Hi, my name is Abdullah KAVAKLI
I'm a Data Scientist.

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I am a data scientist and have knowledge in DevOps and machine learning. Skilled in implementing Linux projects on the cloud, feature engineering, and various machine learning algorithms. Developed successful machine learning models with high accuracy scores. Experienced in NLP techniques for sentiment analysis. Optimized algorithms for faster processing and am passionate about using data science to solve complex problems and help businesses make informed decisions.

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Projects

Credit Card Fraud Detection using Machine Learning

  • Developed a machine learning model to detect fraudulent credit card transactions using Python programming language and Scikit-learn library
  • Achieved a ROC-AUC score of 98% in detecting fraudulent transactions, outperforming traditional rule-based fraud detection systems.
  • Cleaned and preprocessed a real-world dataset containing credit card transactions to identify patterns in the data.
  • Utilized various feature engineering techniques such as scaling, oversampling, undersampling and power transform to optimize the model's accuracy and performance.
  • Trained and fine-tuned multiple machine learning algorithms such as Logistic Regression, Decision Tree, Random Forest, and XGBoost to identify the best model for the dataset.

Source Code
Credit Card

Brain Tumor Classification with CNN

  • Developed a deep learning model using Convolutional Neural Networks (CNNs) to classify brain tumors from MRI images
  • Implemented the model using Python and TensorFlow, achieving a ROC-AUC score of 95% on the test set.
  • Used transfer learning by fine-tuning a pre-trained InceptionResNetV2 model, which significantly reduced the training time and improved the accuracy of the model.

Source Code
MRI

Restaurant Reviews Classification

  • Utilized NLP techniques to preprocess and clean a dataset of restaurant reviews.
  • Engineered features from the text data to represent sentiment and subjectivity.
  • Trained and tested various ML algorithms, including Random Forest, Decision Tree, and Naive Bayes, to predict the sentiment of reviews.
  • Achieved a high ROC AUC score of 93% on the test set

Source Code
Restaurant

Accelerating the PrefixSpan algorithm using GPU

  • Developed and implemented a GPU-accelerated version of the PrefixSpan algorithm, a sequential pattern mining algorithm.
  • Achieved a 75% performance improvement by using the Numba and Numpy libraries.

Cloud Computing

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