Salah Ahammed S S

BTech in Computer Science and Engineering

Trivandrum

Summary

A Computer Science graduate with strong problem-solving skills and a willingness to take risks and learn from challenges. I enjoy working with people, understanding their needs, and helping them use technology with ease. Known for being adaptable, a quick learner, and a clear communicator. I work well in teams, stay calm under pressure, and focus on delivering smooth and successful product implementations.

Work History

Machine Learning Intern

Srishti Innovative

07.2024 - 07.2024

Conducted data preprocessing, feature engineering, and exploratory data analysis using Python, Pandas, and NumPy.
Built and trained ML models using Scikit-learn and PyTorch for classification, regression, and clustering tasks.
Visualized data insights and model results using Matplotlib and Seaborn for internal reports and presentations.
Documented code, workflow, and project findings, ensuring reproducibility and collaboration within the team.

Education

BTech - Computer Science And Engineering

Mar Baselios College of Engineering And Technology

Trivandrum, India

04.2001 -

Skills

C Programming

Python programming

Scikit-learn library

SQL

Power BI

MS Excel

Problem Solving

Stakeholder Communication

Time Management

Adaptability

Projects

Photovoltaic Potential Estimation From Satellite Imagery

Developed an open-source application to assess solar rooftop potential and predict cost savings for buildings in India, inspired by Google’s Project Sunroof.

Integrated OpenStreetMap with Leaflet.js to let users select buildings via an interactive satellite map using polygon drawing.
Processed low-resolution satellite images using Hough Transforms and edge detection to estimate rooftop area and optimal solar panel placement.
Used LSTM neural networks to predict cost savings based on historical electricity consumption and photovoltaic potential data.
Applied Python libraries such as scikit-learn, pvlib, numpy, and geopy for model development and geospatial calculations.
Datasets sourced from Global Solar Atlas and Kaggle; future plans include integrating Mapflow with QGIS for advanced segmentation of rooftops, vegetation, and elevation.

Integrated IoT and Chatbot Solution for Stress Monitoring and Mental Health Support

Developed a low-cost, privacy-conscious system combining IoT sensors and a chatbot to monitor stress and provide mental health support.

Used ESP32, heartbeat, temperature, and GSR sensors to collect physiological signals and classify stress levels using KNN and Random Forest.
Designed a chatbot using NLP techniques (NLTK, Keras) to simulate supportive human interaction and suggest appropriate coping actions.
Employed datasets like WESAD, Stress-lysis, and Mental Health Conversational Data from Kaggle for model training and validation.
Focused on ethical AI with data privacy, real-time moderation, and emergency alert triggers for high-risk users.

Timeline