cojagtech@gmail.com +91 74107 47036

Data Science Projects

Our Projects

Cojag work closely with their customers and help in comprehending their needs and requirements. Then the plan is developed and finally implemented by the team with full expertise to provide the best solution.

Transfer Learning for Brain Tumor Detection

This project focuses on brain tumor detection using multiple transfer learning models, such as VGG16, VGG19, DenseNet121, EfficientNetB7, and others. It explores the impact of using pre-trained weights (ImageNet) versus training from scratch (None) to compare model performance.

Optimizing Skin Cancer Detection Using Transfer Learning and Image Augmentation

This project aims to detect skin cancer using various transfer learning models, including VGG16, VGG19, DenseNet121, and EfficientNetB7. It compares the effectiveness of pre-trained

Efficient Object Detection with YOLOv7

This program enables object detection using the YOLOv7 model, supporting various input sources such as images, videos, and live feeds. It processes data to identify and filter objects based on confidence and intersection-over-union thresholds.

Fraud Detection with Imbalanced Data in Money Laundering Dataset

This project explores methods to handle imbalanced data using a money laundering dataset. The issue is that there are many more normal transactions compared to fraudulent ones.

Network Intrusion Detection: Model Training and Evaluation

The network intrusion detection code typically involves loading and preprocessing the dataset (e.g., NSL-KDD), encoding categorical features, and normalizing numerical ones.

Food Disease Detection via Hyperspectral Imaging and Deep Learni

The Apple disease dataset is converted into hyperspectral images by generating synthetic spectral bands based on RGB values. These images are used to train deep learning models like AlexNet,

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Plot number 45, Fulmati Layout, BabulKheda, Badil Kheda, near Ramteke Nagar Hanuman Mandir, Nagpur, Maharashtra 440027

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