Uber_Pickups.ipynb

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    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/5/58/Uber_logo_2018.svg/1024px-Uber_logo_2018.svg.png\" alt=\"UBER LOGO\" width=\"50%\" />\n",
    "\n",
    "# UBER Pickups "
   ]
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   "source": [
    "## Company's Description 📇\n",
    "\n",
    "<a href=\"http://uber.com/\" target=\"_blank\">Uber</a> is one of the most famous startup in the world. It started as a ride-sharing application for people who couldn't afford a taxi. Now, Uber expanded its activities to Food Delivery with <a href=\"https://www.ubereats.com/fr-en\" target=\"_blank\">Uber Eats</a>, package delivery, freight transportation and even urban transportation with <a href=\"https://www.uber.com/fr/en/ride/uber-bike/\" target=\"_blank\"> Jump Bike</a> and <a href=\"https://www.li.me/\" target=\"_blank\"> Lime </a> that the company funded. \n",
    "\n",
    "\n",
    "The company's goal is to revolutionize transportation accross the globe. It operates now on about 70 countries and 900 cities and generates over $14 billion revenue! 😮\n"
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    "## Project 🚧\n",
    "\n",
    "One of the main pain point that Uber's team found is that sometimes drivers are not around when users need them. For example, a user might be in San Francisco's Financial District whereas Uber drivers are looking for customers in Castro.  \n",
    "\n",
    "(If you are not familiar with the bay area, check out <a href=\"https://www.google.com/maps/place/San+Francisco,+CA,+USA/@37.7515389,-122.4567213,13.43z/data=!4m5!3m4!1s0x80859a6d00690021:0x4a501367f076adff!8m2!3d37.7749295!4d-122.4194155\" target=\"_blank\">Google Maps</a>)\n",
    "\n",
    "Eventhough both neighborhood are not that far away, users would still have to wait 10 to 15 minutes before being picked-up, which is too long. Uber's research shows that users accept to wait 5-7 minutes, otherwise they would cancel their ride. \n",
    "\n",
    "Therefore, Uber's data team would like to work on a project where **their app would recommend hot-zones in major cities to be in at any given time of day.**  "
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    "## Goals 🎯\n",
    "\n",
    "Uber already has data about pickups in major cities. Your objective is to create algorithms that will determine where are the hot-zones that drivers should be in. Therefore you will:\n",
    "\n",
    "* Create an algorithm to find hot zones \n",
    "* Visualize results on a nice dashboard "
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    "## Scope of this project 🖼️\n",
    "\n",
    "To start off, Uber wants to try this feature in New York city. Therefore you will only focus on this city. Data can be found here: \n",
    "\n",
    "👉👉<a href=\"https://full-stack-bigdata-datasets.s3.eu-west-3.amazonaws.com/Machine+Learning+non+Supervis%C3%A9/Projects/uber-trip-data.zip\" target=\"_blank\"> Uber Trip Data</a> 👈👈\n",
    "\n",
    "**You only need to focus on New York City for this project**"
   ]
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    "## Helpers 🦮\n",
    "\n",
    "To help you achieve this project, here are a few tips that should help you: \n",
    "\n",
    "### Clustering is your friend \n",
    "\n",
    "Clustering technics are a perfect fit for the job. Think about it, all the pickup locations can be gathered into different clusters. You can then use **cluster coordinates to pin hot zones** 😉\n",
    "    \n",
    "\n",
    "### Create maps with `plotly` \n",
    "\n",
    "Check out <a href=\"https://plotly.com/\" target=\"_blank\">Plotly</a> documentation, you can create maps and populate them easily. Obviously, there are other libraries but this one should do the job pretty well. \n",
    "\n",
    "\n",
    "### Start small grow big \n",
    "\n",
    "Eventhough Uber wants to have hot-zones per hour and per day of week, you should first **start small**. Pick one day at a given hour and **then start to generalize** your approach. "
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    "## Deliverable 📬\n",
    "\n",
    "To complete this project, your team should: \n",
    "\n",
    "* Have a map with hot-zones using any python library (`plotly` or anything else). \n",
    "* You should **at least** describe hot-zones per day of week. \n",
    "* Compare results with **at least** two unsupervised algorithms like KMeans and DBScan. \n",
    "\n",
    "Your maps should look something like this: \n",
    "\n",
    "<img src=\"https://full-stack-assets.s3.eu-west-3.amazonaws.com/images/Clusters_uber_pickups.png\" alt=\"Uber Cluster Map\" />"
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