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October 31, 2022 17:48
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DWM_Project_Comparison.ipynb
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "provenance": [], | |
| "collapsed_sections": [], | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| }, | |
| "language_info": { | |
| "name": "python" | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/Rocktopus101/ab87a3a1d4e1c6ba0c1861a34986546a/dwm_project_comparison.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Importing Required Libraries\n" | |
| ], | |
| "metadata": { | |
| "id": "TocQG2QXqahG" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "id": "JX4Umg-IqI6q" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import pandas as pd\n", | |
| "import numpy as np\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "%matplotlib inline\n", | |
| "import seaborn as sns\n", | |
| "from sklearn.model_selection import train_test_split\n", | |
| "from sklearn import tree\n", | |
| "from sklearn.naive_bayes import GaussianNB\n", | |
| "from sklearn.naive_bayes import MultinomialNB\n", | |
| "from sklearn.preprocessing import StandardScaler\n", | |
| "from sklearn.metrics import r2_score\n", | |
| "from sklearn.metrics import accuracy_score\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Importing Dataset\n" | |
| ], | |
| "metadata": { | |
| "id": "2e33-3ysxhNh" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from sklearn.datasets import load_digits\n", | |
| "\n", | |
| "digits = load_digits()" | |
| ], | |
| "metadata": { | |
| "id": "OhfreZjisMBi" | |
| }, | |
| "execution_count": 2, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Data Exploration" | |
| ], | |
| "metadata": { | |
| "id": "Rluem0_4xz5V" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "plt.gray()\n", | |
| "for i in range(5):\n", | |
| " plt.matshow(digits.images[i]) " | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 1000 | |
| }, | |
| "id": "OGeN1bplxkCK", | |
| "outputId": "acaa36db-04bb-41f8-c2e2-cb000ab41b26" | |
| }, | |
| "execution_count": 3, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 0 Axes>" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 288x288 with 1 Axes>" | |
| ], | |
| "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAECCAYAAADXWsr9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAL1UlEQVR4nO3df6hX9R3H8ddrptVS0laL0MiMIUSw/IEsitg0w1a4f5YoFCw29I8tkg3K9s/ov/6K9scIxGpBZqQljNhaSkYMtprXbJnaKDFSKgsNsz+U7L0/vsdhznXPvZ3P537v9/18wBe/997vPe/3vdfX95zz/Z5z3o4IARhs3xrrBgCUR9CBBAg6kABBBxIg6EACBB1IoC+CbnuJ7bdtv2N7TeFaj9k+ZHtXyTqn1bvc9jbbu22/ZfuewvXOs/2a7Teaeg+UrNfUnGD7ddvPl67V1Ntv+03bO21vL1xrqu1Ntvfa3mP7uoK1Zjc/06nbUdurO1l4RIzpTdIESe9KmiVpkqQ3JF1dsN6NkuZK2lXp57tM0tzm/hRJ/y7881nS5Ob+REmvSvpB4Z/x15KekvR8pd/pfkkXV6r1hKRfNPcnSZpaqe4ESR9KuqKL5fXDGn2BpHciYl9EnJD0tKSflCoWEa9IOlxq+Wep90FE7GjufyZpj6TpBetFRBxrPpzY3IodFWV7hqRbJa0rVWOs2L5QvRXDo5IUESci4tNK5RdJejci3utiYf0Q9OmS3j/t4wMqGISxZHumpDnqrWVL1plge6ekQ5K2RETJeg9LulfSlwVrnCkkvWh7yPbKgnWulPSxpMebXZN1ti8oWO90yyVt6Gph/RD0FGxPlvSspNURcbRkrYg4GRHXSpohaYHta0rUsX2bpEMRMVRi+V/jhoiYK+kWSb+0fWOhOueot5v3SETMkfS5pKKvIUmS7UmSlkra2NUy+yHoByVdftrHM5rPDQzbE9UL+fqIeK5W3WYzc5ukJYVKXC9pqe396u1yLbT9ZKFa/xURB5t/D0narN7uXwkHJB04bYtok3rBL+0WSTsi4qOuFtgPQf+npO/ZvrJ5Jlsu6U9j3FNnbFu9fbw9EfFQhXqX2J7a3D9f0mJJe0vUioj7I2JGRMxU7+/2UkTcUaLWKbYvsD3l1H1JN0sq8g5KRHwo6X3bs5tPLZK0u0StM6xQh5vtUm/TZExFxBe2fyXpr+q90vhYRLxVqp7tDZJ+KOli2wck/S4iHi1VT7213p2S3mz2myXptxHx50L1LpP0hO0J6j2RPxMRVd72quRSSZt7z586R9JTEfFCwXp3S1rfrIT2SbqrYK1TT16LJa3qdLnNS/kABlg/bLoDKIygAwkQdCABgg4kQNCBBPoq6IUPZxyzWtSj3ljX66ugS6r5y6z6h6Me9cayXr8FHUABRQ6YsT3QR+FMmzZtxN9z/PhxnXvuuaOqN336yE/mO3z4sC666KJR1Tt6dOTn3Bw7dkyTJ08eVb2DB0d+akNEqDk6bsROnjw5qu8bLyLif34xY34I7Hh00003Va334IMPVq23devWqvXWrCl+QthXHDlypGq9fsCmO5AAQQcSIOhAAgQdSICgAwkQdCABgg4kQNCBBFoFvebIJADdGzbozUUG/6DeJWivlrTC9tWlGwPQnTZr9KojkwB0r03Q04xMAgZVZye1NCfK1z5nF0ALbYLeamRSRKyVtFYa/NNUgfGmzab7QI9MAjIYdo1ee2QSgO612kdv5oSVmhUGoDCOjAMSIOhAAgQdSICgAwkQdCABgg4kQNCBBAg6kACTWkah9uSUWbNmVa03mpFT38Thw4er1lu2bFnVehs3bqxa72xYowMJEHQgAYIOJEDQgQQIOpAAQQcSIOhAAgQdSICgAwkQdCCBNiOZHrN9yPauGg0B6F6bNfofJS0p3AeAgoYNekS8IqnuWQcAOsU+OpAAs9eABDoLOrPXgP7FpjuQQJu31zZI+ruk2bYP2P55+bYAdKnNkMUVNRoBUA6b7kACBB1IgKADCRB0IAGCDiRA0IEECDqQAEEHEhiI2Wvz5s2rWq/2LLSrrrqqar19+/ZVrbdly5aq9Wr/f2H2GoAqCDqQAEEHEiDoQAIEHUiAoAMJEHQgAYIOJEDQgQQIOpBAm4tDXm57m+3dtt+yfU+NxgB0p82x7l9I+k1E7LA9RdKQ7S0RsbtwbwA60mb22gcRsaO5/5mkPZKml24MQHdGtI9ue6akOZJeLdEMgDJan6Zqe7KkZyWtjoijZ/k6s9eAPtUq6LYnqhfy9RHx3Nkew+w1oH+1edXdkh6VtCciHirfEoCutdlHv17SnZIW2t7Z3H5cuC8AHWoze+1vklyhFwCFcGQckABBBxIg6EACBB1IgKADCRB0IAGCDiRA0IEEBmL22rRp06rWGxoaqlqv9iy02mr/PjNijQ4kQNCBBAg6kABBBxIg6EACBB1IgKADCRB0IAGCDiRA0IEE2lwF9jzbr9l+o5m99kCNxgB0p82x7sclLYyIY8313f9m+y8R8Y/CvQHoSJurwIakY82HE5sbAxqAcaTVPrrtCbZ3SjokaUtEMHsNGEdaBT0iTkbEtZJmSFpg+5ozH2N7pe3ttrd33SSAb2ZEr7pHxKeStklacpavrY2I+RExv6vmAHSjzavul9ie2tw/X9JiSXtLNwagO21edb9M0hO2J6j3xPBMRDxfti0AXWrzqvu/JM2p0AuAQjgyDkiAoAMJEHQgAYIOJEDQgQQIOpAAQQcSIOhAAsxeG4WtW7dWrTfoav/9jhw5UrVeP2CNDiRA0IEECDqQAEEHEiDoQAIEHUiAoAMJEHQgAYIOJEDQgQRaB70Z4vC6bS4MCYwzI1mj3yNpT6lGAJTTdiTTDEm3SlpXth0AJbRdoz8s6V5JXxbsBUAhbSa13CbpUEQMDfM4Zq8BfarNGv16SUtt75f0tKSFtp8880HMXgP617BBj4j7I2JGRMyUtFzSSxFxR/HOAHSG99GBBEZ0KamIeFnSy0U6AVAMa3QgAYIOJEDQgQQIOpAAQQcSIOhAAgQdSICgAwkMxOy12rO05s2bV7VebbVnodX+fW7cuLFqvX7AGh1IgKADCRB0IAGCDiRA0IEECDqQAEEHEiDoQAIEHUiAoAMJtDoEtrnU82eSTkr6gks6A+PLSI51/1FEfFKsEwDFsOkOJNA26CHpRdtDtleWbAhA99puut8QEQdtf1fSFtt7I+KV0x/QPAHwJAD0oVZr9Ig42Px7SNJmSQvO8hhmrwF9qs001QtsTzl1X9LNknaVbgxAd9psul8qabPtU49/KiJeKNoVgE4NG/SI2Cfp+xV6AVAIb68BCRB0IAGCDiRA0IEECDqQAEEHEiDoQAIEHUjAEdH9Qu3uF/o1Zs2aVbOctm/fXrXeqlWrqta7/fbbq9ar/febP3+wT8eICJ/5OdboQAIEHUiAoAMJEHQgAYIOJEDQgQQIOpAAQQcSIOhAAgQdSKBV0G1Ptb3J9l7be2xfV7oxAN1pO8Dh95JeiIif2p4k6dsFewLQsWGDbvtCSTdK+pkkRcQJSSfKtgWgS2023a+U9LGkx22/bntdM8jhK2yvtL3ddt1TuwAMq03Qz5E0V9IjETFH0ueS1pz5IEYyAf2rTdAPSDoQEa82H29SL/gAxolhgx4RH0p63/bs5lOLJO0u2hWATrV91f1uSeubV9z3SbqrXEsAutYq6BGxUxL73sA4xZFxQAIEHUiAoAMJEHQgAYIOJEDQgQQIOpAAQQcSGIjZa7WtXLmyar377ruvar2hoaGq9ZYtW1a13qBj9hqQFEEHEiDoQAIEHUiAoAMJEHQgAYIOJEDQgQQIOpDAsEG3Pdv2ztNuR22vrtEcgG4Me824iHhb0rWSZHuCpIOSNhfuC0CHRrrpvkjSuxHxXolmAJQx0qAvl7ShRCMAymkd9Oaa7kslbfw/X2f2GtCn2g5wkKRbJO2IiI/O9sWIWCtprTT4p6kC481INt1XiM12YFxqFfRmTPJiSc+VbQdACW1HMn0u6TuFewFQCEfGAQkQdCABgg4kQNCBBAg6kABBBxIg6EACBB1IgKADCZSavfaxpNGcs36xpE86bqcfalGPerXqXRERl5z5ySJBHy3b2yNi/qDVoh71xroem+5AAgQdSKDfgr52QGtRj3pjWq+v9tEBlNFva3QABRB0IAGCDiRA0IEECDqQwH8An6mM7cqa+WgAAAAASUVORK5CYII=\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 288x288 with 1 Axes>" | |
| ], | |
| "image/png": "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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 288x288 with 1 Axes>" | |
| ], | |
| "image/png": "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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 288x288 with 1 Axes>" | |
| ], | |
| "image/png": "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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 288x288 with 1 Axes>" | |
| ], | |
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| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "['DESCR', 'data', 'feature_names', 'frame', 'images', 'target', 'target_names']" | |
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| "df = pd.DataFrame(digits.data, columns=digits.feature_names)\n", | |
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| "\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-9f42bd40-1a9f-4945-a42c-ee58b45bfcad 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-9f42bd40-1a9f-4945-a42c-ee58b45bfcad');\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": 6 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Data Preprocessing" | |
| ], | |
| "metadata": { | |
| "id": "wlSjP2VRyzK4" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "scaler = StandardScaler()" | |
| ], | |
| "metadata": { | |
| "id": "QLyIraznyoeX" | |
| }, | |
| "execution_count": 7, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "x = df\n", | |
| "y = digits.target" | |
| ], | |
| "metadata": { | |
| "id": "WsVXzNrFzCTu" | |
| }, | |
| "execution_count": 8, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "x_scaled = scaler.fit_transform(x)" | |
| ], | |
| "metadata": { | |
| "id": "B1oJwubqzIJU" | |
| }, | |
| "execution_count": 9, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Principle Component Analysis" | |
| ], | |
| "metadata": { | |
| "id": "VtBfm5AczRKx" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from sklearn.decomposition import PCA\n", | |
| "\n", | |
| "pca = PCA(0.95)\n", | |
| "# Retains 95% of useful features and creates new dimensions\n", | |
| "\n", | |
| "x_pca = pca.fit_transform(x_scaled)" | |
| ], | |
| "metadata": { | |
| "id": "5saIlpumzMZz" | |
| }, | |
| "execution_count": 10, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "x_scaled.shape" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "PSdS0t1TziEb", | |
| "outputId": "7d4d608d-7430-479e-aee0-99b4fb5a1b32" | |
| }, | |
| "execution_count": 11, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "(1797, 64)" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 11 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "x_pca.shape" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "enft5aHZznFh", | |
| "outputId": "e822c555-1301-47be-8304-00c95ab7c09f" | |
| }, | |
| "execution_count": 12, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "(1797, 40)" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 12 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Splitting Dataset into Training Set and Testing Set" | |
| ], | |
| "metadata": { | |
| "id": "ugRBzdu40cNL" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "x_train, x_test, y_train, y_test = train_test_split(x_pca, y, random_state=42, test_size=0.2)" | |
| ], | |
| "metadata": { | |
| "id": "2yrHtmUizzij" | |
| }, | |
| "execution_count": 13, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "len(x_train)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "PO4XUN0306rr", | |
| "outputId": "31928d8d-7d77-47de-a025-17ce007586b2" | |
| }, | |
| "execution_count": 14, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "1437" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 14 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "len(x_test)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "U3HFsV2b08vW", | |
| "outputId": "8b396d5d-716d-420b-d386-1ad9fe52370a" | |
| }, | |
| "execution_count": 15, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "360" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 15 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Decision Tree Algorithm" | |
| ], | |
| "metadata": { | |
| "id": "DS1ZJn_i1BXq" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "DT = tree.DecisionTreeClassifier()" | |
| ], | |
| "metadata": { | |
| "id": "kmMoJb020-LY" | |
| }, | |
| "execution_count": 16, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "DT.fit(x_train, y_train)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "hWPXlM_r1QVg", | |
| "outputId": "bee082a5-818a-4ef0-a065-0e51a00bcfe6" | |
| }, | |
| "execution_count": 17, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "DecisionTreeClassifier()" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 17 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "y_pred_dt = DT.predict(x_test)" | |
| ], | |
| "metadata": { | |
| "id": "s15_fWZQ1ZkC" | |
| }, | |
| "execution_count": 18, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "DT.score(x_test, y_test)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "S6G4mwH51zIi", | |
| "outputId": "746216e6-4a86-4b4d-9814-c7ead15369eb" | |
| }, | |
| "execution_count": 19, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "0.8527777777777777" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 19 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "r2_score(y_test, y_pred_dt)\n", | |
| "# R-squared (R2) is a statistical measure that represents the proportion of the \n", | |
| "# variance for a dependent variable that's explained by an independent variable or variables" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "Omj4JdTq111C", | |
| "outputId": "a2716e1e-5eea-49cf-9e8b-aa10424ef929" | |
| }, | |
| "execution_count": 20, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "0.6619755451693168" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 20 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Naive Bayes Algorithm" | |
| ], | |
| "metadata": { | |
| "id": "Wx64lNvW18z4" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "GB = GaussianNB()" | |
| ], | |
| "metadata": { | |
| "id": "FQR9XNak16k7" | |
| }, | |
| "execution_count": 21, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "GB.fit(x_train, y_train)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "KRQS9gyY2K22", | |
| "outputId": "9721eb3a-5e1b-418d-82df-3a97bf913aa1" | |
| }, | |
| "execution_count": 22, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "GaussianNB()" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 22 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "y_pred_nb = GB.predict(x_test)" | |
| ], | |
| "metadata": { | |
| "id": "GtnaAvCP2QQ4" | |
| }, | |
| "execution_count": 23, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "GB.score(x_test, y_test)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "TBnqh8qt2hnH", | |
| "outputId": "3e3a28df-5d5b-457d-e823-4c450dda60b1" | |
| }, | |
| "execution_count": 24, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "0.8916666666666667" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 24 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "r2_score(y_test, y_pred_nb)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "Cdt7pvtK2jd7", | |
| "outputId": "6faa6dba-5600-49f6-8d68-22a7433413a7" | |
| }, | |
| "execution_count": 25, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "0.7403684506513264" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 25 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Confusion Matrix Comparison" | |
| ], | |
| "metadata": { | |
| "id": "SiL8gi5X2_zZ" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from sklearn.metrics import confusion_matrix\n", | |
| "\n", | |
| "cm = confusion_matrix(y_test, y_pred_nb)\n", | |
| "\n", | |
| "plt.figure(figsize=(15,10))\n", | |
| "sns.heatmap(cm, annot=True)\n", | |
| "plt.xlabel('Predicted')\n", | |
| "plt.ylabel(\"Actual\")\n", | |
| "plt.title(\"Naive Bayes Algorithm\", fontsize=\"20\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 644 | |
| }, | |
| "id": "Nn6GV6Bh2ncG", | |
| "outputId": "a67cecf8-93af-4d3a-9a3d-4123eb0aa535" | |
| }, | |
| "execution_count": 26, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "Text(0.5, 1.0, 'Naive Bayes Algorithm')" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 26 | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 1080x720 with 2 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "cm2 = confusion_matrix(y_test, y_pred_dt)\n", | |
| "\n", | |
| "plt.figure(figsize=(15,10))\n", | |
| "sns.heatmap(cm2, annot=True)\n", | |
| "plt.xlabel('Predicted')\n", | |
| "plt.ylabel(\"Actual\")\n", | |
| "plt.title(\"ID3 Algorithm\", fontsize=\"20\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 644 | |
| }, | |
| "id": "NWZjrsib3VC7", | |
| "outputId": "c415dbd2-b921-4366-df20-303b8769ddae" | |
| }, | |
| "execution_count": 27, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "Text(0.5, 1.0, 'ID3 Algorithm')" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 27 | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 1080x720 with 2 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| } | |
| ] | |
| } |
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