{ "cells": [ { "cell_type": "markdown", "id": "733b5975-57c9-4e07-b78c-f963940983e7", "metadata": {}, "source": [ "# Notebook-A6 (Data Analytics-3)" ] }, { "cell_type": "code", "execution_count": 2, "id": "e0be02f6-46d8-4248-8b07-26f76114d095", "metadata": {}, "outputs": [], "source": [ "# Import libraries\n", "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score" ] }, { "cell_type": "code", "execution_count": 4, "id": "524d2df1-e1e9-4f3a-860b-22cfb54b6245", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
| \n", " | sepal.length | \n", "sepal.width | \n", "petal.length | \n", "petal.width | \n", "variety | \n", "
|---|---|---|---|---|---|
| 0 | \n", "5.1 | \n", "3.5 | \n", "1.4 | \n", "0.2 | \n", "Setosa | \n", "
| 1 | \n", "4.9 | \n", "3.0 | \n", "1.4 | \n", "0.2 | \n", "Setosa | \n", "
| 2 | \n", "4.7 | \n", "3.2 | \n", "1.3 | \n", "0.2 | \n", "Setosa | \n", "
| 3 | \n", "4.6 | \n", "3.1 | \n", "1.5 | \n", "0.2 | \n", "Setosa | \n", "
| 4 | \n", "5.0 | \n", "3.6 | \n", "1.4 | \n", "0.2 | \n", "Setosa | \n", "