Analyzes various global datasets to determine the factors influencing the World Happiness Score.

Since the late 1970’s corporations have used machine learning recommendation systems to understand user selections, user trends and consumer demands. Machine learning algorithms in 2022 can now predict future interests, engagement, current taste, new product experimentation and more! I will use The University of Minnesota team lab (Grouplens) MovieLens 10m dataset for this project. Grouplens selected 72,000 users at random to rate at least 20 movies for a combined 10 million ratings (view Reference Section for more information). Goals: I will explore the Movielens 10M data set to conduct the following: I will clean the data, investigate any NAs and examine the outliers that may skew the data needed to achieve the RMSE goal of .86490. Create a series of visualizations and examine each chart to understand what steps we need to complete to reach our RMSE goal. Create a trial recommender model to understand the RMSE. Create a recommendation system based on the code from the reference section and utilize Loss Function (RMSE), User effects, and Regularization. Finalize the machine learning algorithm to achieve the RMSE goal.

In this project, I am exploring a heart disease dataset for a university hospital. The results from my analysis will be used to develop predictive models that assess an individual’s risk of heart disease.

this is the answer to the project2 of reproducible research course

Practicing from the book "An Introduction to Statistical Learning with Applications in R" by Gareth James • Daniela Witten • Trevor Hastie • Robert Tibshirani

Practicing from the book "An Introduction to Statistical Learning with Applications in R" by Gareth James • Daniela Witten • Trevor Hastie • Robert Tibshirani

Analysis of FitBit data provided by Kaggle.

Kathmandu- Dhulikhel