davelina2 Goto Github PK

aipnd-project

Project code for AI Programming with Python ND Program

babynames

An R package containing US baby names from the SSA

comtrade-plot

Shiny App for Visualizing UN Comtrade Shipping Data

davelina

davelina2

Config files for my GitHub profile.

equipmentmaintenance

Simple work with exploratory data analysis and machine learning modelling trying to predict failures in an industrial equipment given its signals of temperature, pressure and vibration.

glm_https-github.com-idataist-generalized-linear-models-in-python.githttps-github.com-idataist-g

lstm_rnn_tutorials_with_demo

LSTM-RNN Tutorial with LSTM and RNN Tutorial with Demo with Demo Projects such as Stock/Bitcoin Time Series Prediction, Sentiment Analysis, Music Generation using Keras-Tensorflow

machine-learning

Content for Udacity's Machine Learning curriculum

machine-learning-project

The project focuses on identifying the speaker accent to be US or not US using binary classification. This project uses various Machine Learning classification methods like Logistic Regression, KNN, Binary Tree and Random Forests. Using the listed methods, evaluated the performance on the baseline models. To increase the accuracy and to prevent the dataset to be over-fitted or under-fitted various feature extraction and regularization techniques like Lasso and Ridge are used in this project. To increase the testing accuracy, fine-tuned the hyperparameters for the classification models.

oil-spill-sar

Oil Spill Detection in Ecuadorian Exclusive Economic Zone

pdsnd_github

GitHub project (Project 3) repository for PDSND

plotly-shiny

Templates for embedding interactive plotly graphs in Shiny apps

practical-statistics-for-data-scientists

Code repository for O'Reilly book

project-bankruptcy-prediction

Capstone Project: Binary Classification with XGBoost to predict the future bankruptcy status of Polish companies. Supervised Machine Learning.

shiny-bioinformatics

http://tinyurl.com/cruk-shiny

suicide-rate-analysis_data-visualization

Business magazine-style report on World Suicide Rate Analysis. Andy Kirk's The Three Principles of Good Visualization Design was followed to create the report. Tools used: RStudio, Tableau and Canva Graphs plotted: 1) Map Chart is used to show the amount of suicide in each country 2) A horizontal bar graph is used to further compare the differences in suicide counts in each country 3) A single line graph is used to show the amount of suicides committed each year during the period 1985-2016 4) Stacked 100% Area graph is used to compare the suicide counts among different age groups, namely 5-14 years,15-24 years, 25-34 years, 35-54 years, 55-74 years 5) Side by side bar chart is used to compare the number of suicide counts among different age groups, sex-wise 6) A pie chart is used to see the composition of causes of deaths in the US in the year 2017 7) Bubble Chart is used to check out the methods by which people commit suicide and to check which one causes the maximum death 8) A line graph is used to compare the Suicide count and happiness index for the years 2006 to 2015 9) The correlation matrix is used to find the correlation between different elements. 10) Side by side line graph is used to compare gender wise suicide percentage in the US 11) Treemaps are used to see the composition of the number of suicides among the states of US 12) Side by side area graph is used to see the availability of different drugs in the US over time