mirror of
https://github.com/metafy-social/python-scripts.git
synced 2024-11-24 20:41:12 +00:00
626 lines
24 KiB
Plaintext
626 lines
24 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/riyajaiswal25/MLProjects/blob/main/DigitRecognitionusingRandomForestClassifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "hdd4dapuroBk"
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},
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"source": [
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"# Digit Recognition using Random Forest Classifier"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "k_cWcYTUsWdE"
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},
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"source": [
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"**Import Basic Library**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"id": "t6uu8CVZrllI"
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "S_X9qpm0s4uq"
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},
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"source": [
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"**Choosing Dataset**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 73
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},
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"id": "ERRZ3tkOOYFA",
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"outputId": "5f8f4aae-398b-4e33-e2c2-53de23174401"
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},
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"text/plain": [
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"<IPython.core.display.HTML object>"
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],
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"text/html": [
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"\n",
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" <input type=\"file\" id=\"files-7634f8da-a56b-480e-a6d2-1cc2533a486a\" name=\"files[]\" multiple disabled\n",
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" style=\"border:none\" />\n",
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" <output id=\"result-7634f8da-a56b-480e-a6d2-1cc2533a486a\">\n",
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" Upload widget is only available when the cell has been executed in the\n",
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" current browser session. Please rerun this cell to enable.\n",
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" </output>\n",
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" <script>// Copyright 2017 Google LLC\n",
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"//\n",
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"// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
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"// you may not use this file except in compliance with the License.\n",
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"// You may obtain a copy of the License at\n",
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"//\n",
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"// http://www.apache.org/licenses/LICENSE-2.0\n",
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"//\n",
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"// Unless required by applicable law or agreed to in writing, software\n",
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"// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
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"// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
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"// See the License for the specific language governing permissions and\n",
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"// limitations under the License.\n",
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"\n",
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"/**\n",
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" * @fileoverview Helpers for google.colab Python module.\n",
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" */\n",
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"(function(scope) {\n",
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"function span(text, styleAttributes = {}) {\n",
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" 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",
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" element.style[key] = styleAttributes[key];\n",
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" }\n",
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" return element;\n",
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"}\n",
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"\n",
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"// Max number of bytes which will be uploaded at a time.\n",
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"const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
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"\n",
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"function _uploadFiles(inputId, outputId) {\n",
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" const steps = uploadFilesStep(inputId, outputId);\n",
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" const outputElement = document.getElementById(outputId);\n",
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" // Cache steps on the outputElement to make it available for the next call\n",
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" // to uploadFilesContinue from Python.\n",
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" outputElement.steps = steps;\n",
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"\n",
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" return _uploadFilesContinue(outputId);\n",
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"}\n",
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"\n",
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"// This is roughly an async generator (not supported in the browser yet),\n",
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"// where there are multiple asynchronous steps and the Python side is going\n",
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"// to poll for completion of each step.\n",
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"// This uses a Promise to block the python side on completion of each step,\n",
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"// then passes the result of the previous step as the input to the next step.\n",
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"function _uploadFilesContinue(outputId) {\n",
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" const outputElement = document.getElementById(outputId);\n",
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" const steps = outputElement.steps;\n",
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"\n",
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" const next = steps.next(outputElement.lastPromiseValue);\n",
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" return Promise.resolve(next.value.promise).then((value) => {\n",
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" // Cache the last promise value to make it available to the next\n",
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" // step of the generator.\n",
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" outputElement.lastPromiseValue = value;\n",
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" return next.value.response;\n",
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" });\n",
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"}\n",
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"\n",
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"/**\n",
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" * Generator function which is called between each async step of the upload\n",
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" * process.\n",
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" * @param {string} inputId Element ID of the input file picker element.\n",
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" * @param {string} outputId Element ID of the output display.\n",
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" * @return {!Iterable<!Object>} Iterable of next steps.\n",
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" */\n",
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"function* uploadFilesStep(inputId, outputId) {\n",
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" const inputElement = document.getElementById(inputId);\n",
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" inputElement.disabled = false;\n",
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"\n",
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" const outputElement = document.getElementById(outputId);\n",
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" outputElement.innerHTML = '';\n",
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"\n",
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" const pickedPromise = new Promise((resolve) => {\n",
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" inputElement.addEventListener('change', (e) => {\n",
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" resolve(e.target.files);\n",
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" });\n",
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" });\n",
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"\n",
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" const cancel = document.createElement('button');\n",
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" inputElement.parentElement.appendChild(cancel);\n",
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" cancel.textContent = 'Cancel upload';\n",
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" const cancelPromise = new Promise((resolve) => {\n",
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" cancel.onclick = () => {\n",
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" resolve(null);\n",
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" };\n",
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" });\n",
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"\n",
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" // Wait for the user to pick the files.\n",
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" const files = yield {\n",
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" promise: Promise.race([pickedPromise, cancelPromise]),\n",
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" response: {\n",
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" action: 'starting',\n",
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" }\n",
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" };\n",
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"\n",
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" cancel.remove();\n",
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"\n",
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" // Disable the input element since further picks are not allowed.\n",
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" inputElement.disabled = true;\n",
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"\n",
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" if (!files) {\n",
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" return {\n",
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" response: {\n",
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" action: 'complete',\n",
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" }\n",
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" };\n",
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" }\n",
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"\n",
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" for (const file of files) {\n",
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" const li = document.createElement('li');\n",
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" li.append(span(file.name, {fontWeight: 'bold'}));\n",
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" li.append(span(\n",
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" `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
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" `last modified: ${\n",
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" file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
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" 'n/a'} - `));\n",
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" const percent = span('0% done');\n",
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" li.appendChild(percent);\n",
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"\n",
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" outputElement.appendChild(li);\n",
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"\n",
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" const fileDataPromise = new Promise((resolve) => {\n",
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" const reader = new FileReader();\n",
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" reader.onload = (e) => {\n",
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" resolve(e.target.result);\n",
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" };\n",
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" reader.readAsArrayBuffer(file);\n",
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" });\n",
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" // Wait for the data to be ready.\n",
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" let fileData = yield {\n",
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" promise: fileDataPromise,\n",
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" response: {\n",
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" action: 'continue',\n",
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" }\n",
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" };\n",
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"\n",
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" // Use a chunked sending to avoid message size limits. See b/62115660.\n",
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" let position = 0;\n",
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" do {\n",
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" const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
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" const chunk = new Uint8Array(fileData, position, length);\n",
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" position += length;\n",
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"\n",
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" const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
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" yield {\n",
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" response: {\n",
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" action: 'append',\n",
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" file: file.name,\n",
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" data: base64,\n",
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" },\n",
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" };\n",
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"\n",
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" let percentDone = fileData.byteLength === 0 ?\n",
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" 100 :\n",
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" Math.round((position / fileData.byteLength) * 100);\n",
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" percent.textContent = `${percentDone}% done`;\n",
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"\n",
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" } while (position < fileData.byteLength);\n",
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" }\n",
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"\n",
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" // All done.\n",
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" yield {\n",
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" response: {\n",
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" action: 'complete',\n",
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" }\n",
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" };\n",
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"}\n",
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"\n",
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"scope.google = scope.google || {};\n",
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"scope.google.colab = scope.google.colab || {};\n",
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"scope.google.colab._files = {\n",
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" _uploadFiles,\n",
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" _uploadFilesContinue,\n",
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"};\n",
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"})(self);\n",
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"</script> "
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]
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},
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"metadata": {}
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},
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Saving train[1].csv to train[1].csv\n"
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]
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}
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],
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"source": [
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"from google.colab import files\n",
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"uploaded = files.upload()"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"**Load Dataset**"
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],
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"metadata": {
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"id": "TJRApm0w0Dct"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"dataset = pd.read_csv('train.csv')"
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],
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"metadata": {
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"id": "GyOvJOoR0Lhq"
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},
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"execution_count": 4,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"**Summarize dataset**"
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],
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"metadata": {
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"id": "0txmydWY0ZEH"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"print(dataset.shape)\n",
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"print(dataset.head(5))"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "AW-9ITV10cIY",
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"outputId": "dce2cb6d-2bdb-41e5-de9e-baf122900140"
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},
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"execution_count": 5,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"(42000, 785)\n",
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" label pixel0 pixel1 pixel2 pixel3 pixel4 pixel5 pixel6 pixel7 \\\n",
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"0 1 0 0 0 0 0 0 0 0 \n",
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"1 0 0 0 0 0 0 0 0 0 \n",
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"2 1 0 0 0 0 0 0 0 0 \n",
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"3 4 0 0 0 0 0 0 0 0 \n",
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"4 0 0 0 0 0 0 0 0 0 \n",
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"\n",
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" pixel8 ... pixel774 pixel775 pixel776 pixel777 pixel778 pixel779 \\\n",
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"0 0 ... 0 0 0 0 0 0 \n",
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"1 0 ... 0 0 0 0 0 0 \n",
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"2 0 ... 0 0 0 0 0 0 \n",
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"3 0 ... 0 0 0 0 0 0 \n",
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"4 0 ... 0 0 0 0 0 0 \n",
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"\n",
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" pixel780 pixel781 pixel782 pixel783 \n",
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"0 0 0 0 0 \n",
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"1 0 0 0 0 \n",
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"2 0 0 0 0 \n",
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"3 0 0 0 0 \n",
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"4 0 0 0 0 \n",
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"\n",
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"[5 rows x 785 columns]\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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"cell_type": "markdown",
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"source": [
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"**Segregate Dataset into X(Input/Independent Variable) & Y(Output/Dependent Variable)**"
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],
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"metadata": {
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"id": "QUh5BKq20viv"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"X = dataset.iloc[:,1:]\n",
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"print(X)\n",
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"print(X.shape)"
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],
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|
|
"metadata": {
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/"
|
||
|
|
},
|
||
|
|
"id": "OP2TX3iX09ND",
|
||
|
|
"outputId": "9c8f44e2-a503-4acf-8978-f6576706e402"
|
||
|
|
},
|
||
|
|
"execution_count": 6,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "stream",
|
||
|
|
"name": "stdout",
|
||
|
|
"text": [
|
||
|
|
" pixel0 pixel1 pixel2 pixel3 pixel4 pixel5 pixel6 pixel7 pixel8 \\\n",
|
||
|
|
"0 0 0 0 0 0 0 0 0 0 \n",
|
||
|
|
"1 0 0 0 0 0 0 0 0 0 \n",
|
||
|
|
"2 0 0 0 0 0 0 0 0 0 \n",
|
||
|
|
"3 0 0 0 0 0 0 0 0 0 \n",
|
||
|
|
"4 0 0 0 0 0 0 0 0 0 \n",
|
||
|
|
"... ... ... ... ... ... ... ... ... ... \n",
|
||
|
|
"41995 0 0 0 0 0 0 0 0 0 \n",
|
||
|
|
"41996 0 0 0 0 0 0 0 0 0 \n",
|
||
|
|
"41997 0 0 0 0 0 0 0 0 0 \n",
|
||
|
|
"41998 0 0 0 0 0 0 0 0 0 \n",
|
||
|
|
"41999 0 0 0 0 0 0 0 0 0 \n",
|
||
|
|
"\n",
|
||
|
|
" pixel9 ... pixel774 pixel775 pixel776 pixel777 pixel778 \\\n",
|
||
|
|
"0 0 ... 0 0 0 0 0 \n",
|
||
|
|
"1 0 ... 0 0 0 0 0 \n",
|
||
|
|
"2 0 ... 0 0 0 0 0 \n",
|
||
|
|
"3 0 ... 0 0 0 0 0 \n",
|
||
|
|
"4 0 ... 0 0 0 0 0 \n",
|
||
|
|
"... ... ... ... ... ... ... ... \n",
|
||
|
|
"41995 0 ... 0 0 0 0 0 \n",
|
||
|
|
"41996 0 ... 0 0 0 0 0 \n",
|
||
|
|
"41997 0 ... 0 0 0 0 0 \n",
|
||
|
|
"41998 0 ... 0 0 0 0 0 \n",
|
||
|
|
"41999 0 ... 0 0 0 0 0 \n",
|
||
|
|
"\n",
|
||
|
|
" pixel779 pixel780 pixel781 pixel782 pixel783 \n",
|
||
|
|
"0 0 0 0 0 0 \n",
|
||
|
|
"1 0 0 0 0 0 \n",
|
||
|
|
"2 0 0 0 0 0 \n",
|
||
|
|
"3 0 0 0 0 0 \n",
|
||
|
|
"4 0 0 0 0 0 \n",
|
||
|
|
"... ... ... ... ... ... \n",
|
||
|
|
"41995 0 0 0 0 0 \n",
|
||
|
|
"41996 0 0 0 0 0 \n",
|
||
|
|
"41997 0 0 0 0 0 \n",
|
||
|
|
"41998 0 0 0 0 0 \n",
|
||
|
|
"41999 0 0 0 0 0 \n",
|
||
|
|
"\n",
|
||
|
|
"[42000 rows x 784 columns]\n",
|
||
|
|
"(42000, 784)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"source": [
|
||
|
|
"Y = dataset.iloc[:,0]\n",
|
||
|
|
"print(Y)\n",
|
||
|
|
"print(Y.shape)"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/"
|
||
|
|
},
|
||
|
|
"id": "2RuBl7671GH4",
|
||
|
|
"outputId": "96d6afef-f2ed-420f-d95c-826a287fa8dd"
|
||
|
|
},
|
||
|
|
"execution_count": 7,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "stream",
|
||
|
|
"name": "stdout",
|
||
|
|
"text": [
|
||
|
|
"0 1\n",
|
||
|
|
"1 0\n",
|
||
|
|
"2 1\n",
|
||
|
|
"3 4\n",
|
||
|
|
"4 0\n",
|
||
|
|
" ..\n",
|
||
|
|
"41995 0\n",
|
||
|
|
"41996 1\n",
|
||
|
|
"41997 7\n",
|
||
|
|
"41998 6\n",
|
||
|
|
"41999 9\n",
|
||
|
|
"Name: label, Length: 42000, dtype: int64\n",
|
||
|
|
"(42000,)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"source": [
|
||
|
|
"**Splitting Dataset into Test and Train**"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"id": "o1j-AGZd1OQV"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"source": [
|
||
|
|
"from sklearn.model_selection import train_test_split\n",
|
||
|
|
"X_train, X_test, y_train, y_test = train_test_split(X,Y, test_size = 0.25, random_state = 0)"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"id": "U_c_R4HA1SeZ"
|
||
|
|
},
|
||
|
|
"execution_count": 8,
|
||
|
|
"outputs": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"source": [
|
||
|
|
"**Training**"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"id": "Gf6EgvAc1vjh"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"source": [
|
||
|
|
"from sklearn.ensemble import RandomForestClassifier\n",
|
||
|
|
"model = RandomForestClassifier()\n",
|
||
|
|
"model.fit(X_train, y_train)"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/"
|
||
|
|
},
|
||
|
|
"id": "RS4TAnDh1yUU",
|
||
|
|
"outputId": "4803259d-f3a1-461f-d3d0-939bc4495a64"
|
||
|
|
},
|
||
|
|
"execution_count": 9,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "execute_result",
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"RandomForestClassifier()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"execution_count": 9
|
||
|
|
}
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"source": [
|
||
|
|
"y_pred = model.predict(X_test)"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"id": "SljeEEbs2JFT"
|
||
|
|
},
|
||
|
|
"execution_count": 10,
|
||
|
|
"outputs": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"source": [
|
||
|
|
"**Model Accuracy**"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"id": "4XEvHILm2OF-"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"source": [
|
||
|
|
"from sklearn.metrics import accuracy_score\n",
|
||
|
|
"print(\"Accuracy of the Model: {0}%\".format(accuracy_score(y_test, y_pred)*100))"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/"
|
||
|
|
},
|
||
|
|
"id": "sHEVc1Qq2Rqy",
|
||
|
|
"outputId": "06be6e32-1ba4-4035-eafb-3b3c2023abd6"
|
||
|
|
},
|
||
|
|
"execution_count": 11,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "stream",
|
||
|
|
"name": "stdout",
|
||
|
|
"text": [
|
||
|
|
"Accuracy of the Model: 96.31428571428572%\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"source": [
|
||
|
|
"import matplotlib.pyplot as plt\n",
|
||
|
|
"index=10\n",
|
||
|
|
"print(\"Predicted \" +str(model.predict(X_test)[index]))\n",
|
||
|
|
"plt.axis('off')\n",
|
||
|
|
"plt.imshow(X_test.iloc[index].values.reshape((28,28)),cmap='gray')"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/",
|
||
|
|
"height": 283
|
||
|
|
},
|
||
|
|
"id": "iymJ1Zpj20gk",
|
||
|
|
"outputId": "ae21ce24-b957-4a30-8f04-ec5c77dd5a53"
|
||
|
|
},
|
||
|
|
"execution_count": 13,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "stream",
|
||
|
|
"name": "stdout",
|
||
|
|
"text": [
|
||
|
|
"Predicted 7\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"output_type": "execute_result",
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.image.AxesImage at 0x7ff128cbac90>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"execution_count": 13
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"output_type": "display_data",
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
],
|
||
|
|
"image/png": "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\n"
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"colab": {
|
||
|
|
"provenance": [],
|
||
|
|
"authorship_tag": "ABX9TyOBzEe2vR1rQh4B8yWT0mhr",
|
||
|
|
"include_colab_link": true
|
||
|
|
},
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"name": "python"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 0
|
||
|
|
}
|