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New Year's Recipes

Here are some delicious recipe ideas to ring in the new year:

* Midnight Kiss Chocolate Truffles: Rich, creamy chocolate truffles infused with a hint of raspberry and topped with edible gold dust.
* Countdown Caprese Salad: A colorful salad of fresh mozzarella, cherry tomatoes, and basil, dressed with a balsamic glaze and served with a side of garlic bread.
* New Year's Eve Sparkling Cider Donuts: Fluffy donuts infused with the sweetness of sparkling cider and topped with a crunchy sugar coating.
* Resolution Roasted Vegetables: A hearty roasted vegetable medley featuring Brussels sprouts, carrots, and sweet potatoes, tossed with olive oil and seasoned with salt and pepper.

These recipes are sure to bring in the new year with style and flavor!

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vivekangadi1/NYC-Parking-Case-Study

New York City is a thriving metropolis. Just like most other metros that size, one of the biggest problems its citizens face is parking. The classic combination of a huge number of cars and a cramped geography is the exact recipe that leads to a huge number of parking tickets. In an attempt to scientifically analyse this phenomenon, the NYC Police Department has collected data for parking tickets. Out of these, the data files from 2014 to 2017 are publicly available on Kaggle. We will try and perform some exploratory analysis on this data. Spark will allow us to analyse the full files at high speeds, as opposed to taking a series of random samples that will approximate the population. For the scope of this analysis, we wish to compare the phenomenon related to parking tickets over three different years - 2015, 2016, 2017. All the analysis steps mentioned below should be done for three different years. Each metric you derive should be compared across the three years. Use the Fiscal years as per the files. You can use calendar year if you like - you will not lose any marks for performing the analysis this way.
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JaiBhagat/NYC-Parking-Tickets-An-Exploratory-Analysis

Problem Statement Big data analytics allows you to analyse data at scale. It has applications in almost every industry in the world. Let’s consider an unconventional application that you wouldn’t ordinarily encounter. New York City is a thriving metropolis. Just like most other metros that size, one of the biggest problems its citizens face, is parking. The classic combination of a huge number of cars, and a cramped geography is the exact recipe that leads to a huge number of parking tickets. In an attempt to scientifically analyse this phenomenon, the NYC Police Department has collected data for parking tickets. Out of these, the data files from 2014 to 2017 are publicly available on Kaggle. We will try and perform some exploratory analysis on this data. Spark will allow us to analyse the full files at high speeds, as opposed to taking a series of random samples that will approximate the population. For the scope of this analysis, we wish to compare phenomenon related to parking tickets over three different years - 2015, 2016, 2017. All the analysis steps mentioned below should be done for 3 different years. Each metric you derive should be compared across the 3 years.
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