Gini_index.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"milk_quality_data.csv\")\n",
        "df.head()"
      ],
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              "    pH  Temprature  Taste  Odor  Fat   Turbidity  Colour   Grade\n",
              "0  6.6          35      1     0     1          0     254    high\n",
              "1  6.6          36      0     1     0          1     253    high\n",
              "2  8.5          70      1     1     1          1     246     low\n",
              "3  9.5          34      1     1     0          1     255     low\n",
              "4  6.6          37      0     0     0          0     255  medium"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pH</th>\n",
              "      <th>Temprature</th>\n",
              "      <th>Taste</th>\n",
              "      <th>Odor</th>\n",
              "      <th>Fat</th>\n",
              "      <th>Turbidity</th>\n",
              "      <th>Colour</th>\n",
              "      <th>Grade</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6.6</td>\n",
              "      <td>35</td>\n",
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              "      <th>1</th>\n",
              "      <td>6.6</td>\n",
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              "      <td>6.6</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>255</td>\n",
              "      <td>medium</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "x=df.iloc[ : , : -1]\n",
        "y=df.iloc[ : , -1 ]\n",
        "print(x)\n",
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LD-VON8QO1HB",
        "outputId": "5501e0e5-1ae8-4878-8a3c-16a5af24df0c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       pH  Temprature  Taste  Odor  Fat   Turbidity  Colour\n",
            "0     6.6          35      1     0     1          0     254\n",
            "1     6.6          36      0     1     0          1     253\n",
            "2     8.5          70      1     1     1          1     246\n",
            "3     9.5          34      1     1     0          1     255\n",
            "4     6.6          37      0     0     0          0     255\n",
            "...   ...         ...    ...   ...   ...        ...     ...\n",
            "1054  6.7          45      1     1     0          0     247\n",
            "1055  6.7          38      1     0     1          0     255\n",
            "1056  3.0          40      1     1     1          1     255\n",
            "1057  6.8          43      1     0     1          0     250\n",
            "1058  8.6          55      0     1     1          1     255\n",
            "\n",
            "[1059 rows x 7 columns]\n",
            "0         high\n",
            "1         high\n",
            "2          low\n",
            "3          low\n",
            "4       medium\n",
            "         ...  \n",
            "1054    medium\n",
            "1055      high\n",
            "1056       low\n",
            "1057      high\n",
            "1058       low\n",
            "Name: Grade, Length: 1059, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.25,random_state=0)\n",
        "print(x_test)\n",
        "print(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "waSkpukYPNsI",
        "outputId": "65508fb2-b2d8-4d18-c86b-ecf56be00414"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       pH  Temprature  Taste  Odor  Fat   Turbidity  Colour\n",
            "418   6.6          37      1     1     1          1     255\n",
            "202   9.0          43      1     0     1          1     250\n",
            "682   6.8          45      0     0     0          1     255\n",
            "316   5.6          50      0     1     1          1     255\n",
            "267   6.8          45      1     1     1          1     245\n",
            "...   ...         ...    ...   ...   ...        ...     ...\n",
            "1021  4.5          38      0     1     1          1     255\n",
            "527   6.5          38      1     0     1          0     255\n",
            "985   6.5          45      1     0     0          0     246\n",
            "966   8.6          55      0     1     1          1     255\n",
            "113   6.8          45      0     0     0          1     255\n",
            "\n",
            "[265 rows x 7 columns]\n",
            "418       high\n",
            "202        low\n",
            "682     medium\n",
            "316        low\n",
            "267       high\n",
            "         ...  \n",
            "1021       low\n",
            "527     medium\n",
            "985     medium\n",
            "966        low\n",
            "113     medium\n",
            "Name: Grade, Length: 265, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "st_x=StandardScaler()\n",
        "x_train=st_x.fit_transform(x_train)\n",
        "x_test=st_x.transform(x_test)\n",
        "print(x_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U-_4l32xPbFp",
        "outputId": "b7074c8a-1135-4a90-e065-1a70487a043b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.07669946 -0.60866162  0.92708402 ... -1.36996773 -0.95323336\n",
            "   0.72584779]\n",
            " [ 0.54401896  2.01309602 -1.07865089 ... -1.36996773 -0.95323336\n",
            "   0.72584779]\n",
            " [ 0.06123796  0.07105333  0.92708402 ... -1.36996773 -0.95323336\n",
            "  -1.14837382]\n",
            " ...\n",
            " [-1.31813632 -0.60866162  0.92708402 ...  0.7299442  -0.95323336\n",
            "   0.72584779]\n",
            " [ 1.64751839 -0.12315094  0.92708402 ...  0.7299442   1.04906106\n",
            "  -0.44554072]\n",
            " [-0.07669946 -0.60866162  0.92708402 ... -1.36996773 -0.95323336\n",
            "   0.72584779]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "classifier= DecisionTreeClassifier(criterion='gini',random_state=0)\n",
        "classifier.fit(x_train,y_train)\n",
        "y_pred=classifier.predict(x_test)\n",
        "print(y_pred)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xXJT0pcSPlfi",
        "outputId": "5fb5eb23-6a7f-48cd-8768-d19cfb5c9a7d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['high' 'low' 'medium' 'low' 'high' 'medium' 'low' 'high' 'high' 'medium'\n",
            " 'low' 'high' 'medium' 'medium' 'high' 'medium' 'high' 'high' 'high' 'low'\n",
            " 'high' 'low' 'medium' 'high' 'high' 'low' 'high' 'high' 'low' 'medium'\n",
            " 'high' 'low' 'low' 'medium' 'high' 'high' 'low' 'medium' 'high' 'low'\n",
            " 'high' 'medium' 'high' 'low' 'medium' 'low' 'high' 'low' 'medium' 'low'\n",
            " 'high' 'medium' 'medium' 'medium' 'low' 'medium' 'medium' 'low' 'medium'\n",
            " 'low' 'medium' 'low' 'medium' 'high' 'low' 'medium' 'low' 'medium'\n",
            " 'medium' 'high' 'high' 'medium' 'medium' 'high' 'medium' 'medium'\n",
            " 'medium' 'low' 'high' 'medium' 'low' 'medium' 'medium' 'low' 'medium'\n",
            " 'medium' 'high' 'low' 'low' 'low' 'medium' 'high' 'medium' 'low' 'high'\n",
            " 'medium' 'low' 'high' 'medium' 'medium' 'high' 'medium' 'low' 'medium'\n",
            " 'high' 'high' 'high' 'high' 'low' 'low' 'high' 'high' 'high' 'high' 'low'\n",
            " 'medium' 'low' 'high' 'medium' 'high' 'medium' 'medium' 'low' 'high'\n",
            " 'high' 'low' 'low' 'low' 'medium' 'high' 'low' 'medium' 'low' 'medium'\n",
            " 'high' 'medium' 'medium' 'medium' 'medium' 'low' 'medium' 'high' 'low'\n",
            " 'low' 'medium' 'high' 'low' 'low' 'low' 'high' 'low' 'medium' 'medium'\n",
            " 'low' 'high' 'high' 'medium' 'medium' 'medium' 'medium' 'medium' 'medium'\n",
            " 'low' 'medium' 'medium' 'high' 'high' 'low' 'high' 'medium' 'low' 'low'\n",
            " 'medium' 'high' 'high' 'low' 'high' 'high' 'high' 'medium' 'medium' 'low'\n",
            " 'low' 'low' 'low' 'high' 'medium' 'high' 'low' 'low' 'high' 'low' 'low'\n",
            " 'low' 'medium' 'low' 'medium' 'medium' 'high' 'medium' 'low' 'high'\n",
            " 'medium' 'low' 'medium' 'medium' 'low' 'medium' 'medium' 'low' 'high'\n",
            " 'medium' 'medium' 'medium' 'medium' 'medium' 'high' 'medium' 'high' 'low'\n",
            " 'high' 'high' 'medium' 'high' 'medium' 'low' 'low' 'medium' 'high' 'high'\n",
            " 'high' 'low' 'low' 'high' 'low' 'medium' 'low' 'low' 'low' 'high' 'low'\n",
            " 'high' 'low' 'medium' 'high' 'low' 'medium' 'medium' 'low' 'low' 'medium'\n",
            " 'low' 'high' 'low' 'low' 'low' 'high' 'low' 'high' 'low' 'low' 'medium'\n",
            " 'medium' 'low' 'medium']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm=confusion_matrix(y_test,y_pred)\n",
        "print(cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_QWaD9BLPxaM",
        "outputId": "6e2ec42a-5431-40cf-9352-163d6d15b84c"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[80  0  1]\n",
            " [ 0 90  1]\n",
            " [ 0  0 93]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "y_pred=classifier.predict(x_test)\n",
        "plt.plot(y_pred)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "7UpXe9G7P7wk",
        "outputId": "7ca01c2d-ac29-42f4-c1ba-8e47a427cf80"
      },
      "execution_count": 11,
      "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": [
        "cm=confusion_matrix(y_test,y_pred)\n",
        "plt.plot(cm)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "7Xn9YpEGP_a0",
        "outputId": "156856f0-4cfd-4b91-b70b-e5ee7a29afef"
      },
      "execution_count": 12,
      "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": [
        "from sklearn import tree\n",
        "\n",
        "classifier = tree.DecisionTreeClassifier()\n",
        "classifier = classifier.fit(x, y)\n",
        "tree.plot_tree(classifier)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 994
        },
        "id": "NdPOyyo8QLmx",
        "outputId": "ed1b1e7a-9d09-4151-a0a2-de5b61e5effb"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.39945652173913043, 0.9545454545454546, 'X[0] <= 7.1\\ngini = 0.653\\nsamples = 1059\\nvalue = [256, 429, 374]'),\n",
              " Text(0.35597826086956524, 0.8636363636363636, 'X[0] <= 6.0\\ngini = 0.648\\nsamples = 849\\nvalue = [256, 219, 374]'),\n",
              " Text(0.3125, 0.7727272727272727, 'gini = 0.0\\nsamples = 169\\nvalue = [0, 169, 0]'),\n",
              " Text(0.39945652173913043, 0.7727272727272727, 'X[4] <= 0.5\\ngini = 0.55\\nsamples = 680\\nvalue = [256, 50, 374]'),\n",
              " Text(0.13043478260869565, 0.6818181818181818, 'X[1] <= 47.5\\ngini = 0.189\\nsamples = 257\\nvalue = [1, 26, 230]'),\n",
              " Text(0.08695652173913043, 0.5909090909090909, 'X[3] <= 0.5\\ngini = 0.017\\nsamples = 232\\nvalue = [1, 1, 230]'),\n",
              " Text(0.043478260869565216, 0.5, 'gini = 0.0\\nsamples = 188\\nvalue = [0, 0, 188]'),\n",
              " Text(0.13043478260869565, 0.5, 'X[5] <= 0.5\\ngini = 0.088\\nsamples = 44\\nvalue = [1, 1, 42]'),\n",
              " Text(0.08695652173913043, 0.4090909090909091, 'gini = 0.0\\nsamples = 42\\nvalue = [0, 0, 42]'),\n",
              " Text(0.17391304347826086, 0.4090909090909091, 'X[6] <= 251.5\\ngini = 0.5\\nsamples = 2\\nvalue = [1, 1, 0]'),\n",
              " Text(0.13043478260869565, 0.3181818181818182, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
              " Text(0.21739130434782608, 0.3181818181818182, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0, 0]'),\n",
              " Text(0.17391304347826086, 0.5909090909090909, 'gini = 0.0\\nsamples = 25\\nvalue = [0, 25, 0]'),\n",
              " Text(0.6684782608695652, 0.6818181818181818, 'X[5] <= 0.5\\ngini = 0.517\\nsamples = 423\\nvalue = [255, 24, 144]'),\n",
              " Text(0.532608695652174, 0.5909090909090909, 'X[1] <= 47.5\\ngini = 0.555\\nsamples = 260\\nvalue = [94, 22, 144]'),\n",
              " Text(0.4891304347826087, 0.5, 'X[0] <= 6.55\\ngini = 0.478\\nsamples = 238\\nvalue = [94, 0, 144]'),\n",
              " Text(0.34782608695652173, 0.4090909090909091, 'X[3] <= 0.5\\ngini = 0.031\\nsamples = 64\\nvalue = [1, 0, 63]'),\n",
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              " Text(0.6304347826086957, 0.4090909090909091, 'X[1] <= 39.0\\ngini = 0.498\\nsamples = 174\\nvalue = [93, 0, 81]'),\n",
              " Text(0.4782608695652174, 0.3181818181818182, 'X[6] <= 249.5\\ngini = 0.126\\nsamples = 74\\nvalue = [69, 0, 5]'),\n",
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              " Text(0.8695652173913043, 0.22727272727272727, 'X[0] <= 6.75\\ngini = 0.495\\nsamples = 51\\nvalue = [23, 0, 28]'),\n",
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              " Text(0.9130434782608695, 0.13636363636363635, 'X[2] <= 0.5\\ngini = 0.383\\nsamples = 31\\nvalue = [23, 0, 8]'),\n",
              " Text(0.8695652173913043, 0.045454545454545456, 'gini = 0.0\\nsamples = 8\\nvalue = [0, 0, 8]'),\n",
              " Text(0.9565217391304348, 0.045454545454545456, 'gini = 0.0\\nsamples = 23\\nvalue = [23, 0, 0]'),\n",
              " Text(0.5760869565217391, 0.5, 'gini = 0.0\\nsamples = 22\\nvalue = [0, 22, 0]'),\n",
              " Text(0.8043478260869565, 0.5909090909090909, 'X[0] <= 6.55\\ngini = 0.024\\nsamples = 163\\nvalue = [161, 2, 0]'),\n",
              " Text(0.7608695652173914, 0.5, 'X[2] <= 0.5\\ngini = 0.147\\nsamples = 25\\nvalue = [23, 2, 0]'),\n",
              " Text(0.717391304347826, 0.4090909090909091, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2, 0]'),\n",
              " Text(0.8043478260869565, 0.4090909090909091, 'gini = 0.0\\nsamples = 23\\nvalue = [23, 0, 0]'),\n",
              " Text(0.8478260869565217, 0.5, 'gini = 0.0\\nsamples = 138\\nvalue = [138, 0, 0]'),\n",
              " Text(0.4429347826086957, 0.8636363636363636, 'gini = 0.0\\nsamples = 210\\nvalue = [0, 210, 0]')]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}