I have successfully completed a 16-week and 8 end-to-end, applied data science projects of the Applied Data Science Lab module at WorldQuant University. The mini-projects included scientific computing, data wrangling, machine learning and natural language processing with Python.
The project highlights:
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HOUSING IN MEXICO: A dataset of 21,000 properties was used to determine if real estate prices are influenced more by property size or location. Imported and cleaned data from a CSV file, built data visualizations, and examined the relationship between two variables using correlation.
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APARTMENT SALES IN BUENOS AIRES: Built a linear regression model to predict apartment prices in Argentina. Created a data pipeline to impute missing values and encode categorical features, and then improved the model performance by reducing overfitting.
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AIR QUALITY IN NAIROBI: Built an ARMA time-series model to predict particulate matter levels in Kenya, extracted data from a MongoDB database using pymongo, and improved model performance through hyperparameter tuning.
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EARTHQUAKE DAMAGE IN NEPAL: Built logistic regression and decision tree models to predict earthquake damage to buildings, extracted data from a SQLite database, and revealed the biases in the data that can lead to discrimination.
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BANKRUPTCY IN POLAND: Built random forest and gradient boosting models to predict whether a company will go bankrupt, navigated the Linux command line, addressed imbalanced data through resampling, and considered the impact of performance metrics precision and recall.
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CUSTOMER SEGMENTATION IN THE US: Built a k-means model to cluster US consumers into groups, used principal component analysis (PCA) for data visualization, and then created an interactive dashboard with Plotly Dash.
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A/B TESTING AT WORLDQUANT UNIVERSITY: Conducted a chi-square test to determine if sending an email can increase program enrollment at WQU, built custom Python classes to implement an ETL process, and created an interactive data application following a three-tiered design pattern.
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VOLATILITY FORECASTING IN INDIA: Created a GARCH time series model to predict asset volatility, acquired stock data through an API, cleaned and stored it in a SQLite database, and then built an API to serve model predictions.
DUE TO COPYRIGHT ISSUES THE CODE CONTENT OF THE PROJECTS WON’T BE UPLOADED!!