Stacking_Classifier_Wine_Quality_Prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Stacking_Classifier_Wine_Quality_Prediction.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BSU91s1jpB6G",
        "outputId": "3df70bc5-4765-4657-fad5-2f91c6a8dc3e"
      },
      "source": [
        "from sklearn.datasets import load_wine\n",
        "wine=load_wine()\n",
        "x=wine.data\n",
        "y=wine.target\n",
        "print(x)\n",
        "print(x.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1.423e+01 1.710e+00 2.430e+00 ... 1.040e+00 3.920e+00 1.065e+03]\n",
            " [1.320e+01 1.780e+00 2.140e+00 ... 1.050e+00 3.400e+00 1.050e+03]\n",
            " [1.316e+01 2.360e+00 2.670e+00 ... 1.030e+00 3.170e+00 1.185e+03]\n",
            " ...\n",
            " [1.327e+01 4.280e+00 2.260e+00 ... 5.900e-01 1.560e+00 8.350e+02]\n",
            " [1.317e+01 2.590e+00 2.370e+00 ... 6.000e-01 1.620e+00 8.400e+02]\n",
            " [1.413e+01 4.100e+00 2.740e+00 ... 6.100e-01 1.600e+00 5.600e+02]]\n",
            "(178, 13)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MbOOAN35rAx1",
        "outputId": "70f0c909-799d-4082-a52b-bfcbc4c90892"
      },
      "source": [
        "print(y)\n",
        "print(y.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]\n",
            "(178,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ud4o0Vc4rOl8"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.ensemble import StackingClassifier\n",
        "clf1=RandomForestClassifier(n_estimators=10, random_state=42)\n",
        "clf2=DecisionTreeClassifier(random_state=42)\n",
        "clf3=KNeighborsClassifier(n_neighbors=4)\n",
        "estimators = [(\"rf\",clf1),(\"dt\",clf2),(\"kn\",clf3)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hNff8DGTzH1g"
      },
      "source": [
        "sclf=StackingClassifier(estimators=estimators,final_estimator=LogisticRegression())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "olB-PWk31Q0-",
        "outputId": "a620a221-ad25-40c8-e6c0-97c606ca9d60"
      },
      "source": [
        "for clf,label in zip([clf1,clf2,clf3,sclf],[\"Random Forest\",\"DecisionTree\",\"KNeighbors\",\"Stacking Classifier\"]):\n",
        "    scores=cross_val_score(clf,x,y,cv=3,scoring='accuracy')\n",
        "    print(\"Accuracy: \",scores.mean(),label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.9046139359698682 Random Forest\n",
            "Accuracy:  0.9042372881355932 DecisionTree\n",
            "Accuracy:  0.6405838041431262 KNeighbors\n",
            "Accuracy:  0.9212806026365349 Stacking Classifier\n"
          ]
        }
      ]
    }
  ]
}