Gabriela Gutiérrez Olguín

Experienced Project Planner and Data Analyst with 4.5 years of expertise in planning, scheduling, and coordinating cross-functional initiatives. Proven track record in managing data-driven projects, optimizing resource allocation, and improving operational workflows for multinational teams. Skilled in prioritizing objectives, identifying risks, and utilizing Excel and Power BI tools for efficiency. Proficient in Agile methodologies, committed to ongoing improvement and fostering collaboration for project success.

• Complete Python Developer in 2024: Zero to Mastery (ZTM)
• Complete Python for Business Data Analytics & Intelligence (ZTM)
• Coursing 100 Days of Coding in Python (Udemy)
• Coursing the Diploma in AI and Innovation by Founderz and Microsoft
• Complete SQL and Database Bootcamp
• 15 Days of SQL: The Complete SQL Masterclass

• Sentiment Analysis - Text Classification Model, Developed a machine learning model to predict sentiment (positive, negative, neutral) from user reviews using natural language processing (NLP). Implemented various models including Logistic Regression, Random Forest, and Naive Bayes, evaluating their performance using accuracy, precision, and recall metrics. Utilized Python (pandas, scikit-learn, NLTK) for data preprocessing, feature extraction, and model development.
• Diabetes Prediction Model, Built a predictive model using a diabetes dataset to predict whether a patient has diabetes based on diagnostic features. Employed machine learning algorithms like Logistic Regression and Decision Trees, optimizing the models through hyperparameter tuning and cross-validation. Achieved a high accuracy rate and visualized model performance using confusion matrices and ROC curves.
• Pneumonia Image Classification, Developed an image classification model to identify pneumonia in chest X-ray images using convolutional neural networks (CNNs). Used TensorFlow/Keras for model creation, training, and evaluation. Achieved impressive classification accuracy with optimized architecture and data augmentation. Gained experience with transfer learning techniques using pre-trained models like ResNet and VGG.