My Projects
Virtual Book Library
Technologies Used : Python, Sqlite
Libraries : CustomTkinter, TKinter, OpenCV, Pillow, Pandas, Pandastable, PyGame
Face Mask Detection
Technologies Used : Python, Deep Learning
Libraries : Numpy, OpenCV, Pillow, Sklearn, Keras, Facenet-Pytorch
Build deep learning models to detect whether a person is wearing a face mask or not. The models are built using Keras, creating a custom CNN model and leveraging various pre-trained architectures such as MobileNet, VGG16, and ResNet50. Each model has been customized and fine-tuned for binary classification (Mask/No Mask).
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Face Analysis App
Technologies Used : Python, Deep Learning
Libraries : Streamlit, OpenCV, Pillow, NumPy, MTCNN, DeepFace,Plotly, Streamlit
A Streamlit-based application for performing various face-related tasks such as detection, extraction, verification, recognition, and analysis using MTCNN and DeepFace.
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Sentiment Analysis on Twitter Data
Technologies Used : Python, Deep Learning, RoBERTa, Kafka
Libraries : Pandas, Matplotlib, NLTK, Deep Translator, Streamlit
Analyzing and interpreting the sentiments expressed in the tweets to gain insights into public opinions, emotions, and trends.
Leveraging machine learning and natural language processing techniques to perform sentiment analysis on Twitter Data.
Implemented web scrapping using Python to collect the data from Twitter.
Utilized a distributed event streaming platform Kafka to handle the real-time data ingestion and processing efficiently.
Predicting Student Percentage of Marks Based on Study Hours
Technologies Used : Python, Machine Learning
Libraries : NumPy, Pandas, Matplotlib, Linear Regression
Implemented a Student Score Prediction model employing Python and the Scikit-Learn machine-learning module.
Using the Supervised Learning technique, the model was trained on a dataset encompassing study hours and
corresponding scores.
Enabling accurate prediction and aiding educators in understanding the potential academic outcomes for students
based on their study habits.
Analyzing Spotify and YouTube Songs Using Python & MySQL
Technologies Used : Python, MySQL
Libraries : NumPy, Pandas
Utilized Python to ensure data cleanliness for a more effective analysis process.
Crafted SQL queries to derive insights from the dataset, focusing on artist information, album categorization, and platform usage patterns.
Further concluded the artist to compile a professional report with the higher popularity and identified which of their songs achieved the most streams on each platform.
Predicting the Optimum Number of Cluster in the "Iris DataSet" and Representing it Visually
Technologies Used : Python, Machine Learning
Libraries : NumPy, Pandas, Matplotlib, Linear Regression
Implemented a Clustering model employing Python and the Scikit-Learn machine-learning module.
Using the Unsupervised Learning technique, the model was trained on a dataset encompassing the features of iris flowers of different species.
Predicting the optimum number of cluster to group the flowers of same species based on their features.
MCQ EXAM APPLICATION
Technologies Used : Python
Libraries : TKinter
Developed an MCQ (Multiple-Choice Question) Exam Application using Python and the Tkinter Module to
facilitate an efficient examination process in schools and colleges.
Implemented a user-friendly interface with Tkinter for easy navigation and a seamless user experience during
the examination.
Exploring India's COVID-19 Journey: A Case Study Analysis
Technologies Used : Python, MySQL
Libraries : NumPy, Pandas
Employed Python for data cleaning to enhance analysis efficiency.
Formulated necessary SQL queries to address specific problem statements.
Derived insights about the recovery rate of COVID-19, the recuperation of hospital beds, and statistics on testing
for COVID-19 cases.