Data Science Bootcamp
CurrentProject 4: using ensemble techniques to build a model to analyze data and figure out key areas to help the growth of a bike share company- Used and tested two different ensemble techniques (Bagging and Boosting) through decision tree, random forest, AdaBoost, Gradient boosting, and XGBoost to figure out the key features that impact bike rentalsProject 3: build linear regression model to predict the price of used phone/tablet and identify influential factors- Preprocessed data… Show more Project 4: using ensemble techniques to build a model to analyze data and figure out key areas to help the growth of a bike share company- Used and tested two different ensemble techniques (Bagging and Boosting) through decision tree, random forest, AdaBoost, Gradient boosting, and XGBoost to figure out the key features that impact bike rentalsProject 3: build linear regression model to predict the price of used phone/tablet and identify influential factors- Preprocessed data into a clean and useable format by managing missing values, eliminating duplicates, performing feature engineering, merging datasets, and splitting data into training and testing sets to evaluate the performance of a machine learning model - Tested for Linear Regression Assumptions: no multicollinearity, linearity of variables, independence of error terms, normality of error terms and no heteroscedasticityProject 2: determine the effectiveness at gathering new subscribers on old vs new landing page - Identified the appropriate statistical tests for each question and performed the tests using SciPyProject 1: analyze data for food delivery company to improve business - Conducted exploratory data analysis and used descriptive statistics to understand data structure, assess data quality, and visualize the data - Performed visual analysis using Seaborn and Matplotlib for univariate and bivariate analysis Presented findings to stakeholder and helped tailor marking efforts Show less