A7_NB_KNN.ipynb

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      "source": [
        "* **Submission of Assignment 7**\n",
        "* **Lakshay Singla**\n",
        "* **102003371**\n",
        "* **3CO15**"
      ],
      "metadata": {
        "id": "4imSdl0nh4KK"
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      "cell_type": "markdown",
      "source": [
        "## **Question 1**"
      ],
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        "id": "Pg0QPU9aK_8n"
      }
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
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        "id": "VAnziOEoLG4D"
      },
      "execution_count": null,
      "outputs": []
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        "df = pd.read_csv('/content/ham_spam.csv', encoding='latin-1')\n",
        "data = df.drop([\"Unnamed: 2\", \"Unnamed: 3\", \"Unnamed: 4\"], axis=1, inplace = True)\n",
        "df.rename(columns = {\"v2\":\"text\", \"v1\":\"output\"}, inplace = True)\n",
        "df.head(2)"
      ],
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              "  output                                               text\n",
              "0    ham  Go until jurong point, crazy.. Available only ...\n",
              "1    ham                      Ok lar... Joking wif u oni..."
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      "cell_type": "code",
      "source": [
        "import nltk\n",
        "from nltk import sent_tokenize,word_tokenize\n",
        "import re\n",
        "from nltk.corpus import stopwords\n",
        "nltk.download('punkt')\n",
        "nltk.download('stopwords')"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P080EBXVLb1s",
        "outputId": "3a10e7a4-5edf-433b-a005-d4c20000e2c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        },
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              "True"
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          "metadata": {},
          "execution_count": 103
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    },
    {
      "cell_type": "markdown",
      "source": [
        "**Preprocessing**"
      ],
      "metadata": {
        "id": "GLbzCLq6cRJW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.stem import PorterStemmer\n",
        "ps = PorterStemmer()"
      ],
      "metadata": {
        "id": "gHGOzjQ8XdKM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stopword = stopwords.words('english')"
      ],
      "metadata": {
        "id": "myeB_jIEZXDU"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "for i in range(len(df)):\n",
        "  text = df.iloc[i][1]\n",
        "  text = word_tokenize(text)\n",
        "  text.append(\" playing\")\n",
        "  new_text = [ps.stem(word.lower()) for word in text if word.lower() not in stopword and word.isalpha()]\n",
        "  new_text = ' '.join(new_text)\n",
        "  df.iloc[i][1] = new_text"
      ],
      "metadata": {
        "id": "4kHE389OLuK7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(2)"
      ],
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            "text/plain": [
              "  output                                               text\n",
              "0    ham  go jurong point crazi avail bugi n great world...\n",
              "1    ham                              ok lar joke wif u oni"
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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      "cell_type": "markdown",
      "source": [
        "**Making the TDM**"
      ],
      "metadata": {
        "id": "VxzCGnCMcLzd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "cv = CountVectorizer(max_features = 1000)"
      ],
      "metadata": {
        "id": "9f2tNnhdY1Ni"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = cv.fit_transform(df['text']).toarray()\n",
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NubgfSwgY9nE",
        "outputId": "3735a9bb-0e6a-4330-a9b6-3f6a81b2a74a"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5572, 1000)"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tdm = pd.DataFrame(X, columns = cv.get_feature_names_out())\n",
        "tdm.head(3)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 1000 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9997b0c2-6a20-4918-b178-cb2a5fb7b7f2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9997b0c2-6a20-4918-b178-cb2a5fb7b7f2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9997b0c2-6a20-4918-b178-cb2a5fb7b7f2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Train Test Splitting**"
      ],
      "metadata": {
        "id": "8iJS_6vdcqvq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "LE = LabelEncoder()\n",
        "y = LE.fit_transform(df['output'])"
      ],
      "metadata": {
        "id": "dZx03MvleHWv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(tdm, y, train_size = 0.7, random_state = 43)"
      ],
      "metadata": {
        "id": "JRsdvFdOcqex"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Applying Naive-Bayes**"
      ],
      "metadata": {
        "id": "S7ngL8w5fJ9G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "classifier = GaussianNB()\n",
        "classifier.fit(x_train, y_train)\n",
        "y_pred = classifier.predict(x_test)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "cm\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZupWELcXdG5j",
        "outputId": "76a94b7d-e551-40c1-e169-7caadb879515"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1144,  296],\n",
              "       [  20,  212]])"
            ]
          },
          "metadata": {},
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nqfV_bbte0Z3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Question 2**"
      ],
      "metadata": {
        "id": "-cut6o-dfdl5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn import datasets\n",
        "iris = datasets.load_iris()"
      ],
      "metadata": {
        "id": "W36ti8VufcfD"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = iris.data\n",
        "y = iris.target"
      ],
      "metadata": {
        "id": "y6fk_zEqgBOM"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "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, train_size = 0.7, random_state = 43)"
      ],
      "metadata": {
        "id": "Hq8LqqGGgn02"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**From Scratch**"
      ],
      "metadata": {
        "id": "QzzNAWIChkhN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Output_Label Probabilities**"
      ],
      "metadata": {
        "id": "ieipPnhqfvGZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "size = x_train.shape[0]\n",
        "label_prob = {}\n",
        "for outcome in np.unique(y_train):\n",
        "  outcome_count = sum(outcome==y_train)\n",
        "  label_prob[outcome] = outcome_count/size\n",
        "print(label_prob)"
      ],
      "metadata": {
        "id": "a7Zm3yFxhnRb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5d8f0b14-e46a-419f-ee93-d650e021a865"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{0: 0.3333333333333333, 1: 0.3142857142857143, 2: 0.3523809523809524}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Computing Liklihood**"
      ],
      "metadata": {
        "id": "9in7xsOGfrfd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "target_names = list(iris.target_names)\n",
        "target_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QLRJ6LTidYT1",
        "outputId": "5f130084-38b9-4a75-b373-6bbc0c894dcc"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['setosa', 'versicolor', 'virginica']"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(x_train,columns = iris.feature_names)\n",
        "df['target'] = y_train\n",
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "DlOOlnfcjEYi",
        "outputId": "cd749075-2e67-484e-f004-d4d28c5b68c3"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
              "0                5.7               2.8                4.5               1.3   \n",
              "1                5.5               2.6                4.4               1.2   \n",
              "2                5.7               2.6                3.5               1.0   \n",
              "3                6.7               3.1                4.4               1.4   \n",
              "4                6.3               3.3                4.7               1.6   \n",
              "\n",
              "   target  \n",
              "0       1  \n",
              "1       1  \n",
              "2       1  \n",
              "3       1  \n",
              "4       1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1fecc775-e558-43db-894b-0475fcbac92e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.7</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.5</td>\n",
              "      <td>2.6</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.7</td>\n",
              "      <td>2.6</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.1</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.3</td>\n",
              "      <td>3.3</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1.6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1fecc775-e558-43db-894b-0475fcbac92e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1fecc775-e558-43db-894b-0475fcbac92e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1fecc775-e558-43db-894b-0475fcbac92e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Calculate Gaussian Prob**"
      ],
      "metadata": {
        "id": "Cx_xd6zkp1EX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def GaussianProb(mean,var,new_x):\n",
        "  exp = np.exp((-(new_x - mean)**2)/(2*var))\n",
        "  start = (1/(np.sqrt(2*np.pi*var))) \n",
        "  return start*exp"
      ],
      "metadata": {
        "id": "FZJDh7Clo6ab"
      },
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Making mean and variance table**"
      ],
      "metadata": {
        "id": "SEEKJErGuTCW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "final_table = []\n",
        "for i in range(3):\n",
        "  new_table = []\n",
        "  temp_df = df[df['target'] == i]\n",
        "  for feature in iris.feature_names:\n",
        "    mean,var = temp_df[feature].mean() , temp_df[feature].var()\n",
        "    new_table.append([mean,var])\n",
        "  final_table.append(new_table)"
      ],
      "metadata": {
        "id": "GiQ6-E_fhB9n"
      },
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_table[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0o7WUY5-3MkK",
        "outputId": "63fd262e-57e3-4a7d-a2c1-90c575cea502"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[5.031428571428571, 0.13457142857142856],\n",
              " [3.4342857142857146, 0.14467226890756302],\n",
              " [1.4828571428571429, 0.03322689075630252],\n",
              " [0.2571428571428571, 0.013109243697478993]]"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Predicting output**"
      ],
      "metadata": {
        "id": "GEfaOPQDuh5C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test = x_test[0]\n",
        "test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_jdRNdLHwOaO",
        "outputId": "397b659e-a4ef-41f0-d5fb-272a41fdf099"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4.8, 3.1, 1.6, 0.2])"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h5GvDvlp8RB8",
        "outputId": "6f9fe281-0efc-40e0-b401-b94e1c795e31"
      },
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.7, 2.8, 4.5, 1.3],\n",
              "       [5.5, 2.6, 4.4, 1.2],\n",
              "       [5.7, 2.6, 3.5, 1. ],\n",
              "       [6.7, 3.1, 4.4, 1.4],\n",
              "       [6.3, 3.3, 4.7, 1.6]])"
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = []\n",
        "for i in range(len(x_test)): \n",
        "  test = x_test[i]\n",
        "  probs = {}\n",
        "  count = 0\n",
        "  for each_label in final_table:  \n",
        "    prob = label_prob[count]  \n",
        "    feature_count = 0 \n",
        "    for stats in each_label:  \n",
        "     prob = prob * GaussianProb(stats[0],stats[1],test[feature_count])\n",
        "     feature_count+=1 \n",
        "    count+=1  \n",
        "    probs[count-1] = prob \n",
        "  output =max(probs, key=probs.get)\n",
        "  y_pred.append(output)"
      ],
      "metadata": {
        "id": "KowAGduTuXaS"
      },
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(y_test)):\n",
        "  print(y_pred[i], y_test[i])"
      ],
      "metadata": {
        "id": "I3UJSxoF9kMT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print (classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bAjXaUnNw9VG",
        "outputId": "6bcda710-a71d-4ec7-e2fc-302562e16391"
      },
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        15\n",
            "           1       1.00      0.94      0.97        17\n",
            "           2       0.93      1.00      0.96        13\n",
            "\n",
            "    accuracy                           0.98        45\n",
            "   macro avg       0.98      0.98      0.98        45\n",
            "weighted avg       0.98      0.98      0.98        45\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**USING INBUILT**"
      ],
      "metadata": {
        "id": "1mOOTaP290ki"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "classifier = GaussianNB()\n",
        "classifier.fit(x_train, y_train)\n",
        "y_pred = classifier.predict(x_test)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tv8hdyzS96kR",
        "outputId": "40ebd76d-2ea2-47aa-c408-6ac4a493e4cf"
      },
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[15,  0,  0],\n",
              "       [ 0, 16,  1],\n",
              "       [ 0,  0, 13]])"
            ]
          },
          "metadata": {},
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print (classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IiZJPdZo9-6v",
        "outputId": "d5fbe553-c4db-41be-f2a5-b31fb7983415"
      },
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        15\n",
            "           1       1.00      0.94      0.97        17\n",
            "           2       0.93      1.00      0.96        13\n",
            "\n",
            "    accuracy                           0.98        45\n",
            "   macro avg       0.98      0.98      0.98        45\n",
            "weighted avg       0.98      0.98      0.98        45\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Question 3**"
      ],
      "metadata": {
        "id": "pUH0GaQNZyS7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Loading the iris dataset**"
      ],
      "metadata": {
        "id": "Ba9Ig2aml_Mv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import datasets\n",
        "iris = datasets.load_iris()"
      ],
      "metadata": {
        "id": "FztaSHFeZ48Q"
      },
      "execution_count": 164,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Setting classifer**"
      ],
      "metadata": {
        "id": "FfiaNOuvmCH1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import GridSearchCV"
      ],
      "metadata": {
        "id": "V2ThWvnrbgwq"
      },
      "execution_count": 165,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier()\n",
        "params = {\n",
        "    'n_neighbors' : [1,2,3,4,5],\n",
        "     'weights' : ['uniform', 'distance'],\n",
        "     'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n",
        "     'p' : [1,2]\n",
        "}"
      ],
      "metadata": {
        "id": "6xr7i1tUbow6"
      },
      "execution_count": 166,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Using GridSearchCV**"
      ],
      "metadata": {
        "id": "u_eKDoTRmKPy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "classifier = GridSearchCV(estimator = knn, param_grid = params)\n",
        "classifier.fit(iris.data,iris.target)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TZJg4dNHcCNM",
        "outputId": "0c2fb404-d54d-4d11-92b6-77ed8b4d7961"
      },
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(estimator=KNeighborsClassifier(),\n",
              "             param_grid={'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n",
              "                         'n_neighbors': [1, 2, 3, 4, 5], 'p': [1, 2],\n",
              "                         'weights': ['uniform', 'distance']})"
            ]
          },
          "metadata": {},
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classifier.best_params_)\n",
        "print(classifier.best_score_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BpCQevwAjCbM",
        "outputId": "c3c92acf-fcff-49ac-ac66-e9e38f06d5df"
      },
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'algorithm': 'auto', 'n_neighbors': 4, 'p': 2, 'weights': 'uniform'}\n",
            "0.9733333333333334\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Submitted by Lakshay Singla**\n",
        "**_____________________ _____________________ _____________________**"
      ],
      "metadata": {
        "id": "OXUVJpHKmPKr"
      }
    }
  ]
}