MURTHAZA SHAIK

Highly-motivated employee with desire to take on new challenges. Strong work ethic, adaptability, and exceptional interpersonal skills. Adept at working effectively unsupervised and quickly mastering new skills.

Semantic Segmentation Project – Cable and Plug Detection

Personal Project | Python, PyTorch, DeepLabV3, CVAT

• Built a semantic segmentation model using DeepLabV3 in PyTorch to detect and classify cables and plugs in images.
• Annotated images using CVAT (Computer Vision Annotation Tool) to generate pixel-wise segmentation masks.
• Implemented custom data loaders and preprocessing pipelines to handle the annotated dataset and grayscale/RGB inputs.
• Trained the model and fine-tuned hyperparameters to improve segmentation performance and accuracy.
• Visualized predictions by overlaying segmentation masks on original images to evaluate model quality.
• Enabled accurate detection and localization of cables and plugs, with potential applications in safety inspection and automation tasks.

Personal Project | Python, Scikit-learn, Pandas, Matplotlib

• Developed a regression model to predict house prices based on features like location, size, number of rooms, and other property attributes.
• Preprocessed data using Pandas, handling missing values, encoding categorical variables, and normalizing numerical features.
• Trained multiple supervised learning models including Linear Regression, Decision Tree Regressor, and Random Forest Regressor.
• Tuned hyperparameters using GridSearchCV to improve model accuracy and reduce overfitting.
• Evaluated model performance using Mean Absolute Error (MAE), Mean Squared Error (MSE), and R² score.
• Visualized data distributions, feature importance, and prediction results using Matplotlib and Seaborn.
• Achieved an R² score of [insert your score, e.g., 0.87] on test data, demonstrating reliable performance in price estimation.