mirror of
https://github.com/deepseek-ai/DeepSeek-Coder
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Merge a5f26434ab into b7ba565956
This commit is contained in:
Burhan
GitHub
commit
0105f9e552
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11___House_price_prediction_using_Linear_Regression.ipynb
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11___House_price_prediction_using_Linear_Regression.ipynb
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12___Exam_mark_prediction_using_Linear_Regression.ipynb
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12___Exam_mark_prediction_using_Linear_Regression.ipynb
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13_Salary_prediction_using_POLYNOMIAL_REGRESSION.ipynb
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13_Salary_prediction_using_POLYNOMIAL_REGRESSION.ipynb
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14_Stock_prediction_using_SVM_REGRESSION.ipynb
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14_Stock_prediction_using_SVM_REGRESSION.ipynb
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17_EvaluatingRegressionModelUsingRSquaredAdjustedRSquared.ipynb
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17_EvaluatingRegressionModelUsingRSquaredAdjustedRSquared.ipynb
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18_RegressionModelSelection.ipynb
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18_RegressionModelSelection.ipynb
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|
||||
{
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"provenance": [],
|
||||
"include_colab_link": true
|
||||
},
|
||||
"kernelspec": {
|
||||
"name": "python3",
|
||||
"display_name": "Python 3"
|
||||
}
|
||||
},
|
||||
"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/18_RegressionModelSelection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "r3cas2_1T98w"
|
||||
},
|
||||
"source": [
|
||||
"# Regression Model Selection"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "IODliia6U1xO"
|
||||
},
|
||||
"source": [
|
||||
"## Importing the basic libraries"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "y98nA5UdU6Hf"
|
||||
},
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import pandas as pd"
|
||||
],
|
||||
"execution_count": 1,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "2hRC6YEod9_8"
|
||||
},
|
||||
"source": [
|
||||
"### Load Dataset from Local Directory"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "tZBTr4JHeAzb",
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/",
|
||||
"height": 73
|
||||
},
|
||||
"outputId": "c5931337-dd18-4b92-f759-1b45c2bf1a32"
|
||||
},
|
||||
"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-b25a93ab-dea3-4fa9-8a92-ccc7f6022ebf\" name=\"files[]\" multiple disabled\n",
|
||||
" style=\"border:none\" />\n",
|
||||
" <output id=\"result-b25a93ab-dea3-4fa9-8a92-ccc7f6022ebf\">\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 dataset.csv to dataset.csv\n"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "jpjZ43YlU8eI"
|
||||
},
|
||||
"source": [
|
||||
"## Importing the dataset"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "pLVaXoYVU_Uy"
|
||||
},
|
||||
"source": [
|
||||
"dataset = pd.read_csv('dataset.csv')\n",
|
||||
"X = dataset.iloc[:, :-1].values\n",
|
||||
"y = dataset.iloc[:, -1].values\n",
|
||||
"ysvm = y.reshape(len(y),1)"
|
||||
],
|
||||
"execution_count": 3,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "tPJXMyyUJbWn"
|
||||
},
|
||||
"source": [
|
||||
"## Splitting the dataset into the Training set and Test set"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "rFOzpjaiJd5B"
|
||||
},
|
||||
"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 = 0.2, random_state = 0)\n",
|
||||
"X_trainsvm, X_testsvm, y_trainsvm, y_testsvm = train_test_split(X, ysvm, test_size = 0.2, random_state = 0)"
|
||||
],
|
||||
"execution_count": 4,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "5bU75GVthaOj"
|
||||
},
|
||||
"source": [
|
||||
"### Importing Machine Learning Algorithms"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "YF8HkKVYhag7"
|
||||
},
|
||||
"source": [
|
||||
"from sklearn.linear_model import LinearRegression\n",
|
||||
"from sklearn.preprocessing import PolynomialFeatures\n",
|
||||
"from sklearn.ensemble import RandomForestRegressor\n",
|
||||
"from sklearn.tree import DecisionTreeRegressor\n",
|
||||
"from sklearn.svm import SVR"
|
||||
],
|
||||
"execution_count": 5,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "g16qFkFQVC35"
|
||||
},
|
||||
"source": [
|
||||
"## Initializing different Regression algorithms"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "SLDKyv1SVUqS"
|
||||
},
|
||||
"source": [
|
||||
"from sklearn.preprocessing import StandardScaler\n",
|
||||
"\n",
|
||||
"modelLR = LinearRegression()\n",
|
||||
"\n",
|
||||
"poly_reg = PolynomialFeatures(degree = 4)\n",
|
||||
"X_poly = poly_reg.fit_transform(X_train)\n",
|
||||
"modelPLR = LinearRegression()\n",
|
||||
"\n",
|
||||
"modelRFR = RandomForestRegressor(n_estimators = 10, random_state = 0)\n",
|
||||
"\n",
|
||||
"modelDTR = DecisionTreeRegressor(random_state = 0)\n",
|
||||
"\n",
|
||||
"modelSVR = SVR(kernel = 'rbf')\n",
|
||||
"\n",
|
||||
"sc_X = StandardScaler()\n",
|
||||
"sc_y = StandardScaler()\n",
|
||||
"X_trainsvm = sc_X.fit_transform(X_trainsvm)\n",
|
||||
"y_trainsvm = sc_y.fit_transform(y_trainsvm)"
|
||||
],
|
||||
"execution_count": 6,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "ePPz0Lu6fXfN"
|
||||
},
|
||||
"source": [
|
||||
"### Training Regression algorithm"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "oHhA2uoyfEK2",
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/",
|
||||
"height": 137
|
||||
},
|
||||
"outputId": "62527e59-2484-46c1-8dde-85d85e397f94"
|
||||
},
|
||||
"source": [
|
||||
"modelLR.fit(X_train, y_train)\n",
|
||||
"modelPLR.fit(X_poly, y_train)\n",
|
||||
"modelRFR.fit(X_train, y_train)\n",
|
||||
"modelDTR.fit(X_train, y_train)\n",
|
||||
"modelSVR.fit(X_trainsvm, y_trainsvm)"
|
||||
],
|
||||
"execution_count": 11,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stderr",
|
||||
"text": [
|
||||
"/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py:1408: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
|
||||
" y = column_or_1d(y, warn=True)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"SVR()"
|
||||
],
|
||||
"text/html": [
|
||||
"<style>#sk-container-id-2 {\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-2 {\n",
|
||||
" color: var(--sklearn-color-text);\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"#sk-container-id-2 pre {\n",
|
||||
" padding: 0;\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-parallel-item {\n",
|
||||
" display: flex;\n",
|
||||
" flex-direction: column;\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
|
||||
" align-self: flex-end;\n",
|
||||
" width: 50%;\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
|
||||
" align-self: flex-start;\n",
|
||||
" width: 50%;\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
|
||||
" width: 0;\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"/* Serial-specific style estimator block */\n",
|
||||
"\n",
|
||||
"#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 label.sk-toggleable__label-arrow:hover:before {\n",
|
||||
" color: var(--sklearn-color-text);\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"/* Toggleable content - dropdown */\n",
|
||||
"\n",
|
||||
"#sk-container-id-2 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-2 div.sk-toggleable__content.fitted {\n",
|
||||
" /* fitted */\n",
|
||||
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"#sk-container-id-2 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-2 div.sk-toggleable__content.fitted pre {\n",
|
||||
" /* unfitted */\n",
|
||||
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-label label.sk-toggleable__label,\n",
|
||||
"#sk-container-id-2 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-2 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-2 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-2 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-2 div.sk-label-container {\n",
|
||||
" text-align: center;\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"/* Estimator-specific */\n",
|
||||
"#sk-container-id-2 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-2 div.sk-estimator.fitted {\n",
|
||||
" /* fitted */\n",
|
||||
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"/* on hover */\n",
|
||||
"#sk-container-id-2 div.sk-estimator:hover {\n",
|
||||
" /* unfitted */\n",
|
||||
" background-color: var(--sklearn-color-unfitted-level-2);\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"#sk-container-id-2 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-2 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-2 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-2 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-2 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-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVR</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVR()</pre></div> </div></div></div></div>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 11
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "Nao9cdO6IgNb"
|
||||
},
|
||||
"source": [
|
||||
"## Predicting the Test set for Validation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "EebHA3EOIkQK"
|
||||
},
|
||||
"source": [
|
||||
"modelLRy_pred = modelLR.predict(X_test)\n",
|
||||
"modelPLRy_pred = modelPLR.predict(poly_reg.transform(X_test))\n",
|
||||
"modelRFRy_pred = modelRFR.predict(X_test)\n",
|
||||
"modelDTRy_pred = modelDTR.predict(X_test)\n",
|
||||
"modelSVRy_pred = sc_y.inverse_transform(modelSVR.predict(sc_X.transform(X_test)).reshape(-1, 1))"
|
||||
],
|
||||
"execution_count": 13,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "V4nELFnnIod1"
|
||||
},
|
||||
"source": [
|
||||
"## Evaluating the Model Performance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "_G2QS1UoIsTZ",
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"outputId": "8c51ca4f-7b43-4d4f-a32b-b2b7bc1f8fe4"
|
||||
},
|
||||
"source": [
|
||||
"from sklearn.metrics import r2_score\n",
|
||||
"print(\"Linear Regression Accuracy: {}\".format(r2_score(y_test, modelLRy_pred)))\n",
|
||||
"print(\"Polynomial Regression Accuracy: {}\".format(r2_score(y_test, modelPLRy_pred)))\n",
|
||||
"print(\"Random Forest Regression Accuracy: {}\".format(r2_score(y_test, modelRFRy_pred)))\n",
|
||||
"print(\"Decision Treee Regression Accuracy: {}\".format(r2_score(y_test, modelDTRy_pred)))\n",
|
||||
"print(\"Support Vector Regression Accuracy: {}\".format(r2_score(y_test, modelSVRy_pred)))"
|
||||
],
|
||||
"execution_count": 14,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": [
|
||||
"Linear Regression Accuracy: 0.9325315554761303\n",
|
||||
"Polynomial Regression Accuracy: 0.9455261540927579\n",
|
||||
"Random Forest Regression Accuracy: 0.9615908334363876\n",
|
||||
"Decision Treee Regression Accuracy: 0.922905874177941\n",
|
||||
"Support Vector Regression Accuracy: 0.9480784049986258\n"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
814
19_ClusterringUsingIncomeSpent.ipynb
Normal file
814
19_ClusterringUsingIncomeSpent.ipynb
Normal file
File diff suppressed because one or more lines are too long
2944
24_MarketBasketAnalysisusingECLAT.ipynb
Normal file
2944
24_MarketBasketAnalysisusingECLAT.ipynb
Normal file
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Load Diff
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1105
26_SentimentalAnalysisNLP.ipynb
Normal file
1105
26_SentimentalAnalysisNLP.ipynb
Normal file
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938
BreastCancerDetection_VariousMLAlgorithm.ipynb
Normal file
938
BreastCancerDetection_VariousMLAlgorithm.ipynb
Normal file
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
228
Flagellar_Motor__new.ipynb
Normal file
228
Flagellar_Motor__new.ipynb
Normal file
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1397
HandwrittenDigitRecognition_SVM.ipynb
Normal file
1397
HandwrittenDigitRecognition_SVM.ipynb
Normal file
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1390
LeafSpeciesDetection_DECISIONTREE.ipynb
Normal file
1390
LeafSpeciesDetection_DECISIONTREE.ipynb
Normal file
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1156
SalaryEstimation_K_NN.ipynb
Normal file
1156
SalaryEstimation_K_NN.ipynb
Normal file
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1835
TitanicSurvivalPrediction_NAIVEBAYES.ipynb
Normal file
1835
TitanicSurvivalPrediction_NAIVEBAYES.ipynb
Normal file
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Load Diff
730
Untitled13.ipynb
Normal file
730
Untitled13.ipynb
Normal file
@ -0,0 +1,730 @@
|
||||
{
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"provenance": [],
|
||||
"authorship_tag": "ABX9TyMJAMTJ2UxcPpBAc/ntokzp",
|
||||
"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/Untitled13.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "iU_l3SQ9A65p"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"source": [
|
||||
"#23_MarketBasketAnalysisusingAPIRIORI\n",
|
||||
"### Importing the basic libraries\n",
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"### Load Dataset from Local Directory\n",
|
||||
"from google.colab import files\n",
|
||||
"uploaded = files.upload()\n",
|
||||
"### Importing the dataset\n",
|
||||
"dataset = pd.read_csv('dataset.csv')\n",
|
||||
"print(dataset.shape)\n",
|
||||
"print(dataset.head(5))\n",
|
||||
"### Data Pre-Processing using vectorized operations\n",
|
||||
"# Extract the relevant columns (assuming first 20)\n",
|
||||
"transactions_df = dataset.iloc[:, :20]\n",
|
||||
"# Convert all values to strings\n",
|
||||
"transactions_df = transactions_df.astype(str)\n",
|
||||
"# Convert DataFrame to list of lists\n",
|
||||
"transactions = transactions_df.values.tolist()\n",
|
||||
"### Training APRIORI\n",
|
||||
"!pip install apyori\n",
|
||||
"from apyori import apriori\n",
|
||||
"rules = apriori(transactions = transactions, min_support = 0.003, min_confidence = 0.2, min_lift = 3, min_length = 2, max_length = 2)\n",
|
||||
"### Result\n",
|
||||
"results = list(rules)\n",
|
||||
"results\n",
|
||||
"### Results in Dataframe\n",
|
||||
"lhs = [tuple(result[2][0][0])[0] for result in results]\n",
|
||||
"rhs = [tuple(result[2][0][1])[0] for result in results]\n",
|
||||
"supports = [result[1] for result in results]\n",
|
||||
"confidences = [result[2][0][2] for result in results]\n",
|
||||
"lifts = [result[2][0][3] for result in results]\n",
|
||||
"resultsinDataFrame = pd.DataFrame(zip(lhs, rhs, supports, confidences, lifts), columns = ['Left Hand Side', 'Right Hand Side', 'Support', 'Confidence', 'Lift'])\n",
|
||||
"resultsinDataFrame"
|
||||
],
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "lFqrYPxIAk31",
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/",
|
||||
"height": 1291
|
||||
},
|
||||
"outputId": "dfb99183-e856-45ba-8a3d-9d4e58d67a90"
|
||||
},
|
||||
"execution_count": 1,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "display_data",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
],
|
||||
"text/html": [
|
||||
"\n",
|
||||
" <input type=\"file\" id=\"files-cd4b8c1e-81ca-47e9-afff-0523698ccd96\" name=\"files[]\" multiple disabled\n",
|
||||
" style=\"border:none\" />\n",
|
||||
" <output id=\"result-cd4b8c1e-81ca-47e9-afff-0523698ccd96\">\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 dataset.csv to dataset.csv\n",
|
||||
"(7500, 20)\n",
|
||||
" shrimp almonds avocado vegetables mix green grapes \\\n",
|
||||
"0 burgers meatballs eggs NaN NaN \n",
|
||||
"1 chutney NaN NaN NaN NaN \n",
|
||||
"2 turkey avocado NaN NaN NaN \n",
|
||||
"3 mineral water milk energy bar whole wheat rice green tea \n",
|
||||
"4 low fat yogurt NaN NaN NaN NaN \n",
|
||||
"\n",
|
||||
" whole weat flour yams cottage cheese energy drink tomato juice \\\n",
|
||||
"0 NaN NaN NaN NaN NaN \n",
|
||||
"1 NaN NaN NaN NaN NaN \n",
|
||||
"2 NaN NaN NaN NaN NaN \n",
|
||||
"3 NaN NaN NaN NaN NaN \n",
|
||||
"4 NaN NaN NaN NaN NaN \n",
|
||||
"\n",
|
||||
" low fat yogurt green tea honey salad mineral water salmon antioxydant juice \\\n",
|
||||
"0 NaN NaN NaN NaN NaN NaN NaN \n",
|
||||
"1 NaN NaN NaN NaN NaN NaN NaN \n",
|
||||
"2 NaN NaN NaN NaN NaN NaN NaN \n",
|
||||
"3 NaN NaN NaN NaN NaN NaN NaN \n",
|
||||
"4 NaN NaN NaN NaN NaN NaN NaN \n",
|
||||
"\n",
|
||||
" frozen smoothie spinach olive oil \n",
|
||||
"0 NaN NaN NaN \n",
|
||||
"1 NaN NaN NaN \n",
|
||||
"2 NaN NaN NaN \n",
|
||||
"3 NaN NaN NaN \n",
|
||||
"4 NaN NaN NaN \n",
|
||||
"Collecting apyori\n",
|
||||
" Downloading apyori-1.1.2.tar.gz (8.6 kB)\n",
|
||||
" Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
|
||||
"Building wheels for collected packages: apyori\n",
|
||||
" Building wheel for apyori (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
|
||||
" Created wheel for apyori: filename=apyori-1.1.2-py3-none-any.whl size=5954 sha256=6f460bf2d21945572fd7d1a90d9524bf5c416704c59847856c42f2fb0d60d484\n",
|
||||
" Stored in directory: /root/.cache/pip/wheels/77/3d/a6/d317a6fb32be58a602b1e8c6b5d6f31f79322da554cad2a5ea\n",
|
||||
"Successfully built apyori\n",
|
||||
"Installing collected packages: apyori\n",
|
||||
"Successfully installed apyori-1.1.2\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" Left Hand Side Right Hand Side Support Confidence Lift\n",
|
||||
"0 light cream chicken 0.004533 0.290598 4.843305\n",
|
||||
"1 mushroom cream sauce escalope 0.005733 0.300699 3.790327\n",
|
||||
"2 pasta escalope 0.005867 0.372881 4.700185\n",
|
||||
"3 fromage blanc honey 0.003333 0.245098 5.178128\n",
|
||||
"4 herb & pepper ground beef 0.016000 0.323450 3.291555\n",
|
||||
"5 tomato sauce ground beef 0.005333 0.377358 3.840147\n",
|
||||
"6 light cream olive oil 0.003200 0.205128 3.120612\n",
|
||||
"7 whole wheat pasta olive oil 0.008000 0.271493 4.130221\n",
|
||||
"8 pasta shrimp 0.005067 0.322034 4.514494"
|
||||
],
|
||||
"text/html": [
|
||||
"\n",
|
||||
" <div id=\"df-008c783b-279a-40b9-a9e4-1b17fd994e2c\" 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>Left Hand Side</th>\n",
|
||||
" <th>Right Hand Side</th>\n",
|
||||
" <th>Support</th>\n",
|
||||
" <th>Confidence</th>\n",
|
||||
" <th>Lift</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>light cream</td>\n",
|
||||
" <td>chicken</td>\n",
|
||||
" <td>0.004533</td>\n",
|
||||
" <td>0.290598</td>\n",
|
||||
" <td>4.843305</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>mushroom cream sauce</td>\n",
|
||||
" <td>escalope</td>\n",
|
||||
" <td>0.005733</td>\n",
|
||||
" <td>0.300699</td>\n",
|
||||
" <td>3.790327</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>pasta</td>\n",
|
||||
" <td>escalope</td>\n",
|
||||
" <td>0.005867</td>\n",
|
||||
" <td>0.372881</td>\n",
|
||||
" <td>4.700185</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>fromage blanc</td>\n",
|
||||
" <td>honey</td>\n",
|
||||
" <td>0.003333</td>\n",
|
||||
" <td>0.245098</td>\n",
|
||||
" <td>5.178128</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>herb & pepper</td>\n",
|
||||
" <td>ground beef</td>\n",
|
||||
" <td>0.016000</td>\n",
|
||||
" <td>0.323450</td>\n",
|
||||
" <td>3.291555</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>5</th>\n",
|
||||
" <td>tomato sauce</td>\n",
|
||||
" <td>ground beef</td>\n",
|
||||
" <td>0.005333</td>\n",
|
||||
" <td>0.377358</td>\n",
|
||||
" <td>3.840147</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>6</th>\n",
|
||||
" <td>light cream</td>\n",
|
||||
" <td>olive oil</td>\n",
|
||||
" <td>0.003200</td>\n",
|
||||
" <td>0.205128</td>\n",
|
||||
" <td>3.120612</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>7</th>\n",
|
||||
" <td>whole wheat pasta</td>\n",
|
||||
" <td>olive oil</td>\n",
|
||||
" <td>0.008000</td>\n",
|
||||
" <td>0.271493</td>\n",
|
||||
" <td>4.130221</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>8</th>\n",
|
||||
" <td>pasta</td>\n",
|
||||
" <td>shrimp</td>\n",
|
||||
" <td>0.005067</td>\n",
|
||||
" <td>0.322034</td>\n",
|
||||
" <td>4.514494</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>\n",
|
||||
" <div class=\"colab-df-buttons\">\n",
|
||||
"\n",
|
||||
" <div class=\"colab-df-container\">\n",
|
||||
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-008c783b-279a-40b9-a9e4-1b17fd994e2c')\"\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-008c783b-279a-40b9-a9e4-1b17fd994e2c 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-008c783b-279a-40b9-a9e4-1b17fd994e2c');\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-99413864-1e27-4eff-b893-b72424d2b6a1\">\n",
|
||||
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-99413864-1e27-4eff-b893-b72424d2b6a1')\"\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-99413864-1e27-4eff-b893-b72424d2b6a1 button');\n",
|
||||
" quickchartButtonEl.style.display =\n",
|
||||
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
||||
" })();\n",
|
||||
" </script>\n",
|
||||
"</div>\n",
|
||||
"\n",
|
||||
" <div id=\"id_608141c4-567b-4f50-88d6-7182c8a13969\">\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",
|
||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"\n",
|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"\n",
|
||||
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|
||||
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||||
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||||
"application/vnd.google.colaboratory.intrinsic+json": {
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
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|
||||
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||||
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||||
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|
||||
"source": [],
|
||||
"metadata": {
|
||||
"id": "rj5duez_ERkd"
|
||||
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|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
}
|
||||
]
|
||||
}
|
||||
1083
accuracy_enhanced_BreastCancerDetection_VariousMLAlgorithm.ipynb
Normal file
1083
accuracy_enhanced_BreastCancerDetection_VariousMLAlgorithm.ipynb
Normal file
File diff suppressed because one or more lines are too long
176
athelete_speed_dashboard5.ipynb
Normal file
176
athelete_speed_dashboard5.ipynb
Normal file
@ -0,0 +1,176 @@
|
||||
{
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"provenance": [],
|
||||
"authorship_tag": "ABX9TyO00Xm/6WwCJ8/dPgpB1Juf",
|
||||
"include_colab_link": true
|
||||
},
|
||||
"kernelspec": {
|
||||
"name": "python3",
|
||||
"display_name": "Python 3"
|
||||
},
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||||
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|
||||
"name": "python"
|
||||
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|
||||
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|
||||
"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/athelete_speed_dashboard5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/",
|
||||
"height": 614
|
||||
},
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||||
"id": "G2vIOdeGG43G",
|
||||
"outputId": "689ccdcc-3ca2-4b1f-e21d-702aa480ff99"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": [
|
||||
"Analysis complete.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# prompt: create visual dashboard for speed,power,mean velocity,horsepower persecond analysis using rpm for atheletes speed analysis device where we calculate athelets speed through revolution per minute through absolute encoder sensor live.the dashboard should be more visual and should use 2d modeling for graphs of all above parameter and clearity in all above graphs ploted.also build an python application for the same .write backend effective and correctly matching front end parameter\n",
|
||||
"\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import numpy as np\n",
|
||||
"import time\n",
|
||||
"import random\n",
|
||||
"import pandas as pd\n",
|
||||
"from IPython.display import clear_output\n",
|
||||
"\n",
|
||||
"class AthleteAnalyzer:\n",
|
||||
" def __init__(self):\n",
|
||||
" self.rpm_data = []\n",
|
||||
" self.time_data = []\n",
|
||||
" self.power_data = []\n",
|
||||
" self.speed_data = []\n",
|
||||
" self.mean_velocity_data = []\n",
|
||||
" self.horsepower_per_second_data = []\n",
|
||||
" self.start_time = time.time()\n",
|
||||
"\n",
|
||||
" def calculate_speed(self, rpm, radius=0.3): # Radius in meters (adjust as needed)\n",
|
||||
" # Calculate linear speed (m/s) from RPM and radius\n",
|
||||
" circumference = 2 * np.pi * radius\n",
|
||||
" speed_mps = (rpm / 60) * circumference\n",
|
||||
" return speed_mps\n",
|
||||
"\n",
|
||||
" def calculate_power(self, rpm, torque=10): # Torque in Nm (adjust as needed)\n",
|
||||
" # Calculate power (watts) from RPM and torque\n",
|
||||
" power_watts = (rpm * 2 * np.pi * torque) / 60\n",
|
||||
" return power_watts\n",
|
||||
"\n",
|
||||
" def calculate_mean_velocity(self,speed_data):\n",
|
||||
" if len(speed_data)==0:\n",
|
||||
" return 0\n",
|
||||
" mean_velocity = sum(speed_data) / len(speed_data)\n",
|
||||
" return mean_velocity\n",
|
||||
"\n",
|
||||
" def calculate_horsepower_per_second(self,power_data):\n",
|
||||
" if len(power_data)==0:\n",
|
||||
" return 0\n",
|
||||
"\n",
|
||||
" horsepower_per_second = (power_data[-1] * 0.001341)\n",
|
||||
" return horsepower_per_second\n",
|
||||
"\n",
|
||||
" def update_data(self, rpm):\n",
|
||||
" current_time = time.time() - self.start_time\n",
|
||||
" self.time_data.append(current_time)\n",
|
||||
" self.rpm_data.append(rpm)\n",
|
||||
"\n",
|
||||
" speed = self.calculate_speed(rpm)\n",
|
||||
" self.speed_data.append(speed)\n",
|
||||
"\n",
|
||||
" power = self.calculate_power(rpm)\n",
|
||||
" self.power_data.append(power)\n",
|
||||
"\n",
|
||||
" mean_velocity=self.calculate_mean_velocity(self.speed_data)\n",
|
||||
" self.mean_velocity_data.append(mean_velocity)\n",
|
||||
"\n",
|
||||
" horsepower_per_second=self.calculate_horsepower_per_second(self.power_data)\n",
|
||||
" self.horsepower_per_second_data.append(horsepower_per_second)\n",
|
||||
"\n",
|
||||
" def create_dashboard(self):\n",
|
||||
"\n",
|
||||
" # Create figure and axes\n",
|
||||
" fig, axs = plt.subplots(3, 2, figsize=(18, 12))\n",
|
||||
"\n",
|
||||
" # RPM Graph\n",
|
||||
" axs[0, 0].plot(self.time_data, self.rpm_data, label=\"RPM\", color=\"blue\")\n",
|
||||
" axs[0, 0].set_xlabel(\"Time (s)\")\n",
|
||||
" axs[0, 0].set_ylabel(\"RPM\")\n",
|
||||
" axs[0, 0].set_title(\"RPM Over Time\")\n",
|
||||
" axs[0, 0].grid(True)\n",
|
||||
"\n",
|
||||
" # Speed Graph\n",
|
||||
" axs[0, 1].plot(self.time_data, self.speed_data, label=\"Speed (m/s)\", color=\"green\")\n",
|
||||
" axs[0, 1].set_xlabel(\"Time (s)\")\n",
|
||||
" axs[0, 1].set_ylabel(\"Speed (m/s)\")\n",
|
||||
" axs[0, 1].set_title(\"Speed Over Time\")\n",
|
||||
" axs[0, 1].grid(True)\n",
|
||||
"\n",
|
||||
" # Power Graph\n",
|
||||
" axs[1, 0].plot(self.time_data, self.power_data, label=\"Power (watts)\", color=\"red\")\n",
|
||||
" axs[1, 0].set_xlabel(\"Time (s)\")\n",
|
||||
" axs[1, 0].set_ylabel(\"Power (watts)\")\n",
|
||||
" axs[1, 0].set_title(\"Power Over Time\")\n",
|
||||
" axs[1, 0].grid(True)\n",
|
||||
"\n",
|
||||
" #Mean Velocity Graph\n",
|
||||
" axs[1, 1].plot(self.time_data, self.mean_velocity_data, label=\"Mean Velocity\", color=\"orange\")\n",
|
||||
" axs[1, 1].set_xlabel(\"Time (s)\")\n",
|
||||
" axs[1, 1].set_ylabel(\"Mean Velocity\")\n",
|
||||
" axs[1, 1].set_title(\"Mean Velocity Over Time\")\n",
|
||||
" axs[1, 1].grid(True)\n",
|
||||
"\n",
|
||||
" #Horse power per second Graph\n",
|
||||
" axs[2, 0].plot(self.time_data, self.horsepower_per_second_data, label=\"Horse Power Per Second\", color=\"purple\")\n",
|
||||
" axs[2, 0].set_xlabel(\"Time (s)\")\n",
|
||||
" axs[2, 0].set_ylabel(\"Horsepower/s\")\n",
|
||||
" axs[2, 0].set_title(\"Horse Power per Second Over Time\")\n",
|
||||
" axs[2, 0].grid(True)\n",
|
||||
"\n",
|
||||
" fig.delaxes(axs[2,1])\n",
|
||||
"\n",
|
||||
" plt.tight_layout()\n",
|
||||
" plt.show()\n",
|
||||
"\n",
|
||||
" def run_analysis(self, duration=30, update_interval=1):\n",
|
||||
" end_time = time.time() + duration\n",
|
||||
" while time.time() < end_time:\n",
|
||||
" # Simulate RPM data from sensor (replace with actual sensor reading)\n",
|
||||
" rpm = random.randint(50, 150)\n",
|
||||
" self.update_data(rpm)\n",
|
||||
" self.create_dashboard()\n",
|
||||
"\n",
|
||||
" # Clear the output and update the dashboard\n",
|
||||
" clear_output(wait=True)\n",
|
||||
"\n",
|
||||
" time.sleep(update_interval)\n",
|
||||
" print(\"Analysis complete.\")\n",
|
||||
"\n",
|
||||
"if __name__ == \"__main__\":\n",
|
||||
" analyzer = AthleteAnalyzer()\n",
|
||||
" analyzer.run_analysis()\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
835
breastcancer_6.ipynb
Normal file
835
breastcancer_6.ipynb
Normal file
@ -0,0 +1,835 @@
|
||||
{
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"provenance": [],
|
||||
"authorship_tag": "ABX9TyPEBJ6UnJGiLAl/F4VbQEn/",
|
||||
"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/breastcancer_6.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "4BYhNI_DVlrd"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"source": [
|
||||
"from IPython import get_ipython\n",
|
||||
"from IPython.display import display\n",
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"from google.colab import files\n",
|
||||
"from sklearn.preprocessing import LabelEncoder\n",
|
||||
"from xgboost import XGBClassifier\n",
|
||||
"from sklearn.metrics import confusion_matrix, accuracy_score\n",
|
||||
"from sklearn.model_selection import cross_val_score"
|
||||
],
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "x58skzkAVrK9"
|
||||
},
|
||||
"execution_count": 1,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"source": [
|
||||
"# Load Dataset from Local Directory\n",
|
||||
"uploaded = files.upload()\n",
|
||||
"\n",
|
||||
"# Importing the dataset\n",
|
||||
"dataset = pd.read_csv('dataset.csv')\n",
|
||||
"print(dataset.shape)\n",
|
||||
"print(dataset.head(5))"
|
||||
],
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/",
|
||||
"height": 460
|
||||
},
|
||||
"id": "Wv58GcVQVsAP",
|
||||
"outputId": "1c7e07ef-7408-4d02-fcc4-54546265d3e3"
|
||||
},
|
||||
"execution_count": 2,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "display_data",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
],
|
||||
"text/html": [
|
||||
"\n",
|
||||
" <input type=\"file\" id=\"files-ef8610b6-acb8-41d0-a661-c5cae3267288\" name=\"files[]\" multiple disabled\n",
|
||||
" style=\"border:none\" />\n",
|
||||
" <output id=\"result-ef8610b6-acb8-41d0-a661-c5cae3267288\">\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",
|
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" element.textContent = text;\n",
|
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" for (const key of Object.keys(styleAttributes)) {\n",
|
||||
" element.style[key] = styleAttributes[key];\n",
|
||||
" }\n",
|
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" 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 dataset.csv to dataset.csv\n",
|
||||
"(683, 11)\n",
|
||||
" Sample code number Clump Thickness Uniformity of Cell Size \\\n",
|
||||
"0 1000025 5 1 \n",
|
||||
"1 1002945 5 4 \n",
|
||||
"2 1015425 3 1 \n",
|
||||
"3 1016277 6 8 \n",
|
||||
"4 1017023 4 1 \n",
|
||||
"\n",
|
||||
" Uniformity of Cell Shape Marginal Adhesion Single Epithelial Cell Size \\\n",
|
||||
"0 1 1 2 \n",
|
||||
"1 4 5 7 \n",
|
||||
"2 1 1 2 \n",
|
||||
"3 8 1 3 \n",
|
||||
"4 1 3 2 \n",
|
||||
"\n",
|
||||
" Bare Nuclei Bland Chromatin Normal Nucleoli Mitoses Class \n",
|
||||
"0 1 3 1 1 2 \n",
|
||||
"1 10 3 2 1 2 \n",
|
||||
"2 2 3 1 1 2 \n",
|
||||
"3 4 3 7 1 2 \n",
|
||||
"4 1 3 1 1 2 \n"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"source": [
|
||||
"# Segregating Dataset\n",
|
||||
"X = dataset.iloc[:, :-1].values\n",
|
||||
"y = dataset.iloc[:, -1].values\n",
|
||||
"\n",
|
||||
"# Encode the target variable 'y'\n",
|
||||
"le = LabelEncoder()\n",
|
||||
"y = le.fit_transform(y)\n",
|
||||
"\n",
|
||||
"# Splitting Dataset into Train & Test\n",
|
||||
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
|
||||
],
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "RcSmj6qPVs6C"
|
||||
},
|
||||
"execution_count": 3,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"source": [
|
||||
"# Training with XGBoost\n",
|
||||
"classifier = XGBClassifier()\n",
|
||||
"classifier.fit(X_train, y_train)"
|
||||
],
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/",
|
||||
"height": 254
|
||||
},
|
||||
"id": "kikaoHatVvp7",
|
||||
"outputId": "37c5e300-7dd3-459b-9d2f-11f86aececa5"
|
||||
},
|
||||
"execution_count": 4,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
|
||||
" colsample_bylevel=None, colsample_bynode=None,\n",
|
||||
" colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
|
||||
" enable_categorical=False, eval_metric=None, feature_types=None,\n",
|
||||
" gamma=None, grow_policy=None, importance_type=None,\n",
|
||||
" interaction_constraints=None, learning_rate=None, max_bin=None,\n",
|
||||
" max_cat_threshold=None, max_cat_to_onehot=None,\n",
|
||||
" max_delta_step=None, max_depth=None, max_leaves=None,\n",
|
||||
" min_child_weight=None, missing=nan, monotone_constraints=None,\n",
|
||||
" multi_strategy=None, n_estimators=None, n_jobs=None,\n",
|
||||
" num_parallel_tree=None, random_state=None, ...)"
|
||||
],
|
||||
"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>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
|
||||
" colsample_bylevel=None, colsample_bynode=None,\n",
|
||||
" colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
|
||||
" enable_categorical=False, eval_metric=None, feature_types=None,\n",
|
||||
" gamma=None, grow_policy=None, importance_type=None,\n",
|
||||
" interaction_constraints=None, learning_rate=None, max_bin=None,\n",
|
||||
" max_cat_threshold=None, max_cat_to_onehot=None,\n",
|
||||
" max_delta_step=None, max_depth=None, max_leaves=None,\n",
|
||||
" min_child_weight=None, missing=nan, monotone_constraints=None,\n",
|
||||
" multi_strategy=None, n_estimators=None, n_jobs=None,\n",
|
||||
" num_parallel_tree=None, random_state=None, ...)</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>XGBClassifier</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
|
||||
" colsample_bylevel=None, colsample_bynode=None,\n",
|
||||
" colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
|
||||
" enable_categorical=False, eval_metric=None, feature_types=None,\n",
|
||||
" gamma=None, grow_policy=None, importance_type=None,\n",
|
||||
" interaction_constraints=None, learning_rate=None, max_bin=None,\n",
|
||||
" max_cat_threshold=None, max_cat_to_onehot=None,\n",
|
||||
" max_delta_step=None, max_depth=None, max_leaves=None,\n",
|
||||
" min_child_weight=None, missing=nan, monotone_constraints=None,\n",
|
||||
" multi_strategy=None, n_estimators=None, n_jobs=None,\n",
|
||||
" num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 4
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"source": [
|
||||
"# Forming Confusion Matrix\n",
|
||||
"y_pred = classifier.predict(X_test)\n",
|
||||
"cm = confusion_matrix(y_test, y_pred)\n",
|
||||
"print(cm)\n",
|
||||
"accuracy_score(y_test, y_pred)\n",
|
||||
"\n",
|
||||
"# K-Fold Cross Validation\n",
|
||||
"accuracies = cross_val_score(estimator=classifier, X=X_train, y=y_train, cv=10)\n",
|
||||
"print(\"Accuracy: {:.2f} %\".format(accuracies.mean() * 100))"
|
||||
],
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "LoXwTRLPVwby",
|
||||
"outputId": "54849688-c935-4532-8d8e-0e29beb8c857"
|
||||
},
|
||||
"execution_count": 5,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": [
|
||||
"[[85 2]\n",
|
||||
" [ 1 49]]\n",
|
||||
"Accuracy: 96.71 %\n"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user