In this assignment, I have tried to perform Exploratory data analysis on Green Taxi Pickup data to conclude price of Taxi fares in longer duration trip fares, number of passengers travelling together in one trip fare and which vendor will be available to provide service for shorter duration trip and longer duration trip respectively.

I have collected data from public accessible sources like

Loading Packages

Let us load the packages needed for visualization and exploratory analysis

In the 90s, little we could imagine that we would be accessing hundreds of thousands of songs one click away.

In that era, we have never imagined that the single app could recommend different songs based on our preferences and playlists.

Yes, this miracle is possible using recommendation systems. In a broad sense, a recommender (or recommendation) system (or engine) is a filtering system which aim is to predict a rating or preference a user would give to an item: a song, in our case.

There are several types of recommender systems, among which the more used ones are content-based…

What is Exploratory Data Analysis ??????

According to edadata source, EDA is so important for 3 reasons (at least) as stated below:

Recommender systems can be broadly divided into two categories :

  1. Collaborative based filtering approach.

Collaborative algorithm uses “User Behavior” for recommending items. They exploit behavior of other users and items in terms of transaction history, ratings, selection and purchase information. Other users behavior and preferences over the items are used to recommend items to the new users. In this case, features of the items are not known

2. Content based filtering approach

The point of content-based is that we have to know the content of both user and item. …

A movie recommendation is significant in our social life due to its strength in providing wonderful entertainment. Such a recommendation system can suggest a set of movies to users based on their interest, or the popularities of the movies. Although, a set of movie recommendation systems have been proposed, most of these either cannot recommend a movie to the existing users efficiently or to a new user by any means.

During my research, I have seen some of the recommender offers generalized recommendations to every user based on movie popularity. The basic idea behind that recommender is the movies which…

Twitter Follower Recommendation Engine

On Twitter, we follow a bunch of people. These are people we have either met in person and want to keep in touch with or am interested in following — or both!

Now we don’t attend many conferences and event, also due to pandemic, everything went online. So finding more people to follow that way is slow going; we probably only meet a handful of new people.

The Interesting people to follow on the internet is also limitless. The problem is finding them in a virtual sea of bots, marketing-only accounts, and complainers.

Twitter does have…

Jinal Kalpesh Shah

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