Naïve_Bayes_Classifier_HAAI_ipynb.ipynb

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      "cell_type": "markdown",
      "source": [
        "Let's apply the Naive Bayes classifier to a dataset. At first, we are importing the necessary Python packages."
      ],
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        "id": "yTh-IFwi9GCL"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's download the iris dataset from its weblink as follows:"
      ],
      "metadata": {
        "id": "bGtaYYtX9ayb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "path = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\""
      ],
      "metadata": {
        "id": "K2a6kUvC9dHD"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, assign the column names to the dataset as follows:"
      ],
      "metadata": {
        "id": "M7kSZyk_9hIL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "headernames = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'Class']"
      ],
      "metadata": {
        "id": "hSo_kumi9k-l"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we have to read the dataset using the Pandas data frame as follows:"
      ],
      "metadata": {
        "id": "M3bsrq4-9p5U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(path, names=headernames)\n",
        "data.shape"
      ],
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        },
        "id": "psmRizH49ual",
        "outputId": "324c38b9-2127-4999-de9e-ee8f01274799"
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      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, observe the data."
      ],
      "metadata": {
        "id": "mmeCe7Ze93P6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
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      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sepal-length  sepal-width  petal-length  petal-width        Class\n",
              "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
              "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
              "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
              "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
              "4           5.0          3.6           1.4          0.2  Iris-setosa"
            ],
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          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's implement the very basic step of data preprocessing."
      ],
      "metadata": {
        "id": "ehB-BrDK-BEX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.iloc[:, :-1].values\n",
        "y = data.iloc[:, 4].values"
      ],
      "metadata": {
        "id": "uoFZM3O5-x8_"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, we are dividing the data into train and test splits, with 70% of the training data and 30% of the testing data."
      ],
      "metadata": {
        "id": "5fM4f2sW-3_t"
      }
    },
    {
      "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=0.30)"
      ],
      "metadata": {
        "id": "niAMttjv-5_D"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's do the data scaling."
      ],
      "metadata": {
        "id": "fvdczuKB--gd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(X_train)\n",
        "X_train = scaler.transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "TBbt4qpy_Dso"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we are training the model with the help of the GaussianNB class of sklearn as follows:"
      ],
      "metadata": {
        "id": "1JM-Udn4_lMW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "classifier = GaussianNB()\n",
        "classifier.fit(X_train, y_train);"
      ],
      "metadata": {
        "id": "1JTJsYdj_hYP"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we have to make the prediction, which can be done in the following way:"
      ],
      "metadata": {
        "id": "CE3FrAov_xoP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = classifier.predict(X_test)"
      ],
      "metadata": {
        "id": "L49mJfZ6_yvV"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's print the results as follows:"
      ],
      "metadata": {
        "id": "uVUWvJbt_2l8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "result = confusion_matrix(y_test, y_pred)\n",
        "print(\"Confusion Matrix:\")\n",
        "print(result)\n",
        "result1 = classification_report(y_test, y_pred)\n",
        "print(\"Classification Report:\",)\n",
        "print (result1)\n",
        "result2 = accuracy_score(y_test,y_pred)\n",
        "print(\"Accuracy:\",result2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EEB1PM8g_-lu",
        "outputId": "d1137ab2-f926-4ba7-bece-19c945afdda4"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[16  0  0]\n",
            " [ 0 14  2]\n",
            " [ 0  1 12]]\n",
            "Classification Report:\n",
            "                 precision    recall  f1-score   support\n",
            "\n",
            "    Iris-setosa       1.00      1.00      1.00        16\n",
            "Iris-versicolor       0.93      0.88      0.90        16\n",
            " Iris-virginica       0.86      0.92      0.89        13\n",
            "\n",
            "       accuracy                           0.93        45\n",
            "      macro avg       0.93      0.93      0.93        45\n",
            "   weighted avg       0.94      0.93      0.93        45\n",
            "\n",
            "Accuracy: 0.9333333333333333\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using the Naive Bayes classifier, we have achieved an accuracy of 93.33% (approximately)."
      ],
      "metadata": {
        "id": "DHNGuCBWAC3Q"
      }
    }
  ]
}