mirror of
https://github.com/deepseek-ai/DeepSeek-Coder.git
synced 2025-02-23 14:19:09 -05:00
1835 lines
69 KiB
Plaintext
1835 lines
69 KiB
Plaintext
{
|
||
"nbformat": 4,
|
||
"nbformat_minor": 0,
|
||
"metadata": {
|
||
"colab": {
|
||
"provenance": [],
|
||
"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/github/Orrm23/DeepSeek-Coder/blob/main/TitanicSurvivalPrediction_NAIVEBAYES.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "Qmi36D7ZPY-M"
|
||
},
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||
"source": [
|
||
"# **Day - 6 Titanic Survival Prediction using NAIVE BAYES**"
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||
]
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||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "Q8lgHC2zPTE4"
|
||
},
|
||
"source": [
|
||
"### *Importing basic Libraries*"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"id": "nKKbpfywIqAq"
|
||
},
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import numpy as np"
|
||
],
|
||
"execution_count": 1,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "xfyZYdDaPnJz"
|
||
},
|
||
"source": [
|
||
"### *Choose Dataset file from Local Directory*"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 73
|
||
},
|
||
"id": "ki0LIHaOP869",
|
||
"outputId": "b9ecc9aa-e308-4031-a096-fd77b3f36d4c"
|
||
},
|
||
"source": [
|
||
"from google.colab import files\n",
|
||
"uploaded = files.upload()"
|
||
],
|
||
"execution_count": 2,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<IPython.core.display.HTML object>"
|
||
],
|
||
"text/html": [
|
||
"\n",
|
||
" <input type=\"file\" id=\"files-0ec9cc56-2e03-40c4-aa43-3d5dac3d68d6\" name=\"files[]\" multiple disabled\n",
|
||
" style=\"border:none\" />\n",
|
||
" <output id=\"result-0ec9cc56-2e03-40c4-aa43-3d5dac3d68d6\">\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 titanicsurvival.csv to titanicsurvival.csv\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "oEx3VSimP_DF"
|
||
},
|
||
"source": [
|
||
"### *Load Dataset*"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"id": "HQVO5TRBQCGP"
|
||
},
|
||
"source": [
|
||
"dataset = pd.read_csv('titanicsurvival.csv')"
|
||
],
|
||
"execution_count": 3,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "Da6ym5z7QHwY"
|
||
},
|
||
"source": [
|
||
"### *Summarize Dataset*"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "Esd6w-GBQLZ5",
|
||
"outputId": "44a2090a-3b0f-4ee2-c37b-328f185d2bba"
|
||
},
|
||
"source": [
|
||
"print(dataset.shape)\n",
|
||
"print(dataset.head(5))"
|
||
],
|
||
"execution_count": 4,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"(891, 5)\n",
|
||
" Pclass Sex Age Fare Survived\n",
|
||
"0 3 male 22.0 7.2500 0\n",
|
||
"1 1 female 38.0 71.2833 1\n",
|
||
"2 3 female 26.0 7.9250 1\n",
|
||
"3 1 female 35.0 53.1000 1\n",
|
||
"4 3 male 35.0 8.0500 0\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "1AALh-8cS6Jd"
|
||
},
|
||
"source": [
|
||
"### *Mapping Text Data to Binary Value*"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "rcr5RdqtS9iD",
|
||
"outputId": "24ecd79c-ef7c-4301-be39-ecad5bb7361e"
|
||
},
|
||
"source": [
|
||
"income_set = set(dataset['Sex'])\n",
|
||
"dataset['Sex'] = dataset['Sex'].map({'female': 0, 'male': 1}).astype(int)\n",
|
||
"print(dataset.head)"
|
||
],
|
||
"execution_count": 5,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"<bound method NDFrame.head of Pclass Sex Age Fare Survived\n",
|
||
"0 3 1 22.0 7.2500 0\n",
|
||
"1 1 0 38.0 71.2833 1\n",
|
||
"2 3 0 26.0 7.9250 1\n",
|
||
"3 1 0 35.0 53.1000 1\n",
|
||
"4 3 1 35.0 8.0500 0\n",
|
||
".. ... ... ... ... ...\n",
|
||
"886 2 1 27.0 13.0000 0\n",
|
||
"887 1 0 19.0 30.0000 1\n",
|
||
"888 3 0 NaN 23.4500 0\n",
|
||
"889 1 1 26.0 30.0000 1\n",
|
||
"890 3 1 32.0 7.7500 0\n",
|
||
"\n",
|
||
"[891 rows x 5 columns]>\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "_j0iPDCWRYAg"
|
||
},
|
||
"source": [
|
||
"### *Segregate Dataset into X(Input/IndependentVariable) & Y(Output/DependentVariable)*"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 424
|
||
},
|
||
"id": "Cqyxx7qQRYp7",
|
||
"outputId": "f7fa1388-e792-4dab-ef8b-6110b7b7d6f5"
|
||
},
|
||
"source": [
|
||
"X = dataset.drop('Survived',axis='columns')\n",
|
||
"X"
|
||
],
|
||
"execution_count": 6,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
" Pclass Sex Age Fare\n",
|
||
"0 3 1 22.0 7.2500\n",
|
||
"1 1 0 38.0 71.2833\n",
|
||
"2 3 0 26.0 7.9250\n",
|
||
"3 1 0 35.0 53.1000\n",
|
||
"4 3 1 35.0 8.0500\n",
|
||
".. ... ... ... ...\n",
|
||
"886 2 1 27.0 13.0000\n",
|
||
"887 1 0 19.0 30.0000\n",
|
||
"888 3 0 NaN 23.4500\n",
|
||
"889 1 1 26.0 30.0000\n",
|
||
"890 3 1 32.0 7.7500\n",
|
||
"\n",
|
||
"[891 rows x 4 columns]"
|
||
],
|
||
"text/html": [
|
||
"\n",
|
||
" <div id=\"df-6578e182-af60-4e65-af5c-bc6cc15ae1ca\" 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>Pclass</th>\n",
|
||
" <th>Sex</th>\n",
|
||
" <th>Age</th>\n",
|
||
" <th>Fare</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>3</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>22.0</td>\n",
|
||
" <td>7.2500</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>38.0</td>\n",
|
||
" <td>71.2833</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>3</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>26.0</td>\n",
|
||
" <td>7.9250</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>35.0</td>\n",
|
||
" <td>53.1000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>3</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>35.0</td>\n",
|
||
" <td>8.0500</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>886</th>\n",
|
||
" <td>2</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>27.0</td>\n",
|
||
" <td>13.0000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>887</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>19.0</td>\n",
|
||
" <td>30.0000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>888</th>\n",
|
||
" <td>3</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>23.4500</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>889</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>26.0</td>\n",
|
||
" <td>30.0000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>890</th>\n",
|
||
" <td>3</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>32.0</td>\n",
|
||
" <td>7.7500</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>891 rows × 4 columns</p>\n",
|
||
"</div>\n",
|
||
" <div class=\"colab-df-buttons\">\n",
|
||
"\n",
|
||
" <div class=\"colab-df-container\">\n",
|
||
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6578e182-af60-4e65-af5c-bc6cc15ae1ca')\"\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 -960 960 960\">\n",
|
||
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
|
||
" </svg>\n",
|
||
" </button>\n",
|
||
"\n",
|
||
" <style>\n",
|
||
" .colab-df-container {\n",
|
||
" display:flex;\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",
|
||
" .colab-df-buttons div {\n",
|
||
" margin-bottom: 4px;\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-6578e182-af60-4e65-af5c-bc6cc15ae1ca 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-6578e182-af60-4e65-af5c-bc6cc15ae1ca');\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",
|
||
"\n",
|
||
"\n",
|
||
"<div id=\"df-53e2a80a-be56-4820-b9d7-3afb34fdeb42\">\n",
|
||
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-53e2a80a-be56-4820-b9d7-3afb34fdeb42')\"\n",
|
||
" title=\"Suggest charts\"\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",
|
||
" <g>\n",
|
||
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
|
||
" </g>\n",
|
||
"</svg>\n",
|
||
" </button>\n",
|
||
"\n",
|
||
"<style>\n",
|
||
" .colab-df-quickchart {\n",
|
||
" --bg-color: #E8F0FE;\n",
|
||
" --fill-color: #1967D2;\n",
|
||
" --hover-bg-color: #E2EBFA;\n",
|
||
" --hover-fill-color: #174EA6;\n",
|
||
" --disabled-fill-color: #AAA;\n",
|
||
" --disabled-bg-color: #DDD;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-quickchart {\n",
|
||
" --bg-color: #3B4455;\n",
|
||
" --fill-color: #D2E3FC;\n",
|
||
" --hover-bg-color: #434B5C;\n",
|
||
" --hover-fill-color: #FFFFFF;\n",
|
||
" --disabled-bg-color: #3B4455;\n",
|
||
" --disabled-fill-color: #666;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-quickchart {\n",
|
||
" background-color: var(--bg-color);\n",
|
||
" border: none;\n",
|
||
" border-radius: 50%;\n",
|
||
" cursor: pointer;\n",
|
||
" display: none;\n",
|
||
" fill: var(--fill-color);\n",
|
||
" height: 32px;\n",
|
||
" padding: 0;\n",
|
||
" width: 32px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-quickchart:hover {\n",
|
||
" background-color: var(--hover-bg-color);\n",
|
||
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
||
" fill: var(--button-hover-fill-color);\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-quickchart-complete:disabled,\n",
|
||
" .colab-df-quickchart-complete:disabled:hover {\n",
|
||
" background-color: var(--disabled-bg-color);\n",
|
||
" fill: var(--disabled-fill-color);\n",
|
||
" box-shadow: none;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-spinner {\n",
|
||
" border: 2px solid var(--fill-color);\n",
|
||
" border-color: transparent;\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" animation:\n",
|
||
" spin 1s steps(1) infinite;\n",
|
||
" }\n",
|
||
"\n",
|
||
" @keyframes spin {\n",
|
||
" 0% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" border-left-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 20% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-left-color: var(--fill-color);\n",
|
||
" border-top-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 30% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-left-color: var(--fill-color);\n",
|
||
" border-top-color: var(--fill-color);\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 40% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" border-top-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 60% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 80% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 90% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"\n",
|
||
" <script>\n",
|
||
" async function quickchart(key) {\n",
|
||
" const quickchartButtonEl =\n",
|
||
" document.querySelector('#' + key + ' button');\n",
|
||
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
|
||
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
|
||
" try {\n",
|
||
" const charts = await google.colab.kernel.invokeFunction(\n",
|
||
" 'suggestCharts', [key], {});\n",
|
||
" } catch (error) {\n",
|
||
" console.error('Error during call to suggestCharts:', error);\n",
|
||
" }\n",
|
||
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
|
||
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
|
||
" }\n",
|
||
" (() => {\n",
|
||
" let quickchartButtonEl =\n",
|
||
" document.querySelector('#df-53e2a80a-be56-4820-b9d7-3afb34fdeb42 button');\n",
|
||
" quickchartButtonEl.style.display =\n",
|
||
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
||
" })();\n",
|
||
" </script>\n",
|
||
"</div>\n",
|
||
"\n",
|
||
" <div id=\"id_10c9f5a8-fe19-4ff8-8074-a84d473ca1d2\">\n",
|
||
" <style>\n",
|
||
" .colab-df-generate {\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-generate: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-generate {\n",
|
||
" background-color: #3B4455;\n",
|
||
" fill: #D2E3FC;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-generate: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",
|
||
" <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X')\"\n",
|
||
" title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
|
||
" </svg>\n",
|
||
" </button>\n",
|
||
" <script>\n",
|
||
" (() => {\n",
|
||
" const buttonEl =\n",
|
||
" document.querySelector('#id_10c9f5a8-fe19-4ff8-8074-a84d473ca1d2 button.colab-df-generate');\n",
|
||
" buttonEl.style.display =\n",
|
||
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
||
"\n",
|
||
" buttonEl.onclick = () => {\n",
|
||
" google.colab.notebook.generateWithVariable('X');\n",
|
||
" }\n",
|
||
" })();\n",
|
||
" </script>\n",
|
||
" </div>\n",
|
||
"\n",
|
||
" </div>\n",
|
||
" </div>\n"
|
||
],
|
||
"application/vnd.google.colaboratory.intrinsic+json": {
|
||
"type": "dataframe",
|
||
"variable_name": "X",
|
||
"summary": "{\n \"name\": \"X\",\n \"rows\": 891,\n \"fields\": [\n {\n \"column\": \"Pclass\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 1,\n \"max\": 3,\n \"num_unique_values\": 3,\n \"samples\": [\n 3,\n 1,\n 2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Sex\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Age\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 14.526497332334044,\n \"min\": 0.42,\n \"max\": 80.0,\n \"num_unique_values\": 88,\n \"samples\": [\n 0.75,\n 22.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Fare\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 49.693428597180905,\n \"min\": 0.0,\n \"max\": 512.3292,\n \"num_unique_values\": 248,\n \"samples\": [\n 11.2417,\n 51.8625\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
|
||
}
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 6
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 458
|
||
},
|
||
"id": "1F1tC2tRRddY",
|
||
"outputId": "dfa4f8ca-945a-465b-ab63-637411837fb8"
|
||
},
|
||
"source": [
|
||
"Y = dataset.Survived\n",
|
||
"Y"
|
||
],
|
||
"execution_count": 7,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"0 0\n",
|
||
"1 1\n",
|
||
"2 1\n",
|
||
"3 1\n",
|
||
"4 0\n",
|
||
" ..\n",
|
||
"886 0\n",
|
||
"887 1\n",
|
||
"888 0\n",
|
||
"889 1\n",
|
||
"890 0\n",
|
||
"Name: Survived, Length: 891, dtype: int64"
|
||
],
|
||
"text/html": [
|
||
"<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>Survived</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>886</th>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>887</th>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>888</th>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>889</th>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>890</th>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>891 rows × 1 columns</p>\n",
|
||
"</div><br><label><b>dtype:</b> int64</label>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 7
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "SibVwENGTpsN"
|
||
},
|
||
"source": [
|
||
"Finding & Removing NA values from our Features X"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "soVDtqhRTwHZ",
|
||
"outputId": "e18b9852-5d91-40cb-c46d-94f3402f13a2"
|
||
},
|
||
"source": [
|
||
"X.columns[X.isna().any()]"
|
||
],
|
||
"execution_count": 8,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"Index(['Age'], dtype='object')"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 8
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"id": "0_jCaFTRXQj1"
|
||
},
|
||
"source": [
|
||
"X.Age = X.Age.fillna(X.Age.mean())"
|
||
],
|
||
"execution_count": 9,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "nYNPgh4cX0bt"
|
||
},
|
||
"source": [
|
||
"### *Test again to check any na value*"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "QSBSGrNfX3NA",
|
||
"outputId": "5a65aec5-9704-4c66-8c2c-9012d961c99b"
|
||
},
|
||
"source": [
|
||
"X.columns[X.isna().any()]"
|
||
],
|
||
"execution_count": 10,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"Index([], dtype='object')"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 10
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "R4ngba4SYEue"
|
||
},
|
||
"source": [
|
||
"### *Splitting Dataset into Train & Test*"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"id": "vy9RTlZ4YFyO"
|
||
},
|
||
"source": [
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.25,random_state =0)"
|
||
],
|
||
"execution_count": 11,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "ocZLLSzgYl9V"
|
||
},
|
||
"source": [
|
||
"### *Training*"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 80
|
||
},
|
||
"id": "tPSuaammYz_4",
|
||
"outputId": "a3a5cc02-a37f-41be-d0d6-514872a484e1"
|
||
},
|
||
"source": [
|
||
"from sklearn.naive_bayes import GaussianNB\n",
|
||
"model = GaussianNB()\n",
|
||
"model.fit(X_train, y_train)"
|
||
],
|
||
"execution_count": 12,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"GaussianNB()"
|
||
],
|
||
"text/html": [
|
||
"<style>#sk-container-id-1 {\n",
|
||
" /* Definition of color scheme common for light and dark mode */\n",
|
||
" --sklearn-color-text: #000;\n",
|
||
" --sklearn-color-text-muted: #666;\n",
|
||
" --sklearn-color-line: gray;\n",
|
||
" /* Definition of color scheme for unfitted estimators */\n",
|
||
" --sklearn-color-unfitted-level-0: #fff5e6;\n",
|
||
" --sklearn-color-unfitted-level-1: #f6e4d2;\n",
|
||
" --sklearn-color-unfitted-level-2: #ffe0b3;\n",
|
||
" --sklearn-color-unfitted-level-3: chocolate;\n",
|
||
" /* Definition of color scheme for fitted estimators */\n",
|
||
" --sklearn-color-fitted-level-0: #f0f8ff;\n",
|
||
" --sklearn-color-fitted-level-1: #d4ebff;\n",
|
||
" --sklearn-color-fitted-level-2: #b3dbfd;\n",
|
||
" --sklearn-color-fitted-level-3: cornflowerblue;\n",
|
||
"\n",
|
||
" /* Specific color for light theme */\n",
|
||
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
|
||
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
|
||
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
|
||
" --sklearn-color-icon: #696969;\n",
|
||
"\n",
|
||
" @media (prefers-color-scheme: dark) {\n",
|
||
" /* Redefinition of color scheme for dark theme */\n",
|
||
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
|
||
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
|
||
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
|
||
" --sklearn-color-icon: #878787;\n",
|
||
" }\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-1 {\n",
|
||
" color: var(--sklearn-color-text);\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-1 pre {\n",
|
||
" padding: 0;\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-1 input.sk-hidden--visually {\n",
|
||
" border: 0;\n",
|
||
" clip: rect(1px 1px 1px 1px);\n",
|
||
" clip: rect(1px, 1px, 1px, 1px);\n",
|
||
" height: 1px;\n",
|
||
" margin: -1px;\n",
|
||
" overflow: hidden;\n",
|
||
" padding: 0;\n",
|
||
" position: absolute;\n",
|
||
" width: 1px;\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-1 div.sk-dashed-wrapped {\n",
|
||
" border: 1px dashed var(--sklearn-color-line);\n",
|
||
" margin: 0 0.4em 0.5em 0.4em;\n",
|
||
" box-sizing: border-box;\n",
|
||
" padding-bottom: 0.4em;\n",
|
||
" background-color: var(--sklearn-color-background);\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-1 div.sk-container {\n",
|
||
" /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
|
||
" but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
|
||
" so we also need the `!important` here to be able to override the\n",
|
||
" default hidden behavior on the sphinx rendered scikit-learn.org.\n",
|
||
" See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
|
||
" display: inline-block !important;\n",
|
||
" position: relative;\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-1 div.sk-text-repr-fallback {\n",
|
||
" display: none;\n",
|
||
"}\n",
|
||
"\n",
|
||
"div.sk-parallel-item,\n",
|
||
"div.sk-serial,\n",
|
||
"div.sk-item {\n",
|
||
" /* draw centered vertical line to link estimators */\n",
|
||
" background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
|
||
" background-size: 2px 100%;\n",
|
||
" background-repeat: no-repeat;\n",
|
||
" background-position: center center;\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* Parallel-specific style estimator block */\n",
|
||
"\n",
|
||
"#sk-container-id-1 div.sk-parallel-item::after {\n",
|
||
" content: \"\";\n",
|
||
" width: 100%;\n",
|
||
" border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
|
||
" flex-grow: 1;\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-1 div.sk-parallel {\n",
|
||
" display: flex;\n",
|
||
" align-items: stretch;\n",
|
||
" justify-content: center;\n",
|
||
" background-color: var(--sklearn-color-background);\n",
|
||
" position: relative;\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-1 div.sk-parallel-item {\n",
|
||
" display: flex;\n",
|
||
" flex-direction: column;\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
|
||
" align-self: flex-end;\n",
|
||
" width: 50%;\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
|
||
" align-self: flex-start;\n",
|
||
" width: 50%;\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
|
||
" width: 0;\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* Serial-specific style estimator block */\n",
|
||
"\n",
|
||
"#sk-container-id-1 div.sk-serial {\n",
|
||
" display: flex;\n",
|
||
" flex-direction: column;\n",
|
||
" align-items: center;\n",
|
||
" background-color: var(--sklearn-color-background);\n",
|
||
" padding-right: 1em;\n",
|
||
" padding-left: 1em;\n",
|
||
"}\n",
|
||
"\n",
|
||
"\n",
|
||
"/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
|
||
"clickable and can be expanded/collapsed.\n",
|
||
"- Pipeline and ColumnTransformer use this feature and define the default style\n",
|
||
"- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
|
||
"*/\n",
|
||
"\n",
|
||
"/* Pipeline and ColumnTransformer style (default) */\n",
|
||
"\n",
|
||
"#sk-container-id-1 div.sk-toggleable {\n",
|
||
" /* Default theme specific background. It is overwritten whether we have a\n",
|
||
" specific estimator or a Pipeline/ColumnTransformer */\n",
|
||
" background-color: var(--sklearn-color-background);\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* Toggleable label */\n",
|
||
"#sk-container-id-1 label.sk-toggleable__label {\n",
|
||
" cursor: pointer;\n",
|
||
" display: flex;\n",
|
||
" width: 100%;\n",
|
||
" margin-bottom: 0;\n",
|
||
" padding: 0.5em;\n",
|
||
" box-sizing: border-box;\n",
|
||
" text-align: center;\n",
|
||
" align-items: start;\n",
|
||
" justify-content: space-between;\n",
|
||
" gap: 0.5em;\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-1 label.sk-toggleable__label .caption {\n",
|
||
" font-size: 0.6rem;\n",
|
||
" font-weight: lighter;\n",
|
||
" color: var(--sklearn-color-text-muted);\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
|
||
" /* Arrow on the left of the label */\n",
|
||
" content: \"▸\";\n",
|
||
" float: left;\n",
|
||
" margin-right: 0.25em;\n",
|
||
" color: var(--sklearn-color-icon);\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
|
||
" color: var(--sklearn-color-text);\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* Toggleable content - dropdown */\n",
|
||
"\n",
|
||
"#sk-container-id-1 div.sk-toggleable__content {\n",
|
||
" max-height: 0;\n",
|
||
" max-width: 0;\n",
|
||
" overflow: hidden;\n",
|
||
" text-align: left;\n",
|
||
" /* unfitted */\n",
|
||
" background-color: var(--sklearn-color-unfitted-level-0);\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
|
||
" /* fitted */\n",
|
||
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-1 div.sk-toggleable__content pre {\n",
|
||
" margin: 0.2em;\n",
|
||
" border-radius: 0.25em;\n",
|
||
" color: var(--sklearn-color-text);\n",
|
||
" /* unfitted */\n",
|
||
" background-color: var(--sklearn-color-unfitted-level-0);\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
|
||
" /* unfitted */\n",
|
||
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
|
||
" /* Expand drop-down */\n",
|
||
" max-height: 200px;\n",
|
||
" max-width: 100%;\n",
|
||
" overflow: auto;\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
|
||
" content: \"▾\";\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* Pipeline/ColumnTransformer-specific style */\n",
|
||
"\n",
|
||
"#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
|
||
" color: var(--sklearn-color-text);\n",
|
||
" background-color: var(--sklearn-color-unfitted-level-2);\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
|
||
" background-color: var(--sklearn-color-fitted-level-2);\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* Estimator-specific style */\n",
|
||
"\n",
|
||
"/* Colorize estimator box */\n",
|
||
"#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
|
||
" /* unfitted */\n",
|
||
" background-color: var(--sklearn-color-unfitted-level-2);\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
|
||
" /* fitted */\n",
|
||
" background-color: var(--sklearn-color-fitted-level-2);\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
|
||
"#sk-container-id-1 div.sk-label label {\n",
|
||
" /* The background is the default theme color */\n",
|
||
" color: var(--sklearn-color-text-on-default-background);\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* On hover, darken the color of the background */\n",
|
||
"#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
|
||
" color: var(--sklearn-color-text);\n",
|
||
" background-color: var(--sklearn-color-unfitted-level-2);\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* Label box, darken color on hover, fitted */\n",
|
||
"#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
|
||
" color: var(--sklearn-color-text);\n",
|
||
" background-color: var(--sklearn-color-fitted-level-2);\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* Estimator label */\n",
|
||
"\n",
|
||
"#sk-container-id-1 div.sk-label label {\n",
|
||
" font-family: monospace;\n",
|
||
" font-weight: bold;\n",
|
||
" display: inline-block;\n",
|
||
" line-height: 1.2em;\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-1 div.sk-label-container {\n",
|
||
" text-align: center;\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* Estimator-specific */\n",
|
||
"#sk-container-id-1 div.sk-estimator {\n",
|
||
" font-family: monospace;\n",
|
||
" border: 1px dotted var(--sklearn-color-border-box);\n",
|
||
" border-radius: 0.25em;\n",
|
||
" box-sizing: border-box;\n",
|
||
" margin-bottom: 0.5em;\n",
|
||
" /* unfitted */\n",
|
||
" background-color: var(--sklearn-color-unfitted-level-0);\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-1 div.sk-estimator.fitted {\n",
|
||
" /* fitted */\n",
|
||
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* on hover */\n",
|
||
"#sk-container-id-1 div.sk-estimator:hover {\n",
|
||
" /* unfitted */\n",
|
||
" background-color: var(--sklearn-color-unfitted-level-2);\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
|
||
" /* fitted */\n",
|
||
" background-color: var(--sklearn-color-fitted-level-2);\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
|
||
"\n",
|
||
"/* Common style for \"i\" and \"?\" */\n",
|
||
"\n",
|
||
".sk-estimator-doc-link,\n",
|
||
"a:link.sk-estimator-doc-link,\n",
|
||
"a:visited.sk-estimator-doc-link {\n",
|
||
" float: right;\n",
|
||
" font-size: smaller;\n",
|
||
" line-height: 1em;\n",
|
||
" font-family: monospace;\n",
|
||
" background-color: var(--sklearn-color-background);\n",
|
||
" border-radius: 1em;\n",
|
||
" height: 1em;\n",
|
||
" width: 1em;\n",
|
||
" text-decoration: none !important;\n",
|
||
" margin-left: 0.5em;\n",
|
||
" text-align: center;\n",
|
||
" /* unfitted */\n",
|
||
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
|
||
" color: var(--sklearn-color-unfitted-level-1);\n",
|
||
"}\n",
|
||
"\n",
|
||
".sk-estimator-doc-link.fitted,\n",
|
||
"a:link.sk-estimator-doc-link.fitted,\n",
|
||
"a:visited.sk-estimator-doc-link.fitted {\n",
|
||
" /* fitted */\n",
|
||
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
|
||
" color: var(--sklearn-color-fitted-level-1);\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* On hover */\n",
|
||
"div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
|
||
".sk-estimator-doc-link:hover,\n",
|
||
"div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
|
||
".sk-estimator-doc-link:hover {\n",
|
||
" /* unfitted */\n",
|
||
" background-color: var(--sklearn-color-unfitted-level-3);\n",
|
||
" color: var(--sklearn-color-background);\n",
|
||
" text-decoration: none;\n",
|
||
"}\n",
|
||
"\n",
|
||
"div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
|
||
".sk-estimator-doc-link.fitted:hover,\n",
|
||
"div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
|
||
".sk-estimator-doc-link.fitted:hover {\n",
|
||
" /* fitted */\n",
|
||
" background-color: var(--sklearn-color-fitted-level-3);\n",
|
||
" color: var(--sklearn-color-background);\n",
|
||
" text-decoration: none;\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* Span, style for the box shown on hovering the info icon */\n",
|
||
".sk-estimator-doc-link span {\n",
|
||
" display: none;\n",
|
||
" z-index: 9999;\n",
|
||
" position: relative;\n",
|
||
" font-weight: normal;\n",
|
||
" right: .2ex;\n",
|
||
" padding: .5ex;\n",
|
||
" margin: .5ex;\n",
|
||
" width: min-content;\n",
|
||
" min-width: 20ex;\n",
|
||
" max-width: 50ex;\n",
|
||
" color: var(--sklearn-color-text);\n",
|
||
" box-shadow: 2pt 2pt 4pt #999;\n",
|
||
" /* unfitted */\n",
|
||
" background: var(--sklearn-color-unfitted-level-0);\n",
|
||
" border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
|
||
"}\n",
|
||
"\n",
|
||
".sk-estimator-doc-link.fitted span {\n",
|
||
" /* fitted */\n",
|
||
" background: var(--sklearn-color-fitted-level-0);\n",
|
||
" border: var(--sklearn-color-fitted-level-3);\n",
|
||
"}\n",
|
||
"\n",
|
||
".sk-estimator-doc-link:hover span {\n",
|
||
" display: block;\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* \"?\"-specific style due to the `<a>` HTML tag */\n",
|
||
"\n",
|
||
"#sk-container-id-1 a.estimator_doc_link {\n",
|
||
" float: right;\n",
|
||
" font-size: 1rem;\n",
|
||
" line-height: 1em;\n",
|
||
" font-family: monospace;\n",
|
||
" background-color: var(--sklearn-color-background);\n",
|
||
" border-radius: 1rem;\n",
|
||
" height: 1rem;\n",
|
||
" width: 1rem;\n",
|
||
" text-decoration: none;\n",
|
||
" /* unfitted */\n",
|
||
" color: var(--sklearn-color-unfitted-level-1);\n",
|
||
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-1 a.estimator_doc_link.fitted {\n",
|
||
" /* fitted */\n",
|
||
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
|
||
" color: var(--sklearn-color-fitted-level-1);\n",
|
||
"}\n",
|
||
"\n",
|
||
"/* On hover */\n",
|
||
"#sk-container-id-1 a.estimator_doc_link:hover {\n",
|
||
" /* unfitted */\n",
|
||
" background-color: var(--sklearn-color-unfitted-level-3);\n",
|
||
" color: var(--sklearn-color-background);\n",
|
||
" text-decoration: none;\n",
|
||
"}\n",
|
||
"\n",
|
||
"#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
|
||
" /* fitted */\n",
|
||
" background-color: var(--sklearn-color-fitted-level-3);\n",
|
||
"}\n",
|
||
"</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GaussianNB</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.naive_bayes.GaussianNB.html\">?<span>Documentation for GaussianNB</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>GaussianNB()</pre></div> </div></div></div></div>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 12
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "v63bNnciZZYS"
|
||
},
|
||
"source": [
|
||
"### *Predicting, wheather Person Survived or Not*"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "s17AtjCOZeEZ",
|
||
"outputId": "fb8f0722-4755-463a-b2c7-3761e564e2b3"
|
||
},
|
||
"source": [
|
||
"pclassNo = int(input(\"Enter Person's Pclass number: \"))\n",
|
||
"gender = int(input(\"Enter Person's Gender 0-female 1-male(0 or 1): \"))\n",
|
||
"age = int(input(\"Enter Person's Age: \"))\n",
|
||
"fare = float(input(\"Enter Person's Fare: \"))\n",
|
||
"person = [[pclassNo,gender,age,fare]]\n",
|
||
"result = model.predict(person)\n",
|
||
"print(result)\n",
|
||
"\n",
|
||
"if result == 1:\n",
|
||
" print(\"Person might be Survived\")\n",
|
||
"else:\n",
|
||
" print(\"Person might not be Survived\")"
|
||
],
|
||
"execution_count": 13,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Enter Person's Pclass number: 20\n",
|
||
"Enter Person's Gender 0-female 1-male(0 or 1): 0\n",
|
||
"Enter Person's Age: 43\n",
|
||
"Enter Person's Fare: 4567\n",
|
||
"[1]\n",
|
||
"Person might be Survived\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but GaussianNB was fitted with feature names\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "1PdvxG-La4H3"
|
||
},
|
||
"source": [
|
||
"### *Prediction for all Test Data*"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "fShPpJ75a6u0",
|
||
"outputId": "8d96d8c6-14fc-4c3d-fe83-35f22246d155"
|
||
},
|
||
"source": [
|
||
"y_pred = model.predict(X_test)\n",
|
||
"print(np.column_stack((y_pred,y_test)))"
|
||
],
|
||
"execution_count": 14,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"[[0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [1 1]\n",
|
||
" [0 1]\n",
|
||
" [1 1]\n",
|
||
" [1 1]\n",
|
||
" [1 1]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [1 1]\n",
|
||
" [1 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 1]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [1 0]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [1 1]\n",
|
||
" [1 0]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [0 1]\n",
|
||
" [0 0]\n",
|
||
" [0 1]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [1 0]\n",
|
||
" [0 1]\n",
|
||
" [0 1]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [0 1]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [1 0]\n",
|
||
" [0 0]\n",
|
||
" [0 1]\n",
|
||
" [0 0]\n",
|
||
" [1 0]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [1 1]\n",
|
||
" [1 1]\n",
|
||
" [0 1]\n",
|
||
" [1 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [1 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 1]\n",
|
||
" [1 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [1 1]\n",
|
||
" [1 1]\n",
|
||
" [1 1]\n",
|
||
" [1 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 1]\n",
|
||
" [1 1]\n",
|
||
" [1 0]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [1 0]\n",
|
||
" [1 1]\n",
|
||
" [1 1]\n",
|
||
" [1 0]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [0 1]\n",
|
||
" [1 0]\n",
|
||
" [1 1]\n",
|
||
" [1 1]\n",
|
||
" [1 1]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [0 1]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 1]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [1 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [1 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [1 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [1 0]\n",
|
||
" [1 1]\n",
|
||
" [1 0]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [1 0]\n",
|
||
" [1 1]\n",
|
||
" [1 0]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [1 0]\n",
|
||
" [0 1]\n",
|
||
" [1 0]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [0 1]\n",
|
||
" [1 1]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [1 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [1 0]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [0 1]\n",
|
||
" [0 0]\n",
|
||
" [0 1]\n",
|
||
" [1 0]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [0 1]\n",
|
||
" [0 0]\n",
|
||
" [1 0]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 1]\n",
|
||
" [0 0]\n",
|
||
" [1 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 0]\n",
|
||
" [0 1]\n",
|
||
" [1 0]\n",
|
||
" [1 1]\n",
|
||
" [0 0]\n",
|
||
" [1 1]\n",
|
||
" [1 1]]\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "lFeW_-qYdszc"
|
||
},
|
||
"source": [
|
||
"### *Accuracy of our Model*"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "HDOFRQ0PdzQS",
|
||
"outputId": "9e0c4bba-ac94-4065-dac9-2e69d47ddebd"
|
||
},
|
||
"source": [
|
||
"from sklearn.metrics import accuracy_score\n",
|
||
"print(\"Accuracy of the Model: {0}%\".format(accuracy_score(y_test, y_pred)*100))"
|
||
],
|
||
"execution_count": 15,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Accuracy of the Model: 77.57847533632287%\n"
|
||
]
|
||
}
|
||
]
|
||
}
|
||
]
|
||
} |