{
"nbformat": 4,
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"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"
},
"source": [
"# **Day - 6 Titanic Survival Prediction using NAIVE BAYES**"
]
},
{
"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",
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" 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]"
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"type": "dataframe",
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"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}"
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},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 458
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"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": [
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"889 1\n",
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},
"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()"
],
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"#sk-container-id-1 {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"#sk-container-id-1 pre {\n",
" padding: 0;\n",
"}\n",
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" margin: 0 0.4em 0.5em 0.4em;\n",
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" display: none;\n",
"}\n",
"\n",
"div.sk-parallel-item,\n",
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" 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",
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"\n",
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"#sk-container-id-1 div.sk-parallel-item::after {\n",
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" border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
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"#sk-container-id-1 div.sk-parallel {\n",
" display: flex;\n",
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"/* 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",
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"\n",
"\n",
"/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
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"- 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",
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"\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",
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" justify-content: space-between;\n",
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"\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",
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"\n",
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"#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
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"#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
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" 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",
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"\n",
"#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
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"#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",
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"\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",
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"\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",
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" [1 1]\n",
" [0 0]\n",
" [1 1]\n",
" [0 1]\n",
" [0 0]\n",
" [0 0]\n",
" [0 0]\n",
" [0 0]\n",
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" [0 0]\n",
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" [0 0]\n",
" [0 0]\n",
" [1 0]\n",
" [0 0]\n",
" [0 0]\n",
" [1 0]\n",
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" [1 0]\n",
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" [0 0]\n",
" [1 1]\n",
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" [1 1]\n",
" [1 0]\n",
" [0 0]\n",
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" [1 0]\n",
" [1 1]\n",
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" [1 1]\n",
" [0 0]\n",
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" [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",
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" [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"
]
}
]
}
]
}