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    "\n",
    "# Lab 10\n",
    "\n",
    "Suppose you have a list of positions of possible clients of Uber in Lisbon (Passageiros.csv). How many cars could you use and where they could be positioned in order to reduce time? Use a cluster analysis appraoch to support the solution of this problem.\n",
    "\n",
    "* import the libraries needed\n",
    "* import dataset from Passageiros.csv file\n",
    "* Verify imported data\n",
    "* verify data types and convert into numeric if needed. Use for example, df['x']=pd.to_numeric(df['x'], errors='coerce')\n",
    "* plot a scatter chart\n",
    "* create a X dataframe including only numeric columns\n",
    "* calculete WCSS using X dataframe:\n",
    "\n",
    "    wcss = []\n",
    "\n",
    "    for i in range(1, 11):\n",
    "\n",
    "        kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)\n",
    "\n",
    "        kmeans.fit(df)\n",
    "\n",
    "        wcss.append(kmeans.inertia_)\n",
    "\n",
    "    plt.plot(range(1, 11), wcss)\n",
    "\n",
    "    plt.title('Elbow Method')\n",
    "\n",
    "    plt.xlabel('Number of clusters')\n",
    "\n",
    "    plt.ylabel('WCSS')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "    plot a scatter chart showing centroids of the clusters estimated\n",
    "\n"
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