The goal of this project is to predict the future stock price of Google using various predictive forecasting models and then analysing the various models. The dataset for Google stocks is obtained from Yahoo Finance using Quantmod package in R. The timeline of the data is from 2015 till present day(4/26/2020).

Problem Statement: In professional football, it is not uncommon for a player to drop out of the team due to contract transfer, contract expiration, or medical reasons. By the means of our analysis, we will be trying to find suitable replacements players based on their skill set, strength, and physical attributes. We will be doing that by using Clustering Analysis which is an unsupervised learning method. We will be building two different models using K-means clustering and Hierarchical clustering and in the end, compare both the models to see which one performs better.

our team has worked on the requirement of NYC Bike Service Inc. and collected their data and weather data from available sources. Since the company maintained its data on trip details in their database, so I was a structured data. Since we had to collect the weather data from the internet, and it was not maintained somewhere properly so we got it as semi structured data. Since the volume of the data was quite big, we used big data integration concepts to integrate the weather and bike-share datasets and then visualize the dependencies that bike tips had on weather, so that the managers of NYC Bike Service Inc. can decide what all can be changed or what more can be made to increase their business with weather changes.

The Data was collected from Kaggle. In this project, we present to you exploratory data analysis, visualizations, and modeling of New York Airbnb data. In this project, we also try to predict the factors that affect the pricing of the Airbnb around New York. This includes creating different kinds of models, model specification, transformation, variable selection and many more.

Authors - Sayak Chakraborty | Vipul Mayank

Anime Data Analysis in R MyAnimeList, often abbreviated as MAL, is an anime and manga social networking and social cataloging application website. The site provides its users with a list-like system to organize and score anime and manga. It facilitates finding users who share similar tastes and provides a large database of anime and manga. Anime without rankings or popularity scores were excluded. Producers, genre, and studio were converted from lists to tidy observations, so there will be repetitions of shows with multiple producers, genres, etc. Problem Statement: This development has been carried out to analyze the various factors that influence the popularity or rank of a particular anime. Implementation: The data was cleaned and shaped accordingly to carry out the analysis and infer the results. Authors: Sayak Chakraborty , Vipul Mayank