ANSHUL KATIYAR

Rajiv Gandhi Institute Of Petroleum Technology,Rae Bareli

Raebareli, Uttar Pradesh, India

Since April 21, 2026

About

I am a B.Tech student in Mathematics and Computing at RGIPT with a strong interest in Machine Learning and Data Science. I have worked on projects involving student performance prediction and time series forecasting, focusing on data preprocessing, feature engineering, and model building. In my time series project, I analyzed trend and seasonality and improved forecasting performance using multivariate models. I have also experimented with models like Random Forest, XGBoost, and LSTM to understand their performance on real-world datasets. I am proficient in Python, scikit-learn, and PyTorch, and I am currently working on improving my skills in building complete ML pipelines and deployment.

Skills

PythonData InterpretationData ModelingData Visualization DesignersMachine LearningData ProcessingArtificial Intelligence

Education

Rajiv Gandhi Institute Of Petroleum Technology,Rae Bareli

B.Tech · Mathematics and Computing

August 13, 2023 – May 2, 2027

Key Strengths

Strong academic foundation in Mathematics and Computing, including relevant courses like Machine Learning, AI, Data Structures, and Algorithms.
Demonstrated practical application of Machine Learning concepts through two significant projects: 'Time Series Forecasting' and 'Student Performance Predictor'.
Proficiency in key ML libraries and tools including Python, PyTorch, scikit-learn, pandas, NumPy, matplotlib, Flask, and Git.
Experience with end-to-end ML workflow, from data ingestion and transformation to model training, evaluation, and deployment (Flask UI).
Exposure to various ML models (MLP, LSTM, 1D CNN for time series; Random Forest, XGBoost, CatBoost, etc. for regression) and model benchmarking.
The candidate's project work and academic background show a strong foundational match for an entry-level ML Engineer role.

Cultural & Operational Fit

Cultural Fit Analysis

The psychometric test score is 0 out of 500, which indicates a complete lack of validated data regarding the candidate's cultural fit, including aspects like logical reasoning, work attitude, stress handling, and team collaboration. Therefore, no conclusions about cultural fit can be drawn from the assessment data.

Soft Skills & Operational Fit

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Identifying your key strengths…

Evaluating your skill match against the job requirements…

Key Strengths

Strong academic foundation in Mathematics and Computing, including relevant courses like Machine Learning, AI, Data Structures, and Algorithms.
Demonstrated practical application of Machine Learning concepts through two significant projects: 'Time Series Forecasting' and 'Student Performance Predictor'.
Proficiency in key ML libraries and tools including Python, PyTorch, scikit-learn, pandas, NumPy, matplotlib, Flask, and Git.
Experience with end-to-end ML workflow, from data ingestion and transformation to model training, evaluation, and deployment (Flask UI).
Exposure to various ML models (MLP, LSTM, 1D CNN for time series; Random Forest, XGBoost, CatBoost, etc. for regression) and model benchmarking.
The candidate's project work and academic background show a strong foundational match for an entry-level ML Engineer role.

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit