Untitled4.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled4.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP2ugC0mM4gQNy4tjCaZBSO",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ghenwafkh/miniproject/blob/master/Untitled4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dOZteF1owOBR",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "**Notebook 3: Pre-Processing the data**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4vdcC8hvwj7N",
        "colab_type": "text"
      },
      "source": [
        "**Goal:**\n",
        "Find the most predictive features of the data and filter it so it will enhance the predictive power of the analytics model.\n",
        "\n",
        "Load data and essential libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "79hbbUUNwoCw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "926c5ad1-f66b-4c76-f4b8-f823637338ed"
      },
      "source": [
        "\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#Load libraries for data processing\n",
        "import pandas as pd #data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import numpy as np\n",
        "from scipy.stats import norm\n",
        "\n",
        "# visualization\n",
        "import seaborn as sns \n",
        "plt.style.use('fivethirtyeight')\n",
        "sns.set_style(\"white\")\n",
        "\n",
        "\n",
        "plt.rcParams['figure.figsize'] = (8,4) \n",
        "#plt.rcParams['axes.titlesize'] = 'large'\n",
        "\n",
        "data = pd.read_csv('data/clean-data.csv', index_col=False)\n",
        "data.drop('Unnamed: 0',axis=1, inplace=True)\n",
        "#data.head()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "83PEQNy6w47N",
        "colab_type": "text"
      },
      "source": [
        "**Label encoding:**\n",
        "\n",
        "Here, I assign the 30 features to a NumPy array X, and transform the class labels from their original string representation (M and B) into integers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PC39zHQRw-Jl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "#Assign predictors to a variable of ndarray (matrix) type\n",
        "array = data.values\n",
        "X = array[:,1:31]\n",
        "y = array[:,0]"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-scy_kLuyEPT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#transform the class labels from their original string representation (M and B) into integers\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y)\n",
        "\n",
        "#Call the transform method of LabelEncorder on two dummy variables\n",
        "#le.transform (['M', 'B'])"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EMleRuBL0Lqf",
        "colab_type": "text"
      },
      "source": [
        "**Assesing Model Accuracy: Split data into training and test sets**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PqzsItJV0Sy4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3bbd736e-ced0-4b26-fa2f-41eff4d2c827"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "##Split data set in train 70% and test 30%\n",
        "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.25, random_state=7)\n",
        "X_train.shape, y_train.shape, X_test.shape, y_test.shape\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((426, 30), (426,), (143, 30), (143,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HqLCVj751aZi",
        "colab_type": "text"
      },
      "source": [
        "**Feature Standardization:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kRPikN6y1her",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Normalize the  data (center around 0 and scale to remove the variance).\n",
        "scaler =StandardScaler()\n",
        "Xs = scaler.fit_transform(X)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7FAhOzLx1_nM",
        "colab_type": "text"
      },
      "source": [
        "**Feature decomposition using Principal Component Analysis( PCA):**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YpZopaz92DvF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.decomposition import PCA\n",
        "# feature extraction\n",
        "pca = PCA(n_components=10)\n",
        "fit = pca.fit(Xs)\n",
        "\n",
        "# summarize components\n",
        "#print(\"Explained Variance: %s\") % fit.explained_variance_ratio_\n",
        "#print(fit.components_)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mZrn40Uv22j6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "b233a39e-d049-4a03-e59b-63a98ec7751a"
      },
      "source": [
        "X_pca = pca.transform(Xs)\n",
        "\n",
        "PCA_df = pd.DataFrame()\n",
        "\n",
        "PCA_df['PCA_1'] = X_pca[:,0]\n",
        "PCA_df['PCA_2'] = X_pca[:,1]\n",
        "\n",
        "plt.plot(PCA_df['PCA_1'][data.diagnosis == 'M'],PCA_df['PCA_2'][data.diagnosis == 'M'],'o', alpha = 0.7, color = 'r')\n",
        "plt.plot(PCA_df['PCA_1'][data.diagnosis == 'B'],PCA_df['PCA_2'][data.diagnosis == 'B'],'o', alpha = 0.7, color = 'b')\n",
        "\n",
        "plt.xlabel('PCA_1')\n",
        "plt.ylabel('PCA_2')\n",
        "plt.legend(['Malignant','Benign'])\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "060dVIif4AUv",
        "colab_type": "text"
      },
      "source": [
        "**Deciding How Many Principal Components to Retain**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LUFUOUuu4DNx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "397cb9c7-c55b-4181-c773-317ee0655954"
      },
      "source": [
        "#The amount of variance that each PC explains\n",
        "var= pca.explained_variance_ratio_\n",
        "#Cumulative Variance explains\n",
        "#var1=np.cumsum(np.round(pca.explained_variance_ratio_, decimals=4)*100)\n",
        "#print(var1)\n",
        "\n",
        "plt.plot(var)\n",
        "plt.title('Scree Plot')\n",
        "plt.xlabel('Principal Component')\n",
        "plt.ylabel('Eigenvalue')\n",
        "\n",
        "leg = plt.legend(['Eigenvalues from PCA'], loc='best', borderpad=0.3,shadow=False,markerscale=0.4)\n",
        "leg.get_frame().set_alpha(0.4)\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}