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  7.     "# **Classification of Spain's municipalities over 10k inhabitants according venue types**"
  8.    ]
  9.   },
  10.   {
  11.    "cell_type": "markdown",
  12.    "metadata": {},
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  14.     "## Index\n",
  15.     "\n",
  16.     "1. Introduction\n",
  17.     "2. Data\n",
  18.     "3. Methodology\n",
  19.     "4. Results\n",
  20.     "5. Discussion\n",
  21.     "6. Conclusion"
  22.    ]
  23.   },
  24.   {
  25.    "cell_type": "markdown",
  26.    "metadata": {},
  27.    "source": [
  28.     "## 1. Introduction"
  29.    ]
  30.   },
  31.   {
  32.    "cell_type": "markdown",
  33.    "metadata": {},
  34.    "source": [
  35.     "Right now, I'm living in Vigo, Spain and I'm thinking about moving to another city. The thing is I like the city I live in, so I want to go to a similar place elsewhere in Spain."
  36.    ]
  37.   },
  38.   {
  39.    "cell_type": "markdown",
  40.    "metadata": {},
  41.    "source": [
  42.     "## 2. Data"
  43.    ]
  44.   },
  45.   {
  46.    "cell_type": "markdown",
  47.    "metadata": {},
  48.    "source": [
  49.     "Main sources of data are:\n",
  50.     "* Dataset containing every city, town and village in Spain, coordinates, number of inhabitants and region.\n",
  51.     "* FourSquare RESTFull API to retrieve venues in a place.\n",
  52.     "\n",
  53.     "With those two, I have enough data to build a classification model."
  54.    ]
  55.   },
  56.   {
  57.    "cell_type": "markdown",
  58.    "metadata": {},
  59.    "source": [
  60.     "## 3. Methodology"
  61.    ]
  62.   },
  63.   {
  64.    "cell_type": "markdown",
  65.    "metadata": {},
  66.    "source": [
  67.     "In this project, I am going to explore how similar are cities between them to help me make the first-step decision.\n",
  68.     "\n",
  69.     "My raw data almost has all the information I need for the analysis, such as 'location /longitude /latitude /inhabitants', especially the location information, which indeed do me a great favor. As a condition, result city must have more than 100,000 inhabitants.\n",
  70.     "\n",
  71.     "I explore cities to obtain number of existing facilities and their type and location with Foursquare API.\n",
  72.     "\n",
  73.     "Finally, cluster all cities using K-means. According to all the venue data, I will focus on using unsupervised learning K-means algorithm to cluster based on similarities between cities.\n",
  74.     "\n",
  75.     "I will also visualize geographic details of each cluster, which should be a starting point to explore them.\n"
  76.    ]
  77.   },
  78.   {
  79.    "cell_type": "markdown",
  80.    "metadata": {},
  81.    "source": [
  82.     "## 4. Results"
  83.    ]
  84.   },
  85.   {
  86.    "cell_type": "markdown",
  87.    "metadata": {},
  88.    "source": [
  89.     "In the end, K-means algorithm shows that Vigo has similarities with some other cities in Spain. The figure present all cities in its cluster."
  90.    ]
  91.   },
  92.   {
  93.    "cell_type": "markdown",
  94.    "metadata": {},
  95.    "source": [
  96.     "![Vigo Similarities](ImgVigo.png)"
  97.    ]
  98.   },
  99.   {
  100.    "cell_type": "markdown",
  101.    "metadata": {},
  102.    "source": [
  103.     "Result may have logic because all point in the cluster are in cities near to the sea, same as Vigo."
  104.    ]
  105.   },
  106.   {
  107.    "cell_type": "markdown",
  108.    "metadata": {},
  109.    "source": [
  110.     "## 5. Discussion"
  111.    ]
  112.   },
  113.   {
  114.    "cell_type": "markdown",
  115.    "metadata": {},
  116.    "source": [
  117.     "The aim of this project is to help me to relocate in a city similar to mine, I can chose the city to which I want to relocate based on the most common venues in it.\n",
  118.     "\n",
  119.     "Due to Spain is full of Spanish and Tapas restaurants, I had to take them out of venues list.\n"
  120.    ]
  121.   },
  122.   {
  123.    "cell_type": "markdown",
  124.    "metadata": {},
  125.    "source": [
  126.     "## 6. Conclusion"
  127.    ]
  128.   },
  129.   {
  130.    "cell_type": "markdown",
  131.    "metadata": {},
  132.    "source": [
  133.     "This project helps me get a better understanding of the cities with respect to the most common venues there. It is always helpful to make use of technology to stay one step ahead i.e. finding out more about places before moving. \n",
  134.     "\n",
  135.     "The future of this project includes taking other factors into consideration to shortlist the cities such as cost of living or weather. "
  136.    ]
  137.   },
  138.   {
  139.    "cell_type": "markdown",
  140.    "metadata": {},
  141.    "source": [
  142.     "***"
  143.    ]
  144.   },
  145.   {
  146.    "cell_type": "markdown",
  147.    "metadata": {},
  148.    "source": [
  149.     "Thank you for reading this report. Have a nice day :)"
  150.    ]
  151.   }
  152.  ],
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