This RMarkdown is created to increase our knowledge and experience as we learn by building a good time series model to forecast number of tourist arrived to Indonesia before year 2018.

This RMarkdown is created to increase our knowledge and experience as we learn by building a good model that can predict and classify each student tech profile based on the number of hours studying, number and type of courses taken, and average score per type of courses taken during the education via online platform called Tortuga Code.

This RMarkdown is created to increase our knowledge and experience as we learn by building a good model that can classify a room occupancy by its temperature, relative humidity, light, carbon dioxide (CO2), and humidity ratio (derived quantity from temperature and relative humidity).

We will use a dataset consists of socio-economic and health factors from various countries to determine the overall development of those countries and categorized those countries using K-means clustering. The reason we want to categorize the overall development of those countries is to identify which country that in needs for help from HELP International organization.

By applying CNN to the Brain Tumor MRI data, we can classify the MRI image result into 4 classes: glioma tumor, meningioma tumor, no tumor, and pituitary tumor.

RMarkdown ini diperuntukkan untuk analisa model linear regresi yang dapat diterapkan pada data laporan kriminalitas di beberapa negara bagian di US pada tahun 1960. Model linear regresi yang dihasilkan diharapkan dapat menunjukkan pengaruh variabel-variabel kondisi sosial-ekonomi kepada target variabel tingkat kriminalitas atau crime_rate.

latihan menggunakan flexdashboard menggunakan data avocado US