mirror of
https://github.com/metafy-social/python-scripts.git
synced 2024-10-06 12:59:32 +00:00
Merge branch 'metafy-social:master' into master
This commit is contained in:
shradha
GitHub
commit
c9963e0220
626
Projects/DigitRecognitionusingRandomForestClassifier.ipynb
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626
Projects/DigitRecognitionusingRandomForestClassifier.ipynb
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|
|
@ -0,0 +1,626 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "view-in-github",
|
||||
"colab_type": "text"
|
||||
},
|
||||
"source": [
|
||||
"<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>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "hdd4dapuroBk"
|
||||
},
|
||||
"source": [
|
||||
"# Digit Recognition using Random Forest Classifier"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "k_cWcYTUsWdE"
|
||||
},
|
||||
"source": [
|
||||
"**Import Basic Library**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"id": "t6uu8CVZrllI"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "S_X9qpm0s4uq"
|
||||
},
|
||||
"source": [
|
||||
"**Choosing Dataset**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/",
|
||||
"height": 73
|
||||
},
|
||||
"id": "ERRZ3tkOOYFA",
|
||||
"outputId": "5f8f4aae-398b-4e33-e2c2-53de23174401"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "display_data",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
],
|
||||
"text/html": [
|
||||
"\n",
|
||||
" <input type=\"file\" id=\"files-7634f8da-a56b-480e-a6d2-1cc2533a486a\" name=\"files[]\" multiple disabled\n",
|
||||
" style=\"border:none\" />\n",
|
||||
" <output id=\"result-7634f8da-a56b-480e-a6d2-1cc2533a486a\">\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 train[1].csv to train[1].csv\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from google.colab import files\n",
|
||||
"uploaded = files.upload()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"**Load Dataset**"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "TJRApm0w0Dct"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"dataset = pd.read_csv('train.csv')"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "GyOvJOoR0Lhq"
|
||||
},
|
||||
"execution_count": 4,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"**Summarize dataset**"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "0txmydWY0ZEH"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"print(dataset.shape)\n",
|
||||
"print(dataset.head(5))"
|
||||
],
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "AW-9ITV10cIY",
|
||||
"outputId": "dce2cb6d-2bdb-41e5-de9e-baf122900140"
|
||||
},
|
||||
"execution_count": 5,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": [
|
||||
"(42000, 785)\n",
|
||||
" label pixel0 pixel1 pixel2 pixel3 pixel4 pixel5 pixel6 pixel7 \\\n",
|
||||
"0 1 0 0 0 0 0 0 0 0 \n",
|
||||
"1 0 0 0 0 0 0 0 0 0 \n",
|
||||
"2 1 0 0 0 0 0 0 0 0 \n",
|
||||
"3 4 0 0 0 0 0 0 0 0 \n",
|
||||
"4 0 0 0 0 0 0 0 0 0 \n",
|
||||
"\n",
|
||||
" pixel8 ... pixel774 pixel775 pixel776 pixel777 pixel778 pixel779 \\\n",
|
||||
"0 0 ... 0 0 0 0 0 0 \n",
|
||||
"1 0 ... 0 0 0 0 0 0 \n",
|
||||
"2 0 ... 0 0 0 0 0 0 \n",
|
||||
"3 0 ... 0 0 0 0 0 0 \n",
|
||||
"4 0 ... 0 0 0 0 0 0 \n",
|
||||
"\n",
|
||||
" pixel780 pixel781 pixel782 pixel783 \n",
|
||||
"0 0 0 0 0 \n",
|
||||
"1 0 0 0 0 \n",
|
||||
"2 0 0 0 0 \n",
|
||||
"3 0 0 0 0 \n",
|
||||
"4 0 0 0 0 \n",
|
||||
"\n",
|
||||
"[5 rows x 785 columns]\n"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"**Segregate Dataset into X(Input/Independent Variable) & Y(Output/Dependent Variable)**"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "QUh5BKq20viv"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"X = dataset.iloc[:,1:]\n",
|
||||
"print(X)\n",
|
||||
"print(X.shape)"
|
||||
],
|
||||
"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
|
||||
}
|
||||
16
README.md
16
README.md
|
|
@ -303,6 +303,13 @@ Thanks a lot for spending your time helping! Keep rocking 🍻
|
|||
</a>
|
||||
</td></tr>
|
||||
<tr>
|
||||
<td align="center">
|
||||
<a href="https://github.com/riyajaiswal25">
|
||||
<img src="https://avatars.githubusercontent.com/u/84279900?v=4" width="100;" alt="riyajaiswal25"/>
|
||||
<br />
|
||||
<sub><b>Riya Jaiswal</b></sub>
|
||||
</a>
|
||||
</td>
|
||||
<td align="center">
|
||||
<a href="https://github.com/sarthakroy2002">
|
||||
<img src="https://avatars.githubusercontent.com/u/47187468?v=4" width="100;" alt="sarthakroy2002"/>
|
||||
|
|
@ -337,15 +344,15 @@ Thanks a lot for spending your time helping! Keep rocking 🍻
|
|||
<br />
|
||||
<sub><b>Null</b></sub>
|
||||
</a>
|
||||
</td>
|
||||
</td></tr>
|
||||
<tr>
|
||||
<td align="center">
|
||||
<a href="https://github.com/yashbrid03">
|
||||
<img src="https://avatars.githubusercontent.com/u/65955929?v=4" width="100;" alt="yashbrid03"/>
|
||||
<br />
|
||||
<sub><b>Yash Nilesh Brid</b></sub>
|
||||
</a>
|
||||
</td></tr>
|
||||
<tr>
|
||||
</td>
|
||||
<td align="center">
|
||||
<a href="https://github.com/mclmza">
|
||||
<img src="https://avatars.githubusercontent.com/u/7353871?v=4" width="100;" alt="mclmza"/>
|
||||
|
|
@ -380,7 +387,8 @@ Thanks a lot for spending your time helping! Keep rocking 🍻
|
|||
<br />
|
||||
<sub><b>Sameer Sahu</b></sub>
|
||||
</a>
|
||||
</td>
|
||||
</td></tr>
|
||||
<tr>
|
||||
<td align="center">
|
||||
<a href="https://github.com/shatanikmahanty">
|
||||
<img src="https://avatars.githubusercontent.com/u/67138059?v=4" width="100;" alt="shatanikmahanty"/>
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user