├── 01. Day1 Python Decision Making └── Day1.ipynb ├── 02. Day2 Python Data Structures └── Day2.ipynb ├── 03. Day3 Python Function and Recursion └── Day3.ipynb ├── 04. Day4 Python Classes and Objects └── Day4.ipynb ├── 05. Day5 Python Modules └── Day5.ipynb ├── 06. Day6 Numpy Basics └── Day6.ipynb ├── 07. Day7 Numpy Random └── Day7.ipynb ├── 08. Day8 Pandas Tutorial └── Day8.ipynb ├── 09. Day9 Pandas Data Manipulation └── Day9.ipynb ├── 10. Day10 Pandas Data Cleaning └── Day10.ipynb ├── 11. Day11 Matplotlib (Part - 1) └── Day11.ipynb ├── 12. Day12 Matplotlib (Part - 2) └── Day12.ipynb ├── 13. Day13 Matplotlib (Scatter & Pie Plot) └── Day13.ipynb ├── 14. Day14 Seaborn (Part - 1) └── Day14.ipynb ├── 15. Day15 Seaborn (Part - 2) └── 15_Day15_Seaborn(part_2).ipynb ├── 16. Day16 Python Revision ├── 16_Day16_Python_Revision(1-5).ipynb └── python.png ├── 17. Day17 Numpy Revision ├── 17_Day17_Numpy_Revision.ipynb └── Numpy.png ├── 18. Day18 Pandas Revision ├── 18_Day18_Pandas_Revision(Day8_Day10).ipynb └── pandas.png ├── 19. Day19 Matplotlib Revision ├── 19_Day19_Matplotlib_Revision.ipynb └── matplotlib.png ├── 20. day20 Seaborn Revision ├── 20_Day20_Seaborn_Revision(Day14_15).ipynb └── seaborn.png ├── 21. Day21 ML Basics └── Day21 ML Basics.ipynb ├── 22. Day22 Supervised Learning ├── Day22 Supervised Learning.ipynb └── Day22 Supervised Learning.pdf ├── 23. Day23 Unsupervised Learning ├── Day23 Unsupervised Learning.pdf └── Day23 Unsupervised learning.ipynb ├── 24. Day24 ML Workflow ├── Day24 ML Workflow.ipynb └── Day24 ML Workflow.jpg ├── 25. Day25 Model Evaluation ├── 25_Day25_Model_Evaluation_Techniques.ipynb └── Day 25 Model evaluation in ml.pdf ├── 26. Day26 Underfitting and Overfitting └── 26_Day26_Underfitting_and_Overfitting.ipynb ├── 27. Day27 Cross-Validation ├── 27-day27-cross-validation (1).pdf └── 27_Day27_Cross_Validation.ipynb ├── 28. Day28 Training and Testing data └── 28_Day28_Training_and_Testing_data.ipynb ├── 29. Day29 EDA Workflow ├── 29.pdf └── Day29 EDA Workflow.pdf ├── 30. Day30 Simple basic EDA ├── Day30_Simple_basic_EDA.ipynb └── simple-basic-eda.pdf ├── 31. Day31 Linear Regression ├── 31-day31-linear-regression.pdf └── 31_Day31_Linear_Regression.ipynb ├── 32. Day32 Multiple Linear Regression ├── 32_Day32_Multiple_Linear_regression.ipynb └── Day32-multiple-linear-regression.pdf ├── 33. Day33 Linear Regression(Revision) └── Day32.pdf ├── 34. Day34 MLR Revision └── 34_Day34_Revision.ipynb ├── 35. Day35 Classification └── Day35 Classification Algorithm.pdf ├── 36. Day36 Logistic Regression └── 36_Day36_Logistic_Regression.ipynb ├── 37. Day37 Logistic Regression(Iris) └── Day37_Logistic_Regression(IRIS).ipynb ├── 38. Day38 Logistic reg. Workflow └── Day38 Logistic Reg. Workflow.pdf ├── 39. Day39 SVM Intro. └── Day 39 SVM.pdf ├── 40. Day40 Linear SVM └── Day40_linear_svm.ipynb ├── 41. Day41 Non-Linear SVM └── Day41_Non_Linear_SVM.ipynb ├── 42. Day42 SVM Regression └── Day42_Implementation_SVM_Regression.ipynb ├── 43. Day43 KNN Introduction ├── Day43 KNN Intro..pdf └── Day43 KNN Introduction.ipynb ├── 44. Day44 KNN Classification └── Day44_KNN_Classification.ipynb ├── 45. Day45 KNN Classification(IRIS) └── Day45_KNN_Classification(Iris).ipynb ├── 46. Day46 KNN Regression ├── Day46_KNN_Regression.ipynb └── Salary_dataset (1).csv ├── 47. Day47 KNN Hyperparameter tuning └── Day47_KNN_Hyperparameter_Tuning.ipynb ├── 48. Decision Tree Concept └── Day48 Decision Trees .pdf ├── 49. Day49 Decision Tree Implementation └── 49_Day49_Decision_Tree_Implementation.ipynb ├── 50. Day50 Decision Tree(Iris) └── 50_Day50_Decision_Tree(Iris).ipynb ├── 51. Day51 RandomForest Concept ├── Day 51 Random Forest Concept.pdf └── Untitled36.ipynb ├── 52. Day52 Random Forest Implementation ├── Day52_Random_Forest_Implementation.ipynb └── car_evaluation.csv ├── 53. Day53 Random Forest(Iris) └── Day53_Random_Forest(Iris)ipynb.ipynb ├── 54. Day54 RF_Hyperparameter Tuning ├── Day54_RandomForest_Hyperparameter_tuning.ipynb └── randomforest-hyperparametertuning.pdf ├── 55. Day55 Ensemble Learning ├── Day55_Ensemble_Learning.ipynb └── Overall Summary of Boosting.png ├── 56. Day56 Naive Bayes └── Day56_Naive_Bayes.ipynb ├── 57. Day57 Intro. to Clustering └── Untitled39.ipynb ├── 58. Day 58 K Means Concept └── Day58_K_Means Concept.ipynb ├── 59. Day59 K Means Implementation └── Day59_K_Means_Clustering.ipynb ├── 60. Day60 Hierarchical Clustering Concept └── Untitled39.ipynb ├── 61. Day61 H-Clustering(Agglomerative Clustering) ├── Day61_Hierarchical_Clustering.ipynb └── Mall_Customers.csv ├── 62. Day62 DBSCAN Concept └── Untitled40.ipynb ├── 63. Day63 DBSCAN Implementation └── Day63_DBSCAN.ipynb ├── 64. Day64 Dimensionality Reduction └── Dimensionality Reduction.png ├── 65. Day65 PCA Concept └── Day65 PCA.pdf ├── 66. Day66 PCA Implementation └── Day_66_PCA.ipynb ├── 67. Day67 Feature Selection Intro. └── Day67 Feature Selection.pdf ├── 68. Day68 Feature Selection - Filter Method ├── Day68_Filter_Method.ipynb └── day68-filter-method.pdf ├── 69. Day69 Feature Selection - Wrapper Method └── Day69 Wrapper Method.pdf ├── 70. Day70 Feature Selection - Embedded Method └── Day 70 Embedded Method.pdf ├── 71. Day71 Intro. to Data Analytics └── Day71 Intro to data analytics.pdf ├── 72. Day72 Intro. to Big Data └── Day72 Intro to Big Data.pdf ├── 73. Day73 Intro. to Excel └── Excel_Cheat_Sheet_.png ├── 74. Day74 Intro. to Power BI └── Bi.jpeg ├── 75. Day75 Simple BI Project └── Untitled45.ipynb ├── 76. Day76 Intro. to Deep Learning └── Day 76 Intro. to deep learning.pdf ├── 77. Day77 Intro. to Neural Networks └── Day77 Intro to Neural Network.pdf ├── 78. Day78 Intro. to Optimizers └── Day78 Intro. to Optimizers.pdf ├── 79. Day79 Intro. to NLP └── Day79 Intro to NLP.pdf ├── 80. Day80 Intro. to Big Data └── Day80 Intro to Big Data.pdf ├── 81. Day81 Intro. to Database └── Day81 Intro to Database.pdf ├── 82. Day82 Intro. to SQL └── Day82 Intro to SQL.pdf ├── 83. Day83 Overview of SQL └── SQL Cheat Sheet📝.pdf ├── 84. Day84 Webscrapping └── webscrapping.pdf ├── 85. Day85 Webscrapping └── Untitled55.ipynb ├── 86-88 Day86-88 Heart Disease Prediction ├── Day86_88_Heart_Disease_Prediction.ipynb └── heart.csv ├── 89-90 Day89-90 Loan Predictions with Comparing 3 models ├── Day89_90_Loan_Predictions.ipynb ├── day89-90-loan-predictions.pdf └── loan_data_set.csv ├── 91-92 Day91-92 Drug Classification with various model ├── Day91_92_Drug_Classification.ipynb ├── day91-92-drug-classification.pdf └── drug200.csv ├── 93-94 Day93-94 Diabetes Prediction with various model ├── Day93_94_Diabetes_Prediction.ipynb ├── day93-94-diabetes-prediction.pdf └── diabetes.csv ├── 95-96 Day95-96 Mall Customer Segmentation ├── Mall_Customer_Segmentation.ipynb ├── Mall_Customers.csv └── mall-customer-segmentation.pdf ├── 97-98 Day97-98 Flight Price Prediction using ML model ├── Data_Train.xlsx ├── Flight_Price_Predictions.ipynb └── flight-price-predictions.pdf ├── 99-100 Day99-100 Car Evaluation Model ├── Car_Evaluation_Model.ipynb ├── car-evaluation-model.pdf └── car_evaluation.csv └── README.md /01. Day1 Python Decision Making /Day1.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "name": "Day1.ipynb", 8 | "authorship_tag": "ABX9TyMui5oj4bU4NV9hp5EoG7wu", 9 | "include_colab_link": true 10 | }, 11 | "kernelspec": { 12 | "name": "python3", 13 | "display_name": "Python 3" 14 | }, 15 | "language_info": { 16 | "name": "python" 17 | } 18 | }, 19 | "cells": [ 20 | { 21 | "cell_type": "markdown", 22 | "metadata": { 23 | "id": "view-in-github", 24 | "colab_type": "text" 25 | }, 26 | "source": [ 27 | "\"Open" 28 | ] 29 | }, 30 | { 31 | "cell_type": "markdown", 32 | "source": [ 33 | " --------------**Python Decision Making**--------\n", 34 | " \"26 September 2023\" - Loga Aswin " 35 | ], 36 | "metadata": { 37 | "id": "jf1UglkchbQy" 38 | } 39 | }, 40 | { 41 | "cell_type": "markdown", 42 | "source": [ 43 | "**Python if...else Statement**" 44 | ], 45 | "metadata": { 46 | "id": "EuZ-uoGnVQru" 47 | } 48 | }, 49 | { 50 | "cell_type": "code", 51 | "source": [ 52 | "number = 10\n", 53 | "\n", 54 | "if number > 0:\n", 55 | " print('True')\n", 56 | "\n", 57 | "else:\n", 58 | " print('False')\n" 59 | ], 60 | "metadata": { 61 | "colab": { 62 | "base_uri": "https://localhost:8080/" 63 | }, 64 | "id": "Psb4HyqfbgPS", 65 | "outputId": "9ab8f7e7-4137-4924-887d-32a63dad323f" 66 | }, 67 | "execution_count": 12, 68 | "outputs": [ 69 | { 70 | "output_type": "stream", 71 | "name": "stdout", 72 | "text": [ 73 | "True\n" 74 | ] 75 | } 76 | ] 77 | }, 78 | { 79 | "cell_type": "markdown", 80 | "source": [ 81 | "**Python if...elif...else Statement**" 82 | ], 83 | "metadata": { 84 | "id": "uWzlk8_abXIq" 85 | } 86 | }, 87 | { 88 | "cell_type": "code", 89 | "source": [ 90 | "a = 10\n", 91 | "b=5\n", 92 | "if a>b:\n", 93 | " print(\"Number is positive\")\n", 94 | "elif a==b:\n", 95 | " print(\"Both are equal\")\n", 96 | "else:\n", 97 | " print(\"Number is negative\")" 98 | ], 99 | "metadata": { 100 | "colab": { 101 | "base_uri": "https://localhost:8080/" 102 | }, 103 | "id": "f3cmqQi-Vovs", 104 | "outputId": "d21cf1fb-e6c6-4cbf-b7ab-520e75abb534" 105 | }, 106 | "execution_count": 11, 107 | "outputs": [ 108 | { 109 | "output_type": "stream", 110 | "name": "stdout", 111 | "text": [ 112 | "Number is positive\n" 113 | ] 114 | } 115 | ] 116 | }, 117 | { 118 | "cell_type": "markdown", 119 | "source": [ 120 | "**Nested-if loops**" 121 | ], 122 | "metadata": { 123 | "id": "fiNnXx3DcIVt" 124 | } 125 | }, 126 | { 127 | "cell_type": "code", 128 | "source": [ 129 | "number = 10\n", 130 | "if (number >= 0):\n", 131 | " if number == 10:\n", 132 | " print('Number is 10')\n", 133 | " else:\n", 134 | " print('Number is positive')\n", 135 | "else:\n", 136 | " print('Number is negative')\n" 137 | ], 138 | "metadata": { 139 | "colab": { 140 | "base_uri": "https://localhost:8080/" 141 | }, 142 | "id": "eNA_MMVRbsDa", 143 | "outputId": "df8a7228-f792-431d-bc05-86aea016d248" 144 | }, 145 | "execution_count": 16, 146 | "outputs": [ 147 | { 148 | "output_type": "stream", 149 | "name": "stdout", 150 | "text": [ 151 | "Number is 10\n" 152 | ] 153 | } 154 | ] 155 | }, 156 | { 157 | "cell_type": "markdown", 158 | "source": [ 159 | " **Python for Loop**\n", 160 | " * Loop are used to repeat the block of code\n", 161 | " * For Eg: If you want to display a message 1000 times, then we use a loop .\n", 162 | " ** 2 types of loops:\n", 163 | " # For Loop\n", 164 | " # While Loop " 165 | ], 166 | "metadata": { 167 | "id": "rFB3caFahlLP" 168 | } 169 | }, 170 | { 171 | "cell_type": "markdown", 172 | "source": [ 173 | "**For Loop**" 174 | ], 175 | "metadata": { 176 | "id": "AOHUWshHkA4o" 177 | } 178 | }, 179 | { 180 | "cell_type": "code", 181 | "source": [ 182 | "# iterate from i = 0 to i = 3\n", 183 | "for i in range(0,4):\n", 184 | " print(i)" 185 | ], 186 | "metadata": { 187 | "colab": { 188 | "base_uri": "https://localhost:8080/" 189 | }, 190 | "id": "ZOCKiwPzjfj6", 191 | "outputId": "6a4c5fca-7112-41eb-bc96-694ffe0377f4" 192 | }, 193 | "execution_count": 19, 194 | "outputs": [ 195 | { 196 | "output_type": "stream", 197 | "name": "stdout", 198 | "text": [ 199 | "0\n", 200 | "1\n", 201 | "2\n", 202 | "3\n" 203 | ] 204 | } 205 | ] 206 | }, 207 | { 208 | "cell_type": "markdown", 209 | "source": [ 210 | "**For Loop with else**" 211 | ], 212 | "metadata": { 213 | "id": "tOizv6cGklLb" 214 | } 215 | }, 216 | { 217 | "cell_type": "code", 218 | "source": [ 219 | "sample = [0,1,2]\n", 220 | "for i in sample:\n", 221 | " print(i)\n", 222 | "else:\n", 223 | " print('No.')" 224 | ], 225 | "metadata": { 226 | "colab": { 227 | "base_uri": "https://localhost:8080/" 228 | }, 229 | "id": "zemGD1lZkj6O", 230 | "outputId": "7b39d6d8-b3c7-423a-b16d-8f6bc3581468" 231 | }, 232 | "execution_count": 21, 233 | "outputs": [ 234 | { 235 | "output_type": "stream", 236 | "name": "stdout", 237 | "text": [ 238 | "0\n", 239 | "1\n", 240 | "2\n", 241 | "No.\n" 242 | ] 243 | } 244 | ] 245 | }, 246 | { 247 | "cell_type": "markdown", 248 | "source": [ 249 | " **Python While loop**\n", 250 | "Python while loop is used to run a block code until a certain condition is met." 251 | ], 252 | "metadata": { 253 | "id": "JL_njppolsjM" 254 | } 255 | }, 256 | { 257 | "cell_type": "code", 258 | "source": [ 259 | "# initialize the variable\n", 260 | "i = 5\n", 261 | "n = 10\n", 262 | "\n", 263 | "# while loop from i = 1 to 5\n", 264 | "while i <= n:\n", 265 | " print(i)\n", 266 | " i = i + 1" 267 | ], 268 | "metadata": { 269 | "colab": { 270 | "base_uri": "https://localhost:8080/" 271 | }, 272 | "id": "xlNCHufbl1i3", 273 | "outputId": "4835ccd5-864d-46f8-9c7b-f0c70003d4d0" 274 | }, 275 | "execution_count": 22, 276 | "outputs": [ 277 | { 278 | "output_type": "stream", 279 | "name": "stdout", 280 | "text": [ 281 | "5\n", 282 | "6\n", 283 | "7\n", 284 | "8\n", 285 | "9\n", 286 | "10\n" 287 | ] 288 | } 289 | ] 290 | }, 291 | { 292 | "cell_type": "markdown", 293 | "source": [ 294 | "**Infinite While Loop**" 295 | ], 296 | "metadata": { 297 | "id": "nH7dfJODmgs2" 298 | } 299 | }, 300 | { 301 | "cell_type": "code", 302 | "source": [ 303 | "age = 42\n", 304 | "while age > 18: # always true\n", 305 | " print('You can vote')" 306 | ], 307 | "metadata": { 308 | "id": "SxIO-hIUn77e" 309 | }, 310 | "execution_count": null, 311 | "outputs": [] 312 | }, 313 | { 314 | "cell_type": "markdown", 315 | "source": [ 316 | "**While loop with else**" 317 | ], 318 | "metadata": { 319 | "id": "sIIZL7Stos1B" 320 | } 321 | }, 322 | { 323 | "cell_type": "code", 324 | "source": [ 325 | "sample = 0\n", 326 | "while sample < 3:\n", 327 | " print('Hi')\n", 328 | " sample = sample + 1\n", 329 | "else:\n", 330 | " print('Bye')" 331 | ], 332 | "metadata": { 333 | "colab": { 334 | "base_uri": "https://localhost:8080/" 335 | }, 336 | "id": "kGO-EgtboCpj", 337 | "outputId": "2b259cb3-eb3c-4b97-8f00-35e81e199655" 338 | }, 339 | "execution_count": 26, 340 | "outputs": [ 341 | { 342 | "output_type": "stream", 343 | "name": "stdout", 344 | "text": [ 345 | "Hi\n", 346 | "Hi\n", 347 | "Hi\n", 348 | "Bye\n" 349 | ] 350 | } 351 | ] 352 | }, 353 | { 354 | "cell_type": "markdown", 355 | "source": [ 356 | " ** Break Statement**\n", 357 | " * Break statement is used to terminate the loop immediately when it is encountered." 358 | ], 359 | "metadata": { 360 | "id": "B-cXuy0eo8bP" 361 | } 362 | }, 363 | { 364 | "cell_type": "code", 365 | "source": [ 366 | "for i in range(5):\n", 367 | " if i == 3:\n", 368 | " break #terminate the loop\n", 369 | " print(i)" 370 | ], 371 | "metadata": { 372 | "colab": { 373 | "base_uri": "https://localhost:8080/" 374 | }, 375 | "id": "cZuTIB2Fo-h2", 376 | "outputId": "3b6b5a65-7e1f-4d37-c211-085a47f002c1" 377 | }, 378 | "execution_count": 27, 379 | "outputs": [ 380 | { 381 | "output_type": "stream", 382 | "name": "stdout", 383 | "text": [ 384 | "0\n", 385 | "1\n", 386 | "2\n" 387 | ] 388 | } 389 | ] 390 | }, 391 | { 392 | "cell_type": "markdown", 393 | "source": [ 394 | "**Continue Statement**" 395 | ], 396 | "metadata": { 397 | "id": "e4KRXuJIpDE5" 398 | } 399 | }, 400 | { 401 | "cell_type": "code", 402 | "source": [ 403 | "for i in range(5):\n", 404 | " if i == 3:\n", 405 | " continue #it skips the current iteration\n", 406 | " print(i)" 407 | ], 408 | "metadata": { 409 | "colab": { 410 | "base_uri": "https://localhost:8080/" 411 | }, 412 | "id": "DXDxZufPpFi5", 413 | "outputId": "0e5c6cea-7566-46c3-9819-671c6b5fa2e2" 414 | }, 415 | "execution_count": 28, 416 | "outputs": [ 417 | { 418 | "output_type": "stream", 419 | "name": "stdout", 420 | "text": [ 421 | "0\n", 422 | "1\n", 423 | "2\n", 424 | "4\n" 425 | ] 426 | } 427 | ] 428 | }, 429 | { 430 | "cell_type": "markdown", 431 | "source": [ 432 | " ** Pass Statement**\n", 433 | " * Pass statement is a null statement which can be used as a placeholder for future code. " 434 | ], 435 | "metadata": { 436 | "id": "L9NlvCSkqRjG" 437 | } 438 | } 439 | ] 440 | } -------------------------------------------------------------------------------- /02. Day2 Python Data Structures/Day2.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyPE7PLYXBsSxWiNJmvr85IS", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "source": [ 32 | " Python Data Types By: Loga Aswin" 33 | ], 34 | "metadata": { 35 | "id": "Y0HcrK81sWtn" 36 | } 37 | }, 38 | { 39 | "cell_type": "markdown", 40 | "source": [ 41 | "**List**\n", 42 | "It is an ordered collection of similar or different types of items separated by commas and enclosed within brackets[]." 43 | ], 44 | "metadata": { 45 | "id": "r0obYYX5ssXV" 46 | } 47 | }, 48 | { 49 | "cell_type": "code", 50 | "execution_count": null, 51 | "metadata": { 52 | "colab": { 53 | "base_uri": "https://localhost:8080/" 54 | }, 55 | "id": "ooppUuiwsTMq", 56 | "outputId": "7474b94a-0344-4d2a-c845-925a0d6f5ae6" 57 | }, 58 | "outputs": [ 59 | { 60 | "output_type": "stream", 61 | "name": "stdout", 62 | "text": [ 63 | "Audi\n", 64 | "\n" 65 | ] 66 | } 67 | ], 68 | "source": [ 69 | "car=[\"polo\",\"Audi\",\"Bmw\"]\n", 70 | "print(car[1])\n", 71 | "print(type(car))\n" 72 | ] 73 | }, 74 | { 75 | "cell_type": "markdown", 76 | "source": [ 77 | "**Append:**\n", 78 | "append() adds an element at the end of the list" 79 | ], 80 | "metadata": { 81 | "id": "f4uTuNEgt2Tu" 82 | } 83 | }, 84 | { 85 | "cell_type": "code", 86 | "source": [ 87 | "sample = [1, 2, 3, 4, 5, 6]\n", 88 | "sample.append(5)\n", 89 | "sample.append([7, 8, 9])\n", 90 | "sample.extend([6, 7, 8])\n", 91 | "print(sample)" 92 | ], 93 | "metadata": { 94 | "colab": { 95 | "base_uri": "https://localhost:8080/" 96 | }, 97 | "id": "fyCgyYIRtqnZ", 98 | "outputId": "f05589a6-caae-422f-ac36-2a52e0b04a30" 99 | }, 100 | "execution_count": null, 101 | "outputs": [ 102 | { 103 | "output_type": "stream", 104 | "name": "stdout", 105 | "text": [ 106 | "[1, 2, 3, 4, 5, 6, 5, [7, 8, 9], 6, 7, 8]\n" 107 | ] 108 | } 109 | ] 110 | }, 111 | { 112 | "cell_type": "markdown", 113 | "source": [ 114 | "**Slicing**" 115 | ], 116 | "metadata": { 117 | "id": "KFqmGSEvvMAV" 118 | } 119 | }, 120 | { 121 | "cell_type": "code", 122 | "source": [ 123 | "list = [1, 2, 3, 4, 5, 6, 7]\n", 124 | "print(list[0:4])\n", 125 | "print(list[::])\n", 126 | "print(list[::-1])\n", 127 | "print(list[-1::])" 128 | ], 129 | "metadata": { 130 | "colab": { 131 | "base_uri": "https://localhost:8080/" 132 | }, 133 | "id": "Q3T29Oy8uWan", 134 | "outputId": "4cac5b1f-26d8-496d-8d69-e180ad24671a" 135 | }, 136 | "execution_count": null, 137 | "outputs": [ 138 | { 139 | "output_type": "stream", 140 | "name": "stdout", 141 | "text": [ 142 | "[1, 2, 3, 4]\n", 143 | "[1, 2, 3, 4, 5, 6, 7]\n", 144 | "[7, 6, 5, 4, 3, 2, 1]\n", 145 | "[7]\n" 146 | ] 147 | } 148 | ] 149 | }, 150 | { 151 | "cell_type": "markdown", 152 | "source": [ 153 | "**Deleting elements in list**" 154 | ], 155 | "metadata": { 156 | "id": "DlI6XVZuvfW7" 157 | } 158 | }, 159 | { 160 | "cell_type": "code", 161 | "source": [ 162 | "list = [1, 2, 3, 4, 5, 6, 7]\n", 163 | "print(list.pop(2))\n", 164 | "print(list)\n", 165 | "list.remove(4)\n", 166 | "print(list)\n", 167 | "list.clear()\n", 168 | "print(list)" 169 | ], 170 | "metadata": { 171 | "colab": { 172 | "base_uri": "https://localhost:8080/" 173 | }, 174 | "id": "mwY7oetiwD4F", 175 | "outputId": "8ce534a9-38d9-4034-dc8c-239721b5b655" 176 | }, 177 | "execution_count": 3, 178 | "outputs": [ 179 | { 180 | "output_type": "stream", 181 | "name": "stdout", 182 | "text": [ 183 | "3\n", 184 | "[1, 2, 4, 5, 6, 7]\n", 185 | "[1, 2, 5, 6, 7]\n", 186 | "[]\n" 187 | ] 188 | } 189 | ] 190 | }, 191 | { 192 | "cell_type": "markdown", 193 | "source": [ 194 | " **Tuples**\n", 195 | "A tuple is a collection of objects that are ordered and immutable." 196 | ], 197 | "metadata": { 198 | "id": "7csCnpAtyC5Y" 199 | } 200 | }, 201 | { 202 | "cell_type": "code", 203 | "source": [ 204 | "# Different types of tuples\n", 205 | "\n", 206 | "# Empty tuple\n", 207 | "tuple = ()\n", 208 | "print(tuple)\n", 209 | "\n", 210 | "# integers\n", 211 | "tuple = (10, 20, 30)\n", 212 | "print(tuple)\n", 213 | "\n", 214 | "# mixed datatypes\n", 215 | "tuple = (1, \"Hello\", 3.4)\n", 216 | "print(tuple)\n", 217 | "\n", 218 | "# nested tuple\n", 219 | "tuple = (\"mouse\", [8, 4, 6], (1, 2, 3))\n", 220 | "print(tuple)" 221 | ], 222 | "metadata": { 223 | "colab": { 224 | "base_uri": "https://localhost:8080/" 225 | }, 226 | "id": "TqHrn4H8yCbr", 227 | "outputId": "647c2e70-4be5-4837-f246-d64ee314248e" 228 | }, 229 | "execution_count": 4, 230 | "outputs": [ 231 | { 232 | "output_type": "stream", 233 | "name": "stdout", 234 | "text": [ 235 | "()\n", 236 | "(10, 20, 30)\n", 237 | "(1, 'Hello', 3.4)\n", 238 | "('mouse', [8, 4, 6], (1, 2, 3))\n" 239 | ] 240 | } 241 | ] 242 | }, 243 | { 244 | "cell_type": "markdown", 245 | "source": [ 246 | "**Slicing**" 247 | ], 248 | "metadata": { 249 | "id": "3j2oiBAQzEBo" 250 | } 251 | }, 252 | { 253 | "cell_type": "code", 254 | "source": [ 255 | "# accessing tuple\n", 256 | "tuple = (1,2,3,4,5,6,7,8,9)\n", 257 | "\n", 258 | "print(tuple[1:4])\n", 259 | "\n", 260 | "print(tuple[:-7])\n", 261 | "\n", 262 | "print(tuple[7:])\n", 263 | "\n", 264 | "print(tuple[:])" 265 | ], 266 | "metadata": { 267 | "colab": { 268 | "base_uri": "https://localhost:8080/" 269 | }, 270 | "id": "fPbpyQiPzGgB", 271 | "outputId": "05c6d3b9-1329-4f03-fdfd-c80c235ba2e9" 272 | }, 273 | "execution_count": 6, 274 | "outputs": [ 275 | { 276 | "output_type": "stream", 277 | "name": "stdout", 278 | "text": [ 279 | "(2, 3, 4)\n", 280 | "(1, 2)\n", 281 | "(8, 9)\n", 282 | "(1, 2, 3, 4, 5, 6, 7, 8, 9)\n" 283 | ] 284 | } 285 | ] 286 | }, 287 | { 288 | "cell_type": "code", 289 | "source": [ 290 | "tuple = (1,2,3,4) #iterating through tuple\n", 291 | "for tuple in tuple:\n", 292 | " print(\"tuple\")" 293 | ], 294 | "metadata": { 295 | "colab": { 296 | "base_uri": "https://localhost:8080/" 297 | }, 298 | "id": "YIqEdWtE0HUH", 299 | "outputId": "89519123-3d40-44ae-b3cc-27841af57dbe" 300 | }, 301 | "execution_count": 9, 302 | "outputs": [ 303 | { 304 | "output_type": "stream", 305 | "name": "stdout", 306 | "text": [ 307 | "tuple\n", 308 | "tuple\n", 309 | "tuple\n", 310 | "tuple\n" 311 | ] 312 | } 313 | ] 314 | }, 315 | { 316 | "cell_type": "markdown", 317 | "source": [ 318 | " **Sets**\n", 319 | "Empty curly braces { } will make an empty dictionary in Python. \n" 320 | ], 321 | "metadata": { 322 | "id": "IQLfaz3G0Rwn" 323 | } 324 | }, 325 | { 326 | "cell_type": "code", 327 | "source": [ 328 | "num = {2, 4, 6, 6, 2, 8}\n", 329 | "print(num)" 330 | ], 331 | "metadata": { 332 | "colab": { 333 | "base_uri": "https://localhost:8080/" 334 | }, 335 | "id": "2FQhCV4u1Irt", 336 | "outputId": "8e5aff86-d94e-4b10-b99f-8aee41d44ec8" 337 | }, 338 | "execution_count": 10, 339 | "outputs": [ 340 | { 341 | "output_type": "stream", 342 | "name": "stdout", 343 | "text": [ 344 | "{8, 2, 4, 6}\n" 345 | ] 346 | } 347 | ] 348 | }, 349 | { 350 | "cell_type": "code", 351 | "source": [ 352 | "num = {45, 39, 30, 75}\n", 353 | "\n", 354 | "print('before:',num)\n", 355 | "num.add(32)\n", 356 | "print('after:', num)" 357 | ], 358 | "metadata": { 359 | "colab": { 360 | "base_uri": "https://localhost:8080/" 361 | }, 362 | "id": "wrPhwPNi1Tbn", 363 | "outputId": "97738c7e-0715-4af6-9ca4-a04cbbb5685e" 364 | }, 365 | "execution_count": 11, 366 | "outputs": [ 367 | { 368 | "output_type": "stream", 369 | "name": "stdout", 370 | "text": [ 371 | "before: {75, 45, 30, 39}\n", 372 | "after: {32, 39, 75, 45, 30}\n" 373 | ] 374 | } 375 | ] 376 | }, 377 | { 378 | "cell_type": "code", 379 | "source": [ 380 | "languages = {'React', 'Java', 'Python'}\n", 381 | "\n", 382 | "print('before:',languages)\n", 383 | "removedValue = languages.discard('Java')\n", 384 | "print('after:', languages)" 385 | ], 386 | "metadata": { 387 | "colab": { 388 | "base_uri": "https://localhost:8080/" 389 | }, 390 | "id": "RpUREBUz1yS_", 391 | "outputId": "efeb0688-2904-4cf7-dbd1-8ad97dbe441d" 392 | }, 393 | "execution_count": 13, 394 | "outputs": [ 395 | { 396 | "output_type": "stream", 397 | "name": "stdout", 398 | "text": [ 399 | "before: {'React', 'Python', 'Java'}\n", 400 | "after: {'React', 'Python'}\n" 401 | ] 402 | } 403 | ] 404 | }, 405 | { 406 | "cell_type": "code", 407 | "source": [ 408 | "num = {2, 4, 6, 6, 2, 8}\n", 409 | "print(len(num))" 410 | ], 411 | "metadata": { 412 | "colab": { 413 | "base_uri": "https://localhost:8080/" 414 | }, 415 | "id": "zUWZVOk62dsM", 416 | "outputId": "cdb9c05a-aaec-49f6-d3c9-61cf4552e796" 417 | }, 418 | "execution_count": 14, 419 | "outputs": [ 420 | { 421 | "output_type": "stream", 422 | "name": "stdout", 423 | "text": [ 424 | "4\n" 425 | ] 426 | } 427 | ] 428 | }, 429 | { 430 | "cell_type": "markdown", 431 | "source": [ 432 | "**Set Intersection**" 433 | ], 434 | "metadata": { 435 | "id": "ihZYZouV3LQN" 436 | } 437 | }, 438 | { 439 | "cell_type": "code", 440 | "source": [ 441 | "A = {1, 3, 5}\n", 442 | "B = {1, 2, 3}\n", 443 | "print('using &:', A & B)\n", 444 | "print('using intersection():', A.intersection(B))" 445 | ], 446 | "metadata": { 447 | "colab": { 448 | "base_uri": "https://localhost:8080/" 449 | }, 450 | "id": "Qm-X-a0M3OFb", 451 | "outputId": "217c70b7-aab0-4f72-f6bc-fd17ab607f5e" 452 | }, 453 | "execution_count": 15, 454 | "outputs": [ 455 | { 456 | "output_type": "stream", 457 | "name": "stdout", 458 | "text": [ 459 | "using &: {1, 3}\n", 460 | "using intersection(): {1, 3}\n" 461 | ] 462 | } 463 | ] 464 | }, 465 | { 466 | "cell_type": "code", 467 | "source": [ 468 | "print('using -:', A - B)\n", 469 | "print('using intersection():', A.difference(B))" 470 | ], 471 | "metadata": { 472 | "colab": { 473 | "base_uri": "https://localhost:8080/" 474 | }, 475 | "id": "j3ndBqTb3Xfu", 476 | "outputId": "d8c6836b-0c80-43c4-c6a8-b3ec940fdac8" 477 | }, 478 | "execution_count": 17, 479 | "outputs": [ 480 | { 481 | "output_type": "stream", 482 | "name": "stdout", 483 | "text": [ 484 | "using -: {5}\n", 485 | "using intersection(): {5}\n" 486 | ] 487 | } 488 | ] 489 | }, 490 | { 491 | "cell_type": "markdown", 492 | "source": [ 493 | "**Dictionary**" 494 | ], 495 | "metadata": { 496 | "id": "tfHTo4XE4CEs" 497 | } 498 | }, 499 | { 500 | "cell_type": "code", 501 | "source": [ 502 | "dict = {1:'a', 2:'b', 5:'c', 4:'d'}\n", 503 | "print(dict)\n", 504 | "print(dict[5])" 505 | ], 506 | "metadata": { 507 | "colab": { 508 | "base_uri": "https://localhost:8080/" 509 | }, 510 | "id": "iANjNgEZ4Ewa", 511 | "outputId": "3edd799b-c6f3-4c67-d6b8-3c4d40223e14" 512 | }, 513 | "execution_count": 18, 514 | "outputs": [ 515 | { 516 | "output_type": "stream", 517 | "name": "stdout", 518 | "text": [ 519 | "{1: 'a', 2: 'b', 5: 'c', 4: 'd'}\n", 520 | "c\n" 521 | ] 522 | } 523 | ] 524 | }, 525 | { 526 | "cell_type": "code", 527 | "source": [ 528 | "print(dict.items())\n", 529 | "print(dict.keys())\n", 530 | "print(dict.values())" 531 | ], 532 | "metadata": { 533 | "colab": { 534 | "base_uri": "https://localhost:8080/" 535 | }, 536 | "id": "FvtWMjgs44MQ", 537 | "outputId": "8a2ca792-c84f-4b0d-f8b3-149becfacf37" 538 | }, 539 | "execution_count": 23, 540 | "outputs": [ 541 | { 542 | "output_type": "stream", 543 | "name": "stdout", 544 | "text": [ 545 | "dict_items([(1, 'a'), (2, 'b'), (5, 'c'), (4, 'd')])\n", 546 | "dict_keys([1, 2, 5, 4])\n", 547 | "dict_values(['a', 'b', 'c', 'd'])\n" 548 | ] 549 | } 550 | ] 551 | } 552 | ] 553 | } -------------------------------------------------------------------------------- /03. Day3 Python Function and Recursion/Day3.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyO6TpIjoiDDPY/hCREjOMA3", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "source": [ 32 | " **Functions** By: Loga Aswin\n", 33 | " * A function is a block of code that performs a specific task. " 34 | ], 35 | "metadata": { 36 | "id": "ckwf5a6-uCRX" 37 | } 38 | }, 39 | { 40 | "cell_type": "code", 41 | "execution_count": 3, 42 | "metadata": { 43 | "colab": { 44 | "base_uri": "https://localhost:8080/" 45 | }, 46 | "id": "ogP0T3mjsQY9", 47 | "outputId": "630dcd20-5fe2-4d43-f0a1-d6ce355e9f19" 48 | }, 49 | "outputs": [ 50 | { 51 | "output_type": "stream", 52 | "name": "stdout", 53 | "text": [ 54 | "Hi World\n" 55 | ] 56 | } 57 | ], 58 | "source": [ 59 | "# user-defined function\n", 60 | "def hello():\n", 61 | " print('Hi World')\n", 62 | "hello() #function calling" 63 | ] 64 | }, 65 | { 66 | "cell_type": "code", 67 | "source": [ 68 | "# Function along parameter\n", 69 | "def addition(n1,n2):\n", 70 | " print(n1+n2)\n", 71 | "addition(50,50)" 72 | ], 73 | "metadata": { 74 | "colab": { 75 | "base_uri": "https://localhost:8080/" 76 | }, 77 | "id": "pob-KeYnu1uw", 78 | "outputId": "6b5a3708-278f-4338-ef9f-d70af24b9281" 79 | }, 80 | "execution_count": 4, 81 | "outputs": [ 82 | { 83 | "output_type": "stream", 84 | "name": "stdout", 85 | "text": [ 86 | "100\n" 87 | ] 88 | } 89 | ] 90 | }, 91 | { 92 | "cell_type": "markdown", 93 | "source": [ 94 | "**Function return type**" 95 | ], 96 | "metadata": { 97 | "id": "0M0mBso7x4Hz" 98 | } 99 | }, 100 | { 101 | "cell_type": "code", 102 | "source": [ 103 | "# function\n", 104 | "def find_square(num):\n", 105 | " result = num * num\n", 106 | " return result\n", 107 | "#calling function\n", 108 | "square = find_square(10)\n", 109 | "\n", 110 | "print('Square:',square)" 111 | ], 112 | "metadata": { 113 | "colab": { 114 | "base_uri": "https://localhost:8080/" 115 | }, 116 | "id": "51vHed1Su8el", 117 | "outputId": "b142eea1-4103-4cae-b274-c3bf6cf4d1e1" 118 | }, 119 | "execution_count": 21, 120 | "outputs": [ 121 | { 122 | "output_type": "stream", 123 | "name": "stdout", 124 | "text": [ 125 | "square: 100\n" 126 | ] 127 | } 128 | ] 129 | }, 130 | { 131 | "cell_type": "markdown", 132 | "source": [ 133 | "**Types of Function Arguments:**\n", 134 | "\n", 135 | "\n", 136 | "1. Default Arguments:\n", 137 | "\n" 138 | ], 139 | "metadata": { 140 | "id": "QuKC9VW2yM3K" 141 | } 142 | }, 143 | { 144 | "cell_type": "code", 145 | "source": [ 146 | "def add_numbers( a = 70, b = 80):\n", 147 | " sum = a + b\n", 148 | " print('Sum:', sum)\n", 149 | "\n", 150 | "# two arguments\n", 151 | "add_numbers(2, 3)\n", 152 | "\n", 153 | "#No arguments\n", 154 | "add_numbers()" 155 | ], 156 | "metadata": { 157 | "colab": { 158 | "base_uri": "https://localhost:8080/" 159 | }, 160 | "id": "RT8cUTw1xs33", 161 | "outputId": "7e30f259-b443-4aaf-b809-ff71e6d1c319" 162 | }, 163 | "execution_count": 22, 164 | "outputs": [ 165 | { 166 | "output_type": "stream", 167 | "name": "stdout", 168 | "text": [ 169 | "Sum: 5\n", 170 | "Sum: 150\n" 171 | ] 172 | } 173 | ] 174 | }, 175 | { 176 | "cell_type": "markdown", 177 | "source": [ 178 | "2. Keyword Arguments" 179 | ], 180 | "metadata": { 181 | "id": "TxravEj5y47N" 182 | } 183 | }, 184 | { 185 | "cell_type": "code", 186 | "source": [ 187 | "def show(first_name, last_name):\n", 188 | " print('First Name:', first_name)\n", 189 | " print('Last Name:', last_name)\n", 190 | "\n", 191 | "show(last_name = 'Aswin', first_name = 'Loga')" 192 | ], 193 | "metadata": { 194 | "colab": { 195 | "base_uri": "https://localhost:8080/" 196 | }, 197 | "id": "aguX-YLfzKH4", 198 | "outputId": "36973655-8ea3-4390-c01e-afab28d67ccf" 199 | }, 200 | "execution_count": 23, 201 | "outputs": [ 202 | { 203 | "output_type": "stream", 204 | "name": "stdout", 205 | "text": [ 206 | "First Name: Loga\n", 207 | "Last Name: Aswin\n" 208 | ] 209 | } 210 | ] 211 | }, 212 | { 213 | "cell_type": "markdown", 214 | "source": [ 215 | "3. Positional Arguments" 216 | ], 217 | "metadata": { 218 | "id": "iTkCZsNQzfNv" 219 | } 220 | }, 221 | { 222 | "cell_type": "code", 223 | "source": [ 224 | "def prints(age,name):\n", 225 | " print(age,name)\n", 226 | "\n", 227 | "prints('Aswin',20)\n", 228 | "prints(20,'Aswin')" 229 | ], 230 | "metadata": { 231 | "colab": { 232 | "base_uri": "https://localhost:8080/" 233 | }, 234 | "id": "f34Dqtw9zpFN", 235 | "outputId": "f2f019b9-2af1-433f-8b33-92cded10e929" 236 | }, 237 | "execution_count": 25, 238 | "outputs": [ 239 | { 240 | "output_type": "stream", 241 | "name": "stdout", 242 | "text": [ 243 | "Aswin 20\n", 244 | "20 Aswin\n" 245 | ] 246 | } 247 | ] 248 | }, 249 | { 250 | "cell_type": "markdown", 251 | "source": [ 252 | "4. Arbitrary Arguments" 253 | ], 254 | "metadata": { 255 | "id": "v7XXz34Ez9EO" 256 | } 257 | }, 258 | { 259 | "cell_type": "code", 260 | "source": [ 261 | "#find sum of multiple numbers\n", 262 | "def find_sum(*numbers):\n", 263 | " result = 0\n", 264 | " for num in numbers:\n", 265 | " result = result + num\n", 266 | " print(\"Sum = \", result)\n", 267 | "\n", 268 | "find_sum(1, 2, 3)" 269 | ], 270 | "metadata": { 271 | "colab": { 272 | "base_uri": "https://localhost:8080/" 273 | }, 274 | "id": "tQMh64Mp0MbP", 275 | "outputId": "71320f7f-3313-4a3f-af34-46d11d84a796" 276 | }, 277 | "execution_count": 26, 278 | "outputs": [ 279 | { 280 | "output_type": "stream", 281 | "name": "stdout", 282 | "text": [ 283 | "Sum = 6\n" 284 | ] 285 | } 286 | ] 287 | }, 288 | { 289 | "cell_type": "markdown", 290 | "source": [ 291 | "**Python Recursion** :\n", 292 | "Recursion is the process of defining something in terms of itself." 293 | ], 294 | "metadata": { 295 | "id": "PRnVoZqC9jWd" 296 | } 297 | }, 298 | { 299 | "cell_type": "code", 300 | "source": [ 301 | "def factorial(x):\n", 302 | " if x == 1:\n", 303 | " return 1\n", 304 | " else:\n", 305 | " return (x * factorial(x-1))\n", 306 | "\n", 307 | "x = int(input(\"Enter the number:\"))\n", 308 | "print(\"The factorial is\", factorial(x))" 309 | ], 310 | "metadata": { 311 | "colab": { 312 | "base_uri": "https://localhost:8080/" 313 | }, 314 | "id": "umAH6LsJ9m_D", 315 | "outputId": "dfcd821c-145d-4fc9-b079-0fa6b44b9809" 316 | }, 317 | "execution_count": 27, 318 | "outputs": [ 319 | { 320 | "output_type": "stream", 321 | "name": "stdout", 322 | "text": [ 323 | "Enter the number:5\n", 324 | "The factorial is 120\n" 325 | ] 326 | } 327 | ] 328 | }, 329 | { 330 | "cell_type": "code", 331 | "source": [ 332 | "#python recursive pattern\n", 333 | "def row(n):\n", 334 | " if n < 1:\n", 335 | " return\n", 336 | " print(\"*\", end=\" \")\n", 337 | " row(n - 1)\n", 338 | "\n", 339 | "def pattern(n):\n", 340 | " if n < 1:\n", 341 | " return\n", 342 | " row(n)\n", 343 | " print(\"\")\n", 344 | " pattern(n - 1)\n", 345 | "\n", 346 | "n = 5\n", 347 | "pattern(n)" 348 | ], 349 | "metadata": { 350 | "colab": { 351 | "base_uri": "https://localhost:8080/" 352 | }, 353 | "id": "fB51DdK89uUo", 354 | "outputId": "a4059ba6-8a3b-4c75-f9bd-0520623b0e7e" 355 | }, 356 | "execution_count": 28, 357 | "outputs": [ 358 | { 359 | "output_type": "stream", 360 | "name": "stdout", 361 | "text": [ 362 | "* * * * * \n", 363 | "* * * * \n", 364 | "* * * \n", 365 | "* * \n", 366 | "* \n" 367 | ] 368 | } 369 | ] 370 | } 371 | ] 372 | } -------------------------------------------------------------------------------- /04. Day4 Python Classes and Objects/Day4.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyPwoU7/aBC7p90IXgxHD0uY", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "source": [ 32 | " **Python Objects and Classes** By: Loga Aswin\n", 33 | "Python classes:\n", 34 | " A class is a blueprint or a template for creating objects. " 35 | ], 36 | "metadata": { 37 | "id": "74onC0ncVCYo" 38 | } 39 | }, 40 | { 41 | "cell_type": "code", 42 | "execution_count": null, 43 | "metadata": { 44 | "id": "YYFSjM6HSssv" 45 | }, 46 | "outputs": [], 47 | "source": [ 48 | "#name of the class\n", 49 | "class Car:\n", 50 | " name = \"\" #defining class\n", 51 | " gear = 0" 52 | ] 53 | }, 54 | { 55 | "cell_type": "markdown", 56 | "source": [ 57 | "Python Objects:\n", 58 | "An object is called an instance of a class" 59 | ], 60 | "metadata": { 61 | "id": "ciloxC5tYa9D" 62 | } 63 | }, 64 | { 65 | "cell_type": "markdown", 66 | "source": [ 67 | "For example: Car is class . So, we take Car1,Car2 as objects" 68 | ], 69 | "metadata": { 70 | "id": "bz8QQFy9YlK8" 71 | } 72 | }, 73 | { 74 | "cell_type": "code", 75 | "source": [ 76 | "# create class\n", 77 | "class Car:\n", 78 | " name = \"\"\n", 79 | " gear = 0\n", 80 | "\n", 81 | "# create objects of class\n", 82 | "Car1 = Car()" 83 | ], 84 | "metadata": { 85 | "id": "29bbG8RWYUWj" 86 | }, 87 | "execution_count": 1, 88 | "outputs": [] 89 | }, 90 | { 91 | "cell_type": "markdown", 92 | "source": [ 93 | "Example Python program of Class and Objects" 94 | ], 95 | "metadata": { 96 | "id": "txtyGZToZHKp" 97 | } 98 | }, 99 | { 100 | "cell_type": "code", 101 | "source": [ 102 | "# define a class\n", 103 | "class Car:\n", 104 | " name = \"\"\n", 105 | " gear = 0\n", 106 | "\n", 107 | "# create object of class\n", 108 | "Car1 = Car()\n", 109 | "\n", 110 | "# access attributes and assign new values\n", 111 | "Car1.gear = 11\n", 112 | "Car1.name = \"Mountain Bike\"\n", 113 | "\n", 114 | "print(f\"Name: {Car1.name}, Gears: {Car1.gear} \")" 115 | ], 116 | "metadata": { 117 | "colab": { 118 | "base_uri": "https://localhost:8080/" 119 | }, 120 | "id": "GZTYqTIwZFaa", 121 | "outputId": "052a3f92-d21c-43e0-aaab-69f624014f9e" 122 | }, 123 | "execution_count": 3, 124 | "outputs": [ 125 | { 126 | "output_type": "stream", 127 | "name": "stdout", 128 | "text": [ 129 | "Name: Mountain Bike, Gears: 11 \n" 130 | ] 131 | } 132 | ] 133 | }, 134 | { 135 | "cell_type": "markdown", 136 | "source": [ 137 | "**Python Constructors**" 138 | ], 139 | "metadata": { 140 | "id": "hnpZA9NxZte7" 141 | } 142 | }, 143 | { 144 | "cell_type": "code", 145 | "source": [ 146 | "class person:\n", 147 | " def __init__(self, name, age):\n", 148 | " self.name = name\n", 149 | " self.age = age\n", 150 | "person1 = person(\"Aswin\",20) #creating a person object\n", 151 | "print(f\"Name: {person1.name}\")\n", 152 | "print(f\"Age: {person1.age}\")\n" 153 | ], 154 | "metadata": { 155 | "id": "6S-_tXCQZvOQ" 156 | }, 157 | "execution_count": null, 158 | "outputs": [] 159 | }, 160 | { 161 | "cell_type": "markdown", 162 | "source": [ 163 | "**Destructor**" 164 | ], 165 | "metadata": { 166 | "id": "bThz196I_UA8" 167 | } 168 | }, 169 | { 170 | "cell_type": "code", 171 | "source": [ 172 | "class MyClass:\n", 173 | " def __init__(self, name):\n", 174 | " self.name = name\n", 175 | "\n", 176 | " def __del__(self):\n", 177 | " print(f\"{self.name} is being destroyed!\")\n", 178 | "\n", 179 | "# Creating objects\n", 180 | "obj1 = MyClass(\"Object 1\")\n", 181 | "obj2 = MyClass(\"Object 2\")\n", 182 | "\n", 183 | "# Deleting references to objects\n", 184 | "del obj1\n", 185 | "del obj2\n" 186 | ], 187 | "metadata": { 188 | "colab": { 189 | "base_uri": "https://localhost:8080/" 190 | }, 191 | "id": "240FfVUy_W88", 192 | "outputId": "308697e0-6e83-48dd-8fda-e23a73b72202" 193 | }, 194 | "execution_count": 1, 195 | "outputs": [ 196 | { 197 | "output_type": "stream", 198 | "name": "stdout", 199 | "text": [ 200 | "Object 1 is being destroyed!\n", 201 | "Object 2 is being destroyed!\n" 202 | ] 203 | } 204 | ] 205 | }, 206 | { 207 | "cell_type": "markdown", 208 | "source": [ 209 | "**Class and Static Variable**" 210 | ], 211 | "metadata": { 212 | "id": "Gaq9iAGlIk1Y" 213 | } 214 | }, 215 | { 216 | "cell_type": "code", 217 | "source": [ 218 | "#Class and Static Variables:\n", 219 | "class Voter:\n", 220 | " # Class variable for voting age\n", 221 | " voting_age = 18\n", 222 | "\n", 223 | " # Class variable to keep track of the total number of voters\n", 224 | " num_voters = 0\n" 225 | ], 226 | "metadata": { 227 | "id": "ULgjXWgXI-aT" 228 | }, 229 | "execution_count": 2, 230 | "outputs": [] 231 | }, 232 | { 233 | "cell_type": "code", 234 | "source": [ 235 | "#Class and Static Methods:\n", 236 | "class Voter:\n", 237 | " voting_age = 18\n", 238 | " num_voters = 0\n", 239 | "\n", 240 | " def __init__(self, age):\n", 241 | " self.age = age\n", 242 | " Voter.num_voters += 1\n", 243 | "\n", 244 | " @staticmethod\n", 245 | " def is_eligible(age):\n", 246 | " return age >= Voter.voting_age\n", 247 | "\n", 248 | " @classmethod\n", 249 | " def get_total_voters(cls):\n", 250 | " return cls.num_voters\n", 251 | "\n", 252 | " def check_eligibility(self):\n", 253 | " if Voter.is_eligible(self.age):\n", 254 | " print(f\"You are eligible to vote at age {self.age}.\")\n", 255 | " else:\n", 256 | " print(f\"Sorry, you are not eligible to vote at age {self.age}.\")\n", 257 | "\n", 258 | "\n", 259 | "# Create voter instances\n", 260 | "voter1 = Voter(20)\n", 261 | "voter2 = Voter(16)\n", 262 | "voter3 = Voter(25)\n", 263 | "\n", 264 | "# Check eligibility and total voters\n", 265 | "voter1.check_eligibility() # Output: You are eligible to vote at age 20.\n", 266 | "voter2.check_eligibility() # Output: Sorry, you are not eligible to vote at age 16.\n", 267 | "voter3.check_eligibility() # Output: You are eligible to vote at age 25.\n", 268 | "print(f\"Total voters: {Voter.get_total_voters()}\") # Output: Total voters: 3\n" 269 | ], 270 | "metadata": { 271 | "colab": { 272 | "base_uri": "https://localhost:8080/" 273 | }, 274 | "id": "CVc-YKTNI4Oy", 275 | "outputId": "43d6a4ed-1b1a-42dc-ee3b-7115c4d388eb" 276 | }, 277 | "execution_count": 5, 278 | "outputs": [ 279 | { 280 | "output_type": "stream", 281 | "name": "stdout", 282 | "text": [ 283 | "You are eligible to vote at age 20.\n", 284 | "Sorry, you are not eligible to vote at age 16.\n", 285 | "You are eligible to vote at age 25.\n", 286 | "Total voters: 3\n" 287 | ] 288 | } 289 | ] 290 | } 291 | ] 292 | } -------------------------------------------------------------------------------- /05. Day5 Python Modules/Day5.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyMIfjK3NDSO3xJprwd54cAP", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "source": [ 32 | " **Python Modules** By: Loga Aswin" 33 | ], 34 | "metadata": { 35 | "id": "XGnLdIrNAie9" 36 | } 37 | }, 38 | { 39 | "cell_type": "markdown", 40 | "source": [ 41 | "There are two types of modules:\n", 42 | "**Built-in module:\n", 43 | "**User-defined module:" 44 | ], 45 | "metadata": { 46 | "id": "VZ2goEeRA5sS" 47 | } 48 | }, 49 | { 50 | "cell_type": "code", 51 | "execution_count": 1, 52 | "metadata": { 53 | "colab": { 54 | "base_uri": "https://localhost:8080/" 55 | }, 56 | "id": "rX7hpXdFAI9l", 57 | "outputId": "dce09334-8a95-491a-8c5f-265085f2d907" 58 | }, 59 | "outputs": [ 60 | { 61 | "output_type": "stream", 62 | "name": "stdout", 63 | "text": [ 64 | "The square root of 25 is 5.0\n", 65 | "The factorial of 5 is 120\n", 66 | "The value of pi is approximately 3.141592653589793\n", 67 | "The sine of 30 degrees is 0.49999999999999994\n", 68 | "The natural logarithm of 2.71828 is 0.999999327347282\n", 69 | "2 raised to the power of 3 is 8.0\n", 70 | "3.7 rounded to the nearest integer is 4\n" 71 | ] 72 | } 73 | ], 74 | "source": [ 75 | "import math\n", 76 | "\n", 77 | "# Calculate the square root of a number\n", 78 | "num = 25\n", 79 | "sqrt_num = math.sqrt(num)\n", 80 | "print(f\"The square root of {num} is {sqrt_num}\")\n", 81 | "\n", 82 | "# Calculate the factorial of a number\n", 83 | "num = 5\n", 84 | "factorial = math.factorial(num)\n", 85 | "print(f\"The factorial of {num} is {factorial}\")\n", 86 | "\n", 87 | "# Calculate the value of pi\n", 88 | "pi_value = math.pi\n", 89 | "print(f\"The value of pi is approximately {pi_value}\")\n", 90 | "\n", 91 | "# Calculate the sine of an angle in radians\n", 92 | "angle_rad = math.radians(30) # Convert 30 degrees to radians\n", 93 | "sin_value = math.sin(angle_rad)\n", 94 | "print(f\"The sine of 30 degrees is {sin_value}\")\n", 95 | "\n", 96 | "# Calculate the natural logarithm (base e) of a number\n", 97 | "num = 2.71828 # Euler's number (approximately)\n", 98 | "ln_value = math.log(num)\n", 99 | "print(f\"The natural logarithm of {num} is {ln_value}\")\n", 100 | "\n", 101 | "# Calculate the power of a number\n", 102 | "base = 2\n", 103 | "exponent = 3\n", 104 | "power_result = math.pow(base, exponent)\n", 105 | "print(f\"{base} raised to the power of {exponent} is {power_result}\")\n", 106 | "\n", 107 | "# Round a number to the nearest integer\n", 108 | "num = 3.7\n", 109 | "rounded_num = math.ceil(num) # ceil() rounds up, floor() rounds down\n", 110 | "print(f\"{num} rounded to the nearest integer is {rounded_num}\")\n" 111 | ] 112 | }, 113 | { 114 | "cell_type": "markdown", 115 | "source": [ 116 | "Date & Time" 117 | ], 118 | "metadata": { 119 | "id": "24cMrgqpDVUR" 120 | } 121 | }, 122 | { 123 | "cell_type": "code", 124 | "source": [ 125 | "import datetime\n", 126 | "\n", 127 | "# Get the current date and time\n", 128 | "current_datetime = datetime.datetime.now()\n", 129 | "print(f\"Current Date and Time: {current_datetime}\")\n", 130 | "\n", 131 | "# Get the current date\n", 132 | "current_date = datetime.date.today()\n", 133 | "print(f\"Current Date: {current_date}\")\n", 134 | "\n", 135 | "# Create a specific date\n", 136 | "specific_date = datetime.date(2023, 9, 30)\n", 137 | "print(f\"Specific Date: {specific_date}\")\n", 138 | "\n", 139 | "# Create a specific time\n", 140 | "specific_time = datetime.time(14, 30, 0)\n", 141 | "print(f\"Specific Time: {specific_time}\")\n", 142 | "\n", 143 | "# Combine date and time into a datetime object\n", 144 | "combined_datetime = datetime.datetime.combine(specific_date, specific_time)\n", 145 | "print(f\"Combined DateTime: {combined_datetime}\")\n", 146 | "\n", 147 | "# Access individual components of a datetime object\n", 148 | "year = current_datetime.year\n", 149 | "month = current_datetime.month\n", 150 | "day = current_datetime.day\n", 151 | "hour = current_datetime.hour\n", 152 | "minute = current_datetime.minute\n", 153 | "second = current_datetime.second\n", 154 | "\n", 155 | "print(f\"Year: {year}\")\n", 156 | "print(f\"Month: {month}\")\n", 157 | "print(f\"Day: {day}\")\n", 158 | "print(f\"Hour: {hour}\")\n", 159 | "print(f\"Minute: {minute}\")\n", 160 | "print(f\"Second: {second}\")\n", 161 | "\n", 162 | "\n" 163 | ], 164 | "metadata": { 165 | "colab": { 166 | "base_uri": "https://localhost:8080/" 167 | }, 168 | "id": "YpHCnwlvC_VP", 169 | "outputId": "4b67699b-1bb6-4637-f9b6-4f377eb5465f" 170 | }, 171 | "execution_count": 5, 172 | "outputs": [ 173 | { 174 | "output_type": "stream", 175 | "name": "stdout", 176 | "text": [ 177 | "Current Date and Time: 2023-09-30 17:05:12.667692\n", 178 | "Current Date: 2023-09-30\n", 179 | "Specific Date: 2023-09-30\n", 180 | "Specific Time: 14:30:00\n", 181 | "Combined DateTime: 2023-09-30 14:30:00\n", 182 | "Year: 2023\n", 183 | "Month: 9\n", 184 | "Day: 30\n", 185 | "Hour: 17\n", 186 | "Minute: 5\n", 187 | "Second: 12\n" 188 | ] 189 | } 190 | ] 191 | }, 192 | { 193 | "cell_type": "markdown", 194 | "source": [ 195 | "**Calender**" 196 | ], 197 | "metadata": { 198 | "id": "EZUUEh9HD4Z4" 199 | } 200 | }, 201 | { 202 | "cell_type": "code", 203 | "source": [ 204 | "import calendar\n", 205 | "print(calendar.month(2022, 8))" 206 | ], 207 | "metadata": { 208 | "colab": { 209 | "base_uri": "https://localhost:8080/" 210 | }, 211 | "id": "QGLAsyVOD8FT", 212 | "outputId": "c7c647b5-13ad-4153-da7e-cb6867cdfac9" 213 | }, 214 | "execution_count": 8, 215 | "outputs": [ 216 | { 217 | "output_type": "stream", 218 | "name": "stdout", 219 | "text": [ 220 | " August 2022\n", 221 | "Mo Tu We Th Fr Sa Su\n", 222 | " 1 2 3 4 5 6 7\n", 223 | " 8 9 10 11 12 13 14\n", 224 | "15 16 17 18 19 20 21\n", 225 | "22 23 24 25 26 27 28\n", 226 | "29 30 31\n", 227 | "\n" 228 | ] 229 | } 230 | ] 231 | }, 232 | { 233 | "cell_type": "code", 234 | "source": [ 235 | "import sys\n", 236 | "print(sys.version)\n", 237 | "print(sys.argv)" 238 | ], 239 | "metadata": { 240 | "colab": { 241 | "base_uri": "https://localhost:8080/" 242 | }, 243 | "id": "BnyrnCZUESO6", 244 | "outputId": "b0f26f66-29cd-4c97-d19b-d480e837f6fd" 245 | }, 246 | "execution_count": 11, 247 | "outputs": [ 248 | { 249 | "output_type": "stream", 250 | "name": "stdout", 251 | "text": [ 252 | "3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0]\n", 253 | "['/usr/local/lib/python3.10/dist-packages/colab_kernel_launcher.py', '-f', '/root/.local/share/jupyter/runtime/kernel-36a70b99-3de4-46ed-acb1-ca2a99929116.json']\n" 254 | ] 255 | } 256 | ] 257 | }, 258 | { 259 | "cell_type": "markdown", 260 | "source": [ 261 | "**User Defined module**" 262 | ], 263 | "metadata": { 264 | "id": "1kLY4DmPGP9u" 265 | } 266 | }, 267 | { 268 | "cell_type": "code", 269 | "source": [ 270 | "num1 = int(input(\"Enter first number:\"))\n", 271 | "\n", 272 | "num2 = int(input(\"Enter second number:\"))\n", 273 | "\n", 274 | "print(\"Addition\", num1 + num2)\n", 275 | "\n", 276 | "print(\"Subtraction\", num1 - num2)\n", 277 | "print(\"Multiplication\", num * num2)\n", 278 | "print(\"Division\",num1/num2)" 279 | ], 280 | "metadata": { 281 | "colab": { 282 | "base_uri": "https://localhost:8080/" 283 | }, 284 | "id": "FUszj6nME_gg", 285 | "outputId": "6266575a-9658-41ef-8a71-31bc8d72622e" 286 | }, 287 | "execution_count": 20, 288 | "outputs": [ 289 | { 290 | "output_type": "stream", 291 | "name": "stdout", 292 | "text": [ 293 | "Enter first number:50\n", 294 | "Enter second number:100\n", 295 | "Addition 150\n", 296 | "Subtraction -50\n", 297 | "Multiplication 370.0\n", 298 | "Division 0.5\n" 299 | ] 300 | } 301 | ] 302 | }, 303 | { 304 | "cell_type": "code", 305 | "source": [ 306 | "def square(number):\n", 307 | " return number ** 2\n", 308 | "\n", 309 | "num = 5\n", 310 | "result = square(num)\n", 311 | "print(f\"The square of {num} is {result}\")\n" 312 | ], 313 | "metadata": { 314 | "colab": { 315 | "base_uri": "https://localhost:8080/" 316 | }, 317 | "id": "UAJvaBJWIBOG", 318 | "outputId": "2bc2fab1-6c68-4bba-abb9-2a673fd3d440" 319 | }, 320 | "execution_count": 25, 321 | "outputs": [ 322 | { 323 | "output_type": "stream", 324 | "name": "stdout", 325 | "text": [ 326 | "The square of 5 is 25\n" 327 | ] 328 | } 329 | ] 330 | }, 331 | { 332 | "cell_type": "code", 333 | "source": [ 334 | "import os\n", 335 | "\n", 336 | "current_directory = os.getcwd()\n", 337 | "print(f\"Current Directory: {current_directory}\")\n" 338 | ], 339 | "metadata": { 340 | "colab": { 341 | "base_uri": "https://localhost:8080/" 342 | }, 343 | "id": "QG93jwHJHpEr", 344 | "outputId": "3dd9a250-19a0-4286-a89f-0d3eeab172ff" 345 | }, 346 | "execution_count": 21, 347 | "outputs": [ 348 | { 349 | "output_type": "stream", 350 | "name": "stdout", 351 | "text": [ 352 | "Current Directory: /content\n" 353 | ] 354 | } 355 | ] 356 | }, 357 | { 358 | "cell_type": "code", 359 | "source": [ 360 | "import json\n", 361 | "\n", 362 | "# Serialize Python dictionary to JSON\n", 363 | "data = {\"name\": \"Alice\", \"age\": 30}\n", 364 | "json_data = json.dumps(data)\n", 365 | "print(\"JSON Data:\", json_data)\n", 366 | "\n", 367 | "# Deserialize JSON to Python dictionary\n", 368 | "parsed_data = json.loads(json_data)\n", 369 | "print(\"Python Dictionary:\", parsed_data)\n" 370 | ], 371 | "metadata": { 372 | "colab": { 373 | "base_uri": "https://localhost:8080/" 374 | }, 375 | "id": "lC36R2QIIpan", 376 | "outputId": "1579ed51-0f99-42b9-8aa1-fb16da2e1b78" 377 | }, 378 | "execution_count": 22, 379 | "outputs": [ 380 | { 381 | "output_type": "stream", 382 | "name": "stdout", 383 | "text": [ 384 | "JSON Data: {\"name\": \"Alice\", \"age\": 30}\n", 385 | "Python Dictionary: {'name': 'Alice', 'age': 30}\n" 386 | ] 387 | } 388 | ] 389 | }, 390 | { 391 | "cell_type": "code", 392 | "source": [ 393 | "import random\n", 394 | "\n", 395 | "# Generate a random integer between 1 and 10 (inclusive)\n", 396 | "random_num = random.randint(1, 10)\n", 397 | "print(\"Random Number:\", random_num)\n", 398 | "\n", 399 | "# Generate a random floating-point number between 0 and 1\n", 400 | "random_float = random.random()\n", 401 | "print(\"Random Float:\", random_float)\n" 402 | ], 403 | "metadata": { 404 | "colab": { 405 | "base_uri": "https://localhost:8080/" 406 | }, 407 | "id": "Ao2IZIQSIwUX", 408 | "outputId": "35c75a3c-4a09-468c-d8ab-99edec79756e" 409 | }, 410 | "execution_count": 23, 411 | "outputs": [ 412 | { 413 | "output_type": "stream", 414 | "name": "stdout", 415 | "text": [ 416 | "Random Number: 2\n", 417 | "Random Float: 0.0068412622002508305\n" 418 | ] 419 | } 420 | ] 421 | }, 422 | { 423 | "cell_type": "code", 424 | "source": [ 425 | "import pickle\n", 426 | "\n", 427 | "# Serialize Python object to a binary string\n", 428 | "data = {\"name\": \"Bob\", \"age\": 25}\n", 429 | "pickle_data = pickle.dumps(data)\n", 430 | "print(\"Pickled Data:\", pickle_data)\n", 431 | "\n", 432 | "# Deserialize Pickle data to Python object\n", 433 | "parsed_data = pickle.loads(pickle_data)\n", 434 | "print(\"Python Object:\", parsed_data)\n" 435 | ], 436 | "metadata": { 437 | "colab": { 438 | "base_uri": "https://localhost:8080/" 439 | }, 440 | "id": "Cs6thqZaIx3t", 441 | "outputId": "16491238-7976-442c-985d-09b224b576bc" 442 | }, 443 | "execution_count": 24, 444 | "outputs": [ 445 | { 446 | "output_type": "stream", 447 | "name": "stdout", 448 | "text": [ 449 | "Pickled Data: b'\\x80\\x04\\x95\\x1a\\x00\\x00\\x00\\x00\\x00\\x00\\x00}\\x94(\\x8c\\x04name\\x94\\x8c\\x03Bob\\x94\\x8c\\x03age\\x94K\\x19u.'\n", 450 | "Python Object: {'name': 'Bob', 'age': 25}\n" 451 | ] 452 | } 453 | ] 454 | } 455 | ] 456 | } -------------------------------------------------------------------------------- /06. Day6 Numpy Basics/Day6.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyN3CJQNeHVfUl8n1TGb58NS", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "source": [ 32 | " **Numpy** --Loga Aswin\n", 33 | "NumPy is a Python library used for working with arrays.\n", 34 | "\n", 35 | "It also has functions for working in domain of linear algebra, fourier transform, and matrices. " 36 | ], 37 | "metadata": { 38 | "id": "NJ3GX_0Zc6cg" 39 | } 40 | }, 41 | { 42 | "cell_type": "code", 43 | "execution_count": 2, 44 | "metadata": { 45 | "id": "3luMg8kVcFDY" 46 | }, 47 | "outputs": [], 48 | "source": [ 49 | "import numpy as np" 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "source": [ 55 | "#checking version\n", 56 | "print(np.version)" 57 | ], 58 | "metadata": { 59 | "colab": { 60 | "base_uri": "https://localhost:8080/" 61 | }, 62 | "id": "s071WN29dPoY", 63 | "outputId": "f4a08d6d-b88d-4947-9f6c-b5ba4335ac75" 64 | }, 65 | "execution_count": 3, 66 | "outputs": [ 67 | { 68 | "output_type": "stream", 69 | "name": "stdout", 70 | "text": [ 71 | "\n" 72 | ] 73 | } 74 | ] 75 | }, 76 | { 77 | "cell_type": "markdown", 78 | "source": [ 79 | "**Creating Array**" 80 | ], 81 | "metadata": { 82 | "id": "gplVSZv5dj7c" 83 | } 84 | }, 85 | { 86 | "cell_type": "code", 87 | "source": [ 88 | "#1D Array\n", 89 | "arr = np.array([1, 2, 3, 4, 5])\n", 90 | "print(arr)\n", 91 | "print(type(arr))\n", 92 | "\n", 93 | "#2D Array\n", 94 | "arr = np.array([[1, 2, 3], [4, 5, 6]])\n", 95 | "print(arr)\n", 96 | "\n", 97 | "# Higher dimensional Array\n", 98 | "arr = np.array([10, 20, 30, 40], ndmin=5)\n", 99 | "\n", 100 | "print(arr)" 101 | ], 102 | "metadata": { 103 | "colab": { 104 | "base_uri": "https://localhost:8080/" 105 | }, 106 | "id": "xZYEbxFOdeHI", 107 | "outputId": "7df8f102-a480-408b-eb41-0eb0238732f8" 108 | }, 109 | "execution_count": 6, 110 | "outputs": [ 111 | { 112 | "output_type": "stream", 113 | "name": "stdout", 114 | "text": [ 115 | "[1 2 3 4 5]\n", 116 | "\n", 117 | "[[1 2 3]\n", 118 | " [4 5 6]]\n", 119 | "[[[[[10 20 30 40]]]]]\n" 120 | ] 121 | } 122 | ] 123 | }, 124 | { 125 | "cell_type": "markdown", 126 | "source": [ 127 | "**Array Slicing**" 128 | ], 129 | "metadata": { 130 | "id": "Vgy1IXA7ebaw" 131 | } 132 | }, 133 | { 134 | "cell_type": "code", 135 | "source": [ 136 | "arr = np.array([1, 2, 3, 4, 5, 6, 7])\n", 137 | "\n", 138 | "print(arr[1:5])\n", 139 | "print(arr[-4:-2])\n" 140 | ], 141 | "metadata": { 142 | "colab": { 143 | "base_uri": "https://localhost:8080/" 144 | }, 145 | "id": "fEK4KbEYeULf", 146 | "outputId": "42023fd0-4ec1-49c6-88a6-6567a8db39cd" 147 | }, 148 | "execution_count": 8, 149 | "outputs": [ 150 | { 151 | "output_type": "stream", 152 | "name": "stdout", 153 | "text": [ 154 | "[2 3 4 5]\n", 155 | "[4 5]\n" 156 | ] 157 | } 158 | ] 159 | }, 160 | { 161 | "cell_type": "markdown", 162 | "source": [ 163 | "**NumPy Function**: insert():\n", 164 | "\n", 165 | "Insert values at given place." 166 | ], 167 | "metadata": { 168 | "id": "WTiofMRoe39B" 169 | } 170 | }, 171 | { 172 | "cell_type": "code", 173 | "source": [ 174 | "a = np.array([1,2,3,4,5])\n", 175 | "a = np.insert(a, 2, 100)\n", 176 | "print(a)" 177 | ], 178 | "metadata": { 179 | "colab": { 180 | "base_uri": "https://localhost:8080/" 181 | }, 182 | "id": "1E9yJkmye5xq", 183 | "outputId": "367c438d-4bb8-4175-db5d-082dca254a4b" 184 | }, 185 | "execution_count": 9, 186 | "outputs": [ 187 | { 188 | "output_type": "stream", 189 | "name": "stdout", 190 | "text": [ 191 | "[ 1 2 100 3 4 5]\n" 192 | ] 193 | } 194 | ] 195 | }, 196 | { 197 | "cell_type": "code", 198 | "source": [ 199 | "#Numpy data types:\n", 200 | "import numpy as np\n", 201 | "arr = np.array([1, 2, 3, 4])\n", 202 | "print(arr.dtype)" 203 | ], 204 | "metadata": { 205 | "colab": { 206 | "base_uri": "https://localhost:8080/" 207 | }, 208 | "id": "8hIqUqI8h5qc", 209 | "outputId": "0f1b6372-16ac-46af-f992-dfcbaf0021f4" 210 | }, 211 | "execution_count": 10, 212 | "outputs": [ 213 | { 214 | "output_type": "stream", 215 | "name": "stdout", 216 | "text": [ 217 | "int64\n" 218 | ] 219 | } 220 | ] 221 | }, 222 | { 223 | "cell_type": "markdown", 224 | "source": [ 225 | "\n", 226 | "Copy creates a new array, independent of the original, while view merely represents the original array. Copies are separate, preserving data isolation, while views share data, causing changes in one to impact the other." 227 | ], 228 | "metadata": { 229 | "id": "5aISIrogm6N5" 230 | } 231 | }, 232 | { 233 | "cell_type": "markdown", 234 | "source": [ 235 | "**COPY:**" 236 | ], 237 | "metadata": { 238 | "id": "Hjcl3GBJmO1m" 239 | } 240 | }, 241 | { 242 | "cell_type": "code", 243 | "source": [ 244 | "import numpy as np\n", 245 | "arr = np.array([1, 2, 3, 4, 5])\n", 246 | "x = arr.copy()\n", 247 | "arr[0] = 42\n", 248 | "\n", 249 | "print(arr)\n", 250 | "print(x)" 251 | ], 252 | "metadata": { 253 | "colab": { 254 | "base_uri": "https://localhost:8080/" 255 | }, 256 | "id": "wImkABVymPkp", 257 | "outputId": "afbbfe81-af81-4fd3-8940-251e58b523b4" 258 | }, 259 | "execution_count": 11, 260 | "outputs": [ 261 | { 262 | "output_type": "stream", 263 | "name": "stdout", 264 | "text": [ 265 | "[42 2 3 4 5]\n", 266 | "[1 2 3 4 5]\n" 267 | ] 268 | } 269 | ] 270 | }, 271 | { 272 | "cell_type": "markdown", 273 | "source": [ 274 | "View:" 275 | ], 276 | "metadata": { 277 | "id": "RiyXoJkOmY2Y" 278 | } 279 | }, 280 | { 281 | "cell_type": "code", 282 | "source": [ 283 | "import numpy as np\n", 284 | "\n", 285 | "arr = np.array([1, 2, 3, 4, 5])\n", 286 | "x = arr.view()\n", 287 | "arr[0] = 42\n", 288 | "\n", 289 | "print(arr)\n", 290 | "print(x)" 291 | ], 292 | "metadata": { 293 | "colab": { 294 | "base_uri": "https://localhost:8080/" 295 | }, 296 | "id": "PHxo2PLTmaK_", 297 | "outputId": "9df8bfeb-cd93-4c72-828b-4d1cde27323d" 298 | }, 299 | "execution_count": 12, 300 | "outputs": [ 301 | { 302 | "output_type": "stream", 303 | "name": "stdout", 304 | "text": [ 305 | "[42 2 3 4 5]\n", 306 | "[42 2 3 4 5]\n" 307 | ] 308 | } 309 | ] 310 | }, 311 | { 312 | "cell_type": "markdown", 313 | "source": [ 314 | "NumPy Array Iterating" 315 | ], 316 | "metadata": { 317 | "id": "d2wmWKAKnpPh" 318 | } 319 | }, 320 | { 321 | "cell_type": "code", 322 | "source": [ 323 | "import numpy as np\n", 324 | "\n", 325 | "arr = np.array([[1, 2, 3], [4, 5, 6]])\n", 326 | "for x in arr:\n", 327 | " print(x)" 328 | ], 329 | "metadata": { 330 | "colab": { 331 | "base_uri": "https://localhost:8080/" 332 | }, 333 | "id": "6m4tUV5lnbIp", 334 | "outputId": "2f2c6666-f48b-42f5-d6a3-d3e0fa5637b1" 335 | }, 336 | "execution_count": 16, 337 | "outputs": [ 338 | { 339 | "output_type": "stream", 340 | "name": "stdout", 341 | "text": [ 342 | "[1 2 3]\n", 343 | "[4 5 6]\n" 344 | ] 345 | } 346 | ] 347 | }, 348 | { 349 | "cell_type": "markdown", 350 | "source": [ 351 | "**Joining NumPy Arrays**" 352 | ], 353 | "metadata": { 354 | "id": "qldSezF-oGMS" 355 | } 356 | }, 357 | { 358 | "cell_type": "code", 359 | "source": [ 360 | "arr1 = np.array([1, 2, 3])\n", 361 | "arr2 = np.array([4, 5, 6])\n", 362 | "\n", 363 | "arr = np.concatenate((arr1, arr2))\n", 364 | "\n", 365 | "print(arr)" 366 | ], 367 | "metadata": { 368 | "colab": { 369 | "base_uri": "https://localhost:8080/" 370 | }, 371 | "id": "Sn-xjMKfoIQk", 372 | "outputId": "96849dc6-083e-4f4d-ead8-086d9c9e2a63" 373 | }, 374 | "execution_count": 19, 375 | "outputs": [ 376 | { 377 | "output_type": "stream", 378 | "name": "stdout", 379 | "text": [ 380 | "[1 2 3 4 5 6]\n" 381 | ] 382 | } 383 | ] 384 | }, 385 | { 386 | "cell_type": "markdown", 387 | "source": [ 388 | "**Using Stack function **" 389 | ], 390 | "metadata": { 391 | "id": "WZnifv40otkI" 392 | } 393 | }, 394 | { 395 | "cell_type": "code", 396 | "source": [ 397 | "import numpy as np\n", 398 | "\n", 399 | "arr1 = np.array([10, 20, 30])\n", 400 | "arr2 = np.array([40, 50, 60])\n", 401 | "arr = np.stack((arr1, arr2), axis=1)\n", 402 | "print(arr)" 403 | ], 404 | "metadata": { 405 | "colab": { 406 | "base_uri": "https://localhost:8080/" 407 | }, 408 | "id": "BEIz-99Do0Io", 409 | "outputId": "46015517-0d16-415d-9c79-a21860d4c0ec" 410 | }, 411 | "execution_count": 23, 412 | "outputs": [ 413 | { 414 | "output_type": "stream", 415 | "name": "stdout", 416 | "text": [ 417 | "[[10 40]\n", 418 | " [20 50]\n", 419 | " [30 60]]\n" 420 | ] 421 | } 422 | ] 423 | }, 424 | { 425 | "cell_type": "markdown", 426 | "source": [ 427 | "**Splitting the array**" 428 | ], 429 | "metadata": { 430 | "id": "l8xVQSI7paUt" 431 | } 432 | }, 433 | { 434 | "cell_type": "code", 435 | "source": [ 436 | "import numpy as np\n", 437 | "arr = np.array([1, 2, 3, 4, 5, 6])\n", 438 | "new = np.array_split(arr, 2)\n", 439 | "print(new)" 440 | ], 441 | "metadata": { 442 | "colab": { 443 | "base_uri": "https://localhost:8080/" 444 | }, 445 | "id": "jCM51WRwpJIO", 446 | "outputId": "50dd1c9b-b9fe-40a2-c401-765ca3a315d0" 447 | }, 448 | "execution_count": 26, 449 | "outputs": [ 450 | { 451 | "output_type": "stream", 452 | "name": "stdout", 453 | "text": [ 454 | "[array([1, 2, 3]), array([4, 5, 6])]\n" 455 | ] 456 | } 457 | ] 458 | }, 459 | { 460 | "cell_type": "markdown", 461 | "source": [ 462 | "**Searching Array**" 463 | ], 464 | "metadata": { 465 | "id": "bubzkPoyplgG" 466 | } 467 | }, 468 | { 469 | "cell_type": "code", 470 | "source": [ 471 | "import numpy as np\n", 472 | "arr = np.array([10, 20, 30, 40, 55, 45, 4])\n", 473 | "x = np.where(arr == 5)\n", 474 | "y = np.where(arr%2 == 0)\n", 475 | "z = np.where(arr%2 == 1)\n", 476 | "print(x)\n", 477 | "print(y)\n", 478 | "print(z)" 479 | ], 480 | "metadata": { 481 | "colab": { 482 | "base_uri": "https://localhost:8080/" 483 | }, 484 | "id": "gtDY1BOApo-7", 485 | "outputId": "16a22993-f297-4862-cbe6-e755a4dcbd53" 486 | }, 487 | "execution_count": 33, 488 | "outputs": [ 489 | { 490 | "output_type": "stream", 491 | "name": "stdout", 492 | "text": [ 493 | "(array([], dtype=int64),)\n", 494 | "(array([0, 1, 2, 3, 6]),)\n", 495 | "(array([4, 5]),)\n" 496 | ] 497 | } 498 | ] 499 | }, 500 | { 501 | "cell_type": "markdown", 502 | "source": [ 503 | "**Sorting**" 504 | ], 505 | "metadata": { 506 | "id": "ftu0HzLqqmRT" 507 | } 508 | }, 509 | { 510 | "cell_type": "code", 511 | "source": [ 512 | "import numpy as np\n", 513 | "\n", 514 | "arr = np.array([3, 2, 0, 1])\n", 515 | "arr1 = np.array(['banana', 'cherry', 'apple'])\n", 516 | "\n", 517 | "print(np.sort(arr))\n", 518 | "print(np.sort(arr1))" 519 | ], 520 | "metadata": { 521 | "colab": { 522 | "base_uri": "https://localhost:8080/" 523 | }, 524 | "id": "RmkxD6O5qoch", 525 | "outputId": "918e6de3-4795-4f46-f3e0-5740f984955f" 526 | }, 527 | "execution_count": 35, 528 | "outputs": [ 529 | { 530 | "output_type": "stream", 531 | "name": "stdout", 532 | "text": [ 533 | "[0 1 2 3]\n", 534 | "['apple' 'banana' 'cherry']\n" 535 | ] 536 | } 537 | ] 538 | }, 539 | { 540 | "cell_type": "markdown", 541 | "source": [ 542 | "**Filter**" 543 | ], 544 | "metadata": { 545 | "id": "IEA7I3abrHUr" 546 | } 547 | }, 548 | { 549 | "cell_type": "code", 550 | "source": [ 551 | "import numpy as np\n", 552 | "\n", 553 | "arr = np.array([41, 40, 42, 43, 44])\n", 554 | "\n", 555 | "x = [True, True, False, True, False]\n", 556 | "\n", 557 | "newarr = arr[x]\n", 558 | "\n", 559 | "print(newarr)" 560 | ], 561 | "metadata": { 562 | "colab": { 563 | "base_uri": "https://localhost:8080/" 564 | }, 565 | "id": "FOOfVI8yq7BA", 566 | "outputId": "a7848b72-a016-41ea-be7f-a3e280071912" 567 | }, 568 | "execution_count": 37, 569 | "outputs": [ 570 | { 571 | "output_type": "stream", 572 | "name": "stdout", 573 | "text": [ 574 | "[41 40 43]\n" 575 | ] 576 | } 577 | ] 578 | } 579 | ] 580 | } -------------------------------------------------------------------------------- /09. Day9 Pandas Data Manipulation/Day9.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyO8IXUviFH58XEkUK0CbuQq", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "source": [ 32 | " **Pandas Data Manipulation** -- Loga Aswin" 33 | ], 34 | "metadata": { 35 | "id": "1-Uu4WG6yR39" 36 | } 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 1, 41 | "metadata": { 42 | "id": "sGk2lNt1p44x" 43 | }, 44 | "outputs": [], 45 | "source": [ 46 | "import pandas as pd" 47 | ] 48 | }, 49 | { 50 | "cell_type": "code", 51 | "source": [ 52 | "# Sample data\n", 53 | "data = {'A': [1, 2, 3, 4, 5],\n", 54 | " 'B': ['chennai', 'chandigarh', 'delhi', 'coimbatore', 'kanpur']}\n", 55 | "df = pd.DataFrame(data)\n" 56 | ], 57 | "metadata": { 58 | "id": "yCvUr5LNyww_" 59 | }, 60 | "execution_count": 3, 61 | "outputs": [] 62 | }, 63 | { 64 | "cell_type": "markdown", 65 | "source": [ 66 | "**1. Filtering Data:**\n", 67 | "\n", 68 | "Filtering rows based on a condition." 69 | ], 70 | "metadata": { 71 | "id": "HgxO96FRzYUG" 72 | } 73 | }, 74 | { 75 | "cell_type": "code", 76 | "source": [ 77 | "filtered_df = df[df['A']>3]\n", 78 | "print(filtered_df)" 79 | ], 80 | "metadata": { 81 | "colab": { 82 | "base_uri": "https://localhost:8080/" 83 | }, 84 | "id": "nogME038zbsF", 85 | "outputId": "d55b95f1-43c2-4a3e-9304-cd73cc139b95" 86 | }, 87 | "execution_count": 5, 88 | "outputs": [ 89 | { 90 | "output_type": "stream", 91 | "name": "stdout", 92 | "text": [ 93 | " A B\n", 94 | "3 4 coimbatore\n", 95 | "4 5 kanpur\n" 96 | ] 97 | } 98 | ] 99 | }, 100 | { 101 | "cell_type": "markdown", 102 | "source": [ 103 | "**2. Selecting Columns:**\n", 104 | "\n", 105 | "Selecting specific columns from a DataFrame." 106 | ], 107 | "metadata": { 108 | "id": "ifavRtcuz6vI" 109 | } 110 | }, 111 | { 112 | "cell_type": "code", 113 | "source": [ 114 | "selected_columns = df[['A','B']]\n", 115 | "print(selected_columns)" 116 | ], 117 | "metadata": { 118 | "colab": { 119 | "base_uri": "https://localhost:8080/" 120 | }, 121 | "id": "reCe8Q3Wz_yf", 122 | "outputId": "4bf76f70-963b-4dfc-f8fd-3ea745332a42" 123 | }, 124 | "execution_count": 6, 125 | "outputs": [ 126 | { 127 | "output_type": "stream", 128 | "name": "stdout", 129 | "text": [ 130 | " A B\n", 131 | "0 1 chennai\n", 132 | "1 2 chandigarh\n", 133 | "2 3 delhi\n", 134 | "3 4 coimbatore\n", 135 | "4 5 kanpur\n" 136 | ] 137 | } 138 | ] 139 | }, 140 | { 141 | "cell_type": "markdown", 142 | "source": [ 143 | "**3.Sorting Data:**\n", 144 | "\n", 145 | "Sorting DataFrame by one or more columns." 146 | ], 147 | "metadata": { 148 | "id": "3-VIgOyF0WWj" 149 | } 150 | }, 151 | { 152 | "cell_type": "code", 153 | "source": [ 154 | "sorted_df = df.sort_values(by='A')\n", 155 | "print(sorted_df)" 156 | ], 157 | "metadata": { 158 | "colab": { 159 | "base_uri": "https://localhost:8080/" 160 | }, 161 | "id": "rS18Syow0bi1", 162 | "outputId": "a32ad09d-6696-4eeb-c001-ab285a84e2b3" 163 | }, 164 | "execution_count": 7, 165 | "outputs": [ 166 | { 167 | "output_type": "stream", 168 | "name": "stdout", 169 | "text": [ 170 | " A B\n", 171 | "0 1 chennai\n", 172 | "1 2 chandigarh\n", 173 | "2 3 delhi\n", 174 | "3 4 coimbatore\n", 175 | "4 5 kanpur\n" 176 | ] 177 | } 178 | ] 179 | }, 180 | { 181 | "cell_type": "code", 182 | "source": [ 183 | "#descing order\n", 184 | "sorted_df_desc = df.sort_values(by='A', ascending=False)\n", 185 | "print(sorted_df_desc)" 186 | ], 187 | "metadata": { 188 | "colab": { 189 | "base_uri": "https://localhost:8080/" 190 | }, 191 | "id": "fl-kgEWq0_b2", 192 | "outputId": "98cc723d-297f-4f04-b0b6-fdaa84450a50" 193 | }, 194 | "execution_count": 12, 195 | "outputs": [ 196 | { 197 | "output_type": "stream", 198 | "name": "stdout", 199 | "text": [ 200 | " A B\n", 201 | "4 5 kanpur\n", 202 | "3 4 coimbatore\n", 203 | "2 3 delhi\n", 204 | "1 2 chandigarh\n", 205 | "0 1 chennai\n" 206 | ] 207 | } 208 | ] 209 | }, 210 | { 211 | "cell_type": "markdown", 212 | "source": [ 213 | "**4. Aggregating Data:**\n", 214 | "\n", 215 | "Calculating summary statistics like mean, sum, count, etc." 216 | ], 217 | "metadata": { 218 | "id": "ElDltx_r29Ns" 219 | } 220 | }, 221 | { 222 | "cell_type": "code", 223 | "source": [ 224 | "mean_A = df['A'].mean()\n", 225 | "print(mean_A)" 226 | ], 227 | "metadata": { 228 | "colab": { 229 | "base_uri": "https://localhost:8080/" 230 | }, 231 | "id": "oHfNYXx-12NK", 232 | "outputId": "89bcc624-0754-4e5c-c5c0-ae89a25bd5cd" 233 | }, 234 | "execution_count": 13, 235 | "outputs": [ 236 | { 237 | "output_type": "stream", 238 | "name": "stdout", 239 | "text": [ 240 | "3.0\n" 241 | ] 242 | } 243 | ] 244 | }, 245 | { 246 | "cell_type": "code", 247 | "source": [ 248 | "value_counts_A = df['A'].value_counts()\n", 249 | "print(value_counts_A)" 250 | ], 251 | "metadata": { 252 | "colab": { 253 | "base_uri": "https://localhost:8080/" 254 | }, 255 | "id": "7ddxttJI2LqI", 256 | "outputId": "09d93b55-f826-43ad-deac-751dbac252c4" 257 | }, 258 | "execution_count": 14, 259 | "outputs": [ 260 | { 261 | "output_type": "stream", 262 | "name": "stdout", 263 | "text": [ 264 | "1 1\n", 265 | "2 1\n", 266 | "3 1\n", 267 | "4 1\n", 268 | "5 1\n", 269 | "Name: A, dtype: int64\n" 270 | ] 271 | } 272 | ] 273 | }, 274 | { 275 | "cell_type": "markdown", 276 | "source": [ 277 | "**5. Handling Missing Data:**\n", 278 | "\n", 279 | "Dealing with missing values in your DataFrame." 280 | ], 281 | "metadata": { 282 | "id": "LmCqQ8zB3B14" 283 | } 284 | }, 285 | { 286 | "cell_type": "code", 287 | "source": [ 288 | "# Sample data\n", 289 | "data_with_missing = {'A': [1, 2, None, 4, 5],\n", 290 | " 'B': ['chennai', 'chandigarh', None, 'coimbatore', 'kanpur']}\n", 291 | "df_missing = pd.DataFrame(data_with_missing )\n", 292 | "\n", 293 | "df_no_missing = df_missing.dropna()\n", 294 | "print(df_no_missing)" 295 | ], 296 | "metadata": { 297 | "colab": { 298 | "base_uri": "https://localhost:8080/" 299 | }, 300 | "id": "HFSLEbGq3FIK", 301 | "outputId": "05bed49a-8888-41a9-8e3e-d03ef162cf1b" 302 | }, 303 | "execution_count": 18, 304 | "outputs": [ 305 | { 306 | "output_type": "stream", 307 | "name": "stdout", 308 | "text": [ 309 | " A B\n", 310 | "0 1.0 chennai\n", 311 | "1 2.0 chandigarh\n", 312 | "3 4.0 coimbatore\n", 313 | "4 5.0 kanpur\n" 314 | ] 315 | } 316 | ] 317 | }, 318 | { 319 | "cell_type": "code", 320 | "source": [ 321 | "# Create two DataFrames\n", 322 | "df1 = pd.DataFrame({'key': ['A', 'B', 'C'], 'value1': [10, 20, 30]})\n", 323 | "df2 = pd.DataFrame({'key': ['B', 'C', 'D'], 'value2': [40, 50, 60]})\n", 324 | "\n", 325 | "# Merge based on 'key' column\n", 326 | "merged_df = pd.merge(df1, df2, on='key', how='inner')\n", 327 | "print(merged_df)\n" 328 | ], 329 | "metadata": { 330 | "colab": { 331 | "base_uri": "https://localhost:8080/" 332 | }, 333 | "id": "es-34AIu7rYa", 334 | "outputId": "8edbec1a-4aaf-4c28-e03f-b61702786170" 335 | }, 336 | "execution_count": 21, 337 | "outputs": [ 338 | { 339 | "output_type": "stream", 340 | "name": "stdout", 341 | "text": [ 342 | " key value1 value2\n", 343 | "0 B 20 40\n", 344 | "1 C 30 50\n" 345 | ] 346 | } 347 | ] 348 | }, 349 | { 350 | "cell_type": "markdown", 351 | "source": [ 352 | "**7. Grouping and Aggregating Data:**\n", 353 | "\n", 354 | "Grouping data by one or more columns and applying aggregate functions.\n" 355 | ], 356 | "metadata": { 357 | "id": "X6WEw4x68LJF" 358 | } 359 | }, 360 | { 361 | "cell_type": "code", 362 | "source": [ 363 | "# Group by 'B' and calculate the sum of 'A' for each group\n", 364 | "grouped_df = df.groupby('B')['A'].sum().reset_index()\n", 365 | "print(grouped_df)\n" 366 | ], 367 | "metadata": { 368 | "colab": { 369 | "base_uri": "https://localhost:8080/" 370 | }, 371 | "id": "8f1TO9af8FMn", 372 | "outputId": "84804a34-5100-4f43-93e8-08326c9c86c6" 373 | }, 374 | "execution_count": 22, 375 | "outputs": [ 376 | { 377 | "output_type": "stream", 378 | "name": "stdout", 379 | "text": [ 380 | " B A\n", 381 | "0 chandigarh 2\n", 382 | "1 chennai 1\n", 383 | "2 coimbatore 4\n", 384 | "3 delhi 3\n", 385 | "4 kanpur 5\n" 386 | ] 387 | } 388 | ] 389 | }, 390 | { 391 | "cell_type": "markdown", 392 | "source": [ 393 | "**8. Pivot Tables:**\n", 394 | "\n", 395 | "Creating pivot tables to summarize and reshape data." 396 | ], 397 | "metadata": { 398 | "id": "ZYymmxax8Rga" 399 | } 400 | }, 401 | { 402 | "cell_type": "code", 403 | "source": [ 404 | "# Create a pivot table to show the mean 'A' for each 'B' category\n", 405 | "pivot_table = df.pivot_table(values='A', index='B', aggfunc='mean')\n", 406 | "print(pivot_table)\n" 407 | ], 408 | "metadata": { 409 | "colab": { 410 | "base_uri": "https://localhost:8080/" 411 | }, 412 | "id": "UG-sgAd18WUy", 413 | "outputId": "fd06e2e4-06d7-4274-d9d0-5359ea9aef70" 414 | }, 415 | "execution_count": 23, 416 | "outputs": [ 417 | { 418 | "output_type": "stream", 419 | "name": "stdout", 420 | "text": [ 421 | " A\n", 422 | "B \n", 423 | "chandigarh 2\n", 424 | "chennai 1\n", 425 | "coimbatore 4\n", 426 | "delhi 3\n", 427 | "kanpur 5\n" 428 | ] 429 | } 430 | ] 431 | }, 432 | { 433 | "cell_type": "markdown", 434 | "source": [ 435 | "**9. Combining Data:**\n", 436 | "\n", 437 | "Concatenating or appending multiple DataFrames vertically or horizontally." 438 | ], 439 | "metadata": { 440 | "id": "F88Z5sg18W-c" 441 | } 442 | }, 443 | { 444 | "cell_type": "code", 445 | "source": [ 446 | "# Concatenate two DataFrames vertically\n", 447 | "df_concatenated = pd.concat([df1, df2], axis=0)\n", 448 | "print(df_concatenated)\n", 449 | "\n", 450 | "# Append one DataFrame to another\n", 451 | "df_appended = df1.append(df2, ignore_index=True)\n", 452 | "print(df_appended)\n" 453 | ], 454 | "metadata": { 455 | "colab": { 456 | "base_uri": "https://localhost:8080/" 457 | }, 458 | "id": "3wRVzK8H8fOc", 459 | "outputId": "47d486ea-ad4a-4b3c-b877-ea1dd06c0146" 460 | }, 461 | "execution_count": 24, 462 | "outputs": [ 463 | { 464 | "output_type": "stream", 465 | "name": "stdout", 466 | "text": [ 467 | " key value1 value2\n", 468 | "0 A 10.0 NaN\n", 469 | "1 B 20.0 NaN\n", 470 | "2 C 30.0 NaN\n", 471 | "0 B NaN 40.0\n", 472 | "1 C NaN 50.0\n", 473 | "2 D NaN 60.0\n", 474 | " key value1 value2\n", 475 | "0 A 10.0 NaN\n", 476 | "1 B 20.0 NaN\n", 477 | "2 C 30.0 NaN\n", 478 | "3 B NaN 40.0\n", 479 | "4 C NaN 50.0\n", 480 | "5 D NaN 60.0\n" 481 | ] 482 | }, 483 | { 484 | "output_type": "stream", 485 | "name": "stderr", 486 | "text": [ 487 | ":6: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", 488 | " df_appended = df1.append(df2, ignore_index=True)\n" 489 | ] 490 | } 491 | ] 492 | }, 493 | { 494 | "cell_type": "markdown", 495 | "source": [ 496 | "**Applying function to the data**" 497 | ], 498 | "metadata": { 499 | "id": "1W3AhCw88nGK" 500 | } 501 | }, 502 | { 503 | "cell_type": "code", 504 | "source": [ 505 | "def square(x):\n", 506 | " return x ** 2\n", 507 | "\n", 508 | "# Apply the custom function to 'A' column\n", 509 | "df['A_squared'] = df['A'].apply(square)\n", 510 | "print(df)" 511 | ], 512 | "metadata": { 513 | "colab": { 514 | "base_uri": "https://localhost:8080/" 515 | }, 516 | "id": "26b6tgMv8ltT", 517 | "outputId": "404cbeec-89f4-4126-9af4-6340efd81931" 518 | }, 519 | "execution_count": 25, 520 | "outputs": [ 521 | { 522 | "output_type": "stream", 523 | "name": "stdout", 524 | "text": [ 525 | " A B A_squared\n", 526 | "0 1 chennai 1\n", 527 | "1 2 chandigarh 4\n", 528 | "2 3 delhi 9\n", 529 | "3 4 coimbatore 16\n", 530 | "4 5 kanpur 25\n" 531 | ] 532 | } 533 | ] 534 | } 535 | ] 536 | } -------------------------------------------------------------------------------- /10. Day10 Pandas Data Cleaning/Day10.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyMje0lQ9HTSRM7Max6ky6oi", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "source": [ 32 | " **Data Cleaning** -- Loga Aswin" 33 | ], 34 | "metadata": { 35 | "id": "IU_Bhzwj52aZ" 36 | } 37 | }, 38 | { 39 | "cell_type": "markdown", 40 | "source": [ 41 | "**Data Cleaning**" 42 | ], 43 | "metadata": { 44 | "id": "WDLBxXak8Sen" 45 | } 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 3, 50 | "metadata": { 51 | "id": "PypkBGNy5y_U" 52 | }, 53 | "outputs": [], 54 | "source": [ 55 | "import pandas as pd\n", 56 | "\n", 57 | "data = {'A': [10, 20, None, 30, 40],\n", 58 | " 'B': [None, 'chennai', 'coimbatore', 'london', 'america']}\n", 59 | "\n", 60 | "df = pd.DataFrame(data)\n" 61 | ] 62 | }, 63 | { 64 | "cell_type": "code", 65 | "source": [ 66 | "print(df)" 67 | ], 68 | "metadata": { 69 | "colab": { 70 | "base_uri": "https://localhost:8080/" 71 | }, 72 | "id": "e1ChO-E89fmR", 73 | "outputId": "8a6cc4c6-911a-4b70-cfd0-351b202140a9" 74 | }, 75 | "execution_count": 4, 76 | "outputs": [ 77 | { 78 | "output_type": "stream", 79 | "name": "stdout", 80 | "text": [ 81 | " A B\n", 82 | "0 10.0 None\n", 83 | "1 20.0 chennai\n", 84 | "2 NaN coimbatore\n", 85 | "3 30.0 london\n", 86 | "4 40.0 america\n" 87 | ] 88 | } 89 | ] 90 | }, 91 | { 92 | "cell_type": "markdown", 93 | "source": [ 94 | "**1. Handling Missing Values:**\n", 95 | "\n", 96 | "Dropping rows or columns with missing values:" 97 | ], 98 | "metadata": { 99 | "id": "IA3cRCnV-Dsj" 100 | } 101 | }, 102 | { 103 | "cell_type": "code", 104 | "source": [ 105 | "clean_df = df.dropna()\n", 106 | "print(clean_df)" 107 | ], 108 | "metadata": { 109 | "colab": { 110 | "base_uri": "https://localhost:8080/" 111 | }, 112 | "id": "CuaJCLfP9lLT", 113 | "outputId": "cb7dd3b1-f0fb-4253-9115-0e01ea5d3164" 114 | }, 115 | "execution_count": 6, 116 | "outputs": [ 117 | { 118 | "output_type": "stream", 119 | "name": "stdout", 120 | "text": [ 121 | " A B\n", 122 | "1 20.0 chennai\n", 123 | "3 30.0 london\n", 124 | "4 40.0 america\n" 125 | ] 126 | } 127 | ] 128 | }, 129 | { 130 | "cell_type": "code", 131 | "source": [ 132 | "clean_df = df.dropna(axis=1)\n", 133 | "print(clean_df)" 134 | ], 135 | "metadata": { 136 | "colab": { 137 | "base_uri": "https://localhost:8080/" 138 | }, 139 | "id": "ap6q0gPk-F1n", 140 | "outputId": "8a4ec087-2d28-4630-c1c0-3519b9787301" 141 | }, 142 | "execution_count": 7, 143 | "outputs": [ 144 | { 145 | "output_type": "stream", 146 | "name": "stdout", 147 | "text": [ 148 | "Empty DataFrame\n", 149 | "Columns: []\n", 150 | "Index: [0, 1, 2, 3, 4]\n" 151 | ] 152 | } 153 | ] 154 | }, 155 | { 156 | "cell_type": "code", 157 | "source": [ 158 | "# filling missing value of A with the mean of the columns\n", 159 | " df['A'].fillna(df['A'].mean(), inplace=True)\n", 160 | "print(df)" 161 | ], 162 | "metadata": { 163 | "colab": { 164 | "base_uri": "https://localhost:8080/" 165 | }, 166 | "id": "vhvIQ8Oc-STM", 167 | "outputId": "b6c3f336-ea14-4d1f-a385-e13dcd92c7e8" 168 | }, 169 | "execution_count": 8, 170 | "outputs": [ 171 | { 172 | "output_type": "stream", 173 | "name": "stdout", 174 | "text": [ 175 | " A B\n", 176 | "0 10.0 None\n", 177 | "1 20.0 chennai\n", 178 | "2 25.0 coimbatore\n", 179 | "3 30.0 london\n", 180 | "4 40.0 america\n" 181 | ] 182 | } 183 | ] 184 | }, 185 | { 186 | "cell_type": "markdown", 187 | "source": [ 188 | "**2. Removing Duplicates:**\n", 189 | "\n", 190 | "Removing duplicate rows:" 191 | ], 192 | "metadata": { 193 | "id": "ImPuRV0D_0HX" 194 | } 195 | }, 196 | { 197 | "cell_type": "code", 198 | "source": [ 199 | "x1 = df.drop_duplicates()\n", 200 | "print(x1)" 201 | ], 202 | "metadata": { 203 | "colab": { 204 | "base_uri": "https://localhost:8080/" 205 | }, 206 | "id": "IlXcjpgs_MQ6", 207 | "outputId": "fe50d831-9092-472d-c56b-b93cb75396d5" 208 | }, 209 | "execution_count": 11, 210 | "outputs": [ 211 | { 212 | "output_type": "stream", 213 | "name": "stdout", 214 | "text": [ 215 | " A B\n", 216 | "0 10.0 None\n", 217 | "1 20.0 chennai\n", 218 | "2 25.0 coimbatore\n", 219 | "3 30.0 london\n", 220 | "4 40.0 america\n" 221 | ] 222 | } 223 | ] 224 | }, 225 | { 226 | "cell_type": "code", 227 | "source": [ 228 | "# Sample data\n", 229 | "data = {'A' : [10,20,30,40,50]}\n", 230 | "df = pd.DataFrame(data)\n", 231 | "\n", 232 | "print(df)" 233 | ], 234 | "metadata": { 235 | "colab": { 236 | "base_uri": "https://localhost:8080/" 237 | }, 238 | "id": "1NOpIG_5AkkZ", 239 | "outputId": "ea8a81b1-5b70-4a73-fb08-3e5102afee6c" 240 | }, 241 | "execution_count": 13, 242 | "outputs": [ 243 | { 244 | "output_type": "stream", 245 | "name": "stdout", 246 | "text": [ 247 | " A\n", 248 | "0 10\n", 249 | "1 20\n", 250 | "2 30\n", 251 | "3 40\n", 252 | "4 50\n" 253 | ] 254 | } 255 | ] 256 | }, 257 | { 258 | "cell_type": "markdown", 259 | "source": [ 260 | "**3. Data Type Conversion:**\n", 261 | "\n", 262 | "Converting data types:" 263 | ], 264 | "metadata": { 265 | "id": "Y_KLXuAHDSQJ" 266 | } 267 | }, 268 | { 269 | "cell_type": "code", 270 | "source": [ 271 | "df['A'] = df['A'].astype(int)\n", 272 | "print(df)" 273 | ], 274 | "metadata": { 275 | "colab": { 276 | "base_uri": "https://localhost:8080/" 277 | }, 278 | "id": "qxSBhbqYDM6V", 279 | "outputId": "26bc3bce-1f13-4dc7-ec11-1a91af42ba0b" 280 | }, 281 | "execution_count": 18, 282 | "outputs": [ 283 | { 284 | "output_type": "stream", 285 | "name": "stdout", 286 | "text": [ 287 | " A\n", 288 | "0 10\n", 289 | "1 20\n", 290 | "2 30\n", 291 | "3 40\n", 292 | "4 50\n" 293 | ] 294 | } 295 | ] 296 | }, 297 | { 298 | "cell_type": "markdown", 299 | "source": [ 300 | "**4.String Cleaning:**" 301 | ], 302 | "metadata": { 303 | "id": "J5_wCgMpDsVp" 304 | } 305 | }, 306 | { 307 | "cell_type": "code", 308 | "source": [ 309 | "data = {'A': [1, 2, 3, 4, 5],\n", 310 | " 'B': [' apple ', 'banana', 'cherry ', 'date', ' elderberry ']}\n", 311 | "df = pd.DataFrame(data)\n", 312 | "\n", 313 | "df['B'] = df['B'].str.strip()\n", 314 | "print(df)\n" 315 | ], 316 | "metadata": { 317 | "colab": { 318 | "base_uri": "https://localhost:8080/" 319 | }, 320 | "id": "rYj4hc3CDVHb", 321 | "outputId": "ca336c18-f31d-4e0e-8b13-46c87291c9f7" 322 | }, 323 | "execution_count": 20, 324 | "outputs": [ 325 | { 326 | "output_type": "stream", 327 | "name": "stdout", 328 | "text": [ 329 | " A B\n", 330 | "0 1 apple\n", 331 | "1 2 banana\n", 332 | "2 3 cherry\n", 333 | "3 4 date\n", 334 | "4 5 elderberry\n" 335 | ] 336 | } 337 | ] 338 | }, 339 | { 340 | "cell_type": "code", 341 | "source": [ 342 | "# # Convert 'B' column to lowercase\n", 343 | "df['B'] = df['B'].str.lower()\n", 344 | "print(df)" 345 | ], 346 | "metadata": { 347 | "colab": { 348 | "base_uri": "https://localhost:8080/" 349 | }, 350 | "id": "StDVtFDcDvOf", 351 | "outputId": "e8ebbf53-f82b-4463-f766-fa04f091f889" 352 | }, 353 | "execution_count": 22, 354 | "outputs": [ 355 | { 356 | "output_type": "stream", 357 | "name": "stdout", 358 | "text": [ 359 | " A B\n", 360 | "0 1 apple\n", 361 | "1 2 banana\n", 362 | "2 3 cherry\n", 363 | "3 4 date\n", 364 | "4 5 elderberry\n" 365 | ] 366 | } 367 | ] 368 | }, 369 | { 370 | "cell_type": "markdown", 371 | "source": [ 372 | "**6. Removing Irrelevant Columns:**" 373 | ], 374 | "metadata": { 375 | "id": "qPdzxmDNEJvB" 376 | } 377 | }, 378 | { 379 | "cell_type": "code", 380 | "source": [ 381 | "# Sample\n", 382 | "data = {'A': [1, 2, 3, 4, 5],\n", 383 | " 'B': ['apple', 'banana', 'cherry', 'date', 'chocolate'],\n", 384 | " 'C': [10, 20, 30, 40, 50]}\n", 385 | "df = pd.DataFrame(data)\n", 386 | "\n", 387 | "# Remove the 'C' column\n", 388 | "df.drop('C', axis=1, inplace=True)\n", 389 | "print(df)\n" 390 | ], 391 | "metadata": { 392 | "colab": { 393 | "base_uri": "https://localhost:8080/" 394 | }, 395 | "id": "aAh54o62D4GN", 396 | "outputId": "a4c63db4-d148-4197-c057-824f6424b0be" 397 | }, 398 | "execution_count": 23, 399 | "outputs": [ 400 | { 401 | "output_type": "stream", 402 | "name": "stdout", 403 | "text": [ 404 | " A B\n", 405 | "0 1 apple\n", 406 | "1 2 banana\n", 407 | "2 3 cherry\n", 408 | "3 4 date\n", 409 | "4 5 chocolate\n" 410 | ] 411 | } 412 | ] 413 | }, 414 | { 415 | "cell_type": "code", 416 | "source": [ 417 | "# Replace the element\n", 418 | "df['B'] = df['B'].replace('cherry', 'orange')\n", 419 | "print(df)" 420 | ], 421 | "metadata": { 422 | "colab": { 423 | "base_uri": "https://localhost:8080/" 424 | }, 425 | "id": "jRSgEC6PERY-", 426 | "outputId": "2f25ffb3-1390-4c00-a9ad-b3d42acb7931" 427 | }, 428 | "execution_count": 24, 429 | "outputs": [ 430 | { 431 | "output_type": "stream", 432 | "name": "stdout", 433 | "text": [ 434 | " A B\n", 435 | "0 1 apple\n", 436 | "1 2 banana\n", 437 | "2 3 orange\n", 438 | "3 4 date\n", 439 | "4 5 chocolate\n" 440 | ] 441 | } 442 | ] 443 | }, 444 | { 445 | "cell_type": "markdown", 446 | "source": [ 447 | " **Data transformation**" 448 | ], 449 | "metadata": { 450 | "id": "TFIXN2RlFL6m" 451 | } 452 | }, 453 | { 454 | "cell_type": "code", 455 | "source": [ 456 | "#apply()\n", 457 | "data = {'A': [10, 20, 30, 40, 50]}\n", 458 | "df = pd.DataFrame(data)\n", 459 | "\n", 460 | "def double_value(x):\n", 461 | " return x * 2\n", 462 | "\n", 463 | "df['A_doubled'] = df['A'].apply(double_value)\n", 464 | "print(df)\n" 465 | ], 466 | "metadata": { 467 | "colab": { 468 | "base_uri": "https://localhost:8080/" 469 | }, 470 | "id": "NwT1bGQNFF03", 471 | "outputId": "7538d8d7-1997-433f-9afd-441c0cdb4741" 472 | }, 473 | "execution_count": 27, 474 | "outputs": [ 475 | { 476 | "output_type": "stream", 477 | "name": "stdout", 478 | "text": [ 479 | " A A_doubled\n", 480 | "0 10 20\n", 481 | "1 20 40\n", 482 | "2 30 60\n", 483 | "3 40 80\n", 484 | "4 50 100\n" 485 | ] 486 | } 487 | ] 488 | }, 489 | { 490 | "cell_type": "code", 491 | "source": [ 492 | "# map()\n", 493 | "data = {'Category': ['A', 'B', 'A', 'C', 'B']}\n", 494 | "df = pd.DataFrame(data)\n", 495 | "\n", 496 | "category_mapping = {'A': 1, 'B': 2, 'C': 3}\n", 497 | "\n", 498 | "df['Category_Num'] = df['Category'].map(category_mapping)\n", 499 | "print(df)\n" 500 | ], 501 | "metadata": { 502 | "colab": { 503 | "base_uri": "https://localhost:8080/" 504 | }, 505 | "id": "X-ETWSJ_FZ94", 506 | "outputId": "be53c057-6e27-45ec-a439-81439845f583" 507 | }, 508 | "execution_count": 28, 509 | "outputs": [ 510 | { 511 | "output_type": "stream", 512 | "name": "stdout", 513 | "text": [ 514 | " Category Category_Num\n", 515 | "0 A 1\n", 516 | "1 B 2\n", 517 | "2 A 1\n", 518 | "3 C 3\n", 519 | "4 B 2\n" 520 | ] 521 | } 522 | ] 523 | }, 524 | { 525 | "cell_type": "code", 526 | "source": [ 527 | "# applymap()\n", 528 | "data = {'A': [1, 2, 3],\n", 529 | " 'B': [4, 5, 6]}\n", 530 | "df = pd.DataFrame(data)\n", 531 | "\n", 532 | "def square(x):\n", 533 | " return x ** 2\n", 534 | "\n", 535 | "df_squared = df.applymap(square)\n", 536 | "print(df_squared)\n" 537 | ], 538 | "metadata": { 539 | "colab": { 540 | "base_uri": "https://localhost:8080/" 541 | }, 542 | "id": "gjFhYP-EFi-4", 543 | "outputId": "dff40887-ecbf-43b5-a63e-bd071b1eb2b6" 544 | }, 545 | "execution_count": 31, 546 | "outputs": [ 547 | { 548 | "output_type": "stream", 549 | "name": "stdout", 550 | "text": [ 551 | " A B\n", 552 | "0 1 16\n", 553 | "1 4 25\n", 554 | "2 9 36\n" 555 | ] 556 | } 557 | ] 558 | } 559 | ] 560 | } -------------------------------------------------------------------------------- /16. Day16 Python Revision/16_Day16_Python_Revision(1-5).ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyMHkQGejFM821KlTne6WcYr", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "source": [ 32 | "**Revision of Python from Day1 upto Day5**" 33 | ], 34 | "metadata": { 35 | "id": "jj3fKQsI9AAZ" 36 | } 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": null, 41 | "metadata": { 42 | "id": "sIOtiS368vbB" 43 | }, 44 | "outputs": [], 45 | "source": [] 46 | } 47 | ] 48 | } 49 | -------------------------------------------------------------------------------- /16. 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Day17 Numpy Revision/17_Day17_Numpy_Revision.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyMtC1F/f6bmP3KvNJIBxNmo", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "source": [ 32 | "**Numpy Revision**" 33 | ], 34 | "metadata": { 35 | "id": "-BCTB_I-W_-Z" 36 | } 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": null, 41 | "metadata": { 42 | "id": "M_1KGeY3NHgF" 43 | }, 44 | "outputs": [], 45 | "source": [] 46 | } 47 | ] 48 | } -------------------------------------------------------------------------------- /17. 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Day18 Pandas Revision/18_Day18_Pandas_Revision(Day8_Day10).ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyO+hsOEM7a+iLeGmQhCVviA", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "source": [ 32 | "**Pandas Revision**" 33 | ], 34 | "metadata": { 35 | "id": "D9cfDGsdONMT" 36 | } 37 | } 38 | ] 39 | } -------------------------------------------------------------------------------- /18. 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Day19 Matplotlib Revision/19_Day19_Matplotlib_Revision.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyP4Y4hm9rsx5mw4kMu0m0Xo", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "source": [ 32 | "**Matplotlib Revision**" 33 | ], 34 | "metadata": { 35 | "id": "Pu0S5Su_r1NS" 36 | } 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": null, 41 | "metadata": { 42 | "id": "tYibCnjxrdAp" 43 | }, 44 | "outputs": [], 45 | "source": [] 46 | } 47 | ] 48 | } -------------------------------------------------------------------------------- /19. 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Day25 Model Evaluation/25_Day25_Model_Evaluation_Techniques.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyPtXU74B4d2ccOzLouqtygh", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": null, 32 | "metadata": { 33 | "id": "BBJbkbwKoK4E" 34 | }, 35 | "outputs": [], 36 | "source": [] 37 | } 38 | ] 39 | } -------------------------------------------------------------------------------- /25. Day25 Model Evaluation/Day 25 Model evaluation in ml.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aswinramesh04/100-Days-of-DataScience/560ae496989e1a1b80cd707c87cd359058594949/25. Day25 Model Evaluation/Day 25 Model evaluation in ml.pdf -------------------------------------------------------------------------------- /26. Day26 Underfitting and Overfitting/26_Day26_Underfitting_and_Overfitting.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyPtXU74B4d2ccOzLouqtygh", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": null, 32 | "metadata": { 33 | "id": "_fL5GgDovaAy" 34 | }, 35 | "outputs": [], 36 | "source": [] 37 | } 38 | ] 39 | } -------------------------------------------------------------------------------- /27. Day27 Cross-Validation/27-day27-cross-validation (1).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aswinramesh04/100-Days-of-DataScience/560ae496989e1a1b80cd707c87cd359058594949/27. Day27 Cross-Validation/27-day27-cross-validation (1).pdf -------------------------------------------------------------------------------- /27. Day27 Cross-Validation/27_Day27_Cross_Validation.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyPtcOkt/ScwIQe1LoYRWqsV", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "source": [ 32 | " Day27: Cross-Validation By: Loga Aswin" 33 | ], 34 | "metadata": { 35 | "id": "0ZKdNpMEy6LC" 36 | } 37 | }, 38 | { 39 | "cell_type": "markdown", 40 | "source": [ 41 | "**What is Cross-Validation?**\n", 42 | "\n", 43 | "> Cross-validation is a technique for evaluating a machine learning model and testing its performance. It is a way to test how good a machine learning model is. It's like giving a test to see if the model can solve real-world problems. CV is commonly used in applied ML tasks.\n", 44 | "\n", 45 | "**The Core Algorithm of Cross-Validation**\n", 46 | "\n", 47 | "Cross-validation methods share a common algorithmic structure:\n", 48 | "> **Data Split:** The dataset is divided into two distinct subsets—one for training and the other for testing.\n", 49 | "\n", 50 | "> **Model Training:** The machine learning model is trained on the training dataset.\n", 51 | "\n", 52 | "> **Model Validation:** The trained model is then validated using the test dataset.\n", 53 | "\n", 54 | "> **Repetitions:** Steps 1 to 3 are repeated a number of times, which depends on the specific cross-validation technique employed." 55 | ], 56 | "metadata": { 57 | "id": "js1WejJakum_" 58 | } 59 | }, 60 | { 61 | "cell_type": "markdown", 62 | "source": [ 63 | "**There are plenty of CV techniques. Some of them are commonly used:**\n", 64 | "\n", 65 | "**1. Hold-out cross-validation:**\n", 66 | "We simply split the data into two parts.\n", 67 | "\n", 68 | "How It Works:\n", 69 | "\n", 70 | "**Data Split:** You divide your dataset into two parts: the training set and the test set. Typically, 80% of the data goes into the training set, and 20% goes into the test set, but you can adjust these percentages as needed.\n", 71 | "\n", 72 | "**Model Training:** You teach your model on the training set.\n", 73 | "\n", 74 | "**Model Testing:** You test your model on the test set.\n", 75 | "\n", 76 | "**Result:** You save the outcome of the test. That's it!\n", 77 | "\n" 78 | ], 79 | "metadata": { 80 | "id": "lXcnHZi4tApr" 81 | } 82 | }, 83 | { 84 | "cell_type": "code", 85 | "source": [ 86 | "import numpy as np\n", 87 | "from sklearn.model_selection import train_test_split\n", 88 | "\n", 89 | "# sample data\n", 90 | "data = np.arange(20).reshape((10, 2))\n", 91 | "labels = np.array([0, 0, 1, 1, 1, 0, 0, 1, 1, 0])\n", 92 | "\n", 93 | "# Split the data into training and testing sets\n", 94 | "X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.3, random_state=42)\n", 95 | "\n", 96 | "# Now X_train and y_train contain 70% of the data for training, and X_test and y_test contain 30% for testing.\n" 97 | ], 98 | "metadata": { 99 | "id": "0ox7qpolvVgH" 100 | }, 101 | "execution_count": 4, 102 | "outputs": [] 103 | }, 104 | { 105 | "cell_type": "markdown", 106 | "source": [ 107 | "**2. k-Fold cross-validation:**\n", 108 | "We divide the data into 'k' parts and test the model with each part.\n", 109 | "\n", 110 | "**The algorithm of the k-Fold technique:**\n", 111 | "\n", 112 | "Divide into Folds: Split your data into 'k' equal parts, like dividing a pie into slices.\n", 113 | "\n", 114 | "Test One Slice: Take one slice (fold) as a test set and the others as training sets.\n", 115 | "\n", 116 | "Train Model: Train a new model using the training slices.\n", 117 | "\n", 118 | "Test Model: Test the model on the slice you set aside.\n", 119 | "\n", 120 | "Repeat: Do this 'k' times, each time with a different slice as the test set.\n", 121 | "\n", 122 | "Average Results: Average the results from all 'k' tests to see how well your model works overall.\n", 123 | "\n", 124 | "**In general, it is always better to use k-Fold technique instead of hold-out.**" 125 | ], 126 | "metadata": { 127 | "id": "4b2R2S0awQ1B" 128 | } 129 | }, 130 | { 131 | "cell_type": "markdown", 132 | "source": [ 133 | "**3. Leave-one-out cross-validation:**\n", 134 | "In this method, each data point is used as a testing instance while the rest are used for training.\n", 135 | "\n", 136 | "**4. Leave-P-Out:** Similar to Leave-One-Out, but we use 'p' pieces at a time.\n", 137 | "\n", 138 | "**5. Stratified K-Folds:** Like K-Folds, but it keeps the same kind of data in each part.\n", 139 | "\n", 140 | "**6. Repeated K-Folds:** We repeat K-Folds many times with different data splits.\n", 141 | "\n", 142 | "**7. Nested K-Folds:** A combination of K-Folds used for different kinds of tests.\n", 143 | "\n", 144 | "**8. Time Series Cross-Validation:** Specifically designed for time series data to account for temporal dependencies." 145 | ], 146 | "metadata": { 147 | "id": "39a2a2wsxREq" 148 | } 149 | }, 150 | { 151 | "cell_type": "markdown", 152 | "source": [ 153 | "**Cross-Validation in Machine Learning:**\n", 154 | "\n", 155 | "**scikit-learn (sklearn):** This popular Python library provides various tools for machine learning, including functions for easy cross-validation techniques such as k-Fold and stratified sampling.\n", 156 | "\n", 157 | "**CatBoost:** CatBoost is a gradient boosting library that supports cross-validation methods to evaluate its models. It has built-in cross-validation functionality.\n", 158 | "\n", 159 | "**Cross-Validation in Deep Learning:**\n", 160 | "\n", 161 | "**Keras:** Keras is an open-source neural network library that can be used with popular deep learning frameworks like TensorFlow and Theano. It doesn't have specific built-in cross-validation functions, but you can use scikit-learn or other tools for that purpose.\n", 162 | "\n", 163 | "**PyTorch:** PyTorch is a deep learning framework with a rich ecosystem of libraries. You can use PyTorch in combination with scikit-learn for cross-validation or implement custom cross-validation techniques.\n", 164 | "\n", 165 | "**MxNet (MXNet):** MXNet is another deep learning framework that can be integrated with cross-validation libraries for evaluating deep learning models." 166 | ], 167 | "metadata": { 168 | "id": "fB-lPCXTzEdm" 169 | } 170 | } 171 | ] 172 | } -------------------------------------------------------------------------------- /28. Day28 Training and Testing data/28_Day28_Training_and_Testing_data.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyPtXU74B4d2ccOzLouqtygh", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": null, 32 | "metadata": { 33 | "id": "AdhHsa0qVtls" 34 | }, 35 | "outputs": [], 36 | "source": [] 37 | } 38 | ] 39 | } -------------------------------------------------------------------------------- /29. 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Day34 MLR Revision/34_Day34_Revision.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyPtXU74B4d2ccOzLouqtygh", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": null, 32 | "metadata": { 33 | "id": "oy0o-uVFlZT6" 34 | }, 35 | "outputs": [], 36 | "source": [] 37 | } 38 | ] 39 | } -------------------------------------------------------------------------------- /35. 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Day43 KNN Introduction/Day43 KNN Introduction.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyPtXU74B4d2ccOzLouqtygh", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": null, 32 | "metadata": { 33 | "id": "Hu99tOhd3R1S" 34 | }, 35 | "outputs": [], 36 | "source": [] 37 | } 38 | ] 39 | } -------------------------------------------------------------------------------- /46. Day46 KNN Regression/Salary_dataset (1).csv: -------------------------------------------------------------------------------- 1 | ,YearsExperience,Salary 2 | 0,1.2000000000000002,39344.0 3 | 1,1.4000000000000001,46206.0 4 | 2,1.6,37732.0 5 | 3,2.1,43526.0 6 | 4,2.3000000000000003,39892.0 7 | 5,3.0,56643.0 8 | 6,3.1,60151.0 9 | 7,3.3000000000000003,54446.0 10 | 8,3.3000000000000003,64446.0 11 | 9,3.8000000000000003,57190.0 12 | 10,4.0,63219.0 13 | 11,4.1,55795.0 14 | 12,4.1,56958.0 15 | 13,4.199999999999999,57082.0 16 | 14,4.6,61112.0 17 | 15,5.0,67939.0 18 | 16,5.199999999999999,66030.0 19 | 17,5.3999999999999995,83089.0 20 | 18,6.0,81364.0 21 | 19,6.1,93941.0 22 | 20,6.8999999999999995,91739.0 23 | 21,7.199999999999999,98274.0 24 | 22,8.0,101303.0 25 | 23,8.299999999999999,113813.0 26 | 24,8.799999999999999,109432.0 27 | 25,9.1,105583.0 28 | 26,9.6,116970.0 29 | 27,9.7,112636.0 30 | 28,10.4,122392.0 31 | 29,10.6,121873.0 32 | -------------------------------------------------------------------------------- /48. 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Day55 Ensemble Learning/Day55_Ensemble_Learning.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyNK6W3JWmRFgqMOtUZekNoH", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "source": [ 32 | " Day55: Ensemble Learning By: Loga Aswin" 33 | ], 34 | "metadata": { 35 | "id": "bn8SyMunuDOH" 36 | } 37 | }, 38 | { 39 | "cell_type": "markdown", 40 | "source": [ 41 | "**Ensemble learning**\n", 42 | "\n", 43 | "> A machine learning technique that combines predictions from multiple models to improve accuracy.\n", 44 | "\n", 45 | "\n", 46 | "> Aims to mitigate errors or biases that may exist in individual models.\n", 47 | "\n", 48 | "\n", 49 | "> Utilizes the strengths of different models to create a more precise prediction.\n", 50 | "\n", 51 | "\n", 52 | "\n" 53 | ], 54 | "metadata": { 55 | "id": "IySlxU63OWFW" 56 | } 57 | }, 58 | { 59 | "cell_type": "markdown", 60 | "source": [ 61 | "**Simple Ensemble Techniques:**\n", 62 | "\n", 63 | "\n", 64 | "> **Max Voting:** The predictions by each model are considered as a 'vote'. The predictions which we get the majority of the models agree on are used as the final prediction.\n", 65 | "\n", 66 | "\n", 67 | "\n" 68 | ], 69 | "metadata": { 70 | "id": "SUF8hZECWDqq" 71 | } 72 | }, 73 | { 74 | "cell_type": "code", 75 | "source": [ 76 | "from sklearn.ensemble import VotingClassifier\n", 77 | "from sklearn.linear_model import LogisticRegression\n", 78 | "from sklearn.tree import DecisionTreeClassifier\n", 79 | "from sklearn.svm import SVC\n", 80 | "from sklearn.datasets import make_classification\n", 81 | "from sklearn.model_selection import train_test_split\n", 82 | "from sklearn.metrics import accuracy_score\n", 83 | "\n", 84 | "# Generating some sample data\n", 85 | "X, y = make_classification(n_samples=1000, n_features=20, random_state=42)\n", 86 | "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", 87 | "\n", 88 | "# Initializing models\n", 89 | "model1 = LogisticRegression()\n", 90 | "model2 = DecisionTreeClassifier()\n", 91 | "model3 = SVC(probability=True)\n", 92 | "\n", 93 | "# Max Voting classifier\n", 94 | "model = VotingClassifier(estimators=[('lr', model1), ('dt', model2), ('svc', model3)], voting='hard')\n", 95 | "\n", 96 | "# Training model\n", 97 | "model.fit(X_train, y_train)\n", 98 | "\n", 99 | "# Predicting test results\n", 100 | "y_pred = model.predict(X_test)\n", 101 | "\n", 102 | "# Calculating accuracy\n", 103 | "accuracy = accuracy_score(y_test, y_pred)\n", 104 | "print(\"Max Voting Accuracy:\", accuracy)\n" 105 | ], 106 | "metadata": { 107 | "colab": { 108 | "base_uri": "https://localhost:8080/" 109 | }, 110 | "id": "RxpkwPY7WPxB", 111 | "outputId": "77d1e3a5-4c88-4d8b-c719-f9094955eda1" 112 | }, 113 | "execution_count": 14, 114 | "outputs": [ 115 | { 116 | "output_type": "stream", 117 | "name": "stdout", 118 | "text": [ 119 | "Max Voting Accuracy: 0.855\n" 120 | ] 121 | } 122 | ] 123 | }, 124 | { 125 | "cell_type": "markdown", 126 | "source": [ 127 | "\n", 128 | "> **Averaging**: Averaging aggregates predictions by taking the average probability (for classification) or the mean prediction (for regression) across multiple models.\n", 129 | "\n", 130 | "\n", 131 | "> [We Use probability=True, is used to enable the prediction of probabilities for classes in models that support it, providing more information for soft voting]\n", 132 | "\n", 133 | "\n", 134 | "\n", 135 | "\n" 136 | ], 137 | "metadata": { 138 | "id": "ugmqCuVdZ6lp" 139 | } 140 | }, 141 | { 142 | "cell_type": "code", 143 | "source": [ 144 | "from sklearn.ensemble import VotingClassifier\n", 145 | "\n", 146 | "# Averaging classifier\n", 147 | "model = VotingClassifier(estimators=[('lr', model1), ('dt', model2), ('svc', model3)], voting='soft')\n", 148 | "\n", 149 | "# Train model\n", 150 | "model.fit(X_train, y_train)\n", 151 | "\n", 152 | "# Predicting test result\n", 153 | "y_pred = model.predict(X_test)\n", 154 | "\n", 155 | "# Calculating accuracy\n", 156 | "accuracy = accuracy_score(y_test, y_pred)\n", 157 | "print(\"Averaging Accuracy:\", accuracy)\n" 158 | ], 159 | "metadata": { 160 | "colab": { 161 | "base_uri": "https://localhost:8080/" 162 | }, 163 | "id": "YMkRARvWaSUq", 164 | "outputId": "dfde9ce6-b5d7-469f-b66b-0539068fdb7e" 165 | }, 166 | "execution_count": 13, 167 | "outputs": [ 168 | { 169 | "output_type": "stream", 170 | "name": "stdout", 171 | "text": [ 172 | "Averaging Accuracy: 0.87\n" 173 | ] 174 | } 175 | ] 176 | }, 177 | { 178 | "cell_type": "markdown", 179 | "source": [ 180 | "> **Weighted Averaging**: All models are assigned different weights defining the importance of each model for prediction.\n", 181 | "\n" 182 | ], 183 | "metadata": { 184 | "id": "uaHW7WZAed2B" 185 | } 186 | }, 187 | { 188 | "cell_type": "code", 189 | "source": [ 190 | "# Define weights for models\n", 191 | "weights = [0.3, 0.4, 0.3]\n", 192 | "\n", 193 | "model = VotingClassifier(estimators=[('lr', model1), ('dt', model2), ('svc', model3)], voting='soft', weights=weights)\n", 194 | "\n", 195 | "# Training model\n", 196 | "model.fit(X_train, y_train)\n", 197 | "\n", 198 | "# Predicting test results\n", 199 | "y_pred = model.predict(X_test)\n", 200 | "\n", 201 | "# Calculating accuracy\n", 202 | "accuracy = accuracy_score(y_test, y_pred)\n", 203 | "print(\"Weighted Averaging Accuracy:\", accuracy)" 204 | ], 205 | "metadata": { 206 | "colab": { 207 | "base_uri": "https://localhost:8080/" 208 | }, 209 | "id": "YqX9MVg6evmc", 210 | "outputId": "3b4baca8-2f6b-43c8-9330-ba97d945df55" 211 | }, 212 | "execution_count": 16, 213 | "outputs": [ 214 | { 215 | "output_type": "stream", 216 | "name": "stdout", 217 | "text": [ 218 | "Weighted Averaging Accuracy: 0.895\n" 219 | ] 220 | } 221 | ] 222 | }, 223 | { 224 | "cell_type": "markdown", 225 | "source": [ 226 | "**Advanced Ensemble Techniques:**\n", 227 | "\n", 228 | "\n", 229 | "> **Stacking**: A new model is built on the predictions of other models.\n", 230 | "\n", 231 | "\n", 232 | "> **Blending**: A new model is built on the predictions of other models and the actual values of the training set.\n", 233 | "\n", 234 | "**Algorithms based on Bagging and Boosting:**\n", 235 | "\n", 236 | "> **Bagging**: Multiple subsets are created from the original dataset, selecting observations with replacement. A base model is created on each of these subsets." 237 | ], 238 | "metadata": { 239 | "id": "f0MoQJ3WfgzL" 240 | } 241 | }, 242 | { 243 | "cell_type": "code", 244 | "source": [ 245 | "from sklearn.ensemble import BaggingClassifier\n", 246 | "from sklearn import tree\n", 247 | "model = BaggingClassifier(tree.DecisionTreeClassifier(random_state=1))\n", 248 | "model.fit(X_train, y_train)\n", 249 | "model.score(X_test, y_test)" 250 | ], 251 | "metadata": { 252 | "colab": { 253 | "base_uri": "https://localhost:8080/" 254 | }, 255 | "id": "Tuo97U86grxF", 256 | "outputId": "f68a482b-015b-4637-b45d-35ce8ebb63cb" 257 | }, 258 | "execution_count": 22, 259 | "outputs": [ 260 | { 261 | "output_type": "execute_result", 262 | "data": { 263 | "text/plain": [ 264 | "0.875" 265 | ] 266 | }, 267 | "metadata": {}, 268 | "execution_count": 22 269 | } 270 | ] 271 | }, 272 | { 273 | "cell_type": "code", 274 | "source": [ 275 | "from sklearn.ensemble import BaggingRegressor\n", 276 | "model = BaggingRegressor(tree.DecisionTreeRegressor(random_state=1))\n", 277 | "model.fit(X_train, y_train)\n", 278 | "model.score(X_test,y_test)" 279 | ], 280 | "metadata": { 281 | "colab": { 282 | "base_uri": "https://localhost:8080/" 283 | }, 284 | "id": "rGaNygETiTCs", 285 | "outputId": "5607cfc6-1e5e-4dff-8891-08524e18430e" 286 | }, 287 | "execution_count": 24, 288 | "outputs": [ 289 | { 290 | "output_type": "execute_result", 291 | "data": { 292 | "text/plain": [ 293 | "0.6504873882021907" 294 | ] 295 | }, 296 | "metadata": {}, 297 | "execution_count": 24 298 | } 299 | ] 300 | }, 301 | { 302 | "cell_type": "markdown", 303 | "source": [ 304 | "> **Boosting**: A sequential process, where each subsequent model attempts to correct the errors of the previous model." 305 | ], 306 | "metadata": { 307 | "id": "CQZ8ZhAZifZm" 308 | } 309 | }, 310 | { 311 | "cell_type": "markdown", 312 | "source": [ 313 | "**AdaBoost:**\n", 314 | "\n", 315 | "\n", 316 | "> **AdaBoost** (Adaptive Boosting) is an ensemble learning algorithm that combines multiple weak learners to create a strong learner.\n", 317 | "\n", 318 | "\n", 319 | "> It is an iterative algorithm that sequentially builds weak learners, where each weak learner focuses on the hardest examples from the previous round.\n", 320 | "\n", 321 | "\n", 322 | "> AdaBoost is known for its ability to handle noisy data and its robustness to overfitting.\n", 323 | "\n", 324 | "\n", 325 | "\n", 326 | "\n", 327 | "\n" 328 | ], 329 | "metadata": { 330 | "id": "ofwOmSVEld1v" 331 | } 332 | }, 333 | { 334 | "cell_type": "code", 335 | "source": [ 336 | "# Sample code for classification\n", 337 | "from sklearn.ensemble import AdaBoostClassifier\n", 338 | "model = AdaBoostClassifier(random_state=1)\n", 339 | "model.fit(X_train, y_train)\n", 340 | "model.score(X_test,y_test)" 341 | ], 342 | "metadata": { 343 | "colab": { 344 | "base_uri": "https://localhost:8080/" 345 | }, 346 | "id": "kUmR-sNzi2eD", 347 | "outputId": "9b71ee43-eb50-4ce3-a827-a1dd8c0f5f01" 348 | }, 349 | "execution_count": 26, 350 | "outputs": [ 351 | { 352 | "output_type": "execute_result", 353 | "data": { 354 | "text/plain": [ 355 | "0.87" 356 | ] 357 | }, 358 | "metadata": {}, 359 | "execution_count": 26 360 | } 361 | ] 362 | }, 363 | { 364 | "cell_type": "markdown", 365 | "source": [ 366 | "**Sample code for regression problem:**" 367 | ], 368 | "metadata": { 369 | "id": "D2y_LU_rl0zW" 370 | } 371 | }, 372 | { 373 | "cell_type": "code", 374 | "source": [ 375 | "from sklearn.ensemble import AdaBoostRegressor\n", 376 | "model = AdaBoostRegressor()\n", 377 | "model.fit(X_train, y_train)\n", 378 | "model.score(X_test,y_test)" 379 | ], 380 | "metadata": { 381 | "colab": { 382 | "base_uri": "https://localhost:8080/" 383 | }, 384 | "id": "rJQa0Z4tjMoV", 385 | "outputId": "14d98074-7549-4c19-cbc5-90113a0f7826" 386 | }, 387 | "execution_count": 28, 388 | "outputs": [ 389 | { 390 | "output_type": "execute_result", 391 | "data": { 392 | "text/plain": [ 393 | "0.4132620642489211" 394 | ] 395 | }, 396 | "metadata": {}, 397 | "execution_count": 28 398 | } 399 | ] 400 | }, 401 | { 402 | "cell_type": "markdown", 403 | "source": [ 404 | "**Gradient Boosting Machines (GBM)**:\n", 405 | "\n", 406 | "\n", 407 | "\n", 408 | "> **Gradient Boosting Machines (GBM)** is an ensemble learning algorithm that builds a sequence of weak learners, where each weak learner is trained to minimize the gradient of the loss function with respect to the predictions of the previous weak learner.\n", 409 | "\n", 410 | "\n", 411 | "> GBM is a powerful algorithm that can achieve high accuracy on a variety of tasks.\n" 412 | ], 413 | "metadata": { 414 | "id": "Crx-IYSYmaXb" 415 | } 416 | }, 417 | { 418 | "cell_type": "code", 419 | "source": [ 420 | "from sklearn.ensemble import GradientBoostingClassifier\n", 421 | "model= GradientBoostingClassifier(learning_rate=0.01,random_state=1)\n", 422 | "model.fit(X_train, y_train)\n", 423 | "model.score(X_test,y_test)" 424 | ], 425 | "metadata": { 426 | "colab": { 427 | "base_uri": "https://localhost:8080/" 428 | }, 429 | "id": "WVs1ayZHm4oC", 430 | "outputId": "4e07d322-81d0-456b-c0a1-436919b56891" 431 | }, 432 | "execution_count": 30, 433 | "outputs": [ 434 | { 435 | "output_type": "execute_result", 436 | "data": { 437 | "text/plain": [ 438 | "0.89" 439 | ] 440 | }, 441 | "metadata": {}, 442 | "execution_count": 30 443 | } 444 | ] 445 | }, 446 | { 447 | "cell_type": "code", 448 | "source": [ 449 | "# Sample code for Regressor\n", 450 | "from sklearn.ensemble import GradientBoostingRegressor\n", 451 | "model= GradientBoostingRegressor()\n", 452 | "model.fit(X_train, y_train)\n", 453 | "model.score(X_test,y_test)" 454 | ], 455 | "metadata": { 456 | "colab": { 457 | "base_uri": "https://localhost:8080/" 458 | }, 459 | "id": "6QOAzcAvnQSq", 460 | "outputId": "d2fad824-294c-46cd-8ff7-f2f3d91b9f91" 461 | }, 462 | "execution_count": 32, 463 | "outputs": [ 464 | { 465 | "output_type": "execute_result", 466 | "data": { 467 | "text/plain": [ 468 | "0.6132034021878043" 469 | ] 470 | }, 471 | "metadata": {}, 472 | "execution_count": 32 473 | } 474 | ] 475 | }, 476 | { 477 | "cell_type": "markdown", 478 | "source": [ 479 | "**XGBoost:**\n", 480 | "\n", 481 | "\n", 482 | "> **XGBoost** is an optimized version of GBM that includes several improvements, such as:\n", 483 | "\n", 484 | "1. Parallel Processing: XGBoost implements parallel processing and is faster than GBM .\n", 485 | "2. Regularization techniques: XGBoost uses regularization techniques to prevent overfitting, which is a common problem in machine learning.\n", 486 | "\n", 487 | "[*Since XGBoost takes care of the missing values itself, you do not have to impute the missing values. ]" 488 | ], 489 | "metadata": { 490 | "id": "s2qDcV9gnol3" 491 | } 492 | }, 493 | { 494 | "cell_type": "code", 495 | "source": [ 496 | "import xgboost as xgb\n", 497 | "model=xgb.XGBClassifier(random_state=1,learning_rate=0.01)\n", 498 | "model.fit(X_train, y_train)\n", 499 | "model.score(X_test,y_test)" 500 | ], 501 | "metadata": { 502 | "colab": { 503 | "base_uri": "https://localhost:8080/" 504 | }, 505 | "id": "iiyDgkiLoT9u", 506 | "outputId": "0f97b048-71b5-4278-bf12-9391b645be2f" 507 | }, 508 | "execution_count": 33, 509 | "outputs": [ 510 | { 511 | "output_type": "execute_result", 512 | "data": { 513 | "text/plain": [ 514 | "0.88" 515 | ] 516 | }, 517 | "metadata": {}, 518 | "execution_count": 33 519 | } 520 | ] 521 | }, 522 | { 523 | "cell_type": "code", 524 | "source": [ 525 | "import xgboost as xgb\n", 526 | "model=xgb.XGBRegressor()\n", 527 | "model.fit(X_train, y_train)\n", 528 | "model.score(X_test,y_test)" 529 | ], 530 | "metadata": { 531 | "colab": { 532 | "base_uri": "https://localhost:8080/" 533 | }, 534 | "id": "ek2h3F2no1YI", 535 | "outputId": "56074860-971c-42d0-a4c2-6e9ce5540290" 536 | }, 537 | "execution_count": 34, 538 | "outputs": [ 539 | { 540 | "output_type": "execute_result", 541 | "data": { 542 | "text/plain": [ 543 | "0.6251452430469582" 544 | ] 545 | }, 546 | "metadata": {}, 547 | "execution_count": 34 548 | } 549 | ] 550 | }, 551 | { 552 | "cell_type": "markdown", 553 | "source": [ 554 | "**LightGBM:**\n", 555 | "\n", 556 | "\n", 557 | "> **LightGBM** is another optimized version of GBM that is known for its speed and efficiency.\n", 558 | "\n", 559 | "\n", 560 | "> It uses a novel tree-growing algorithm that is specifically designed for boosting algorithms.\n", 561 | "\n", 562 | "\n", 563 | "> **LightGBM** also includes several other optimizations that make it faster than XGBoost, such as:\n", 564 | "\n", 565 | "\n", 566 | "\n", 567 | "1. Parallel processing: LightGBM can be trained on multiple CPUs or GPUs, which can significantly reduce training time.\n", 568 | "2. Histogram-based tree learning: LightGBM uses a histogram-based tree learning algorithm that is faster than traditional tree learning algorithms.\n", 569 | "\n", 570 | "\n", 571 | "\n", 572 | "\n", 573 | "\n", 574 | "\n", 575 | "\n", 576 | "\n", 577 | "\n" 578 | ], 579 | "metadata": { 580 | "id": "989ytLaFpahj" 581 | } 582 | }, 583 | { 584 | "cell_type": "code", 585 | "source": [ 586 | "import lightgbm as lgb\n", 587 | "\n", 588 | "model = lgb.LGBMClassifier(n_estimators=100, learning_rate=0.1, random_state=42)\n", 589 | "\n", 590 | "model.fit(X_train, y_train)\n", 591 | "\n", 592 | "y_pred = lgb_classifier.predict(X_test)\n", 593 | "\n", 594 | "accuracy = accuracy_score(y_test, y_pred)\n", 595 | "print(\"LightGBM Accuracy:\", accuracy)" 596 | ], 597 | "metadata": { 598 | "colab": { 599 | "base_uri": "https://localhost:8080/" 600 | }, 601 | "id": "9lSCSHGApH9H", 602 | "outputId": "8ecd6f06-b7fa-4644-c1d2-11f9f44931df" 603 | }, 604 | "execution_count": 36, 605 | "outputs": [ 606 | { 607 | "output_type": "stream", 608 | "name": "stdout", 609 | "text": [ 610 | "[LightGBM] [Info] Number of positive: 393, number of negative: 407\n", 611 | "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.\n", 612 | "You can set `force_col_wise=true` to remove the overhead.\n", 613 | "[LightGBM] [Info] Total Bins 5100\n", 614 | "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 20\n", 615 | "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491250 -> initscore=-0.035004\n", 616 | "[LightGBM] [Info] Start training from score -0.035004\n", 617 | "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", 618 | "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", 619 | "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", 620 | "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", 621 | "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", 622 | "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", 623 | "LightGBM Accuracy: 0.895\n" 624 | ] 625 | } 626 | ] 627 | } 628 | ] 629 | } -------------------------------------------------------------------------------- /55. Day55 Ensemble Learning/Overall Summary of Boosting.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aswinramesh04/100-Days-of-DataScience/560ae496989e1a1b80cd707c87cd359058594949/55. Day55 Ensemble Learning/Overall Summary of Boosting.png -------------------------------------------------------------------------------- /57. Day57 Intro. to Clustering/Untitled39.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyPtXU74B4d2ccOzLouqtygh", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": null, 32 | "metadata": { 33 | "id": "h8tPm52tl4CW" 34 | }, 35 | "outputs": [], 36 | "source": [] 37 | } 38 | ] 39 | } -------------------------------------------------------------------------------- /58. Day 58 K Means Concept/Day58_K_Means Concept.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyPtXU74B4d2ccOzLouqtygh", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": null, 32 | "metadata": { 33 | "id": "5EOngT6sJMPX" 34 | }, 35 | "outputs": [], 36 | "source": [] 37 | } 38 | ] 39 | } -------------------------------------------------------------------------------- /60. Day60 Hierarchical Clustering Concept/Untitled39.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyPtXU74B4d2ccOzLouqtygh", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": null, 32 | "metadata": { 33 | "id": "tZnKuZB8Qlo4" 34 | }, 35 | "outputs": [], 36 | "source": [] 37 | } 38 | ] 39 | } -------------------------------------------------------------------------------- /61. Day61 H-Clustering(Agglomerative Clustering)/Mall_Customers.csv: -------------------------------------------------------------------------------- 1 | CustomerID,Genre,Age,Annual Income (k$),Spending Score (1-100) 2 | 0001,Male,19,15,39 3 | 0002,Male,21,15,81 4 | 0003,Female,20,16,6 5 | 0004,Female,23,16,77 6 | 0005,Female,31,17,40 7 | 0006,Female,22,17,76 8 | 0007,Female,35,18,6 9 | 0008,Female,23,18,94 10 | 0009,Male,64,19,3 11 | 0010,Female,30,19,72 12 | 0011,Male,67,19,14 13 | 0012,Female,35,19,99 14 | 0013,Female,58,20,15 15 | 0014,Female,24,20,77 16 | 0015,Male,37,20,13 17 | 0016,Male,22,20,79 18 | 0017,Female,35,21,35 19 | 0018,Male,20,21,66 20 | 0019,Male,52,23,29 21 | 0020,Female,35,23,98 22 | 0021,Male,35,24,35 23 | 0022,Male,25,24,73 24 | 0023,Female,46,25,5 25 | 0024,Male,31,25,73 26 | 0025,Female,54,28,14 27 | 0026,Male,29,28,82 28 | 0027,Female,45,28,32 29 | 0028,Male,35,28,61 30 | 0029,Female,40,29,31 31 | 0030,Female,23,29,87 32 | 0031,Male,60,30,4 33 | 0032,Female,21,30,73 34 | 0033,Male,53,33,4 35 | 0034,Male,18,33,92 36 | 0035,Female,49,33,14 37 | 0036,Female,21,33,81 38 | 0037,Female,42,34,17 39 | 0038,Female,30,34,73 40 | 0039,Female,36,37,26 41 | 0040,Female,20,37,75 42 | 0041,Female,65,38,35 43 | 0042,Male,24,38,92 44 | 0043,Male,48,39,36 45 | 0044,Female,31,39,61 46 | 0045,Female,49,39,28 47 | 0046,Female,24,39,65 48 | 0047,Female,50,40,55 49 | 0048,Female,27,40,47 50 | 0049,Female,29,40,42 51 | 0050,Female,31,40,42 52 | 0051,Female,49,42,52 53 | 0052,Male,33,42,60 54 | 0053,Female,31,43,54 55 | 0054,Male,59,43,60 56 | 0055,Female,50,43,45 57 | 0056,Male,47,43,41 58 | 0057,Female,51,44,50 59 | 0058,Male,69,44,46 60 | 0059,Female,27,46,51 61 | 0060,Male,53,46,46 62 | 0061,Male,70,46,56 63 | 0062,Male,19,46,55 64 | 0063,Female,67,47,52 65 | 0064,Female,54,47,59 66 | 0065,Male,63,48,51 67 | 0066,Male,18,48,59 68 | 0067,Female,43,48,50 69 | 0068,Female,68,48,48 70 | 0069,Male,19,48,59 71 | 0070,Female,32,48,47 72 | 0071,Male,70,49,55 73 | 0072,Female,47,49,42 74 | 0073,Female,60,50,49 75 | 0074,Female,60,50,56 76 | 0075,Male,59,54,47 77 | 0076,Male,26,54,54 78 | 0077,Female,45,54,53 79 | 0078,Male,40,54,48 80 | 0079,Female,23,54,52 81 | 0080,Female,49,54,42 82 | 0081,Male,57,54,51 83 | 0082,Male,38,54,55 84 | 0083,Male,67,54,41 85 | 0084,Female,46,54,44 86 | 0085,Female,21,54,57 87 | 0086,Male,48,54,46 88 | 0087,Female,55,57,58 89 | 0088,Female,22,57,55 90 | 0089,Female,34,58,60 91 | 0090,Female,50,58,46 92 | 0091,Female,68,59,55 93 | 0092,Male,18,59,41 94 | 0093,Male,48,60,49 95 | 0094,Female,40,60,40 96 | 0095,Female,32,60,42 97 | 0096,Male,24,60,52 98 | 0097,Female,47,60,47 99 | 0098,Female,27,60,50 100 | 0099,Male,48,61,42 101 | 0100,Male,20,61,49 102 | 0101,Female,23,62,41 103 | 0102,Female,49,62,48 104 | 0103,Male,67,62,59 105 | 0104,Male,26,62,55 106 | 0105,Male,49,62,56 107 | 0106,Female,21,62,42 108 | 0107,Female,66,63,50 109 | 0108,Male,54,63,46 110 | 0109,Male,68,63,43 111 | 0110,Male,66,63,48 112 | 0111,Male,65,63,52 113 | 0112,Female,19,63,54 114 | 0113,Female,38,64,42 115 | 0114,Male,19,64,46 116 | 0115,Female,18,65,48 117 | 0116,Female,19,65,50 118 | 0117,Female,63,65,43 119 | 0118,Female,49,65,59 120 | 0119,Female,51,67,43 121 | 0120,Female,50,67,57 122 | 0121,Male,27,67,56 123 | 0122,Female,38,67,40 124 | 0123,Female,40,69,58 125 | 0124,Male,39,69,91 126 | 0125,Female,23,70,29 127 | 0126,Female,31,70,77 128 | 0127,Male,43,71,35 129 | 0128,Male,40,71,95 130 | 0129,Male,59,71,11 131 | 0130,Male,38,71,75 132 | 0131,Male,47,71,9 133 | 0132,Male,39,71,75 134 | 0133,Female,25,72,34 135 | 0134,Female,31,72,71 136 | 0135,Male,20,73,5 137 | 0136,Female,29,73,88 138 | 0137,Female,44,73,7 139 | 0138,Male,32,73,73 140 | 0139,Male,19,74,10 141 | 0140,Female,35,74,72 142 | 0141,Female,57,75,5 143 | 0142,Male,32,75,93 144 | 0143,Female,28,76,40 145 | 0144,Female,32,76,87 146 | 0145,Male,25,77,12 147 | 0146,Male,28,77,97 148 | 0147,Male,48,77,36 149 | 0148,Female,32,77,74 150 | 0149,Female,34,78,22 151 | 0150,Male,34,78,90 152 | 0151,Male,43,78,17 153 | 0152,Male,39,78,88 154 | 0153,Female,44,78,20 155 | 0154,Female,38,78,76 156 | 0155,Female,47,78,16 157 | 0156,Female,27,78,89 158 | 0157,Male,37,78,1 159 | 0158,Female,30,78,78 160 | 0159,Male,34,78,1 161 | 0160,Female,30,78,73 162 | 0161,Female,56,79,35 163 | 0162,Female,29,79,83 164 | 0163,Male,19,81,5 165 | 0164,Female,31,81,93 166 | 0165,Male,50,85,26 167 | 0166,Female,36,85,75 168 | 0167,Male,42,86,20 169 | 0168,Female,33,86,95 170 | 0169,Female,36,87,27 171 | 0170,Male,32,87,63 172 | 0171,Male,40,87,13 173 | 0172,Male,28,87,75 174 | 0173,Male,36,87,10 175 | 0174,Male,36,87,92 176 | 0175,Female,52,88,13 177 | 0176,Female,30,88,86 178 | 0177,Male,58,88,15 179 | 0178,Male,27,88,69 180 | 0179,Male,59,93,14 181 | 0180,Male,35,93,90 182 | 0181,Female,37,97,32 183 | 0182,Female,32,97,86 184 | 0183,Male,46,98,15 185 | 0184,Female,29,98,88 186 | 0185,Female,41,99,39 187 | 0186,Male,30,99,97 188 | 0187,Female,54,101,24 189 | 0188,Male,28,101,68 190 | 0189,Female,41,103,17 191 | 0190,Female,36,103,85 192 | 0191,Female,34,103,23 193 | 0192,Female,32,103,69 194 | 0193,Male,33,113,8 195 | 0194,Female,38,113,91 196 | 0195,Female,47,120,16 197 | 0196,Female,35,120,79 198 | 0197,Female,45,126,28 199 | 0198,Male,32,126,74 200 | 0199,Male,32,137,18 201 | 0200,Male,30,137,83 -------------------------------------------------------------------------------- /62. Day62 DBSCAN Concept/Untitled40.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyPtXU74B4d2ccOzLouqtygh", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": null, 32 | "metadata": { 33 | "id": "QLrOcfrg6Ny5" 34 | }, 35 | "outputs": [], 36 | "source": [] 37 | } 38 | ] 39 | } -------------------------------------------------------------------------------- /64. Day64 Dimensionality Reduction/Dimensionality Reduction.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aswinramesh04/100-Days-of-DataScience/560ae496989e1a1b80cd707c87cd359058594949/64. Day64 Dimensionality Reduction/Dimensionality Reduction.png -------------------------------------------------------------------------------- /65. Day65 PCA Concept/Day65 PCA.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aswinramesh04/100-Days-of-DataScience/560ae496989e1a1b80cd707c87cd359058594949/65. Day65 PCA Concept/Day65 PCA.pdf -------------------------------------------------------------------------------- /67. Day67 Feature Selection Intro./Day67 Feature Selection.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aswinramesh04/100-Days-of-DataScience/560ae496989e1a1b80cd707c87cd359058594949/67. Day67 Feature Selection Intro./Day67 Feature Selection.pdf -------------------------------------------------------------------------------- /68. Day68 Feature Selection - Filter Method/Day68_Filter_Method.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyO63T7+gNURH/HZJ84v3KuM", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "source": [ 32 | " Day 68 Filter Method By: Loga Aswin" 33 | ], 34 | "metadata": { 35 | "id": "AmnNAYIJwlgx" 36 | } 37 | }, 38 | { 39 | "cell_type": "code", 40 | "source": [ 41 | "import pandas as pd\n", 42 | "from sklearn.feature_selection import SelectKBest, f_classif, mutual_info_classif, chi2\n", 43 | "from sklearn.datasets import load_breast_cancer # Changed dataset to breast cancer\n", 44 | "\n", 45 | "# Load breast cancer dataset\n", 46 | "breast_cancer = load_breast_cancer()\n", 47 | "df = pd.DataFrame(breast_cancer.data, columns=breast_cancer.feature_names)\n", 48 | "target = breast_cancer.target" 49 | ], 50 | "metadata": { 51 | "id": "gWAxaNelyfmt" 52 | }, 53 | "execution_count": 19, 54 | "outputs": [] 55 | }, 56 | { 57 | "cell_type": "markdown", 58 | "source": [ 59 | "**Various Methods Used in Filter Method**" 60 | ], 61 | "metadata": { 62 | "id": "4MEjIuXE32bC" 63 | } 64 | }, 65 | { 66 | "cell_type": "markdown", 67 | "source": [ 68 | "**1. Correlation-based Feature Selection**:\n", 69 | "Identifies top features based on their correlation with the target variable." 70 | ], 71 | "metadata": { 72 | "id": "apG9LRGQzO35" 73 | } 74 | }, 75 | { 76 | "cell_type": "code", 77 | "source": [ 78 | "corr_scores = df.corrwith(pd.Series(target, name=\"Target\")).abs().sort_values(ascending=False)\n", 79 | "k_corr = 3\n", 80 | "selected_corr = corr_scores.index[:k_corr]\n", 81 | "\n", 82 | "print(\"Top features:\")\n", 83 | "print(selected_corr)" 84 | ], 85 | "metadata": { 86 | "colab": { 87 | "base_uri": "https://localhost:8080/" 88 | }, 89 | "id": "who3SrvPzFR4", 90 | "outputId": "fc9bff38-d851-420e-aa1a-d37a32c9f36f" 91 | }, 92 | "execution_count": 13, 93 | "outputs": [ 94 | { 95 | "output_type": "stream", 96 | "name": "stdout", 97 | "text": [ 98 | "Top features:\n", 99 | "Index(['worst concave points', 'worst perimeter', 'mean concave points'], dtype='object')\n" 100 | ] 101 | } 102 | ] 103 | }, 104 | { 105 | "cell_type": "markdown", 106 | "source": [ 107 | "**2. Mutual Information-based Feature Selection**: Selects features with the highest mutual information with the target." 108 | ], 109 | "metadata": { 110 | "id": "YJalF05MzahI" 111 | } 112 | }, 113 | { 114 | "cell_type": "code", 115 | "source": [ 116 | "k_mi = 3\n", 117 | "selected_mi = SelectKBest(score_func=mutual_info_classif, k=k_mi).fit(df, target)\n", 118 | "selected_mi = df.columns[selected_mi.get_support()]\n", 119 | "\n", 120 | "print(\"Top features:\")\n", 121 | "print(selected_mi)" 122 | ], 123 | "metadata": { 124 | "colab": { 125 | "base_uri": "https://localhost:8080/" 126 | }, 127 | "id": "pVRFytI5zSUb", 128 | "outputId": "3b2e2806-8927-4d17-80d2-5c14941576ba" 129 | }, 130 | "execution_count": 14, 131 | "outputs": [ 132 | { 133 | "output_type": "stream", 134 | "name": "stdout", 135 | "text": [ 136 | "Top features using mutual information-based selection:\n", 137 | "Index(['worst radius', 'worst perimeter', 'worst area'], dtype='object')\n" 138 | ] 139 | } 140 | ] 141 | }, 142 | { 143 | "cell_type": "markdown", 144 | "source": [ 145 | "**3. Chi-square Test**:Uses chi-square test to find features most related to the target in categorical data." 146 | ], 147 | "metadata": { 148 | "id": "LNmh-xoMzhoE" 149 | } 150 | }, 151 | { 152 | "cell_type": "code", 153 | "source": [ 154 | "chi2_feat = SelectKBest(chi2, k=3)\n", 155 | "X_kbest = chi2_feat.fit_transform(df, target)\n", 156 | "\n", 157 | "print(\"Shape before and after chi-square test:\")\n", 158 | "print(df.shape)\n", 159 | "print(X_kbest.shape)" 160 | ], 161 | "metadata": { 162 | "colab": { 163 | "base_uri": "https://localhost:8080/" 164 | }, 165 | "id": "hSx5bCkLzdQ8", 166 | "outputId": "11b3c2b4-1dd8-4a87-8dd0-9103707a780b" 167 | }, 168 | "execution_count": 15, 169 | "outputs": [ 170 | { 171 | "output_type": "stream", 172 | "name": "stdout", 173 | "text": [ 174 | "Shape before and after chi-square test:\n", 175 | "(569, 30)\n", 176 | "(569, 3)\n" 177 | ] 178 | } 179 | ] 180 | }, 181 | { 182 | "cell_type": "markdown", 183 | "source": [ 184 | "**4. Fisher's Score**: Utilizes Fisher's Score to pick the most discriminative features for classification.\n" 185 | ], 186 | "metadata": { 187 | "id": "vOG8IDA9zkwG" 188 | } 189 | }, 190 | { 191 | "cell_type": "code", 192 | "source": [ 193 | "k_fisher = 2\n", 194 | "fisher_selector = SelectKBest(score_func=f_classif, k=k_fisher)\n", 195 | "X_new = fisher_selector.fit_transform(df, target)\n", 196 | "\n", 197 | "# get indices\n", 198 | "sel_indices = fisher_selector.get_support(indices=True)\n", 199 | "selected_fisher = [breast_cancer.feature_names[i] for i in sel_indices]\n", 200 | "\n", 201 | "print(\"Top features using Fisher's Score:\")\n", 202 | "print(selected_fisher)" 203 | ], 204 | "metadata": { 205 | "colab": { 206 | "base_uri": "https://localhost:8080/" 207 | }, 208 | "id": "zIa-la1jzlY2", 209 | "outputId": "40e063b6-4d3a-4f54-acba-d96c66f75b2d" 210 | }, 211 | "execution_count": 20, 212 | "outputs": [ 213 | { 214 | "output_type": "stream", 215 | "name": "stdout", 216 | "text": [ 217 | "Top features using Fisher's Score:\n", 218 | "['worst perimeter', 'worst concave points']\n" 219 | ] 220 | } 221 | ] 222 | }, 223 | { 224 | "cell_type": "markdown", 225 | "source": [ 226 | "**5. Missing Value Ratio:**\n", 227 | "\n", 228 | "Filters features based on a threshold for the ratio of missing values.\n", 229 | "\n", 230 | "\n", 231 | "\n", 232 | "\n" 233 | ], 234 | "metadata": { 235 | "id": "j0wYdvOp0qso" 236 | } 237 | }, 238 | { 239 | "cell_type": "code", 240 | "source": [ 241 | "from sklearn.impute import SimpleImputer\n", 242 | "\n", 243 | "thresh_missing = 0.3\n", 244 | "missing_ratio = df.isnull().mean()\n", 245 | "\n", 246 | "selected_missing = df.columns[missing_ratio < thresh_missing]\n", 247 | "\n", 248 | "# Impute missing values if needed\n", 249 | "imputer = SimpleImputer(strategy='mean')\n", 250 | "df[selected_missing] = imputer.fit_transform(df[selected_missing])\n", 251 | "\n", 252 | "print(\"Selected features after handling missing values:\")\n", 253 | "print(selected_missing)" 254 | ], 255 | "metadata": { 256 | "colab": { 257 | "base_uri": "https://localhost:8080/" 258 | }, 259 | "id": "L8DgTiOJz5We", 260 | "outputId": "9814664b-b17b-4e95-c261-b2b67bc7580d" 261 | }, 262 | "execution_count": 21, 263 | "outputs": [ 264 | { 265 | "output_type": "stream", 266 | "name": "stdout", 267 | "text": [ 268 | "Selected features after handling missing values:\n", 269 | "Index(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n", 270 | " 'mean smoothness', 'mean compactness', 'mean concavity',\n", 271 | " 'mean concave points', 'mean symmetry', 'mean fractal dimension',\n", 272 | " 'radius error', 'texture error', 'perimeter error', 'area error',\n", 273 | " 'smoothness error', 'compactness error', 'concavity error',\n", 274 | " 'concave points error', 'symmetry error', 'fractal dimension error',\n", 275 | " 'worst radius', 'worst texture', 'worst perimeter', 'worst area',\n", 276 | " 'worst smoothness', 'worst compactness', 'worst concavity',\n", 277 | " 'worst concave points', 'worst symmetry', 'worst fractal dimension'],\n", 278 | " dtype='object')\n" 279 | ] 280 | } 281 | ] 282 | } 283 | ] 284 | } -------------------------------------------------------------------------------- /68. Day68 Feature Selection - Filter Method/day68-filter-method.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aswinramesh04/100-Days-of-DataScience/560ae496989e1a1b80cd707c87cd359058594949/68. Day68 Feature Selection - Filter Method/day68-filter-method.pdf -------------------------------------------------------------------------------- /69. Day69 Feature Selection - Wrapper Method/Day69 Wrapper Method.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aswinramesh04/100-Days-of-DataScience/560ae496989e1a1b80cd707c87cd359058594949/69. Day69 Feature Selection - Wrapper Method/Day69 Wrapper Method.pdf -------------------------------------------------------------------------------- /70. 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Day75 Simple BI Project/Untitled45.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyPtXU74B4d2ccOzLouqtygh", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": null, 32 | "metadata": { 33 | "id": "HomFoMKy_PRT" 34 | }, 35 | "outputs": [], 36 | "source": [] 37 | } 38 | ] 39 | } -------------------------------------------------------------------------------- /76. 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Day85 Webscrapping/Untitled55.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyPtXU74B4d2ccOzLouqtygh", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": null, 32 | "metadata": { 33 | "id": "Qq_G3v3sOQSl" 34 | }, 35 | "outputs": [], 36 | "source": [] 37 | } 38 | ] 39 | } -------------------------------------------------------------------------------- /86-88 Day86-88 Heart Disease Prediction/heart.csv: 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59,1,0,140,177,0,1,162,1,0,2,1,3,0 212 | 57,1,2,128,229,0,0,150,0,0.4,1,1,3,0 213 | 61,1,0,120,260,0,1,140,1,3.6,1,1,3,0 214 | 39,1,0,118,219,0,1,140,0,1.2,1,0,3,0 215 | 61,0,0,145,307,0,0,146,1,1,1,0,3,0 216 | 56,1,0,125,249,1,0,144,1,1.2,1,1,2,0 217 | 43,0,0,132,341,1,0,136,1,3,1,0,3,0 218 | 62,0,2,130,263,0,1,97,0,1.2,1,1,3,0 219 | 63,1,0,130,330,1,0,132,1,1.8,2,3,3,0 220 | 65,1,0,135,254,0,0,127,0,2.8,1,1,3,0 221 | 48,1,0,130,256,1,0,150,1,0,2,2,3,0 222 | 63,0,0,150,407,0,0,154,0,4,1,3,3,0 223 | 55,1,0,140,217,0,1,111,1,5.6,0,0,3,0 224 | 65,1,3,138,282,1,0,174,0,1.4,1,1,2,0 225 | 56,0,0,200,288,1,0,133,1,4,0,2,3,0 226 | 54,1,0,110,239,0,1,126,1,2.8,1,1,3,0 227 | 70,1,0,145,174,0,1,125,1,2.6,0,0,3,0 228 | 62,1,1,120,281,0,0,103,0,1.4,1,1,3,0 229 | 35,1,0,120,198,0,1,130,1,1.6,1,0,3,0 230 | 59,1,3,170,288,0,0,159,0,0.2,1,0,3,0 231 | 64,1,2,125,309,0,1,131,1,1.8,1,0,3,0 232 | 47,1,2,108,243,0,1,152,0,0,2,0,2,0 233 | 57,1,0,165,289,1,0,124,0,1,1,3,3,0 234 | 55,1,0,160,289,0,0,145,1,0.8,1,1,3,0 235 | 64,1,0,120,246,0,0,96,1,2.2,0,1,2,0 236 | 70,1,0,130,322,0,0,109,0,2.4,1,3,2,0 237 | 51,1,0,140,299,0,1,173,1,1.6,2,0,3,0 238 | 58,1,0,125,300,0,0,171,0,0,2,2,3,0 239 | 60,1,0,140,293,0,0,170,0,1.2,1,2,3,0 240 | 77,1,0,125,304,0,0,162,1,0,2,3,2,0 241 | 35,1,0,126,282,0,0,156,1,0,2,0,3,0 242 | 70,1,2,160,269,0,1,112,1,2.9,1,1,3,0 243 | 59,0,0,174,249,0,1,143,1,0,1,0,2,0 244 | 64,1,0,145,212,0,0,132,0,2,1,2,1,0 245 | 57,1,0,152,274,0,1,88,1,1.2,1,1,3,0 246 | 56,1,0,132,184,0,0,105,1,2.1,1,1,1,0 247 | 48,1,0,124,274,0,0,166,0,0.5,1,0,3,0 248 | 56,0,0,134,409,0,0,150,1,1.9,1,2,3,0 249 | 66,1,1,160,246,0,1,120,1,0,1,3,1,0 250 | 54,1,1,192,283,0,0,195,0,0,2,1,3,0 251 | 69,1,2,140,254,0,0,146,0,2,1,3,3,0 252 | 51,1,0,140,298,0,1,122,1,4.2,1,3,3,0 253 | 43,1,0,132,247,1,0,143,1,0.1,1,4,3,0 254 | 62,0,0,138,294,1,1,106,0,1.9,1,3,2,0 255 | 67,1,0,100,299,0,0,125,1,0.9,1,2,2,0 256 | 59,1,3,160,273,0,0,125,0,0,2,0,2,0 257 | 45,1,0,142,309,0,0,147,1,0,1,3,3,0 258 | 58,1,0,128,259,0,0,130,1,3,1,2,3,0 259 | 50,1,0,144,200,0,0,126,1,0.9,1,0,3,0 260 | 62,0,0,150,244,0,1,154,1,1.4,1,0,2,0 261 | 38,1,3,120,231,0,1,182,1,3.8,1,0,3,0 262 | 66,0,0,178,228,1,1,165,1,1,1,2,3,0 263 | 52,1,0,112,230,0,1,160,0,0,2,1,2,0 264 | 53,1,0,123,282,0,1,95,1,2,1,2,3,0 265 | 63,0,0,108,269,0,1,169,1,1.8,1,2,2,0 266 | 54,1,0,110,206,0,0,108,1,0,1,1,2,0 267 | 66,1,0,112,212,0,0,132,1,0.1,2,1,2,0 268 | 55,0,0,180,327,0,2,117,1,3.4,1,0,2,0 269 | 49,1,2,118,149,0,0,126,0,0.8,2,3,2,0 270 | 54,1,0,122,286,0,0,116,1,3.2,1,2,2,0 271 | 56,1,0,130,283,1,0,103,1,1.6,0,0,3,0 272 | 46,1,0,120,249,0,0,144,0,0.8,2,0,3,0 273 | 61,1,3,134,234,0,1,145,0,2.6,1,2,2,0 274 | 67,1,0,120,237,0,1,71,0,1,1,0,2,0 275 | 58,1,0,100,234,0,1,156,0,0.1,2,1,3,0 276 | 47,1,0,110,275,0,0,118,1,1,1,1,2,0 277 | 52,1,0,125,212,0,1,168,0,1,2,2,3,0 278 | 58,1,0,146,218,0,1,105,0,2,1,1,3,0 279 | 57,1,1,124,261,0,1,141,0,0.3,2,0,3,0 280 | 58,0,1,136,319,1,0,152,0,0,2,2,2,0 281 | 61,1,0,138,166,0,0,125,1,3.6,1,1,2,0 282 | 42,1,0,136,315,0,1,125,1,1.8,1,0,1,0 283 | 52,1,0,128,204,1,1,156,1,1,1,0,0,0 284 | 59,1,2,126,218,1,1,134,0,2.2,1,1,1,0 285 | 40,1,0,152,223,0,1,181,0,0,2,0,3,0 286 | 61,1,0,140,207,0,0,138,1,1.9,2,1,3,0 287 | 46,1,0,140,311,0,1,120,1,1.8,1,2,3,0 288 | 59,1,3,134,204,0,1,162,0,0.8,2,2,2,0 289 | 57,1,1,154,232,0,0,164,0,0,2,1,2,0 290 | 57,1,0,110,335,0,1,143,1,3,1,1,3,0 291 | 55,0,0,128,205,0,2,130,1,2,1,1,3,0 292 | 61,1,0,148,203,0,1,161,0,0,2,1,3,0 293 | 58,1,0,114,318,0,2,140,0,4.4,0,3,1,0 294 | 58,0,0,170,225,1,0,146,1,2.8,1,2,1,0 295 | 67,1,2,152,212,0,0,150,0,0.8,1,0,3,0 296 | 44,1,0,120,169,0,1,144,1,2.8,0,0,1,0 297 | 63,1,0,140,187,0,0,144,1,4,2,2,3,0 298 | 63,0,0,124,197,0,1,136,1,0,1,0,2,0 299 | 59,1,0,164,176,1,0,90,0,1,1,2,1,0 300 | 57,0,0,140,241,0,1,123,1,0.2,1,0,3,0 301 | 45,1,3,110,264,0,1,132,0,1.2,1,0,3,0 302 | 68,1,0,144,193,1,1,141,0,3.4,1,2,3,0 303 | 57,1,0,130,131,0,1,115,1,1.2,1,1,3,0 304 | 57,0,1,130,236,0,0,174,0,0,1,1,2,0 305 | -------------------------------------------------------------------------------- /89-90 Day89-90 Loan Predictions with Comparing 3 models/day89-90-loan-predictions.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aswinramesh04/100-Days-of-DataScience/560ae496989e1a1b80cd707c87cd359058594949/89-90 Day89-90 Loan Predictions with Comparing 3 models/day89-90-loan-predictions.pdf -------------------------------------------------------------------------------- /91-92 Day91-92 Drug Classification with various model/day91-92-drug-classification.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aswinramesh04/100-Days-of-DataScience/560ae496989e1a1b80cd707c87cd359058594949/91-92 Day91-92 Drug Classification with various model/day91-92-drug-classification.pdf -------------------------------------------------------------------------------- /91-92 Day91-92 Drug Classification with various model/drug200.csv: -------------------------------------------------------------------------------- 1 | Age,Sex,BP,Cholesterol,Na_to_K,Drug 2 | 23,F,HIGH,HIGH,25.355,DrugY 3 | 47,M,LOW,HIGH,13.093,drugC 4 | 47,M,LOW,HIGH,10.114,drugC 5 | 28,F,NORMAL,HIGH,7.798,drugX 6 | 61,F,LOW,HIGH,18.043,DrugY 7 | 22,F,NORMAL,HIGH,8.607,drugX 8 | 49,F,NORMAL,HIGH,16.275,DrugY 9 | 41,M,LOW,HIGH,11.037,drugC 10 | 60,M,NORMAL,HIGH,15.171,DrugY 11 | 43,M,LOW,NORMAL,19.368,DrugY 12 | 47,F,LOW,HIGH,11.767,drugC 13 | 34,F,HIGH,NORMAL,19.199,DrugY 14 | 43,M,LOW,HIGH,15.376,DrugY 15 | 74,F,LOW,HIGH,20.942,DrugY 16 | 50,F,NORMAL,HIGH,12.703,drugX 17 | 16,F,HIGH,NORMAL,15.516,DrugY 18 | 69,M,LOW,NORMAL,11.455,drugX 19 | 43,M,HIGH,HIGH,13.972,drugA 20 | 23,M,LOW,HIGH,7.298,drugC 21 | 32,F,HIGH,NORMAL,25.974,DrugY 22 | 57,M,LOW,NORMAL,19.128,DrugY 23 | 63,M,NORMAL,HIGH,25.917,DrugY 24 | 47,M,LOW,NORMAL,30.568,DrugY 25 | 48,F,LOW,HIGH,15.036,DrugY 26 | 33,F,LOW,HIGH,33.486,DrugY 27 | 28,F,HIGH,NORMAL,18.809,DrugY 28 | 31,M,HIGH,HIGH,30.366,DrugY 29 | 49,F,NORMAL,NORMAL,9.381,drugX 30 | 39,F,LOW,NORMAL,22.697,DrugY 31 | 45,M,LOW,HIGH,17.951,DrugY 32 | 18,F,NORMAL,NORMAL,8.75,drugX 33 | 74,M,HIGH,HIGH,9.567,drugB 34 | 49,M,LOW,NORMAL,11.014,drugX 35 | 65,F,HIGH,NORMAL,31.876,DrugY 36 | 53,M,NORMAL,HIGH,14.133,drugX 37 | 46,M,NORMAL,NORMAL,7.285,drugX 38 | 32,M,HIGH,NORMAL,9.445,drugA 39 | 39,M,LOW,NORMAL,13.938,drugX 40 | 39,F,NORMAL,NORMAL,9.709,drugX 41 | 15,M,NORMAL,HIGH,9.084,drugX 42 | 73,F,NORMAL,HIGH,19.221,DrugY 43 | 58,F,HIGH,NORMAL,14.239,drugB 44 | 50,M,NORMAL,NORMAL,15.79,DrugY 45 | 23,M,NORMAL,HIGH,12.26,drugX 46 | 50,F,NORMAL,NORMAL,12.295,drugX 47 | 66,F,NORMAL,NORMAL,8.107,drugX 48 | 37,F,HIGH,HIGH,13.091,drugA 49 | 68,M,LOW,HIGH,10.291,drugC 50 | 23,M,NORMAL,HIGH,31.686,DrugY 51 | 28,F,LOW,HIGH,19.796,DrugY 52 | 58,F,HIGH,HIGH,19.416,DrugY 53 | 67,M,NORMAL,NORMAL,10.898,drugX 54 | 62,M,LOW,NORMAL,27.183,DrugY 55 | 24,F,HIGH,NORMAL,18.457,DrugY 56 | 68,F,HIGH,NORMAL,10.189,drugB 57 | 26,F,LOW,HIGH,14.16,drugC 58 | 65,M,HIGH,NORMAL,11.34,drugB 59 | 40,M,HIGH,HIGH,27.826,DrugY 60 | 60,M,NORMAL,NORMAL,10.091,drugX 61 | 34,M,HIGH,HIGH,18.703,DrugY 62 | 38,F,LOW,NORMAL,29.875,DrugY 63 | 24,M,HIGH,NORMAL,9.475,drugA 64 | 67,M,LOW,NORMAL,20.693,DrugY 65 | 45,M,LOW,NORMAL,8.37,drugX 66 | 60,F,HIGH,HIGH,13.303,drugB 67 | 68,F,NORMAL,NORMAL,27.05,DrugY 68 | 29,M,HIGH,HIGH,12.856,drugA 69 | 17,M,NORMAL,NORMAL,10.832,drugX 70 | 54,M,NORMAL,HIGH,24.658,DrugY 71 | 18,F,HIGH,NORMAL,24.276,DrugY 72 | 70,M,HIGH,HIGH,13.967,drugB 73 | 28,F,NORMAL,HIGH,19.675,DrugY 74 | 24,F,NORMAL,HIGH,10.605,drugX 75 | 41,F,NORMAL,NORMAL,22.905,DrugY 76 | 31,M,HIGH,NORMAL,17.069,DrugY 77 | 26,M,LOW,NORMAL,20.909,DrugY 78 | 36,F,HIGH,HIGH,11.198,drugA 79 | 26,F,HIGH,NORMAL,19.161,DrugY 80 | 19,F,HIGH,HIGH,13.313,drugA 81 | 32,F,LOW,NORMAL,10.84,drugX 82 | 60,M,HIGH,HIGH,13.934,drugB 83 | 64,M,NORMAL,HIGH,7.761,drugX 84 | 32,F,LOW,HIGH,9.712,drugC 85 | 38,F,HIGH,NORMAL,11.326,drugA 86 | 47,F,LOW,HIGH,10.067,drugC 87 | 59,M,HIGH,HIGH,13.935,drugB 88 | 51,F,NORMAL,HIGH,13.597,drugX 89 | 69,M,LOW,HIGH,15.478,DrugY 90 | 37,F,HIGH,NORMAL,23.091,DrugY 91 | 50,F,NORMAL,NORMAL,17.211,DrugY 92 | 62,M,NORMAL,HIGH,16.594,DrugY 93 | 41,M,HIGH,NORMAL,15.156,DrugY 94 | 29,F,HIGH,HIGH,29.45,DrugY 95 | 42,F,LOW,NORMAL,29.271,DrugY 96 | 56,M,LOW,HIGH,15.015,DrugY 97 | 36,M,LOW,NORMAL,11.424,drugX 98 | 58,F,LOW,HIGH,38.247,DrugY 99 | 56,F,HIGH,HIGH,25.395,DrugY 100 | 20,M,HIGH,NORMAL,35.639,DrugY 101 | 15,F,HIGH,NORMAL,16.725,DrugY 102 | 31,M,HIGH,NORMAL,11.871,drugA 103 | 45,F,HIGH,HIGH,12.854,drugA 104 | 28,F,LOW,HIGH,13.127,drugC 105 | 56,M,NORMAL,HIGH,8.966,drugX 106 | 22,M,HIGH,NORMAL,28.294,DrugY 107 | 37,M,LOW,NORMAL,8.968,drugX 108 | 22,M,NORMAL,HIGH,11.953,drugX 109 | 42,M,LOW,HIGH,20.013,DrugY 110 | 72,M,HIGH,NORMAL,9.677,drugB 111 | 23,M,NORMAL,HIGH,16.85,DrugY 112 | 50,M,HIGH,HIGH,7.49,drugA 113 | 47,F,NORMAL,NORMAL,6.683,drugX 114 | 35,M,LOW,NORMAL,9.17,drugX 115 | 65,F,LOW,NORMAL,13.769,drugX 116 | 20,F,NORMAL,NORMAL,9.281,drugX 117 | 51,M,HIGH,HIGH,18.295,DrugY 118 | 67,M,NORMAL,NORMAL,9.514,drugX 119 | 40,F,NORMAL,HIGH,10.103,drugX 120 | 32,F,HIGH,NORMAL,10.292,drugA 121 | 61,F,HIGH,HIGH,25.475,DrugY 122 | 28,M,NORMAL,HIGH,27.064,DrugY 123 | 15,M,HIGH,NORMAL,17.206,DrugY 124 | 34,M,NORMAL,HIGH,22.456,DrugY 125 | 36,F,NORMAL,HIGH,16.753,DrugY 126 | 53,F,HIGH,NORMAL,12.495,drugB 127 | 19,F,HIGH,NORMAL,25.969,DrugY 128 | 66,M,HIGH,HIGH,16.347,DrugY 129 | 35,M,NORMAL,NORMAL,7.845,drugX 130 | 47,M,LOW,NORMAL,33.542,DrugY 131 | 32,F,NORMAL,HIGH,7.477,drugX 132 | 70,F,NORMAL,HIGH,20.489,DrugY 133 | 52,M,LOW,NORMAL,32.922,DrugY 134 | 49,M,LOW,NORMAL,13.598,drugX 135 | 24,M,NORMAL,HIGH,25.786,DrugY 136 | 42,F,HIGH,HIGH,21.036,DrugY 137 | 74,M,LOW,NORMAL,11.939,drugX 138 | 55,F,HIGH,HIGH,10.977,drugB 139 | 35,F,HIGH,HIGH,12.894,drugA 140 | 51,M,HIGH,NORMAL,11.343,drugB 141 | 69,F,NORMAL,HIGH,10.065,drugX 142 | 49,M,HIGH,NORMAL,6.269,drugA 143 | 64,F,LOW,NORMAL,25.741,DrugY 144 | 60,M,HIGH,NORMAL,8.621,drugB 145 | 74,M,HIGH,NORMAL,15.436,DrugY 146 | 39,M,HIGH,HIGH,9.664,drugA 147 | 61,M,NORMAL,HIGH,9.443,drugX 148 | 37,F,LOW,NORMAL,12.006,drugX 149 | 26,F,HIGH,NORMAL,12.307,drugA 150 | 61,F,LOW,NORMAL,7.34,drugX 151 | 22,M,LOW,HIGH,8.151,drugC 152 | 49,M,HIGH,NORMAL,8.7,drugA 153 | 68,M,HIGH,HIGH,11.009,drugB 154 | 55,M,NORMAL,NORMAL,7.261,drugX 155 | 72,F,LOW,NORMAL,14.642,drugX 156 | 37,M,LOW,NORMAL,16.724,DrugY 157 | 49,M,LOW,HIGH,10.537,drugC 158 | 31,M,HIGH,NORMAL,11.227,drugA 159 | 53,M,LOW,HIGH,22.963,DrugY 160 | 59,F,LOW,HIGH,10.444,drugC 161 | 34,F,LOW,NORMAL,12.923,drugX 162 | 30,F,NORMAL,HIGH,10.443,drugX 163 | 57,F,HIGH,NORMAL,9.945,drugB 164 | 43,M,NORMAL,NORMAL,12.859,drugX 165 | 21,F,HIGH,NORMAL,28.632,DrugY 166 | 16,M,HIGH,NORMAL,19.007,DrugY 167 | 38,M,LOW,HIGH,18.295,DrugY 168 | 58,F,LOW,HIGH,26.645,DrugY 169 | 57,F,NORMAL,HIGH,14.216,drugX 170 | 51,F,LOW,NORMAL,23.003,DrugY 171 | 20,F,HIGH,HIGH,11.262,drugA 172 | 28,F,NORMAL,HIGH,12.879,drugX 173 | 45,M,LOW,NORMAL,10.017,drugX 174 | 39,F,NORMAL,NORMAL,17.225,DrugY 175 | 41,F,LOW,NORMAL,18.739,DrugY 176 | 42,M,HIGH,NORMAL,12.766,drugA 177 | 73,F,HIGH,HIGH,18.348,DrugY 178 | 48,M,HIGH,NORMAL,10.446,drugA 179 | 25,M,NORMAL,HIGH,19.011,DrugY 180 | 39,M,NORMAL,HIGH,15.969,DrugY 181 | 67,F,NORMAL,HIGH,15.891,DrugY 182 | 22,F,HIGH,NORMAL,22.818,DrugY 183 | 59,F,NORMAL,HIGH,13.884,drugX 184 | 20,F,LOW,NORMAL,11.686,drugX 185 | 36,F,HIGH,NORMAL,15.49,DrugY 186 | 18,F,HIGH,HIGH,37.188,DrugY 187 | 57,F,NORMAL,NORMAL,25.893,DrugY 188 | 70,M,HIGH,HIGH,9.849,drugB 189 | 47,M,HIGH,HIGH,10.403,drugA 190 | 65,M,HIGH,NORMAL,34.997,DrugY 191 | 64,M,HIGH,NORMAL,20.932,DrugY 192 | 58,M,HIGH,HIGH,18.991,DrugY 193 | 23,M,HIGH,HIGH,8.011,drugA 194 | 72,M,LOW,HIGH,16.31,DrugY 195 | 72,M,LOW,HIGH,6.769,drugC 196 | 46,F,HIGH,HIGH,34.686,DrugY 197 | 56,F,LOW,HIGH,11.567,drugC 198 | 16,M,LOW,HIGH,12.006,drugC 199 | 52,M,NORMAL,HIGH,9.894,drugX 200 | 23,M,NORMAL,NORMAL,14.02,drugX 201 | 40,F,LOW,NORMAL,11.349,drugX 202 | -------------------------------------------------------------------------------- /93-94 Day93-94 Diabetes Prediction with various model/day93-94-diabetes-prediction.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aswinramesh04/100-Days-of-DataScience/560ae496989e1a1b80cd707c87cd359058594949/93-94 Day93-94 Diabetes Prediction with various model/day93-94-diabetes-prediction.pdf -------------------------------------------------------------------------------- /95-96 Day95-96 Mall Customer Segmentation/Mall_Customers.csv: -------------------------------------------------------------------------------- 1 | CustomerID,Genre,Age,Annual Income (k$),Spending Score (1-100) 2 | 0001,Male,19,15,39 3 | 0002,Male,21,15,81 4 | 0003,Female,20,16,6 5 | 0004,Female,23,16,77 6 | 0005,Female,31,17,40 7 | 0006,Female,22,17,76 8 | 0007,Female,35,18,6 9 | 0008,Female,23,18,94 10 | 0009,Male,64,19,3 11 | 0010,Female,30,19,72 12 | 0011,Male,67,19,14 13 | 0012,Female,35,19,99 14 | 0013,Female,58,20,15 15 | 0014,Female,24,20,77 16 | 0015,Male,37,20,13 17 | 0016,Male,22,20,79 18 | 0017,Female,35,21,35 19 | 0018,Male,20,21,66 20 | 0019,Male,52,23,29 21 | 0020,Female,35,23,98 22 | 0021,Male,35,24,35 23 | 0022,Male,25,24,73 24 | 0023,Female,46,25,5 25 | 0024,Male,31,25,73 26 | 0025,Female,54,28,14 27 | 0026,Male,29,28,82 28 | 0027,Female,45,28,32 29 | 0028,Male,35,28,61 30 | 0029,Female,40,29,31 31 | 0030,Female,23,29,87 32 | 0031,Male,60,30,4 33 | 0032,Female,21,30,73 34 | 0033,Male,53,33,4 35 | 0034,Male,18,33,92 36 | 0035,Female,49,33,14 37 | 0036,Female,21,33,81 38 | 0037,Female,42,34,17 39 | 0038,Female,30,34,73 40 | 0039,Female,36,37,26 41 | 0040,Female,20,37,75 42 | 0041,Female,65,38,35 43 | 0042,Male,24,38,92 44 | 0043,Male,48,39,36 45 | 0044,Female,31,39,61 46 | 0045,Female,49,39,28 47 | 0046,Female,24,39,65 48 | 0047,Female,50,40,55 49 | 0048,Female,27,40,47 50 | 0049,Female,29,40,42 51 | 0050,Female,31,40,42 52 | 0051,Female,49,42,52 53 | 0052,Male,33,42,60 54 | 0053,Female,31,43,54 55 | 0054,Male,59,43,60 56 | 0055,Female,50,43,45 57 | 0056,Male,47,43,41 58 | 0057,Female,51,44,50 59 | 0058,Male,69,44,46 60 | 0059,Female,27,46,51 61 | 0060,Male,53,46,46 62 | 0061,Male,70,46,56 63 | 0062,Male,19,46,55 64 | 0063,Female,67,47,52 65 | 0064,Female,54,47,59 66 | 0065,Male,63,48,51 67 | 0066,Male,18,48,59 68 | 0067,Female,43,48,50 69 | 0068,Female,68,48,48 70 | 0069,Male,19,48,59 71 | 0070,Female,32,48,47 72 | 0071,Male,70,49,55 73 | 0072,Female,47,49,42 74 | 0073,Female,60,50,49 75 | 0074,Female,60,50,56 76 | 0075,Male,59,54,47 77 | 0076,Male,26,54,54 78 | 0077,Female,45,54,53 79 | 0078,Male,40,54,48 80 | 0079,Female,23,54,52 81 | 0080,Female,49,54,42 82 | 0081,Male,57,54,51 83 | 0082,Male,38,54,55 84 | 0083,Male,67,54,41 85 | 0084,Female,46,54,44 86 | 0085,Female,21,54,57 87 | 0086,Male,48,54,46 88 | 0087,Female,55,57,58 89 | 0088,Female,22,57,55 90 | 0089,Female,34,58,60 91 | 0090,Female,50,58,46 92 | 0091,Female,68,59,55 93 | 0092,Male,18,59,41 94 | 0093,Male,48,60,49 95 | 0094,Female,40,60,40 96 | 0095,Female,32,60,42 97 | 0096,Male,24,60,52 98 | 0097,Female,47,60,47 99 | 0098,Female,27,60,50 100 | 0099,Male,48,61,42 101 | 0100,Male,20,61,49 102 | 0101,Female,23,62,41 103 | 0102,Female,49,62,48 104 | 0103,Male,67,62,59 105 | 0104,Male,26,62,55 106 | 0105,Male,49,62,56 107 | 0106,Female,21,62,42 108 | 0107,Female,66,63,50 109 | 0108,Male,54,63,46 110 | 0109,Male,68,63,43 111 | 0110,Male,66,63,48 112 | 0111,Male,65,63,52 113 | 0112,Female,19,63,54 114 | 0113,Female,38,64,42 115 | 0114,Male,19,64,46 116 | 0115,Female,18,65,48 117 | 0116,Female,19,65,50 118 | 0117,Female,63,65,43 119 | 0118,Female,49,65,59 120 | 0119,Female,51,67,43 121 | 0120,Female,50,67,57 122 | 0121,Male,27,67,56 123 | 0122,Female,38,67,40 124 | 0123,Female,40,69,58 125 | 0124,Male,39,69,91 126 | 0125,Female,23,70,29 127 | 0126,Female,31,70,77 128 | 0127,Male,43,71,35 129 | 0128,Male,40,71,95 130 | 0129,Male,59,71,11 131 | 0130,Male,38,71,75 132 | 0131,Male,47,71,9 133 | 0132,Male,39,71,75 134 | 0133,Female,25,72,34 135 | 0134,Female,31,72,71 136 | 0135,Male,20,73,5 137 | 0136,Female,29,73,88 138 | 0137,Female,44,73,7 139 | 0138,Male,32,73,73 140 | 0139,Male,19,74,10 141 | 0140,Female,35,74,72 142 | 0141,Female,57,75,5 143 | 0142,Male,32,75,93 144 | 0143,Female,28,76,40 145 | 0144,Female,32,76,87 146 | 0145,Male,25,77,12 147 | 0146,Male,28,77,97 148 | 0147,Male,48,77,36 149 | 0148,Female,32,77,74 150 | 0149,Female,34,78,22 151 | 0150,Male,34,78,90 152 | 0151,Male,43,78,17 153 | 0152,Male,39,78,88 154 | 0153,Female,44,78,20 155 | 0154,Female,38,78,76 156 | 0155,Female,47,78,16 157 | 0156,Female,27,78,89 158 | 0157,Male,37,78,1 159 | 0158,Female,30,78,78 160 | 0159,Male,34,78,1 161 | 0160,Female,30,78,73 162 | 0161,Female,56,79,35 163 | 0162,Female,29,79,83 164 | 0163,Male,19,81,5 165 | 0164,Female,31,81,93 166 | 0165,Male,50,85,26 167 | 0166,Female,36,85,75 168 | 0167,Male,42,86,20 169 | 0168,Female,33,86,95 170 | 0169,Female,36,87,27 171 | 0170,Male,32,87,63 172 | 0171,Male,40,87,13 173 | 0172,Male,28,87,75 174 | 0173,Male,36,87,10 175 | 0174,Male,36,87,92 176 | 0175,Female,52,88,13 177 | 0176,Female,30,88,86 178 | 0177,Male,58,88,15 179 | 0178,Male,27,88,69 180 | 0179,Male,59,93,14 181 | 0180,Male,35,93,90 182 | 0181,Female,37,97,32 183 | 0182,Female,32,97,86 184 | 0183,Male,46,98,15 185 | 0184,Female,29,98,88 186 | 0185,Female,41,99,39 187 | 0186,Male,30,99,97 188 | 0187,Female,54,101,24 189 | 0188,Male,28,101,68 190 | 0189,Female,41,103,17 191 | 0190,Female,36,103,85 192 | 0191,Female,34,103,23 193 | 0192,Female,32,103,69 194 | 0193,Male,33,113,8 195 | 0194,Female,38,113,91 196 | 0195,Female,47,120,16 197 | 0196,Female,35,120,79 198 | 0197,Female,45,126,28 199 | 0198,Male,32,126,74 200 | 0199,Male,32,137,18 201 | 0200,Male,30,137,83 -------------------------------------------------------------------------------- /95-96 Day95-96 Mall Customer Segmentation/mall-customer-segmentation.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aswinramesh04/100-Days-of-DataScience/560ae496989e1a1b80cd707c87cd359058594949/95-96 Day95-96 Mall Customer Segmentation/mall-customer-segmentation.pdf -------------------------------------------------------------------------------- /97-98 Day97-98 Flight Price Prediction using ML model/Data_Train.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aswinramesh04/100-Days-of-DataScience/560ae496989e1a1b80cd707c87cd359058594949/97-98 Day97-98 Flight Price Prediction using ML model/Data_Train.xlsx -------------------------------------------------------------------------------- /97-98 Day97-98 Flight Price Prediction using ML model/flight-price-predictions.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aswinramesh04/100-Days-of-DataScience/560ae496989e1a1b80cd707c87cd359058594949/97-98 Day97-98 Flight Price Prediction using ML model/flight-price-predictions.pdf -------------------------------------------------------------------------------- /99-100 Day99-100 Car Evaluation Model/car-evaluation-model.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aswinramesh04/100-Days-of-DataScience/560ae496989e1a1b80cd707c87cd359058594949/99-100 Day99-100 Car Evaluation Model/car-evaluation-model.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 100-DaysOfCode-DataScience --------------------------------------------------------------------------------