Ensemble_Majority_Voting.ipynb

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    "colab": {
      "name": "Ensemble with Majority Voting.ipynb",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s5ZdiUemDKm1"
      },
      "source": [
        "# ENSEMBLE WITH MAJORITY VOTING "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ni_7e3g-DKm4"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.naive_bayes import GaussianNB \n",
        "from sklearn import metrics"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9RAY9TuKDKm5"
      },
      "source": [
        "#### Database read  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0DL60-aWDKm5",
        "outputId": "04d7c266-a109-4b17-f1bc-920e0aee78e4"
      },
      "source": [
        "df = pd.read_csv('ReplicatedAcousticFeatures-ParkinsonDatabase.csv')\n",
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(240, 48)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
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      "metadata": {
        "id": "ZaF8pMnmDKm5",
        "outputId": "6e4c7034-6968-4462-a5ff-b9d5bc11a3b4"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "        ID  Recording  Status  Gender  Jitter_rel  Jitter_abs  Jitter_RAP  \\\n",
              "0  CONT-01          1       0       1     0.25546    0.000015    0.001467   \n",
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              "0    0.001673  0.030256  0.26313  ...  1.407701  1.417218  1.380352  1.420670   \n",
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              "\n",
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              "0  1.451240  1.440295  1.403678  1.405495  1.416705  1.354610  \n",
              "1  1.354242  1.365692  1.322870  1.314549  1.318999  1.323508  \n",
              "2  1.455996  1.368882  1.438053  1.388910  1.305469  1.305402  \n",
              "3  1.472926  1.643177  1.551286  1.638346  1.604008  1.621456  \n",
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cx1wUiQwDKm6"
      },
      "source": [
        "#### Drop data "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "basgDAtGDKm6",
        "outputId": "6b59c169-8c4c-429e-8009-f697129d06c6"
      },
      "source": [
        "df.drop(['ID'],axis = 1, inplace = True)\n",
        "label = df['Status'] \n",
        "df.drop(['Status'], axis = 1, inplace = True)\n",
        "df = df.values\n",
        "samples,features = df.shape\n",
        "print(samples,features)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "240 46\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U0D1aUwiDKm6"
      },
      "source": [
        "data = df.astype(np.float)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d-ywcnVmDKm7"
      },
      "source": [
        "#### Import train_test_split function and Split dataset into training set and test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0p2X2M8DDKm7"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(data, label, test_size=0.2,random_state=100)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qc0DA00sDKm7"
      },
      "source": [
        "#### Base models "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E1o1z-mkDKm7",
        "outputId": "868478af-cf80-457c-ebf5-b154522e685d"
      },
      "source": [
        "model1 = DecisionTreeClassifier()\n",
        "model2 = GaussianNB()\n",
        "model3= LogisticRegression()\n",
        "\n",
        "model1.fit(X_train,y_train)\n",
        "model2.fit(X_train,y_train)\n",
        "model3.fit(X_train,y_train)\n",
        "\n",
        "pred1=model1.predict(X_test)\n",
        "pred2=model2.predict(X_test)\n",
        "pred3=model3.predict(X_test)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "C:\\Users\\HP\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lAiuwzpADKm8",
        "outputId": "370fe79d-75d6-46b1-a53e-b41fe4b49128"
      },
      "source": [
        "print(model1)\n",
        "print(model2)\n",
        "print(model3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DecisionTreeClassifier()\n",
            "GaussianNB()\n",
            "LogisticRegression()\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xh_gQzZtDKm8",
        "outputId": "28d1f917-3e0b-4bd9-dd86-d1d592cde5fd"
      },
      "source": [
        "print(\"decision tree classifier prediction:\\n\",pred1)\n",
        "print(\"naive bayes classifier prediction:\\n\",pred2)\n",
        "print(\"logistic regression classifier prediction:\\n\",pred3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "decision tree classifier prediction:\n",
            " [0 1 1 0 1 0 0 1 0 1 1 0 0 0 1 0 0 1 1 0 1 0 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1\n",
            " 1 0 0 0 1 1 1 0 1 0 0]\n",
            "naive bayes classifier prediction:\n",
            " [0 1 1 1 1 0 1 1 0 1 1 0 0 0 1 1 0 1 1 0 0 0 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1\n",
            " 1 0 0 0 1 1 1 0 1 0 0]\n",
            "logistic regression classifier prediction:\n",
            " [0 1 1 0 1 0 1 1 0 1 1 0 0 0 1 0 1 0 1 0 0 0 1 1 0 1 1 0 0 0 1 1 0 1 0 1 1\n",
            " 1 0 0 0 1 1 1 0 0 0 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XVxBCDxRDKm8"
      },
      "source": [
        "#### Accuracy of each base models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "knljFozQDKm8",
        "outputId": "2724c95f-d283-4bfb-ddd6-3548eda837ed"
      },
      "source": [
        "print(\"Accuracy of decision tree classifier:\",metrics.accuracy_score(y_test, pred1))\n",
        "print(\"Accuracy of naive bayes classifier:\",metrics.accuracy_score(y_test, pred2))\n",
        "print(\"Accuracy of logistic regression classifier:\",metrics.accuracy_score(y_test, pred3))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of decision tree classifier: 0.7916666666666666\n",
            "Accuracy of naive bayes classifier: 0.7916666666666666\n",
            "Accuracy of logistic regression classifier: 0.8333333333333334\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g38JsCdTDKm9"
      },
      "source": [
        "#### Final prediction from majority voting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LaYXbOb3DKm9"
      },
      "source": [
        "final_pred_majority_voting = np.array([])\n",
        "for i in range(0,len(X_test)):\n",
        "    if pred1[i]+pred2[i]+pred3[i]>1:\n",
        "        fpredict = 1\n",
        "    else:\n",
        "        fpredict = 0\n",
        "    final_pred_majority_voting = np.append(final_pred_majority_voting,fpredict)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QkPL0AwQDKm9",
        "outputId": "18854776-f9c8-4370-9c48-4db4e9f43c7c"
      },
      "source": [
        "print(\"final prediction using majority voting:\\n\",final_pred_majority_voting)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "final prediction using majority voting:\n",
            " [0. 1. 1. 0. 1. 0. 1. 1. 0. 1. 1. 0. 0. 0. 1. 0. 0. 1. 1. 0. 0. 0. 1. 1.\n",
            " 1. 1. 1. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 1. 1. 1. 0. 1. 0. 0.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lIqKPfAaDKm9"
      },
      "source": [
        "#### Accuracy of final prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7hSh6B5mDKm9",
        "outputId": "735f0cbd-c6e0-411a-e82e-b24a0d12efca"
      },
      "source": [
        "print(\"Accuracy of majority voting:\",metrics.accuracy_score(y_test, final_pred_majority_voting))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of majority voting: 0.7916666666666666\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v7YgkXVzDKm-"
      },
      "source": [
        "#### RANKING OF ALL MODELS BASED ON THEIR ACCURACY"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8wUbKq3pDKm-",
        "outputId": "2eab6d5f-3909-4156-d9d4-d66c20fc938d"
      },
      "source": [
        "print(\"RANKING OF ALL MODELS BASED ON THEIR ACCURACY\")\n",
        "print(\"1.Logistic regression classifier :\",0.8333333333333334)\n",
        "print(\"2.Majority voting Ensemble bayes classifier: \",0.7916666666666666 )\n",
        "print(\"3.Deision tree classifier:\",0.7916666666666666)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RANKING OF ALL MODELS BASED ON THEIR ACCURACY\n",
            "1.Logistic regression classifier : 0.8333333333333334\n",
            "2.Majority voting Ensemble bayes classifier:  0.7916666666666666\n",
            "3.Deision tree classifier: 0.7916666666666666\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}