Clustering Categorical Data - Solution.ipynb

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   "source": [
    "# Clustering Categorical Data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the relevant libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load data from the csv file: <i> 'Categorical.csv'</i>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Load the data\n",
    "raw_data = pd.read_csv('Categorical.csv')\n",
    "# Check the data\n",
    "raw_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove the duplicate index column from the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = raw_data.copy()\n",
    "data = data.drop(['Unnamed: 0'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Map the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the <i>'continent'</i> category for this analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_mapped = data.copy()\n",
    "data_mapped['continent'] = data_mapped['continent'].map({'North America':0,'Europe':1,'Asia':2,'Africa':3,'South America':4, 'Oceania':5,'Seven seas (open ocean)':6, 'Antarctica':7})\n",
    "data_mapped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data_mapped.iloc[:,3:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use 4 clusters initially."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "kmeans = KMeans(4)\n",
    "kmeans.fit(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "identified_clusters = kmeans.fit_predict(x)\n",
    "identified_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data_with_clusters = data_mapped.copy()\n",
    "data_with_clusters['Cluster'] = identified_clusters\n",
    "data_with_clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "plt.scatter(data['Longitude'], data['Latitude'], c=data_with_clusters['Cluster'], cmap = 'rainbow')\n",
    "plt.xlim(-180,180)\n",
    "plt.ylim(-90, 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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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