Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Dat8 | 1,549 | 8 months ago | Jupyter Notebook | |||||||
General Assembly's 2015 Data Science course in Washington, DC | ||||||||||
Data Science Toolkit | 185 | a year ago | 1 | HTML | ||||||
Collection of stats, modeling, and data science tools in Python and R. | ||||||||||
Ds_and_ml_projects | 110 | 9 months ago | 10 | Jupyter Notebook | ||||||
Data Science & Machine Learning projects and tutorials in python from beginner to advanced level. | ||||||||||
Aiml Projects | 44 | 2 years ago | mit | Jupyter Notebook | ||||||
Projects I completed as a part of Great Learning's PGP - Artificial Intelligence and Machine Learning | ||||||||||
Srqm | 39 | 4 years ago | 10 | TeX | ||||||
An introductory statistics course for social scientists, using Stata | ||||||||||
Data Science Portfolio | 10 | 3 years ago | Jupyter Notebook | |||||||
Predicting Baseball Statistics | 7 | 2 years ago | Jupyter Notebook | |||||||
Predicting Baseball Statistics: Classification and Regression Applications in Python Using scikit-learn | ||||||||||
Machine Learning | 4 | 3 years ago | mit | Jupyter Notebook | ||||||
Machine Learning codes | ||||||||||
Pgdds Iiit Bangalore | 4 | 5 years ago | 1 | R | ||||||
A set of projects I worked on as part of my PG Diploma in Data Science Program | ||||||||||
Bank Marketing Analysis | 4 | 4 years ago | mit | Jupyter Notebook | ||||||
The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit. |
Here I have explored, analysed and fit an interpretable prediction Logistic Regression Model to explain the factors that contribute the most to Alzheimer's Disease.
I have mainly focused on building the Logistic Regression Model, and have done the EDA mostly to see how the other factors measured for each individual interprets to a person having AD or not. The Model I came up with is really effective and explains the class predictions quite well.
RUN: The Finished Report can be seen in Data-Analysis.html
(To be downloaded and opened in own browser, gives better experience), but one can view it right on Github through the Data-Analysis.md
file. The supporting code that generated the file can be seen in Data-Analysis.Rmd
(RMarkdown File). The Dataset used for this analysis was obtained from ADNI.