ioassignment2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOKKklxyoESz4Uy0PDlxHOZ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AlaynaMonteiro/Pesu_io_assignments/blob/main/ioassignment2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importing** **the** **Libraries**"
      ],
      "metadata": {
        "id": "dBCO3BsAbM5c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "2YU3x3WsbDiu"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importing** **the** **dataset**"
      ],
      "metadata": {
        "id": "xWL08LIFbWqn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv('housing.csv')\n",
        "x = dataset"
      ],
      "metadata": {
        "id": "BSFofFZ9HJ0H"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "45F_t5oQb5cm",
        "outputId": "fdca3e52-8e49-4bc4-c0c7-d49817a374cd"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
            "0      8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
            "1      8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
            "2      7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
            "3      5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
            "4      3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
            "...       ...       ...       ...        ...         ...       ...       ...   \n",
            "20635  1.5603      25.0  5.045455   1.133333       845.0  2.560606     39.48   \n",
            "20636  2.5568      18.0  6.114035   1.315789       356.0  3.122807     39.49   \n",
            "20637  1.7000      17.0  5.205543   1.120092      1007.0  2.325635     39.43   \n",
            "20638  1.8672      18.0  5.329513   1.171920       741.0  2.123209     39.43   \n",
            "20639  2.3886      16.0  5.254717   1.162264      1387.0  2.616981     39.37   \n",
            "\n",
            "       Longitude  MedHouseVal  \n",
            "0        -122.23        4.526  \n",
            "1        -122.22        3.585  \n",
            "2        -122.24        3.521  \n",
            "3        -122.25        3.413  \n",
            "4        -122.25        3.422  \n",
            "...          ...          ...  \n",
            "20635    -121.09        0.781  \n",
            "20636    -121.21        0.771  \n",
            "20637    -121.22        0.923  \n",
            "20638    -121.32        0.847  \n",
            "20639    -121.24        0.894  \n",
            "\n",
            "[20640 rows x 9 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Feature Scaling**"
      ],
      "metadata": {
        "id": "gC9Iic_eisdi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "x_1 = sc.fit_transform(x)"
      ],
      "metadata": {
        "id": "zel4dFPwiiC0"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Applying PCA**"
      ],
      "metadata": {
        "id": "yVV-2IZki0Sx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "pca = PCA(n_components = 2)\n",
        "x_new = pca.fit_transform(x_1)"
      ],
      "metadata": {
        "id": "Sf8qOEaxi5Z9"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_new)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G_dDtLU5i_i5",
        "outputId": "1faa7929-97df-4728-e6bd-18c2fda13382"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 2.34654276  0.34644059]\n",
            " [ 1.76136708  0.43812372]\n",
            " [ 2.33031157 -0.12620806]\n",
            " ...\n",
            " [ 0.68738046 -2.0567699 ]\n",
            " [ 0.82843475 -2.07324029]\n",
            " [ 0.77143988 -1.83274305]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Using the elbow method to find the optimal number of clusters**"
      ],
      "metadata": {
        "id": "Vr9hizeIcPkw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "wcss = []\n",
        "for i in range(1, 11):\n",
        "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
        "    kmeans.fit(x_new)\n",
        "    wcss.append(kmeans.inertia_)\n",
        "plt.plot(range(1, 11), wcss)\n",
        "plt.title('The Elbow Method')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('WCSS')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 839
        },
        "id": "PoHb43QgcdfT",
        "outputId": "0777214b-8a58-4ecf-eb4a-e092cf23df61"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Training the K-means model on the dataset**"
      ],
      "metadata": {
        "id": "WOFgmKRwc9rY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans = KMeans(n_clusters = 5, init = 'k-means++', random_state = 0)\n",
        "y_kmeans = kmeans.fit_predict(x_new)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pLrix7_Lcvsj",
        "outputId": "70c97497-5fe6-4f56-b381-1229a2591d4f"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualising the clusters**"
      ],
      "metadata": {
        "id": "_sud6W2ldJZb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(x_new[y_kmeans == 0, 0], x_new[y_kmeans == 0, 1], s = 30, c = 'crimson', label = 'Cluster 1')\n",
        "plt.scatter(x_new[y_kmeans == 1, 0], x_new[y_kmeans == 1, 1], s = 30, c = 'blue', label = 'Cluster 2')\n",
        "plt.scatter(x_new[y_kmeans == 2, 0], x_new[y_kmeans == 2, 1], s = 30, c = 'black', label = 'Cluster 3')\n",
        "plt.scatter(x_new[y_kmeans == 3, 0], x_new[y_kmeans == 3, 1], s = 30, c = 'cyan', label = 'Cluster 4')\n",
        "plt.scatter(x_new[y_kmeans == 4, 0], x_new[y_kmeans == 4, 1], s = 30, c = 'magenta', label = 'Cluster 5')\n",
        "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 20, c = 'yellow', label = 'Centroids')\n",
        "plt.title('Clusters')\n",
        "plt.xlabel('MedHouseVal')\n",
        "plt.ylabel('MedInc')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "FacvX75xdITM",
        "outputId": "ea6b3d64-3b9e-4b6e-bdca-e359374b4e94"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}