Pinned Repositories
508-Assignment-
CIS 508 Assignments and Notebooks
A-Visual-History-of-Nobel-Prize-Winners
Exploratory Data Analysis on Nobel Prize Data
Analyzing-TV-Data
Analyzing Super Bowl TV data , for halftime performances, viewer count,. - Datacamp Guided Project
Dr.-Semmelweis-and-the-Discovery-of-Handwashing
In this notebook, we're going to reanalyze the data that made Semmelweis discover the importance of handwashing. Let's start by looking at the data that made Semmelweis realize that something was wrong with the procedures at Vienna General Hospital.
Exploratory-Data-Analysis-on-Netflix-Data
A DataCamp Guided Project, exploratory analysis to find out if the movie duration is declining over time.
Exploring-the-Bitcoin-Cryptocurrency-Market
Since the launch of Bitcoin in 2008, hundreds of similar projects based on the blockchain technology have emerged. We call these cryptocurrencies (also coins or cryptos in the Internet slang). Some are extremely valuable nowadays, and others may have the potential to become extremely valuable in the future1. In fact, on the 6th of December of 2017, Bitcoin has a market capitalization above $200 billion
Hotel-Booking-Cancellation-Prediction
Machine-Learnig-With-PySpark-Content
all things ML with Pyspark
Mini-Project-User-Based-Recommendation-System
1 Mini-Project: User-Based Recommendation System Let’s say we have 6 users, and they have rated 8 different movies on a scale of 1 to 10. Note that not all users have rated all movies. UserMovieRatings = { 'Amy': {'Family Plot':10, 'Rebecca':5, 'Spellbound':9, 'Star Trek':6}, 'Bill': {'Apocalypto':8, 'Braveheart':3, 'Rebecca':10, 'Spellbound':5, 'Star Trek':7}, 'Cathy': {'Spaceballs':7, 'The Ice Storm':4, 'Family Plot':5, 'Rebecca':9, 'Spellbound':1}, 'Dave': {'Braveheart':5, 'Rebecca':7, 'Spellbound':4}, 'Ernie': {'Apocalypto':3, 'Braveheart':8, 'Rebecca':1, 'Star Trek':7}, 'Fiona': {'The Ice Storm':3, 'Family Plot':10, 'Rebecca':6, 'Spellbound':10}} You can build a simple User-Based Recommendation System as follows: • Let’s say you want to make recommendations for UserX • Given a UserX, you can find the most similar user or the “nearest neighbor” of UserX by calculating the manhattan distance between UserX and every other user (not including UserX). • The person with the smallest manhattan distance is considered the most similar user. Let’s call this person UserXNN. • You can now find recommendations for UserX by considering all the movies that UserXNN has rated but that UserX has not.
XGBoost-Repo
SampleXGBoost
waghmareps12's Repositories
waghmareps12/Hotel-Booking-Cancellation-Prediction
waghmareps12/Machine-Learnig-With-PySpark-Content
all things ML with Pyspark
waghmareps12/Mini-Project-User-Based-Recommendation-System
1 Mini-Project: User-Based Recommendation System Let’s say we have 6 users, and they have rated 8 different movies on a scale of 1 to 10. Note that not all users have rated all movies. UserMovieRatings = { 'Amy': {'Family Plot':10, 'Rebecca':5, 'Spellbound':9, 'Star Trek':6}, 'Bill': {'Apocalypto':8, 'Braveheart':3, 'Rebecca':10, 'Spellbound':5, 'Star Trek':7}, 'Cathy': {'Spaceballs':7, 'The Ice Storm':4, 'Family Plot':5, 'Rebecca':9, 'Spellbound':1}, 'Dave': {'Braveheart':5, 'Rebecca':7, 'Spellbound':4}, 'Ernie': {'Apocalypto':3, 'Braveheart':8, 'Rebecca':1, 'Star Trek':7}, 'Fiona': {'The Ice Storm':3, 'Family Plot':10, 'Rebecca':6, 'Spellbound':10}} You can build a simple User-Based Recommendation System as follows: • Let’s say you want to make recommendations for UserX • Given a UserX, you can find the most similar user or the “nearest neighbor” of UserX by calculating the manhattan distance between UserX and every other user (not including UserX). • The person with the smallest manhattan distance is considered the most similar user. Let’s call this person UserXNN. • You can now find recommendations for UserX by considering all the movies that UserXNN has rated but that UserX has not.
waghmareps12/XGBoost-Repo
SampleXGBoost
waghmareps12/508-Assignment-
CIS 508 Assignments and Notebooks
waghmareps12/A-Visual-History-of-Nobel-Prize-Winners
Exploratory Data Analysis on Nobel Prize Data
waghmareps12/Analyzing-TV-Data
Analyzing Super Bowl TV data , for halftime performances, viewer count,. - Datacamp Guided Project
waghmareps12/Dr.-Semmelweis-and-the-Discovery-of-Handwashing
In this notebook, we're going to reanalyze the data that made Semmelweis discover the importance of handwashing. Let's start by looking at the data that made Semmelweis realize that something was wrong with the procedures at Vienna General Hospital.
waghmareps12/Exploratory-Data-Analysis-on-Netflix-Data
A DataCamp Guided Project, exploratory analysis to find out if the movie duration is declining over time.
waghmareps12/Exploring-the-Bitcoin-Cryptocurrency-Market
Since the launch of Bitcoin in 2008, hundreds of similar projects based on the blockchain technology have emerged. We call these cryptocurrencies (also coins or cryptos in the Internet slang). Some are extremely valuable nowadays, and others may have the potential to become extremely valuable in the future1. In fact, on the 6th of December of 2017, Bitcoin has a market capitalization above $200 billion
waghmareps12/github-slideshow
A robot powered training repository :robot:
waghmareps12/my_portfolio
waghmareps12/Predicting-Credit-Card-Approvals
Commercial banks receive a lot of applications for credit cards. Many of them get rejected for many reasons, like high loan balances, low income levels, or too many inquiries on an individual's credit report, for example. Manually analyzing these applications is mundane, error-prone, and time-consuming (and time is money!). Luckily, this task can be automated with the power of machine learning and pretty much every commercial bank does so nowadays. In this notebook, we will build an automatic credit card approval predictor using machine learning techniques, just like the real banks do.
waghmareps12/Prediction-using-Decision-Tree
Prediction using Decision Tree Classifier on Iris Dataset
waghmareps12/Prediction-using-Supervised-ML-Python
Linear Regression TSF Data Science Internship Task
waghmareps12/Prediction-Using-UNSupervised-ML-Python
KMeans Clustering on IRIS Dataset
waghmareps12/The-Android-App-Market-on-Google-Play
DATACAMP GUIDED PROJECT ON ANDROID APP STODE
waghmareps12/The-GitHub-History-of-the-Scala-Language
1. Scala's real-world project repository data¶ With almost 30k commits and a history spanning over ten years, Scala is a mature programming language. It is a general-purpose programming language that has recently become another prominent language for data scientists. Scala is also an open source project. Open source projects have the advantage that their entire development histories -- who made changes, what was changed, code reviews, etc. -- are publicly available. We're going to read in, clean up, and visualize the real world project repository of Scala that spans data from a version control system (Git) as well as a project hosting site (GitHub). We will find out who has had the most influence on its development and who are the experts.