in real-time based on market demand, competition, and customer behavior. Conducted market research, built pricing algorithms, and integrated them into the platform. Used data analysis to identify optimal pricing strategies and monitored their impact on key metrics. Findings: Increased Revenue: Experienced a 10% increase in revenue after implementing dynamic pricing. Improved Conversion Rate: Achieved a 5% increase in conversion rate by offering more competitive prices. Enhanced Competitive Advantage: Effectively positioned products and captured a larger market share. Identified Customer Segments: Gain insights into different customer segments and their willingness to pay.
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