Clustering Categorical Data - Exercise.ipynb

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    "# Clustering Categorical Data "
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   "source": [
    "You are given much more country data. Using the same methodology as the one in the lecture, group all the countries in 2 clusters. \n",
    "\n",
    "<b> Already done that? Okay! </b>\n",
    "    \n",
    "There are other features: name and continent. \n",
    "\n",
    "Encode the continent one and use it in the clustering solution. Think about the difference with the previous exercise."
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   "source": [
    "## Import the relevant libraries"
   ]
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   "source": [
    "## Load the data"
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    "Load data from the csv file: <i> 'Categorical.csv'</i>."
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    "Remove the duplicate index column from the dataset. "
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   "source": [
    "## Map the data"
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    "Use the <i>'continent'</i> category for this analysis."
   ]
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   "source": [
    "## Select the features"
   ]
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   "source": [
    "## Clustering"
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    "Use 4 clusters initially."
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   "source": [
    "## Clustering results"
   ]
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   "source": [
    "## Plot the data"
   ]
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   "source": [
    "Since you already have all the code necessary, go back and play around with the number of clusters. Try 3, 7 and 8 and see if the results match your expectations. "
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   "source": [
    "Simply go back to the beggining of the <b> Clustering </b> section and change <i> kmeans = KMeans(4) </i> to <i> kmeans = KMeans(3) </i>. Then run the remaining cells until the end."
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