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"Talent without working hard is nothing." --CR7 🐐 | Data Science Student | Open to Internships
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Assessing your cultural and operational fit
Amrita Vishwa Vidyapeetham
Data Science
June 15, 2026 – Present
tyre-manufacturing-quality-analytics
June 9, 2026 – Present
Manufacturing operations generate thousands of production records daily. Internal audit teams need systematic tools to monitor quality KPIs, detect out-of-control processes, and flag high-risk batches before they escalate into production failures.
View Projectclubiq-football-analytics
May 7, 2026 – Present
Full Stack Football Analytics Platform — Match Intelligence, Player Ratings & Load Monitoring
View Projectportfolio
April 23, 2026 – Present
Personal portfolio website — AI & Data Science researcher
View ProjectFootball-Match-Outcome-Prediction-
April 21, 2026 – Present
Predicts football match outcomes across 5 European leagues using ELO ratings, rolling form, betting market probabilities & H2H stats. Gradient Boosting (0.55 acc, 0.91 xPts MAE) beats ELO baseline. SHAP explainability + 3-layer anti-leakage architecture.
View ProjectExplainable-Match-Summaries
April 21, 2026 – Present
Factually grounded UEFA Champions League match summaries using RAG + local Mistral-7B + SHAP explainability. Sentence-BERT + FAISS retrieval achieves mean cosine similarity 0.903 vs 0.373 baseline. Zero hallucinations within knowledge base scope.
View ProjectFootballRole-DL
April 21, 2026 – Present
Classifies football players into Attacker, Midfielder, Defender roles from PAMAP2 IoT wearable data. LSTM, BiLSTM, and TCN-Transformer architectures. 99.24% accuracy, LOSO 98.89%±0.42%. SHAP sensor attribution per role.
View ProjectFootball-Player-Fatigue-Prediction-Wearable-IoT-Sensors-ML
April 20, 2026 – Present
Three-class football player fatigue prediction from PAMAP2 wearable IoT data. Karvonen heart rate labeling, SMOTE balancing, LOSO cross-validation, personalized Random Forest. 97.96% LOSO accuracy + coach substitution-alert dashboard.
View ProjectCricketGraph-DL-IPL-Match-Outcome-Prediction-Player-Impact-Analysis
April 20, 2026 – Present
Spatio-temporal graph deep learning for IPL T20 match outcome prediction. GAT player-interaction graph + BiLSTM + cross-attention Transformer. Ball-by-ball win probability, run forecasting & Player Impact Score with SHAP explainability.
View ProjectDetecting-Sarcasm-as-Sentiment-Incongruence
April 20, 2026 – Present
Multi-task RoBERTa for sarcasm detection via Sentiment Incongruence Auto-Labeling. Focal Loss + WeightedRandomSampler + LIME explainability. F1-Macro 0.977, AUC 0.997. Zero-shot cross-domain eval on ABSA & Amazon. IEEE paper.
View ProjectCultural Fit Analysis
The candidate's projects are heavily concentrated in sports analytics and academic research, which demonstrates deep interest and specialization. While this indicates passion, the lack of diversity in project domains outside of sports and academic research might suggest a narrower range of experience in different industry contexts. The target role is 'Data Science', which aligns with the technical skills, but the specific application areas might need to be broadened for a general data science role. The candidate's experience level is listed as 0, which suggests entry-level despite the advanced project work, potentially indicating a recent graduate or someone transitioning. This could impact cultural fit in a senior role requiring broader industry exposure.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong problem-solving orientation and an ability to apply complex technical solutions to real-world problems. The focus on explainability (SHAP, LIME) suggests an understanding of the importance of interpretability in AI/ML models. However, without specific assessment data on communication, teamwork, or stress handling, a comprehensive evaluation of soft skills and operational fit is not possible.