Shimaali Riyasudeen
- Bangalore, Karnataka, India
- shimariyas13@gmail.com
- +91 73974-11738
CEG-ian with a passion for data science seeking internship or full-term employment opportunities in the field. Excels in analyzing and interpreting data to create meaningful insights and solve complex problems.
Experience
QA Engineer
The QA Engineer has extensive experience in creating and maintaining test plans, and specifications, and executing test cases for an E-commerce platform. They have a strong focus on identifying defects and ensuring product quality for customers. Their expertise also includes performing integration and system testing for web-based applications.
Data Scientist - Intern
Trained in Data analysis, machine learning, Python, and R to develop models and conduct experiments.
Projects
Classification and Localization of Fetal Brain Abnormalities using U-NET Model.
- python
- deep learning
- neural networks
- convolutional neural network
- machine learning algorithms
- Implementation of Fetal Brain Tumour Detection
- Implemented color channels using the u-net model (Semantic Segmentation)
- To increase the accuracy and decrease the diagnosing time
- Classified the Fetal brain and detected abnormalities
Built an ML model to predict the result of IPL matches using past recorded data
- python
- machine learning algorithms
- decision tree
- random forest
- logistic regression
- feature extraction
- k-fold cross validation
- sklearn packages
- Implemented Sklearn packages and Machine Learning Algorithms
- Choose the best algorithm for the prediction
- Accuracy and Cross-Validation score among the three algorithms to find out the best that fits the cricket data
Publications
Effective Machine Learning Based Classification Algorithm for Medical Dataset
To find the Suitable Machine Learning Algorithm for medical datasets among the 7 Machine learning Algorithms Naive Bayes, Random Forest, Support Vector Machine, Decision Tree, KNN, KSVM.
Human Face Recognition for Attendance System
Implemented Open CV algorithm to recognize image processing for the Human Face recognition for the attendance system