Humanizing
Data
Here are a few projects in the form of Jupyter notebooks to illustrate my Python coding antics of late.
(SQL & Tableau forthcoming)
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Since I discovered data science formally via MIT, I realized I had been using data in my work all along,
and I started to see (eat, sleep, & dream!) data in everything I do!
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Although I do enjoy predictive modeling and leveraging AI in analyses, my first love is data mining and engineering to craft business recommendations, combining technical discipline with interpersonal connection -- enhancing my clients' horizon of impact.
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Do feel free to click on the underlined titles below, to take a look at the notebooks of interest, jam-packed with code, data analysis and feature engineering, and conclusions or recommendations.
Food Delivery Aggregator
Skills: Exploratory Data Analysis & Visualization, Python, Numpy, Pandas, Matplotlib, Seaborn, Summary & Recommendations
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Predict Patients' Length-of-Stay upon Admission
Skills: EDA & Visualization, k-NN Imputation, Linear Regression Modeling, Statistical Analysis (Pearson Correlation Coefficient, RMSE, MAE, R2 & MAPE)
Determine Employee Risk of Attrition
Skills: EDA & Visualization, Logistic Regression Modeling, Decision Tree Classifier, Random Forest Classifier, K-NN, GridSearchCV Hyperparameter Tuning, model evaluations and comparison
Detect Patterns in Player Performances
Skills: EDA & Visualization, Cluster Analyses: PCA, K-Means, K-Mediods, Hierarchical, Gaussian Mixture Model, DBSCAN), Python, Numpy, Pandas, Matplotlib, Seaborn, Sklearn
Skills: Regression Models - Decision Tree Regressor, Bagging, Random Forest Regressor, AdaBoost, Gradient Boost, XGBoost, model evaluations and comparison
Identify Defaulter Profile & Credit Risk Features
Skills: EDA & Visualization, Feature Engineering, Logistic Regression Modeling, Decision Tree Classifier, Random Forest Classifier, Hyperparameter Tuning, model evaluations and comparison, recommendations for deployment
