HW2_Part1.ipynb

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              "      Unnamed: 0       age  marital  default   balance  housing  loan  \\\n",
              "0              0  0.451613        0        1 -0.518096        0     0   \n",
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              "...          ...       ...      ...      ...       ...      ...   ...   \n",
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              "2241        2250  0.290323        0        1  0.537606        0     1   \n",
              "\n",
              "      contact       day  month  ...  management  retired  selfemployed  \\\n",
              "0           1  0.866667      0  ...           1        0             0   \n",
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              "...       ...       ...    ...  ...         ...      ...           ...   \n",
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              "2238        1  0.333333      0  ...           0        0             0   \n",
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              "2240        1  0.533333      0  ...           0        0             0   \n",
              "2241        1  0.466667      0  ...           1        0             0   \n",
              "\n",
              "      services  student technician  unemployed  primary  secondary  tertiary  \n",
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          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# Your code for LP1 goes here\n",
        "# Feel free to add cells below this for LP1\n",
        "# Your code for LP1 goes here\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "bankDataFrame=pd.read_csv('bank_clean.csv')\n",
        "bankDataFrame"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "# Initially scalling is doen on duration column. \n",
        "# After doing z-score normalization the duration values range changed to -1 to 1.\n",
        "# I have cleaned the bank data and worked on it.\n",
        "\n",
        "bankCluster=bankMarketing[[\"duration\",\"campaign\",\"pdays\",\"previous\"]]\n",
        "\n",
        "kmean =KMeans(n_clusters=3)\n",
        "label=kmean.fit_predict(bankCluster)\n",
        "bankCluster[\"predict\"]=label\n",
        "# predict\n",
        "bankCluster"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "Sjv7dQ49QCuW",
        "outputId": "9912af35-c25f-416b-fbd0-a45dde9f2098"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  if sys.path[0] == '':\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      duration  campaign     pdays  previous  predict\n",
              "0    -0.560314  2.092004 -0.417772 -0.290286        2\n",
              "1    -0.310969  4.439824 -0.417772 -0.290286        2\n",
              "2     0.444510 -0.591219  1.433583  1.177731        1\n",
              "3    -0.891534  0.750393 -0.417772 -0.290286        0\n",
              "4    -0.046737 -0.591219 -0.417772 -0.290286        0\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Getting unique labels\n",
        " \n",
        "u_labels = np.unique(label)\n",
        " \n",
        "bankClusterArray = np.array(bankCluster) \n",
        " \n",
        "for i in u_labels:\n",
        "    plt.scatter(bankClusterArray[label == i , 0] , bankClusterArray[label == i , 1] , label = i)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "2uLBXhvUQzXV",
        "outputId": "c91963b8-91b4-4013-b0fb-8c92d60e3606"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Your code for HP1 goes here\n",
        "# Feel free to add cells below this for HP1\n",
        "kmeans_range=range(1,10)\n",
        "squaredErrorSum=[]\n",
        "for kmeans in kmeans_range:\n",
        "    KMEAN=KMeans(n_clusters=kmeans)\n",
        "    KMEAN.fit(bankCluster)\n",
        "    squaredErrorSum.append(KMEAN.inertia_)"
      ],
      "metadata": {
        "id": "To8auBFFQ9pi"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "squaredErrorSum"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KL7uovgwRGpL",
        "outputId": "d0610765-fb19-4ca6-e769-a8792ca10471"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[9591.075825156111,\n",
              " 6693.641022165044,\n",
              " 5010.655836659971,\n",
              " 3797.0557571624245,\n",
              " 2930.1739390663734,\n",
              " 2621.197293607489,\n",
              " 2354.0107884169024,\n",
              " 2109.715899907708,\n",
              " 1872.9813419204786]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.xlabel('k')\n",
        "plt.ylabel('squared error sum')\n",
        "plt.plot(kmeans_range, squaredErrorSum)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "ztVJ9AsNRNMZ",
        "outputId": "0d7cf7e0-5e57-4bc8-af39-4c0e10bccda7"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fad24a1d990>]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the graph we can observe we have got elbow at K=5.Therefor 5 is the best value for K."
      ],
      "metadata": {
        "id": "ohthDyjKRXI9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "[LP2]\n",
        "\n",
        "Perform Principal Component Analysis (PCA) on the filtered dataset and extract the top two principal components.\n",
        "Plot the principal components in a graph using appropriate labels on the x and y axes."
      ],
      "metadata": {
        "id": "l6JQwvdRRyAT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Your code for LP2 goes here\n",
        "# Feel free to add cells below this for LP2\n",
        "\n",
        "from sklearn.decomposition import PCA\n",
        "pca=PCA(n_components=2)\n",
        "pca.fit(bankCluster)\n",
        "pcaData=pca.transform(bankCluster)\n",
        "bankCluster.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qfb8YjutRzs3",
        "outputId": "d5daf88b-85f6-4b2b-d586-f4295ead2d7b"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2242, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pcaData.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pjZu0BNoR6i9",
        "outputId": "eaf18a9f-cdad-4981-866b-cae8f9ce4277"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2242, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pcaData"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l_377epQR9xU",
        "outputId": "a3dfb91b-2696-4b36-c557-386075079082"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.24858576,  2.51743737],\n",
              "       [-0.42175658,  4.35269574],\n",
              "       [ 2.02572423, -0.46830337],\n",
              "       ...,\n",
              "       [-0.5321158 , -0.15123535],\n",
              "       [-0.51843303, -0.21650599],\n",
              "       [ 3.32817928,  0.09576705]])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "%matplotlib inline\n",
        "\n",
        "plt.figure(figsize=(8,6))\n",
        "plt.scatter(pcaData[:,0],pcaData[:,1])\n",
        "plt.xlabel(\"PC 1\")\n",
        "plt.ylabel(\"PC 2\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "id": "uGxBP5eUR_6t",
        "outputId": "46eb97cc-5355-4d23-a73b-ccf1f46e50f5"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'PC 2')"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "[HP2] Did we lose information by picking only the top two principal components? How much information from the four columns in the filtered components are captured in the the two top principal components? Explain your thoughts briefly."
      ],
      "metadata": {
        "id": "DK7wvVJYSJ3v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Your code for HP2 goes here\n",
        "# Feel free to add cells below this for HP2\n",
        "pca.explained_variance_ratio_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wyDlQITASTt5",
        "outputId": "e3c00bd8-1b40-486b-a077-b79cf0a792f7"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.3707352, 0.2654879])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "37.07% of original data's variance can be seen in principal component 1. 26.54% of original data's variance can be seen in principal component 2.\n",
        "\n",
        "We saved arround 37.3% of original data variance by using both PC1 and PC2 and lost arround 36.39% variance is lost. If we want to preserve variance we need to increase the number of principal components."
      ],
      "metadata": {
        "id": "IEqbDpPKSah3"
      }
    }
  ]
}