Untitled24.ipynb

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      "cell_type": "code",
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        "id": "F66wIU-0UoeH",
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          "text": [
            "[[55  3]\n",
            " [ 1 21]]\n",
            "0.95\n"
          ]
        }
      ],
      "source": [
        "# Importing the libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "#Importing the dataset\n",
        "dataset = pd.read_csv('/content/Social_Network_Ads.csv')\n",
        "X = dataset.iloc[:, [2, 3]].values\n",
        "y = dataset.iloc[:, -1].values\n",
        "#Splitting the dataset into the Training set and Test set\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)\n",
        "# Feature Scaling\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)\n",
        "\n",
        "# Training the K-NN model on the Training set\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)\n",
        "\n",
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "ac = accuracy_score (y_test, y_pred)\n",
        "print(cm)\n",
        "print(ac)"
      ]
    }
  ]
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