guevara-erik Goto Github PK

amazon-alexa-reviews-sentiment-analysis

Problem to solve: discover insights into consumer reviews and assist with machine learning models.You can also train your machine models for sentiment analysis and analyze customer reviews how many positive reviews ? and how many negative reviews ? Data Information: The dataset consists of 3000 Amazon customer reviews Rating Date Variation Verified Reviews Feedback

bubble-sorting-algorithm

Bubble Sort is the simplest sorting algorithm that works by repeatedly swapping the adjacent elements if they are in the wrong order

guevara-erik

I was part of an Applied Data Scientist at the Massachusetts Institute of Technology, with experience in machine learning modeling using python and data science stacks, including Numpy, Pandas, Scikit-Learn, and Tableau.

hello-aws-data-services

Sample code for AWS data service and ML courses on LinkedIn Learning

hiring-prediction-decision-tree

Problem to solve: Predict if a candidate would be hired based on specific characteristics; what are the most important features a candidate must have to have higher possibilities of getting the job?

home-loans-defaulted-customers-eda-machine-learning

Home Loans Defaulted Customers - Machine Learning, Prediction (Classification)

ipywidgets

Interactive Widgets for the Jupyter Notebook

restaurant-tips-exploratory-data-analysis

Problem to solve: Who is the group that gives a more generous tip? what are the hidden patterns in this restaurant dataset? What is the relationship between the gender, the group size, day of the week, if the customer is a smoker or not, or if the service is for lunch or dinner in relationship with the tip given by the customer?

russia-gdp-oil-linear-regression

Problem to solve: Determine what role oil plays in the Russia economy and predict outcomes in the years to come. Data: year (From 2004 to 2018), oilprice (The spot price of a barrel (159 liters) of benchmark crude oil in US dollars, gpd (Gross domestic product in billions of US dollars

study-score-correlation-univariate-linear-regression

Problem to solve: How long do you have to study to get a specific score. We can try to see a pattern in that data and predict a score based on how many hours the subject studies.

titanic-disaster-binary-classification

Problem to solve: On the Titanic disaster predict who would survive based on 4 features: sex, age, fare and class.

tv-radio-newspaper-ads-performance-multiple-linear-regression

Problem to solve: An exciting application of regression is to quantify the effect of advertisement on sales, considering the newspaper, TV, and radio as the advertisement channels.

widgets

wolrldwide-suicide-rates-bivariate-analysis

Problem to solve: find the patterns for increased suicide rates (1985 to 2016) among different cohorts globally, across the socioeconomic spectrum, using exploratory data analysis. Using bivariate analysis, I try to determine if there is any relationship between two variables.