Yulimar Andrea Rivero Alvarez

P

Pima Indians Diabetes Analysis

• Conducted a comprehensive and meticulous exploratory data analysis (EDA) on datasets encompassing demographic details, health metrics, genetic markers, and lifestyle factors prevalent among the Pima Indian population. • Utilized advanced methodologies & visualization techniques to reveal correlations, trends & patterns within the data. This approach aimed to glean insights into the prevalence of diabetes, its associated risk factors & potential intervention strategies within the tribe.

N

Netflix Movie Recommendation

• Undertook exploratory data analysis (EDA) to scrutinize user ratings, movie genres, and viewing histories. Employed collaborative filtering techniques to discern correlations between users and suggest movies aligned with similar user preferences. • Executed recommendation algorithms, including collaborative filtering and matrix factorization, to produce personalized movie recommendations derived from user ratings and movie attributes.

H

Hotel Booking Cancellation Prediction

• Conducted meticulous and thorough exploratory data analysis (EDA) to unveil intricate patterns & correlations within the booking data. Employed a combination of statistical techniques and machine learning algorithms to pinpoint significant predictors of cancellations, including factors such as booking lead time, room type, and seasonal trends. • Expertly leveraged machine learning algorithms such as logistic regression, decision trees, and Support Vector Machines (SVM) to meticulously develop and validate the predictive model. Rigorously evaluated the model's performance using comprehensive metrics like accuracy, precision, recall, and F1-score to ensure its reliability and efficacy were thoroughly assessed.

M

Mansfield Forecasting Models

• Utilized rigorous time series analysis of historical sales data to discern trends, seasonality, and patterns. Employed sophisticated statistical methods, including decomposition techniques and moving averages, to analyze and visualize sales trends. • Deployed advanced time series forecasting techniques such as ARIMA models, exponential smoothing methods, and machine learning algorithms like Linear Regression and LSTM to construct the sales forecasting model, ensuring robust and accurate predictions.

U

Uncovering Causes and Solutions for the Superstore

• Conducted in-depth statistical analysis and utilized data visualization techniques to identify factors contributing to a high rate of returned orders at the Superstore, pinpointing correlations and potential root causes such as customer expectations and shipping methods. • Presented actionable solutions to mitigate return rates and enhance overall customer satisfaction, including proposals to improve product quality, streamline shipping processes for faster delivery, and implement proactive customer communication strategies to address potential issues and improve customer retention preemptively.

R

Restaurant Analysis

• Utilized advanced statistical methods and data visualization techniques to analyze datasets, uncovering key trends and patterns in customer preferences and revenue generation across various restaurant locations and cuisine types. • Formulated actionable recommendations to improve customer engagement and satisfaction by analyzing user behavior data, including average visits and customer feedback, and identifying opportunities for enhancement in service delivery and product offerings.

N

No Show to Appointments

• Conducted comprehensive exploratory data analysis (EDA) on historical appointment data and patient demographics, employing statistical methods and visualization techniques to identify patterns, correlations, and trends crucial for informed decision-making. • Implemented a variety of machine learning algorithms, including Naive Bayes, Logistic Regression, and Random Forests, to develop a prediction model for appointment scheduling. Evaluated model performance using a range of metrics such as sensitivity, specificity, precision, G-Mean, accuracy, and AUC to ensure reliability and effectiveness in predicting patient appointments.

Yulimar Andrea Rivero Alvarez

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