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zaraprvz@gmail.comPHONE NUMBER AVAILABLESKILLSLANGUAGESPassionate and results-driven Junior MachineLearning Engineer with aproven track record indeveloping and deployinghigh-impact machinelearning models.Demonstrated ability toimprove business outcomesthrough data-driveninsights, effectivecommunication skills, andcollaboration with cross-functional teams.programming Languages :Python, R, SQLMachine Learning :Regression, Classification,Clustering, NeuralNetworks, NLPFrameworks & Libraries :TensorFlow, Keras, Scikit-learn, Pandas, NumPyData Visualization :Matplotlib, Seaborn,TableauData Preprocessing : DataCleaning, FeatureEngineering, Data Scaling,ImputationModel Evaluation : Cross-Validation, ROC Curve,Confusion Matrix, pAUCJupyter Notebook, Git,DockerFrench Advanced .Diploma in AdvancedFrenchFrench Advanced .Diploma in AdvancedFrenchJUNIOR MACHINE LEARNING ENGINEERzarapervezEXPERIENCEJUNIOR MACHINE LEARNING ENGINEERUniversity of Kansas.Reduced Downtime : Implemented a predictive maintenance system for manufacturing equipment, reducing unplanned downtime by 30%. Enhanced Model Performance : Conducted data preprocessing and feature engineering on time-series sensor data, resulting in a 25% improvement in model performance. Innovative Solutions : Developed and validated machine learning models using time-series analysis and anomaly detection techniques.JUNIOR DATA SCIENTIST INTERNRandTech Solutions.Developed and optimized machine learning models for predicting customer churn, achieving an AUC ROC score of 0.87, leading to a 10% reduction in churn rate. Collaborated with cross-functional teams to analyze customer feedback, resulting in a 20% improvement in product features. Created interactive dashboards using Tableau to present insights to stakeholders, resulting in a 20% increase in customer satisfaction scores. Conducted feature engineering and data preprocessing, improving model accuracy by 15%.DATA ANALYST INTERNSaint Anthony Hospital.Analyzed large datasets to identify trends and patterns, providing actionable insights that informed business strategies and resulted in a 15% increase in operational efficiency. Automated data cleaning processes using Python, reducing manual work by 50% and saving the company 100 hours of labor monthly. Assisted in the development of data visualization reports, enhancing data-driven decision-making processes.EDUCATIONBACHELOR OF SCIENCE IN COMPUTER SCIENCESPECIALIZATION : MACHINE LEARNINGCalifornia Institute of Technology Caltech.ASSOCIATE DEGREE IN COMPUTER SCIENCEOakton Community College.PROJECTSDiabetes Prediction Using Machine LearningHYPERLINK "https://github.com/zaraprvz/Diabetes-Prediction-Capstone-Project" House Price Predictionhttps://github.com/zaraprvz/Diabetes-Prediction-Capstone-Project Processing image data for deep learninghttps://github.com/zaraprvz/Diabetes-Prediction-Capstone-Project Objective: Developed a machine learning model to predict the likelihood of diabetes in patients using the Diabetes dataset.Exploratory Data Analysis (EDA): Conducted comprehensive EDA with visualizations and statistical analysis to identify patterns and correlations.Model Selection & Training: Experimented with algorithms including Logistic Regression, Decision Trees, Random Forest, and Gradient Boosting.Hyperparameter Tuning: Optimized model hyperparameters using Grid Search and Random Search.Model Evaluation: Assessed models using accuracy, precision, recall, F1-score, and AUC-ROC metrics.Results: Achieved a model accuracy of 85%, with significant improvements in precision and recall through feature engineering and hyperparameter tuning. Demonstrated the effectiveness of ensemble methods in improving predictive performance.Built a regression model to predict house prices based on features such as location, size, and amenities. Python, Scikit-Learn, Pandas, Matplotlib. Achieved a high degree of accuracy, assisting real estate agents in pricing decisions Enhanced image data preprocessing pipeline to improve model accuracy and robustness in various computer vision tasks.achieved [specific outcome, e.g., 95% accuracy] on the validation set, improving baseline performance by [percentage, e.g., 10%]. E-commerce Recommendation SystemBuilt a hybrid recommendation model combining collaborative filtering and content-based filtering, increasing average order value by 15%.