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Researcher | Data Scientist | Mechanical engineer
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IoT-home
April 8, 2022 – July 7, 2024
Convert home devices to IoT decvices using Raspberrypi
View Projectengine-failure-detection
May 19, 2020 – September 30, 2022
This is an experimental prject to predict engine failure using deep learning networks. Initial work is hosted in heroku
View Projectchatbot
May 13, 2020 – December 8, 2022
A.I-Chatbot Mar 2020 – May 2020 Project descriptionBusiness Objective: An e-commerce company wants to build an algorithm to retrieve top 5 Question and answers based on the user given user input. Solution : The input given by the user is first cleaned and features extracted using 3 different algorithms. LSTM Deep Learning classifier will first identify the type of user question (Yes/No ,Open-ended , etc.) The second algorithm Gensim model will identify the 'Product' from the user user input , and the final K-means clustering algorithm will find the cluster in which the user input will fall . This three predictions are embedded to the user input and fed into Cosine similarity algorithm to find similar questions from the Tfidf vector space. See the deployed model in heroku http://aiquestionsearch.herokuapp.com/
View ProjectAIOps-incident-impact-prediction
February 25, 2020 – February 25, 2020
The dataset is having incidents raised by customers.Which contains an event log of an incident management process extracted from a service desk platform of an IT company.Using this log data,I created a model that will predict and classify the impact as High Medium and Low. Model was built using Uber's Ludwig framework
View Projectnlp-classisfication
February 13, 2020 – December 8, 2022
Text classification using Deep learning
View Projectairo
December 11, 2019 – September 29, 2020
A.I is used to optimize the conventional full-time sensor monitored zigzag motion of the line follower robot by training it in the same route. Here the path of the line follower is convoluted to find the anomalies and then these instances of anomalies (timeframe points) is used for creating a reward function.Then by using reinforcement learning , sensor detection timings , motion control parameters ( frequency, magnitude of each motor current) etc are optimized for fast and accurate movement
View ProjectCultural Fit Analysis
The candidate's project portfolio shows a strong personal drive and curiosity in AI/ML and IoT, which aligns with an innovative and learning-oriented culture. The diversity of personal projects (NLP, predictive maintenance, AIOps, robotics, IoT) indicates a broad interest in applying data science across different domains. However, without professional experience or team-based projects, it's difficult to assess collaboration and adaptability within a team environment.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's project descriptions indicate a proactive, self-driven approach to learning and applying new technologies, which could suggest good problem-solving and initiative. However, there is no information on teamwork, communication in a professional setting, or stress handling.