Swapnil Kapse

Eager to embark on a career journey in the dynamic field of Information Technology, a strong foundation in Python was built during academic pursuits. Hands-on experience was gained through internships and personal projects. The passion for technology, coupled with the ability to quickly adapt and learn, drives enthusiasm to contribute to innovative IT solutions. An opportunity is sought to leverage skills and collaborate with a forward-thinking team to make a positive impact in the IT corporate world.

MTA: Introduction to Programming Using Python - Certified 2021

Infant Cry Analysis for Mood Detection:

Mentor: Dr. Lalit Kulkarni | Team Size: 4

Key Skills: Python, Audio Processing, Deep Learning

• Infant cry analysis for mood detection is a process that involves extracting and analyzing acoustic features from infant cries to determine the underlying emotional state.

• By utilizing techniques such as signal processing and deep learning, it aims to classify the cries into different mood categories such as hungry, discomfort, tired, belly pain and burping.

• It relies on features such as pitch, intensity, duration, and spectral characteristics to capture the distinct patterns associated with different moods.

• I worked on the database connection, audio processing and deep learning models for this project.

Heart Disease Detection:

Team Size: 1

Key Skills: Machine Learning Data Science Python Data Visualization

• Likeliness of Heart Disease in a human being using Machine Learning by gathering 13 different readings for a person.

• Visualization was done by using Box Plot, Count Plot, Pair Plot, Heat Map, Linear Plotting.

• Best accuracy of 0.89 was achieved by K-Nearest Neighbor model

Automated Attendance System:

Mentor: Anjali Shejul | Team Size: 4

Key Skills: Deep Learning Face Recognition Python Database MongoDB

• Automated Attendance System is being done by face recognition. 

• Entry and exit point for a student will be recorded.

• Total time of a student for which student is present in the class will be calculated according to the entry and exit point recorded.

• If a student total present time does not meet the requirement set by the organization or teacher he/she will be marked absent.

• Visualization was done by using Box Plot, Count Plot, Pair Plot, Heat Map, Linear Plotting.

• Best accuracy of 0.89 was achieved by K-Nearest Neighbor model.