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Software Development Engineer with less than a year in Machine Learning & Full-stack development
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Assessing your cultural and operational fit
Final-year Computer Science Engineering student (CGPA 8.79) with hands-on experience building machine learning applications and full-stack web systems. Proficient in Python, Java, and C with a strong grasp of Data Structures, Algorithms, and OOP principles. Delivered three end-to-end ML projects spanning healthcare, agriculture, and deep learning integrating trained models into live web interfaces. Eager to contribute as a Software Development Engineer in a product-driven team.
Vidyavardhaka College of Engineering, Mysuru
B.E. · Computer Science and Engineering
August 1, 2022 – June 30, 2026
Government Junior College, Bannikuppe
Class 12 · Pre-University Course (Science)
N/A – May 31, 2022
Chronic Kidney Disease Prediction System
January 1, 2025 – Present
Built an ML-powered web app using Flask to predict Chronic Kidney Disease from clinical data in real time. Preprocessed patient datasets - missing-value imputation, feature encoding, normalization boosting model accuracy. Compared Naive Bayes and Random Forest classifiers; achieved high precision via hyperparameter tuning and cross-validation.
Crop Disease Detection System (IEEE Project)
January 1, 2024 – Present
Designed a CNN-based plant-disease classifier that identifies leaf diseases from uploaded images with high accuracy. Applied augmentation, resizing, and normalization to a labeled agricultural dataset, improving model generalisation. Deployed the model in a web diagnostic platform enabling farmers to receive instant disease classification results.
CropScan - Soil-Based Crop Recommendation System
January 1, 2024 – Present
Developed an interactive Streamlit app recommending optimal crops from soil parameters (pH, NPK, humidity, rainfall). Benchmarked Random Forest, Decision Tree, and Naive Bayes; Random Forest delivered the highest prediction accuracy. Displayed model confidence scores alongside recommendations, helping farmers make data-driven cultivation decisions.
Data Structures & Algorithms (LeetCode, HackerRank)
LeetCode, HackerRank
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest in applying technology to real-world problems, particularly in healthcare and agriculture. This aligns with a product-driven team culture focused on impact. The diversity of projects (CNN-based image classification, soil-based recommendation, disease prediction) shows a broad technical curiosity. However, as a final-year student with no professional experience, the candidate's adaptability to corporate cultural nuances, stakeholder management, and long-term project lifecycle contributions is yet to be proven.
Soft Skills & Operational Fit
The candidate's project descriptions suggest an ability to work on complex problems and deliver functional solutions. Participation in IEEE and VCode Club indicates a collaborative spirit and engagement in technical communities. However, without direct work experience, specific soft skills like leadership, conflict resolution, or advanced teamwork cannot be fully assessed. The candidate appears to be a self-starter with a focus on practical application of learned concepts.