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ShahStavan/22bce539_practical_5.ipynb

Created September 29, 2023 19:24
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22bce539_practical_5.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyP8jfPENwh78S9kTzOjASEA",
"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/ShahStavan/b9796007943a71a60bdd2a8e31b97d35/22bce539_practical_5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"Name & Roll no. :- Shah Stavan, 22bce539\n",
"\n",
"Subject & Course code :- ML, 2CS501\n",
"\n",
"Date : 29/09/2023"
],
"metadata": {
"id": "_sFJmwl1QBOR"
}
},
{
"cell_type": "markdown",
"source": [
"# Practical 5 Naive Bayes\n",
"Naïve-Bayes – Multivariate Bernoulli, Multinomial and Gaussian using sklearn"
],
"metadata": {
"id": "Z6p8pDGdQK9w"
}
},
{
"cell_type": "markdown",
"source": [
"Text Feature Extraction using Common Vectorizer"
],
"metadata": {
"id": "X7eA9V3tQUxj"
}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zv1FHGGnP2gI",
"outputId": "b37e893f-9827-46ea-c25d-5f67920ad4af"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"List of unique words : ['and' 'documentation' 'first' 'is' 'library' 'of' 'one' 'paper'\n",
" 'research' 'second' 'the' 'third' 'this']\n",
"Document Term Matrix : \n",
"[[1 1 1 1 1 1 0 1 1 0 1 0 1]\n",
" [1 1 0 1 0 0 0 1 1 2 1 0 1]\n",
" [1 0 0 0 0 0 1 0 0 0 1 1 0]\n",
" [0 0 1 1 0 0 0 1 1 0 1 0 1]]\n"
]
}
],
"source": [
"from sklearn.feature_extraction.text import CountVectorizer\n",
"\n",
"# Create a CountVectorizer instance\n",
"vectorizer = CountVectorizer()\n",
"\n",
"# Define the corpus (collection of documents)\n",
"corpus = [\n",
" 'This is the first Research Paper and Documentation of Library.',\n",
" 'This is the second second Research Paper and Documentation.',\n",
" 'And the third one.',\n",
" 'Is this the first Research Paper?'\n",
"]\n",
"\n",
"# Transform the text data into a Document-Term Matrix (DTM)\n",
"X = vectorizer.fit_transform(corpus)\n",
"\n",
"# Get the list of unique words (features)\n",
"unique_words = vectorizer.get_feature_names_out()\n",
"\n",
"# Print the unique words\n",
"print(\"List of unique words : \", unique_words)\n",
"\n",
"# Print the Document-Term Matrix (DTM)\n",
"print(\"Document Term Matrix : \")\n",
"print(X.toarray())\n"
]
},
{
"cell_type": "markdown",
"source": [
"1. Importing Python Libraries and Dependencies\n"
],
"metadata": {
"id": "jk4OBVk5QwHI"
}
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import confusion_matrix\n",
"from sklearn.naive_bayes import MultinomialNB\n",
"from sklearn.naive_bayes import BernoulliNB\n",
"from sklearn.naive_bayes import GaussianNB"
],
"metadata": {
"id": "i7CusTIOQvwP"
},
"execution_count": 6,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"2. Upload the Dataset"
],
"metadata": {
"id": "GqQCUz63RGZ4"
}
},
{
"cell_type": "code",
"source": [
"from google.colab import files\n",
"uploaded = files.upload()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 73
},
"id": "Sq6QkzGlQ8WR",
"outputId": "3936c5cd-b08a-48bf-f18c-2e9b2e0f35d1"
},
"execution_count": 3,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <input type=\"file\" id=\"files-404040ae-d7be-4d65-99b6-dff1c6c94cca\" name=\"files[]\" multiple disabled\n",
" style=\"border:none\" />\n",
" <output id=\"result-404040ae-d7be-4d65-99b6-dff1c6c94cca\">\n",
" Upload widget is only available when the cell has been executed in the\n",
" current browser session. Please rerun this cell to enable.\n",
" </output>\n",
" <script>// Copyright 2017 Google LLC\n",
"//\n",
"// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"// you may not use this file except in compliance with the License.\n",
"// You may obtain a copy of the License at\n",
"//\n",
"// http://www.apache.org/licenses/LICENSE-2.0\n",
"//\n",
"// Unless required by applicable law or agreed to in writing, software\n",
"// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"// See the License for the specific language governing permissions and\n",
"// limitations under the License.\n",
"\n",
"/**\n",
" * @fileoverview Helpers for google.colab Python module.\n",
" */\n",
"(function(scope) {\n",
"function span(text, styleAttributes = {}) {\n",
" const element = document.createElement('span');\n",
" element.textContent = text;\n",
" for (const key of Object.keys(styleAttributes)) {\n",
" element.style[key] = styleAttributes[key];\n",
" }\n",
" return element;\n",
"}\n",
"\n",
"// Max number of bytes which will be uploaded at a time.\n",
"const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
"\n",
"function _uploadFiles(inputId, outputId) {\n",
" const steps = uploadFilesStep(inputId, outputId);\n",
" const outputElement = document.getElementById(outputId);\n",
" // Cache steps on the outputElement to make it available for the next call\n",
" // to uploadFilesContinue from Python.\n",
" outputElement.steps = steps;\n",
"\n",
" return _uploadFilesContinue(outputId);\n",
"}\n",
"\n",
"// This is roughly an async generator (not supported in the browser yet),\n",
"// where there are multiple asynchronous steps and the Python side is going\n",
"// to poll for completion of each step.\n",
"// This uses a Promise to block the python side on completion of each step,\n",
"// then passes the result of the previous step as the input to the next step.\n",
"function _uploadFilesContinue(outputId) {\n",
" const outputElement = document.getElementById(outputId);\n",
" const steps = outputElement.steps;\n",
"\n",
" const next = steps.next(outputElement.lastPromiseValue);\n",
" return Promise.resolve(next.value.promise).then((value) => {\n",
" // Cache the last promise value to make it available to the next\n",
" // step of the generator.\n",
" outputElement.lastPromiseValue = value;\n",
" return next.value.response;\n",
" });\n",
"}\n",
"\n",
"/**\n",
" * Generator function which is called between each async step of the upload\n",
" * process.\n",
" * @param {string} inputId Element ID of the input file picker element.\n",
" * @param {string} outputId Element ID of the output display.\n",
" * @return {!Iterable<!Object>} Iterable of next steps.\n",
" */\n",
"function* uploadFilesStep(inputId, outputId) {\n",
" const inputElement = document.getElementById(inputId);\n",
" inputElement.disabled = false;\n",
"\n",
" const outputElement = document.getElementById(outputId);\n",
" outputElement.innerHTML = '';\n",
"\n",
" const pickedPromise = new Promise((resolve) => {\n",
" inputElement.addEventListener('change', (e) => {\n",
" resolve(e.target.files);\n",
" });\n",
" });\n",
"\n",
" const cancel = document.createElement('button');\n",
" inputElement.parentElement.appendChild(cancel);\n",
" cancel.textContent = 'Cancel upload';\n",
" const cancelPromise = new Promise((resolve) => {\n",
" cancel.onclick = () => {\n",
" resolve(null);\n",
" };\n",
" });\n",
"\n",
" // Wait for the user to pick the files.\n",
" const files = yield {\n",
" promise: Promise.race([pickedPromise, cancelPromise]),\n",
" response: {\n",
" action: 'starting',\n",
" }\n",
" };\n",
"\n",
" cancel.remove();\n",
"\n",
" // Disable the input element since further picks are not allowed.\n",
" inputElement.disabled = true;\n",
"\n",
" if (!files) {\n",
" return {\n",
" response: {\n",
" action: 'complete',\n",
" }\n",
" };\n",
" }\n",
"\n",
" for (const file of files) {\n",
" const li = document.createElement('li');\n",
" li.append(span(file.name, {fontWeight: 'bold'}));\n",
" li.append(span(\n",
" `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
" `last modified: ${\n",
" file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
" 'n/a'} - `));\n",
" const percent = span('0% done');\n",
" li.appendChild(percent);\n",
"\n",
" outputElement.appendChild(li);\n",
"\n",
" const fileDataPromise = new Promise((resolve) => {\n",
" const reader = new FileReader();\n",
" reader.onload = (e) => {\n",
" resolve(e.target.result);\n",
" };\n",
" reader.readAsArrayBuffer(file);\n",
" });\n",
" // Wait for the data to be ready.\n",
" let fileData = yield {\n",
" promise: fileDataPromise,\n",
" response: {\n",
" action: 'continue',\n",
" }\n",
" };\n",
"\n",
" // Use a chunked sending to avoid message size limits. See b/62115660.\n",
" let position = 0;\n",
" do {\n",
" const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
" const chunk = new Uint8Array(fileData, position, length);\n",
" position += length;\n",
"\n",
" const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
" yield {\n",
" response: {\n",
" action: 'append',\n",
" file: file.name,\n",
" data: base64,\n",
" },\n",
" };\n",
"\n",
" let percentDone = fileData.byteLength === 0 ?\n",
" 100 :\n",
" Math.round((position / fileData.byteLength) * 100);\n",
" percent.textContent = `${percentDone}% done`;\n",
"\n",
" } while (position < fileData.byteLength);\n",
" }\n",
"\n",
" // All done.\n",
" yield {\n",
" response: {\n",
" action: 'complete',\n",
" }\n",
" };\n",
"}\n",
"\n",
"scope.google = scope.google || {};\n",
"scope.google.colab = scope.google.colab || {};\n",
"scope.google.colab._files = {\n",
" _uploadFiles,\n",
" _uploadFilesContinue,\n",
"};\n",
"})(self);\n",
"</script> "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saving emails.csv to emails.csv\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"\n",
"# Load the dataset\n",
"data_file_path = 'emails.csv'\n",
"df = pd.read_csv(data_file_path)\n",
"\n",
"# Display the first few rows of the dataset\n",
"print(df.head())\n",
"\n",
"# Split the data into features (X) and target (y)\n",
"y = df['Prediction']\n",
"X = df.drop(['Prediction', 'Email No.'], axis=1)\n",
"\n",
"# Split the data into training and testing sets\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=10)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zJM_wznNRL6p",
"outputId": "5b017cf3-a031-4560-8534-06a4ab1f5b9e"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" Email No. the to ect and for of a you hou ... connevey jay \\\n",
"0 Email 1 0 0 1 0 0 0 2 0 0 ... 0 0 \n",
"1 Email 2 8 13 24 6 6 2 102 1 27 ... 0 0 \n",
"2 Email 3 0 0 1 0 0 0 8 0 0 ... 0 0 \n",
"3 Email 4 0 5 22 0 5 1 51 2 10 ... 0 0 \n",
"4 Email 5 7 6 17 1 5 2 57 0 9 ... 0 0 \n",
"\n",
" valued lay infrastructure military allowing ff dry Prediction \n",
"0 0 0 0 0 0 0 0 0 \n",
"1 0 0 0 0 0 1 0 0 \n",
"2 0 0 0 0 0 0 0 0 \n",
"3 0 0 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 1 0 0 \n",
"\n",
"[5 rows x 3002 columns]\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Practical 5 A : Multivariate Bernoulli NB"
],
"metadata": {
"id": "BXoEX9QfSSSc"
}
},
{
"cell_type": "code",
"source": [
"# Create and train the Bernoulli Naive Bayes model\n",
"model = BernoulliNB()\n",
"model.fit(X_train, y_train)\n",
"\n",
"# Make predictions on the test data\n",
"y_pred = model.predict(X_test)\n",
"\n",
"# Calculate and print the accuracy\n",
"accuracy_score = model.score(X_test, y_test)\n",
"print(\"Accuracy: {:.2f}%\".format(accuracy_score * 100))\n",
"\n",
"# Calculate and print the confusion matrix\n",
"confusion = confusion_matrix(y_test, y_pred)\n",
"print(\"Confusion Matrix:\")\n",
"print(confusion)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ftRIJhYZSVKr",
"outputId": "67f84f4f-802f-4c5c-d9fb-598f1de80992"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Accuracy: 87.34%\n",
"Confusion Matrix:\n",
"[[680 57]\n",
" [ 74 224]]\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Practical 5 B : Multinomial NB"
],
"metadata": {
"id": "7VtTcmFmSeYx"
}
},
{
"cell_type": "code",
"source": [
"# Create and train the Multinomial Naive Bayes model\n",
"model = MultinomialNB()\n",
"model.fit(X_train, y_train)\n",
"\n",
"# Predict the labels for the test dataset\n",
"y_pred = model.predict(X_test)\n",
"\n",
"# Calculate and print the accuracy\n",
"accuracy_score = model.score(X_test, y_test)\n",
"print(\"Accuracy: {:.2f}%\".format(accuracy_score * 100))\n",
"\n",
"# Calculate and print the confusion matrix\n",
"confusion = confusion_matrix(y_test, y_pred)\n",
"print(\"Confusion Matrix:\")\n",
"print(confusion)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "d2dREuZISgs4",
"outputId": "749737cc-ac72-48fc-93c7-b041bd1f89b3"
},
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Accuracy: 94.20%\n",
"Confusion Matrix:\n",
"[[693 44]\n",
" [ 16 282]]\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Practical 5 C : Gaussian NB"
],
"metadata": {
"id": "Wn22wKr-TanR"
}
},
{
"cell_type": "code",
"source": [
"# Initialize and train the Gaussian Naive Bayes model\n",
"model = GaussianNB()\n",
"model.fit(X_train, y_train)\n",
"\n",
"# Make predictions on the test data\n",
"y_pred = model.predict(X_test)\n",
"\n",
"# Calculate and print the accuracy\n",
"accuracy_score = model.score(X_test, y_test)\n",
"print(\"Accuracy: {:.2f}%\".format(accuracy_score * 100))\n",
"\n",
"\n",
"# Print the confusion matrix\n",
"confusion = confusion_matrix(y_test, y_pred)\n",
"print(\"Confusion Matrix:\")\n",
"print(confusion)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0MnwEfu7TbYz",
"outputId": "36ca289f-ac50-4bea-dcc2-cb27afabdc20"
},
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Accuracy: 93.82%\n",
"Confusion Matrix:\n",
"[[692 45]\n",
" [ 19 279]]\n"
]
}
]
}
]
}
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