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Earlier Project links are on the Projects Sub-menu under "DataScience Projects - set2"
Journey of Analytics
|Project Title||Project Tutorial Description||Download Link|
|Automated email for Google Trends||Pull Google Trends search data, analyze and then send an email with results. ||Download code|
|Report automation||Automated emails with R. Connect to database, perform calculations, build stunning viz and send as email attachments. ||Download code|
Analyzing data from Fitness trackers. (Fitbit).
Heart rate, sleep quality, calories burned, etc. Tutorial Blog post .
Using 5 machine learning algorithms to predict birth weight of
newborn infants using predictors such as gestation, mother's ethnicity, single or twins/multiples, time when prenatal care started, etc.
Comparison of accuracy and sensitivity from all 5 models. Explanation blog .
|Machine learning models|
|Ames Housing Sale Prices||Kaggle starter script. Predict house SalePrices using 5 different machine learning algorithms - linear regression, classification trees, neural network, randomforest model, generalized linear model (GLM). Tutorial bloglink.||Ames Housing|
|Parallel Programming||Parallel Programming in R, including sql queries to retrieve data from database tables. Blogpost link.||Code files|
|Dashboards with R|
R Dashboard using:
a) storyboard layout, (b) simple layout with ShinyR elements. Blogpost link.
1) Monte Carlo Simulations in R (blogpost)
2) US weather analysis using K-means clustering.
Rent analysis using Zillow data to investigate most expensive US cities and the price trends over time.
Latitude-longitude data for US cities is given separately.