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Coursera_Capstone

Review criterialess This capstone project will be graded by your peers. This capstone project is worth

70% of your total grade. The project will be completed over the course of 2 weeks. Week 1 submissions will be worth 30% whereas week 2 submissions will be worth 40% of your total grade.

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-------> -------> -------> -------> -------> -------> -------> -------> -------> Peer-graded Assignment: Capstone Project - The Battle of Neighborhoods (Week 2) Submit by June 30, 11:59 PM PDT Important Information It is especially important to submit this assignment before the deadline, June 30, 11:59 PM PDT, because it must be graded by others. If you submit late, there may not be enough classmates around to review your work. This makes it difficult

• and in some cases, impossible - to produce a grade. Submit on time to avoid these risks. InstructionsMy submissionDiscussions In this week, you will continue working on your capstone project. Please remember by the end of this week, you will need to submit the following:

A full report consisting of all of the following components (15 marks): Introduction where you discuss the business problem and who would be interested in this project. Data where you describe the data that will be used to solve the problem and the source of the data. Methodology section which represents the main component of the report where you discuss and describe any exploratory data analysis that you did, any inferential statistical testing that you performed, and what machine learnings were used and why. Results section where you discuss the results. Discussion section where you discuss any observations you noted and any recommendations you can make based on the results. Conclusion section where you conclude the report. 2. A link to your Notebook on your Github repository pushed showing your code. (15 marks)

3. Your choice of a presentation or blogpost. (10 marks)

Here are examples of previous outstanding submissions that should give you an idea of what your report would look like, what your notebook would look like in terms of clean, clear, and well-commented code, and what your presentation would look like or your blogpost would look like:

Report: https://cocl.us/coursera_capstone_report Notebook: https://cocl.us/coursera_capstone_notebook Presentation: https://cocl.us/coursera_capstone_presentation Blogpost: https://cocl.us/coursera_capstone_blogpost

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Peer-graded Assignment: Capstone Project - The Battle of Neighborhoods (Week 1) You passed! Congratulations. You earned 30 / 30 points. Review the feedback below and continue the course when you are ready. You can also help more classmates by reviewing their submissions. InstructionsMy submissionDiscussions Now that you have been equipped with the skills and the tools to use location data to explore a geographical location, over the course of two weeks, you will have the opportunity to be as creative as you want and come up with an idea to leverage the Foursquare location data to explore or compare neighbourhoods or cities of your choice or to come up with a problem that you can use the Foursquare location data to solve. If you cannot think of an idea or a problem, here are some ideas to get you started:

In Module 3, we explored New York City and the city of Toronto and segmented and clustered their neighbourhoods. Both cities are very diverse and are the financial capitals of their respective countries. One interesting idea would be to compare the neighbourhoods of the two cities and determine how similar or dissimilar they are. Is New York City more like Toronto or Paris or some other multicultural city? I will leave it to you to refine this idea. In a city of your choice, if someone is looking to open a restaurant, where would you recommend that they open it? Similarly, if a contractor is trying to start their own business, where would you recommend that they setup their office? These are just a couple of many ideas and problems that can be solved using location data in addition to other datasets. No matter what you decide to do, make sure to provide sufficient justification of why you think what you want to do or solve is important and why would a client or a group of people be interested in your project.

Review criterialess This capstone project will be graded by your peers. This capstone project is worth

70% of your total grade. The project will be completed over the course of 2 weeks. Week 1 submissions will be worth 30% whereas week 2 submissions will be worth 40% of your total grade.

For this week, you will required to submit the following:

A description of the problem and a discussion of the background. (15 marks) A description of the data and how it will be used to solve the problem. (15 marks) For the second week, the final deliverables of the project will be:

A link to your Notebook on your Github repository, showing your code. (15 marks) A full report consisting of all of the following components (15 marks): Introduction where you discuss the business problem and who would be interested in this project. Data where you describe the data that will be used to solve the problem and the source of the data. Methodology section which represents the main component of the report where you discuss and describe any exploratory data analysis that you did, any inferential statistical testing that you performed, and what machine learnings were used and why. Results section where you discuss the results. Discussion section where you discuss any observations you noted and any recommendations you can make based on the results. Conclusion section where you conclude the report. 3. Your choice of a presentation or blogpost. (10 marks)

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**Jupyter IPython Notebook_capstone project.

https://github.com/lindangulopez/Coursera_Capstone/blob/master/Capstone%20Project%20June%202019.ipynb

**A relocation adviser has to make recommendations to person who has two job offers, one in Manhattan, New York and another in York, Toronto. The client is Parisian and would like to maintain their lifestyle.

***** They want to keep the habit of taking walks in gardens and ***** is picky eater (they only eat at French restaurants), ***** wants to be in a crime free environment and ***** most of all away from congestion and public transport problems!

They are undecided as both jobs are equally attractive and money is not a problem for them. In addition both cities are very diverse and are the financial capitals of their respective countries. Though attracted to the “big city buzz”, comparable with Paris, with their prestigious hubs, a treasure cases of recreational activities, global cities are also prone to have high levels of crime, congestion and public transport problems than are deemed uncomfortable by the client. So we will compare the both the types of venues their neighbourhoods have to offer to determine their similarity to Paris and as these results only look at recreational activities, use the EIU’s World’s Most Liveable City index to weight in on the choice of: ***Whether Manhattan, New York or York, Toronto be a better home away from home from Reuilly, Paris?

***Interested and Affected Parties, IAPs.

Although at a Beginner Level, these Data Science tools and the shared data set allow for a powerful virtual

play ground for data exploration, so please do fork on Gitbub and collaborate!!!

This notebook is approachable enough to be replicated it can be used for collaboration and not only to expand the business problem of relocation options, as demonstrated here, but also be the stem for approaching other problems/opportunity which depend on the share data and methods used here.

***Data Sources & Types https://github.com/lindangulopez/Coursera_Capstone

The stem data was sourced from two tables , both of which have been converted to pandas dataframes then manipulated in this Lab. The data generated in this lab as well as relevant data generated in two previous Labs have been cleaned and stored as csv files with geo-location coordinates, their corresponding IPython notebooks with dataframes, as well as csv and json files plus in html format can be forked at Github

The data to be used, in the second part of the assignment, to cluster and compare the three cities are available on Github. In prior labs these two cleaned data sets were created, you can download them from Github they have a list of the following neighbourhoods with their geo-ordinates as well as venues, gleaned with the Folium App.

*****Manhattan, New York & *****York, Toronto Neighbourhoods.

**Data from this notebook as well as the accompanying data sets and outputs is also available on this Github repository

**Stem Data Sources:

****A Wikipedia Table:

The city of Paris is divided into twenty, administrative districts, referred to as Arrondissements. The number of the Arrondissement, the equivalent of boroughs, is indicated by the last two digits in of its postal codes, from 75001 up to 75020. The client lives in the 12th Arrondissement of Paris close to 'Parc de Bercy' and is attached to its park and the nearby restaurants. This borough is called Reuilly and is situated on the right bank of the river Seine. Each of Paris' 20 boroughs are divided into 4 quartiers, the equivalent of neighbourhoods. He lives in the 49th quarter, which is the Bercy neighbourhood.

****Global Liveability Index Report:

This index assesses which locations around the world provide the best or the worst living conditions by quantifying the challenges that might be presented to an individual's lifestyle in 140 cities worldwide. Each city is assigned a score for over 30 qualitative and quantitative factors across five broad categories of, which I did some catching up on from these articles:

***Stability ***Health-care ***Culture and environment ***Education and Infrastructure

The New York, Toronto and Paris ranking by Global Liveability Index Ranking are given and will be used to adjust the machine learnng outcome, by not more than 15%.

The following maps and data were generated from the stem data sources:

***A Wikipedia Table:

List of Paris's Neighborhoods, csv file. Map of Paris's Neighborhoods, html file. Map of Reuilly's Neighborhoods, html file. List of Reuilly's Nearby Venues, csv file. List of up to Top 100 Reuilly Venues, csv file. List of up to Reuilly Venues Sorted, csv file. ****Global Liveability Index Report:

Global Liveability Index, csv file This IPython notebook contains code and is annotated to answer question from the first part of the above assignment. The focus is on how to search for, select, mine and upload potential data as well as store and clean dataframes with pandas and nympy, using python3. The outcome and notebook itself, will be used in the creation of a deliverable, a blog post. The blog post and other deliverables are part of the later half of the assignment and will be available a week after this submission.

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