├── #100daysofmlcodeday - 3© harshit ahluwalia.png ├── 1_xc5CSmK9d8oeKYxKxenEGg.gif ├── AI vs ML.jpg ├── Beginner's Data Science Learning Plean for 2019 (1).png ├── Cheat Sheet.png ├── DAY 1-Working with Pandas.ipynb ├── Data Analysis Workflow.jpg ├── Data Science Resources.jpg ├── Data explorationin python using.png ├── Day 2 Simple Linear Regression.ipynb ├── Day 2 Simple Linear Regression.png ├── Day 2 images ├── image1.PNG ├── image2.PNG ├── image3.PNG ├── image4.PNG ├── image5.PNG ├── image6.PNG └── image7.PNG ├── Day 2- image1.PNG ├── Day-5_K-Means_Clustering.png ├── How to Learn Machine Learning.jpg ├── How to learn machine learning in 30 days.jpg ├── LICENSE ├── Linear Regression Notes ├── Linear Regression Note - 1.jpg ├── Linear Regression Note - 2.jpg └── Linear Regression Note - 3.jpg ├── Logistics Regression Notes ├── Logistics Regression Note - 1.jpg ├── Logistics Regression Note - 2.jpg ├── Logistics Regression Note - 3.jpg ├── Logistics Regression Note - 4.jpg ├── Logistics Regression Note - 5.jpg ├── Logistics Regression Note - 6.jpg └── Logistics Regression Note - 7.jpg ├── Machine Learning Strategy.jpg ├── README.md ├── Top_Algorithms_for_Predications.png ├── Week 1 Quick Guide to learn Python for Data Science.jpg ├── Youtube first Video Infographic.jpg ├── collage.jpg ├── test.csv └── train.csv /#100daysofmlcodeday - 3© harshit ahluwalia.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/harshitahluwalia7895/100DaysOfMLCode/758affbb64ae77458a193b70f64d590946a2a23f/#100daysofmlcodeday - 3© harshit ahluwalia.png -------------------------------------------------------------------------------- /1_xc5CSmK9d8oeKYxKxenEGg.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/harshitahluwalia7895/100DaysOfMLCode/758affbb64ae77458a193b70f64d590946a2a23f/1_xc5CSmK9d8oeKYxKxenEGg.gif -------------------------------------------------------------------------------- /AI vs ML.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/harshitahluwalia7895/100DaysOfMLCode/758affbb64ae77458a193b70f64d590946a2a23f/AI vs ML.jpg -------------------------------------------------------------------------------- /Beginner's Data Science Learning Plean for 2019 (1).png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/harshitahluwalia7895/100DaysOfMLCode/758affbb64ae77458a193b70f64d590946a2a23f/Beginner's Data Science Learning Plean for 2019 (1).png -------------------------------------------------------------------------------- /Cheat Sheet.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/harshitahluwalia7895/100DaysOfMLCode/758affbb64ae77458a193b70f64d590946a2a23f/Cheat Sheet.png -------------------------------------------------------------------------------- /Data Analysis Workflow.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/harshitahluwalia7895/100DaysOfMLCode/758affbb64ae77458a193b70f64d590946a2a23f/Data Analysis Workflow.jpg -------------------------------------------------------------------------------- /Data Science Resources.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/harshitahluwalia7895/100DaysOfMLCode/758affbb64ae77458a193b70f64d590946a2a23f/Data Science Resources.jpg -------------------------------------------------------------------------------- /Data explorationin python using.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/harshitahluwalia7895/100DaysOfMLCode/758affbb64ae77458a193b70f64d590946a2a23f/Data explorationin python using.png -------------------------------------------------------------------------------- /Day 2 Simple Linear Regression.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Brief Understanding About SImple Linear Regression\n", 8 | "![image1.PNG](https://image.ibb.co/d7xVdz/1_xc5_CSm_K9d8oe_KYx_Kxen_EGg.gif)" 9 | ] 10 | }, 11 | { 12 | "cell_type": "markdown", 13 | "metadata": {}, 14 | "source": [ 15 | "# Understanding the concept of simple linear regression\n", 16 | "In simple words linear regression is predicting the value of a variable Y(dependent variable) based on some variable X (independent variable) provided there is a linear relationship between X and Y.\n", 17 | "

\n", 18 | "This linear relationship between the 2 variables can be represented by a straight line (called **regression line**)." 19 | ] 20 | }, 21 | { 22 | "attachments": {}, 23 | "cell_type": "markdown", 24 | "metadata": {}, 25 | "source": [ 26 | "![image1.PNG](https://image.ibb.co/kgFu5e/Day_2_image1.png)" 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "metadata": {}, 32 | "source": [ 33 | "Now to determine if there is a linear relationship between 2 variables we can simply plot a scatter plot of variable Y with variable X .If the plotted points are randomly scattered that it can be inferred that the variables are not related." 34 | ] 35 | }, 36 | { 37 | "cell_type": "markdown", 38 | "metadata": {}, 39 | "source": [ 40 | "![image1.PNG](https://image.ibb.co/deBqdz/image2.png)" 41 | ] 42 | }, 43 | { 44 | "cell_type": "markdown", 45 | "metadata": {}, 46 | "source": [ 47 | "![image1.PNG](https://image.ibb.co/jUznQe/image3.png)" 48 | ] 49 | }, 50 | { 51 | "cell_type": "markdown", 52 | "metadata": {}, 53 | "source": [ 54 | "When regression line is drawn some points will lie on the regression line other points will lie in the close vicinity of it. This is because our regression line is a **probabilistic model** and our prediction is approximate. So there will be some errors/deviations from actual/observed value of variable Y.\n", 55 | "![image1.PNG](https://image.ibb.co/hBskBK/image4.png)" 56 | ] 57 | }, 58 | { 59 | "cell_type": "markdown", 60 | "metadata": {}, 61 | "source": [ 62 | "But when the linear relationship exist between X and Y we can plot more than one line through these points. Now how do we know which one is the **best fit**?" 63 | ] 64 | }, 65 | { 66 | "cell_type": "markdown", 67 | "metadata": {}, 68 | "source": [ 69 | "![image1.PNG](https://image.ibb.co/b1Gqdz/image5.png)" 70 | ] 71 | }, 72 | { 73 | "cell_type": "markdown", 74 | "metadata": {}, 75 | "source": [ 76 | "To help us choose the best line we use the concept of “least squares”.\n", 77 | "
\n", 78 | "**Least Squares**
\n", 79 | "Y=b0 + b1X + e
\n", 80 | "
\n", 81 | "This the mathematical representation for the regression line where\n", 82 | "

\n", 83 | "Y-Dependant variable.\n", 84 | "

\n", 85 | "X-Independent variable.\n", 86 | "

\n", 87 | "b0 –intercept of the regression line.\n", 88 | "

\n", 89 | "b1-slope of the regression line.\n", 90 | "

\n", 91 | "e- error/deviation from actual/observed value of variable Y.\n", 92 | "

\n", 93 | "Suppose we fit n points of the form (x1,y1) ,(x2,y2)…..(xn,yn)to the above regression line then\n", 94 | "

\n", 95 | "![image1.PNG](https://image.ibb.co/gipMke/equation1.png)\n", 96 | "\n", 97 | "
\n", 98 | "
\n", 99 | "Where ei is the difference between ith observed response value and the ith response value that is predicted by our regression line\n", 100 | "
\n" 101 | ] 102 | }, 103 | { 104 | "cell_type": "markdown", 105 | "metadata": {}, 106 | "source": [ 107 | "Our aim here is to minimize this error so that we can get the best possible regression line.\n", 108 | "\n", 109 | "Now this error ei can be positive or negative but we are only interested in the magnitude of the error and not in its sign. Hence we square the errors and minimize the sum of **squared errors(SSE)." 110 | ] 111 | }, 112 | { 113 | "cell_type": "markdown", 114 | "metadata": {}, 115 | "source": [ 116 | "![image1.PNG](https://image.ibb.co/bMFu5e/equation2.png)\n" 117 | ] 118 | }, 119 | { 120 | "cell_type": "markdown", 121 | "metadata": {}, 122 | "source": [ 123 | "![image1.PNG](https://image.ibb.co/esnJWK/image6.png)\n" 124 | ] 125 | }, 126 | { 127 | "cell_type": "markdown", 128 | "metadata": {}, 129 | "source": [ 130 | "(In the above graph the green line is the best fit.)\n", 131 | "\n", 132 | "## How do we minimize the sum of squared errors(SSE)?\n", 133 | "Remember that b1 and b0 are still unknown to us.\n", 134 | "\n", 135 | "In the least square approach we minimize sum of squared errors(SSE) by choosing the value of b1 and b0 to be (not diving into math of it)" 136 | ] 137 | }, 138 | { 139 | "cell_type": "markdown", 140 | "metadata": {}, 141 | "source": [ 142 | "![image1.PNG](https://image.ibb.co/nNpAdz/image7.png)\n" 143 | ] 144 | }, 145 | { 146 | "cell_type": "markdown", 147 | "metadata": {}, 148 | "source": [ 149 | "# lets practice some code " 150 | ] 151 | }, 152 | { 153 | "cell_type": "code", 154 | "execution_count": 1, 155 | "metadata": {}, 156 | "outputs": [], 157 | "source": [ 158 | "# Importing important libraries\n", 159 | "import numpy as np\n", 160 | "import pandas as pd\n", 161 | "import matplotlib.pyplot as plt" 162 | ] 163 | }, 164 | { 165 | "cell_type": "code", 166 | "execution_count": 2, 167 | "metadata": {}, 168 | "outputs": [], 169 | "source": [ 170 | "#importing the training and test set\n", 171 | "training=pd.read_csv('train.csv')\n", 172 | "testing=pd.read_csv('test.csv')" 173 | ] 174 | }, 175 | { 176 | "cell_type": "code", 177 | "execution_count": 4, 178 | "metadata": {}, 179 | "outputs": [ 180 | { 181 | "data": { 182 | "text/html": [ 183 | "
\n", 184 | "\n", 197 | "\n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | "
xy
024.021.549452
150.047.464463
215.017.218656
338.036.586398
487.087.288984
\n", 233 | "
" 234 | ], 235 | "text/plain": [ 236 | " x y\n", 237 | "0 24.0 21.549452\n", 238 | "1 50.0 47.464463\n", 239 | "2 15.0 17.218656\n", 240 | "3 38.0 36.586398\n", 241 | "4 87.0 87.288984" 242 | ] 243 | }, 244 | "execution_count": 4, 245 | "metadata": {}, 246 | "output_type": "execute_result" 247 | } 248 | ], 249 | "source": [ 250 | "training.head()" 251 | ] 252 | }, 253 | { 254 | "cell_type": "code", 255 | "execution_count": 5, 256 | "metadata": {}, 257 | "outputs": [ 258 | { 259 | "data": { 260 | "text/html": [ 261 | "
\n", 262 | "\n", 275 | "\n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | " \n", 291 | " \n", 292 | " \n", 293 | " \n", 294 | " \n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | " \n", 305 | " \n", 306 | " \n", 307 | " \n", 308 | " \n", 309 | " \n", 310 | "
xy
07779.775152
12123.177279
22225.609262
32017.857388
43641.849864
\n", 311 | "
" 312 | ], 313 | "text/plain": [ 314 | " x y\n", 315 | "0 77 79.775152\n", 316 | "1 21 23.177279\n", 317 | "2 22 25.609262\n", 318 | "3 20 17.857388\n", 319 | "4 36 41.849864" 320 | ] 321 | }, 322 | "execution_count": 5, 323 | "metadata": {}, 324 | "output_type": "execute_result" 325 | } 326 | ], 327 | "source": [ 328 | "testing.head()" 329 | ] 330 | }, 331 | { 332 | "cell_type": "code", 333 | "execution_count": 6, 334 | "metadata": {}, 335 | "outputs": [], 336 | "source": [ 337 | "training.drop(training.index[[213]], inplace=True)" 338 | ] 339 | }, 340 | { 341 | "cell_type": "code", 342 | "execution_count": 7, 343 | "metadata": {}, 344 | "outputs": [], 345 | "source": [ 346 | "# Importing thhe Imputer class used to impute the missing values\n", 347 | "from sklearn.preprocessing import Imputer\n", 348 | "imputer1=Imputer(missing_values='NaN', strategy='mean',axis=0)\n", 349 | "imputer1=imputer1.fit(training)\n", 350 | "training=imputer1.transform(training)" 351 | ] 352 | }, 353 | { 354 | "cell_type": "code", 355 | "execution_count": 8, 356 | "metadata": {}, 357 | "outputs": [], 358 | "source": [ 359 | "# Train Test and Split\n", 360 | "x_train=training[:,:-1]\n", 361 | "x_test=testing.iloc[:,:-1].values\n", 362 | "y_train=training[:,1]\n", 363 | "y_test=testing.iloc[:,1].values" 364 | ] 365 | }, 366 | { 367 | "cell_type": "code", 368 | "execution_count": 9, 369 | "metadata": {}, 370 | "outputs": [ 371 | { 372 | "data": { 373 | "text/plain": [ 374 | "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" 375 | ] 376 | }, 377 | "execution_count": 9, 378 | "metadata": {}, 379 | "output_type": "execute_result" 380 | } 381 | ], 382 | "source": [ 383 | "#Training the model\n", 384 | "from sklearn.linear_model import LinearRegression\n", 385 | "regressor=LinearRegression()\n", 386 | "regressor.fit(x_train,y_train)" 387 | ] 388 | }, 389 | { 390 | "cell_type": "code", 391 | "execution_count": 10, 392 | "metadata": {}, 393 | "outputs": [ 394 | { 395 | "data": { 396 | "image/png": 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\n", 397 | "text/plain": [ 398 | "
" 399 | ] 400 | }, 401 | "metadata": {}, 402 | "output_type": "display_data" 403 | } 404 | ], 405 | "source": [ 406 | "# PLotting the training set\n", 407 | "plt.scatter(x_train,y_train, color='red')\n", 408 | "plt.plot(x_train,regressor.predict(x_train),color='blue')\n", 409 | "plt.title('(Trainig set)')\n", 410 | "plt.xlabel('X_Plane')\n", 411 | "plt.ylabel('Y_Plane')\n", 412 | "plt.show()" 413 | ] 414 | }, 415 | { 416 | "cell_type": "code", 417 | "execution_count": 11, 418 | "metadata": {}, 419 | "outputs": [ 420 | { 421 | "data": { 422 | "image/png": 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\n", 423 | "text/plain": [ 424 | "
" 425 | ] 426 | }, 427 | "metadata": {}, 428 | "output_type": "display_data" 429 | } 430 | ], 431 | "source": [ 432 | "# Plotting the test set\n", 433 | "\n", 434 | "plt.scatter(x_test,y_test, color='red')\n", 435 | "plt.plot(x_train,regressor.predict(x_train),color='blue')\n", 436 | "plt.title('(Test set)')\n", 437 | "plt.xlabel('X_Plane')\n", 438 | "plt.ylabel('Y_Plane')\n", 439 | "plt.show()" 440 | ] 441 | }, 442 | { 443 | "cell_type": "code", 444 | "execution_count": null, 445 | "metadata": {}, 446 | "outputs": [], 447 | "source": [] 448 | } 449 | ], 450 | "metadata": { 451 | "kernelspec": { 452 | "display_name": "Python 3", 453 | "language": "python", 454 | "name": "python3" 455 | }, 456 | "language_info": { 457 | "codemirror_mode": { 458 | "name": "ipython", 459 | "version": 3 460 | }, 461 | "file_extension": ".py", 462 | "mimetype": "text/x-python", 463 | "name": "python", 464 | "nbconvert_exporter": "python", 465 | "pygments_lexer": "ipython3", 466 | "version": "3.6.5" 467 | } 468 | }, 469 | "nbformat": 4, 470 | "nbformat_minor": 2 471 | } 472 | -------------------------------------------------------------------------------- /Day 2 Simple Linear Regression.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/harshitahluwalia7895/100DaysOfMLCode/758affbb64ae77458a193b70f64d590946a2a23f/Day 2 Simple Linear Regression.png -------------------------------------------------------------------------------- /Day 2 images/image1.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/harshitahluwalia7895/100DaysOfMLCode/758affbb64ae77458a193b70f64d590946a2a23f/Day 2 images/image1.PNG -------------------------------------------------------------------------------- /Day 2 images/image2.PNG: -------------------------------------------------------------------------------- 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shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /Linear Regression Notes/Linear Regression Note - 1.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/harshitahluwalia7895/100DaysOfMLCode/758affbb64ae77458a193b70f64d590946a2a23f/Linear Regression Notes/Linear Regression Note - 1.jpg -------------------------------------------------------------------------------- /Linear Regression Notes/Linear Regression Note - 2.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/harshitahluwalia7895/100DaysOfMLCode/758affbb64ae77458a193b70f64d590946a2a23f/Linear Regression Notes/Linear Regression Note - 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I will post the Schedule sooon that on which week you have to study which topic 10 | 11 | # How to Learn Machine Learning 12 | While i don't want to overstate the complexity of the field, 30 days is awfully short. 13 | 14 | ![alt text](https://github.com/harshitahluwalia7895/100DaysOfMLCode/blob/master/How%20to%20learn%20machine%20learning%20in%2030%20days.jpg) 15 | 16 | * Spend most of my time on the basics of **statistics** 17 | * Then have a look at 1 or 2 very common techniques. (e.g., linear regression and logistic regression) 18 | * Take a dataset that interests you and do some descriptive statistics on it (counts, max, min, median, plots etc) and discover as many weird things in the data as possible. Weird meaning stuff that does not seem right. 19 | * Now try to answer a question for yourself on the above dataset. Do this by (A) solving the weird stuff, (B) getting the data into a format that works for (C) one of the common techniques you studied. It is okay if you hack the code together with lots of **googling**. (do sanity checks on your results though ;) 20 | 21 | **Basic Statistics** 22 |
One of the most easy pitfalls is to just take off-the-shelf implementations of algorithms and throw them against your problem. But most algorithms are based on assumptions and all of them have some limitations. A good grasp of basic statistics will help you: 23 | * Determine whether the assumptions hold. 24 | * What they mean for your choice of algorithm 25 | * Reason about the limitations they imply 26 | * The impact if they are not present (which is not always dramatic) 27 | * Any time spent here will pay dividends every time you have a look at a new algorithm. So no worries if this takes up nearly all your time. 28 | 29 | **Common Techniques** 30 | * Early on, you actually better go deep than broad, because many concepts/elements return any way in other algorithms. 31 | * I mention two types of regressions because in many cases, you'll get a decent answer with these techniques. Also, it is in some sense amazing how something that is basically 'draw trendline' in Excel actually goes so deep. Not that all of it is taken that heavily into account in practice, but it still is good to have it in the back of your head. Especially for those times where you get weird results. 32 | 33 | **Weird Data Stuff** 34 | * This is the largest timesink, always. And it is very important, hence the mantra 'garbage in, garbage out'. Take any real-world dataset which has not been pre-cleaned and you'll find weird things:
35 | * A hugely overrepresented value (companies who like to code missing as 999...)
36 | Duplicate ID's 37 | * A variable which is actually an ID (amazing how many student dreams are shattered by pointing this one out if they have a nearly perfect model ;)) 38 | * Missing values 39 | * Mislabeled cases, misspellings... 40 | * Everything is on state level, except for this one state for which they are reporting counties instead. 41 | * You need to experience it to acknowledge it. And almost any real-world dataset + a critical eye will make you do just that. ;) 42 | 43 | **Try It** 44 | * Well, you didn't learn all this not to use it, right? Also, making sense of your results is important. And being critical for them as well. It is so easy to make a logical mistake which is not programming mistake. I.e., the software will run, but the result will be very wrong. 45 | 46 | * If you want to go all the way, take your results to a friend/family and try to explain high-level what you did, what the results are and what they mean. Again speaking from teaching experience, there are people who are really good at the technical stuff, but cannot transfer the relevant implications of it to a non-technical person. 47 |
48 |
49 | 50 | 51 | 52 | ##### So You want to Learn Machine Learning in 30 Days . you need to Devote About ML & work hard ,in Machine Learning There are Various Concepts are there . 53 | 54 | Better to take any Online Course. then note down all course content and Prepare Schedule for 30 Days . i will Suggest you Best Online Machine Learning Course. 55 | 56 | [Machine Learning by Standford University, Mentor - Andrew Ng](https://www.coursera.org/learn/machine-learning?ranMID=40328&ranEAID=QhjctqYUCD0&ranSiteID=QhjctqYUCD0-DfXMi.hmANw62KhzaGwemA&siteID=QhjctqYUCD0-DfXMi.hmANw62KhzaGwemA&utm_content=10&utm_medium=partners&utm_source=linkshare&utm_campaign=QhjctqYUCD0) 57 | 58 | #### Topics include: 59 |
60 | (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). 61 |
62 | (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). 63 |
64 | (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). 65 |
66 | The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas. 67 |
68 |
69 | 70 | [Machine Learning A-Z™: Hands-On Python & R In Data Science](https://www.udemy.com/machinelearning/?ranMID=39197&ranEAID=QhjctqYUCD0&ranSiteID=QhjctqYUCD0-mecwt8rl2iQ4lG6NQ9dD8w&siteID=QhjctqYUCD0-mecwt8rl2iQ4lG6NQ9dD8w&LSNPUBID=QhjctqYUCD0) 71 | 72 | This course is fun and exciting, but at the same time we dive deep into Machine Learning. It is structured the following way: 73 | 74 | * Part 1 - Data Preprocessing 75 | * Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression 76 | * Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification 77 | * Part 4 - Clustering: K-Means, Hierarchical Clustering 78 | * Part 5 - Association Rule Learning: Apriori, Eclat 79 | * Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling 80 | * Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP 81 | * Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks 82 | * Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA 83 | * Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost. 84 | Moreover, the course is packed with practical exercises which are based on live examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. 85 | 86 | # Beginner Data Science
87 | 88 | ![alt text](https://github.com/harshitahluwalia7895/100DaysOfMLCode/blob/master/Beginner's%20Data%20Science%20Learning%20Plean%20for%202019%20(1).png) 89 | 90 | ### Books for April - May
91 | You can Download all the required books from here
92 | [Mastering Feature Engineering](http://dl.farinsoft.ir/ebooks/Mastering-Feature-Engineering-Principles-Techniques.pdf)
93 | [R for Data Science](https://r4ds.had.co.nz/)
94 | [Python for Data Analysis](https://www.bu.edu/tech/files/2017/09/Python-for-Data-Analysis.pdf)
95 | 96 | 97 | ### Books for June - Aug
98 | You can Download all the required books from here
99 | [The Element of Stastistics Learning](https://web.stanford.edu/~hastie/Papers/ESLII.pdf)
100 | [An Introduction to Stastistics Learning](https://www-bcf.usc.edu/~gareth/ISL/ISLR%20Sixth%20Printing.pdf)
101 | [Machine Learning with R](https://www.packtpub.com/packt/free-ebook/r-machine-learning)
102 | 103 | 104 | 105 | # Workflow on Data Analysis 106 | 107 | ![alt text](https://github.com/harshitahluwalia7895/100DaysOfMLCode/blob/master/Data%20Analysis%20Workflow.jpg) 108 | 109 | # Will Help all the freshers in machine learning for getting started 110 | # Day 1-Working with Pandas 111 | 112 | ![alt text](https://github.com/harshitahluwalia7895/100DaysOfMLCode/blob/master/Data%20explorationin%20python%20using.png) 113 | 114 | ### for code of Day-1 click [here](https://github.com/harshitahluwalia7895/100DaysOfMLCode/blob/master/DAY%201-Working%20with%20Pandas.ipynb) 115 | 116 | 117 | # Day 2-Simple Linear Regression 118 | 119 | ![alt text](https://github.com/harshitahluwalia7895/100DaysOfMLCode/blob/master/Day%202%20Simple%20Linear%20Regression.png) 120 | ![alt text](https://github.com/harshitahluwalia7895/100DaysOfMLCode/blob/master/1_xc5CSmK9d8oeKYxKxenEGg.gif) 121 | 122 | ### for code of Day-2 click [here](https://github.com/harshitahluwalia7895/100DaysOfMLCode/blob/master/Day%202%20Simple%20Linear%20Regression.ipynb) 123 | 124 | # Day 3-Logisitics Regression 125 | 126 | ![alt text](https://github.com/harshitahluwalia7895/100DaysOfMLCode/blob/master/%23100daysofmlcodeday%20-%203%C2%A9%20harshit%20ahluwalia.png) 127 | 128 | ### for code of Day-3 click [here]() 129 | 130 | # Day 4 Cheet Sheet on Scikit Learn 131 | 132 | ![alt text](https://github.com/harshitahluwalia7895/100DaysOfMLCode/blob/master/Cheat%20Sheet.png) 133 | 134 | # Day 5 K-Means Clustering 135 | 136 | ![alt text](https://github.com/harshitahluwalia7895/100DaysOfMLCode/blob/master/Day-5_K-Means_Clustering.png) 137 | 138 | 139 | 140 | # Top Algorithms for Prediction 141 | 142 | ![alt text](https://github.com/harshitahluwalia7895/100DaysOfMLCode/blob/master/Top_Algorithms_for_Predications.png) 143 | 144 | # Machine Learning vs Artificial Intelligence . What's the difference ? let's see..... 145 | 146 | ![alt text](https://github.com/harshitahluwalia7895/100DaysOfMLCode/blob/master/AI%20vs%20ML.jpg) 147 | 148 | 149 | # Data Science Resources 150 | 151 | ![alt text](https://github.com/harshitahluwalia7895/100DaysOfMLCode/blob/master/Data%20Science%20Resources.jpg) 152 | 153 | 154 | # Infographics for Youtube Channel 155 | ![alt_text](https://github.com/harshitahluwalia7895/100DaysOfMLCode/blob/master/Machine%20Learning%20Strategy.jpg) 156 | 157 | ##### Infographics for Python 158 | ![alt_text](https://github.com/harshitahluwalia7895/100DaysOfMLCode/blob/master/Youtube%20first%20Video%20Infographic.jpg) 159 | 160 | # 26 weeks of ML Code 161 | # Infographics and articles from Analytics Vidhya 162 | 163 | ### Week 1 : Git Basics & Introduction to Python 164 | ![alt_text](https://github.com/harshitahluwalia7895/100DaysOfMLCode/blob/master/Week%201%20Quick%20Guide%20to%20learn%20Python%20for%20Data%20Science.jpg) 165 | 166 | Download infographic (https://bit.ly/2HH9JcG) 167 | 168 | more to read (https://bit.ly/2HH9JcG) 169 | 170 | 171 | -------------------------------------------------------------------------------- /Top_Algorithms_for_Predications.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/harshitahluwalia7895/100DaysOfMLCode/758affbb64ae77458a193b70f64d590946a2a23f/Top_Algorithms_for_Predications.png -------------------------------------------------------------------------------- /Week 1 Quick Guide to learn Python for Data Science.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/harshitahluwalia7895/100DaysOfMLCode/758affbb64ae77458a193b70f64d590946a2a23f/Week 1 Quick Guide to learn Python for Data Science.jpg -------------------------------------------------------------------------------- /Youtube first Video Infographic.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/harshitahluwalia7895/100DaysOfMLCode/758affbb64ae77458a193b70f64d590946a2a23f/Youtube first Video Infographic.jpg -------------------------------------------------------------------------------- /collage.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/harshitahluwalia7895/100DaysOfMLCode/758affbb64ae77458a193b70f64d590946a2a23f/collage.jpg -------------------------------------------------------------------------------- /test.csv: -------------------------------------------------------------------------------- 1 | x,y 2 | 77,79.77515201 3 | 21,23.17727887 4 | 22,25.60926156 5 | 20,17.85738813 6 | 36,41.84986439 7 | 15,9.805234876 8 | 62,58.87465933 9 | 95,97.61793701 10 | 20,18.39512747 11 | 5,8.746747654 12 | 4,2.811415826 13 | 19,17.09537241 14 | 96,95.14907176 15 | 62,61.38800663 16 | 36,40.24701716 17 | 15,14.82248589 18 | 65,66.95806869 19 | 14,16.63507984 20 | 87,90.65513736 21 | 69,77.22982636 22 | 89,92.11906278 23 | 51,46.91387709 24 | 89,89.82634442 25 | 27,21.71380347 26 | 97,97.41206981 27 | 58,57.01631363 28 | 79,78.31056542 29 | 21,19.1315097 30 | 93,93.03483388 31 | 27,26.59112396 32 | 99,97.55155344 33 | 31,31.43524822 34 | 33,35.12724777 35 | 80,78.61042432 36 | 28,33.07112825 37 | 47,51.69967172 38 | 53,53.62235225 39 | 69,69.46306072 40 | 28,27.42497237 41 | 33,36.34644189 42 | 91,95.06140858 43 | 71,68.16724757 44 | 50,50.96155532 45 | 76,78.04237454 46 | 4,5.607664865 47 | 37,36.11334779 48 | 70,67.2352155 49 | 68,65.01324035 50 | 40,38.14753871 51 | 35,34.31141446 52 | 94,95.28503937 53 | 88,87.84749912 54 | 52,54.08170635 55 | 31,31.93063515 56 | 59,59.61247085 57 | 0,-1.040114209 58 | 39,47.49374765 59 | 64,62.60089773 60 | 69,70.9146434 61 | 57,56.14834113 62 | 13,14.05572877 63 | 72,68.11367147 64 | 76,75.59701346 65 | 61,59.225745 66 | 82,85.45504157 67 | 18,17.76197116 68 | 41,38.68888682 69 | 50,50.96343637 70 | 55,51.83503872 71 | 13,17.0761107 72 | 46,46.56141773 73 | 13,10.34754461 74 | 79,77.91032969 75 | 53,50.17008622 76 | 15,13.25690647 77 | 28,31.32274932 78 | 81,73.9308764 79 | 69,74.45114379 80 | 52,52.01932286 81 | 84,83.68820499 82 | 68,70.3698748 83 | 27,23.44479161 84 | 56,49.83051801 85 | 48,49.88226593 86 | 40,41.04525583 87 | 39,33.37834391 88 | 82,81.29750133 89 | 100,105.5918375 90 | 59,56.82457013 91 | 43,48.67252645 92 | 67,67.02150613 93 | 38,38.43076389 94 | 63,58.61466887 95 | 91,89.12377509 96 | 60,60.9105427 97 | 14,13.83959878 98 | 21,16.89085185 99 | 87,84.06676818 100 | 73,70.34969772 101 | 32,33.38474138 102 | 2,-1.63296825 103 | 82,88.54475895 104 | 19,17.44047622 105 | 74,75.69298554 106 | 42,41.97607107 107 | 12,12.59244741 108 | 1,0.275307261 109 | 90,98.13258005 110 | 89,87.45721555 111 | 0,-2.344738542 112 | 41,39.3294153 113 | 16,16.68715211 114 | 94,96.58888601 115 | 97,97.70342201 116 | 66,67.01715955 117 | 24,25.63476257 118 | 17,13.41310757 119 | 90,95.15647284 120 | 13,9.744164258 121 | 0,-3.467883789 122 | 64,62.82816355 123 | 96,97.27405461 124 | 98,95.58017185 125 | 12,7.468501839 126 | 41,45.44599591 127 | 47,46.69013968 128 | 78,74.4993599 129 | 20,21.63500655 130 | 89,91.59548851 131 | 29,26.49487961 132 | 64,67.38654703 133 | 75,74.25362837 134 | 12,12.07991648 135 | 25,21.32273728 136 | 28,29.31770045 137 | 30,26.48713683 138 | 65,68.94699774 139 | 59,59.10598995 140 | 64,64.37521087 141 | 53,60.20758349 142 | 71,70.34329706 143 | 97,97.1082562 144 | 73,75.7584178 145 | 9,10.80462727 146 | 12,12.11219941 147 | 63,63.28312382 148 | 99,98.03017721 149 | 60,63.19354354 150 | 35,34.8534823 151 | 2,-2.819913974 152 | 60,59.8313966 153 | 32,29.38505024 154 | 94,97.00148372 155 | 84,85.18657275 156 | 63,61.74063192 157 | 22,18.84798163 158 | 81,78.79008525 159 | 93,95.12400481 160 | 33,30.48881287 161 | 7,10.41468095 162 | 42,38.98317436 163 | 46,46.11021062 164 | 54,52.45103628 165 | 16,21.16523945 166 | 49,52.28620611 167 | 43,44.18863945 168 | 95,97.13832018 169 | 66,67.22008001 170 | 21,18.98322306 171 | 35,24.3884599 172 | 80,79.44769523 173 | 37,40.03504862 174 | 54,53.32005764 175 | 56,54.55446979 176 | 1,-2.761182595 177 | 32,37.80182795 178 | 58,57.48741435 179 | 32,36.06292994 180 | 46,49.83538167 181 | 72,74.68953276 182 | 17,14.86159401 183 | 97,101.0697879 184 | 93,99.43577876 185 | 91,91.69240746 186 | 37,34.12473248 187 | 4,6.079390073 188 | 54,59.07247174 189 | 51,56.43046022 190 | 27,30.49412933 191 | 46,48.35172635 192 | 92,89.73153611 193 | 73,72.86282528 194 | 77,80.97144285 195 | 91,91.36566374 196 | 61,60.07137496 197 | 99,99.87382707 198 | 4,8.655714172 199 | 72,69.39858505 200 | 19,19.38780134 201 | 57,53.11628433 202 | 78,78.39683006 203 | 26,25.75612514 204 | 74,75.07484683 205 | 90,92.88772282 206 | 66,69.45498498 207 | 13,13.12109842 208 | 40,48.09843134 209 | 77,79.3142548 210 | 67,68.48820749 211 | 75,73.2300846 212 | 23,24.68362712 213 | 45,41.90368917 214 | 59,62.22635684 215 | 44,45.96396877 216 | 23,23.52647153 217 | 55,51.80035866 218 | 55,51.10774273 219 | 95,95.79747345 220 | 12,9.241138977 221 | 4,7.646529763 222 | 7,9.281699753 223 | 100,103.5266162 224 | 48,47.41006725 225 | 42,42.03835773 226 | 96,96.11982476 227 | 39,38.05766408 228 | 100,105.4503788 229 | 87,88.80306911 230 | 14,15.49301141 231 | 14,12.42624606 232 | 37,40.00709598 233 | 5,5.634030902 234 | 88,87.36938931 235 | 91,89.73951993 236 | 65,66.61499643 237 | 74,72.9138853 238 | 56,57.19103506 239 | 16,11.21710477 240 | 5,0.676076749 241 | 28,28.15668543 242 | 92,95.3958003 243 | 46,52.05490703 244 | 54,59.70864577 245 | 39,36.79224762 246 | 44,37.08457698 247 | 31,24.18437976 248 | 68,67.28725332 249 | 86,82.870594 250 | 90,89.899991 251 | 38,36.94173178 252 | 21,19.87562242 253 | 95,90.71481654 254 | 56,61.09367762 255 | 60,60.11134958 256 | 65,64.83296316 257 | 78,81.40381769 258 | 89,92.40217686 259 | 6,2.576625376 260 | 67,63.80768172 261 | 36,38.67780759 262 | 16,16.82839701 263 | 100,99.78687252 264 | 45,44.68913433 265 | 73,71.00377824 266 | 57,51.57326718 267 | 20,19.87846479 268 | 76,79.50341495 269 | 34,34.58876491 270 | 55,55.7383467 271 | 72,68.19721905 272 | 55,55.81628509 273 | 8,9.391416798 274 | 56,56.01448111 275 | 72,77.9969477 276 | 58,55.37049953 277 | 6,11.89457829 278 | 96,94.79081712 279 | 23,25.69041546 280 | 58,53.52042319 281 | 23,18.31396758 282 | 19,21.42637785 283 | 25,30.41303282 284 | 64,67.68142149 285 | 21,17.0854783 286 | 59,60.91792707 287 | 19,14.99514319 288 | 16,16.74923937 289 | 42,41.46923883 290 | 43,42.84526108 291 | 61,59.12912974 292 | 92,91.30863673 293 | 11,8.673336357 294 | 41,39.31485292 295 | 1,5.313686205 296 | 8,5.405220518 297 | 71,68.5458879 298 | 46,47.33487629 299 | 55,54.09063686 300 | 62,63.29717058 301 | 47,52.45946688 -------------------------------------------------------------------------------- /train.csv: -------------------------------------------------------------------------------- 1 | x,y 2 | 24,21.54945196 3 | 50,47.46446305 4 | 15,17.21865634 5 | 38,36.58639803 6 | 87,87.28898389 7 | 36,32.46387493 8 | 12,10.78089683 9 | 81,80.7633986 10 | 25,24.61215147 11 | 5,6.963319071 12 | 16,11.23757338 13 | 16,13.53290206 14 | 24,24.60323899 15 | 39,39.40049976 16 | 54,48.43753838 17 | 60,61.69900319 18 | 26,26.92832418 19 | 73,70.4052055 20 | 29,29.34092408 21 | 31,25.30895192 22 | 68,69.02934339 23 | 87,84.99484703 24 | 58,57.04310305 25 | 54,50.5921991 26 | 84,83.02772202 27 | 58,57.05752706 28 | 49,47.95883341 29 | 20,24.34226432 30 | 90,94.68488281 31 | 48,48.03970696 32 | 4,7.08132338 33 | 25,21.99239907 34 | 42,42.33151664 35 | 0,0.329089443 36 | 60,61.92303698 37 | 93,91.17716423 38 | 39,39.45358014 39 | 7,5.996069607 40 | 21,22.59015942 41 | 68,61.18044414 42 | 84,85.02778957 43 | 0,-1.28631089 44 | 58,61.94273962 45 | 19,21.96033347 46 | 36,33.66194193 47 | 19,17.60946242 48 | 59,58.5630564 49 | 51,52.82390762 50 | 19,22.1363481 51 | 33,35.07467353 52 | 85,86.18822311 53 | 44,42.63227697 54 | 5,4.09817744 55 | 59,61.2229864 56 | 14,17.70677576 57 | 9,11.85312574 58 | 75,80.23051695 59 | 69,62.64931741 60 | 10,9.616859804 61 | 17,20.02797699 62 | 58,61.7510743 63 | 74,71.61010303 64 | 21,23.77154623 65 | 51,51.90142035 66 | 19,22.66073682 67 | 50,50.02897927 68 | 24,26.68794368 69 | 0,0.376911899 70 | 12,6.806419002 71 | 75,77.33986001 72 | 21,28.90260209 73 | 64,66.7346608 74 | 5,0.707510638 75 | 58,57.07748383 76 | 32,28.41453196 77 | 41,44.46272123 78 | 7,7.459605998 79 | 4,2.316708112 80 | 5,4.928546187 81 | 49,52.50336074 82 | 90,91.19109623 83 | 3,8.489164326 84 | 11,6.963371967 85 | 32,31.97989959 86 | 83,81.4281205 87 | 25,22.62365422 88 | 83,78.52505087 89 | 26,25.80714057 90 | 76,73.51081775 91 | 95,91.775467 92 | 53,49.21863516 93 | 77,80.50445387 94 | 42,50.05636123 95 | 25,25.46292549 96 | 54,55.32164264 97 | 55,59.1244888 98 | 0,1.100686692 99 | 73,71.98020786 100 | 35,30.13666408 101 | 86,83.88427405 102 | 90,89.91004752 103 | 13,8.335654576 104 | 46,47.88388961 105 | 46,45.00397413 106 | 32,31.15664574 107 | 8,9.190375682 108 | 71,74.83135003 109 | 28,30.23177607 110 | 24,24.21914027 111 | 56,57.87219151 112 | 49,50.61728392 113 | 79,78.67470043 114 | 90,86.236707 115 | 89,89.10409255 116 | 41,43.26595082 117 | 27,26.68273277 118 | 58,59.46383041 119 | 26,28.90055826 120 | 31,31.300416 121 | 70,71.1433266 122 | 71,68.4739206 123 | 39,39.98238856 124 | 7,4.075776144 125 | 48,47.85817542 126 | 56,51.20390217 127 | 45,43.9367213 128 | 41,38.13626679 129 | 3,3.574661632 130 | 37,36.4139958 131 | 24,22.21908523 132 | 68,63.5312572 133 | 47,49.86702787 134 | 27,21.53140009 135 | 68,64.05710234 136 | 74,70.77549842 137 | 95,92.15749762 138 | 79,81.22259156 139 | 21,25.10114067 140 | 95,94.08853397 141 | 54,53.25166165 142 | 56,59.16236621 143 | 80,75.24148428 144 | 26,28.22325833 145 | 25,25.33323728 146 | 8,6.364615703 147 | 95,95.4609216 148 | 94,88.64183756 149 | 54,58.70318693 150 | 7,6.815491279 151 | 99,99.40394676 152 | 36,32.77049249 153 | 48,47.0586788 154 | 65,60.53321778 155 | 42,40.30929858 156 | 93,89.42222685 157 | 86,86.82132066 158 | 26,26.11697543 159 | 51,53.26657596 160 | 100,96.62327888 161 | 94,95.78441027 162 | 6,6.047286687 163 | 24,24.47387908 164 | 75,75.96844763 165 | 7,3.829381009 166 | 53,52.51703683 167 | 73,72.80457527 168 | 16,14.10999096 169 | 80,80.86087062 170 | 77,77.01988215 171 | 89,86.26972444 172 | 80,77.13735466 173 | 55,51.47649476 174 | 19,17.34557531 175 | 56,57.72853572 176 | 47,44.15029394 177 | 56,59.24362743 178 | 2,-1.053275611 179 | 82,86.79002254 180 | 57,60.14031858 181 | 44,44.04222058 182 | 26,24.5227488 183 | 52,52.95305521 184 | 41,43.16133498 185 | 44,45.67562576 186 | 3,-2.830749501 187 | 31,29.19693178 188 | 97,96.49812401 189 | 21,22.5453232 190 | 17,20.10741433 191 | 7,4.035430253 192 | 61,61.14568518 193 | 10,13.97163653 194 | 52,55.34529893 195 | 10,12.18441166 196 | 65,64.00077658 197 | 71,70.3188322 198 | 4,-0.936895047 199 | 24,18.91422276 200 | 26,23.87590331 201 | 51,47.5775361 202 | 42,43.2736092 203 | 62,66.48278755 204 | 74,75.72605529 205 | 77,80.59643338 206 | 3,-2.235879852 207 | 50,47.04654956 208 | 24,21.59635575 209 | 37,32.87558963 210 | 58,57.95782956 211 | 52,52.24760027 212 | 27,24.58286902 213 | 14,12.12573805 214 | 100,100.0158026 215 | 3530.15736917 216 | 72,74.04682658 217 | 5,1.611947467 218 | 71,70.36836307 219 | 54,52.26831735 220 | 84,83.1286166 221 | 42,43.64765048 222 | 54,49.44785426 223 | 74,72.6356699 224 | 54,52.78130641 225 | 53,57.11195136 226 | 78,79.1050629 227 | 97,101.6228548 228 | 49,53.5825402 229 | 71,68.92139297 230 | 48,46.9666961 231 | 51,51.02642868 232 | 89,85.52073551 233 | 99,99.51685756 234 | 93,94.63911256 235 | 49,46.78357742 236 | 18,21.21321959 237 | 65,58.37266004 238 | 83,87.22059677 239 | 100,102.4967859 240 | 41,43.88314335 241 | 52,53.06655757 242 | 29,26.33464785 243 | 97,98.52008934 244 | 7,9.400497579 245 | 51,52.94026699 246 | 58,53.83020877 247 | 50,45.94511142 248 | 67,65.0132736 249 | 89,86.5069584 250 | 76,75.63280796 251 | 35,36.78035027 252 | 99,100.5328916 253 | 31,29.04466136 254 | 52,51.70352433 255 | 11,9.199954718 256 | 66,71.70015848 257 | 50,49.82634062 258 | 39,37.49971096 259 | 60,53.65084683 260 | 35,33.92561965 261 | 53,49.92639685 262 | 14,8.148154262 263 | 49,49.72359037 264 | 16,16.16712757 265 | 76,75.30033002 266 | 13,9.577368568 267 | 51,48.38088357 268 | 70,72.95331671 269 | 98,92.59573853 270 | 86,88.85523586 271 | 100,99.00361771 272 | 46,45.09439571 273 | 51,46.94362684 274 | 50,48.33449605 275 | 91,94.92329574 276 | 48,47.78165248 277 | 81,81.28960746 278 | 38,37.83155021 279 | 40,39.69185252 280 | 79,76.92664854 281 | 96,88.02990531 282 | 60,56.99178872 283 | 70,72.58929383 284 | 44,44.98103442 285 | 11,11.99017641 286 | 6,1.919513328 287 | 5,1.628826073 288 | 72,66.27746655 289 | 55,57.53887255 290 | 95,94.70291077 291 | 41,41.21469904 292 | 25,25.04169243 293 | 1,3.778209914 294 | 55,50.50711779 295 | 4,9.682408486 296 | 48,48.88147608 297 | 55,54.40348599 298 | 75,71.70233156 299 | 68,69.35848388 300 | 100,99.98491591 301 | 25,26.03323718 302 | 75,75.48910307 303 | 34,36.59623056 304 | 38,40.95102191 305 | 92,86.78316267 306 | 21,15.50701184 307 | 88,85.86077871 308 | 75,79.20610113 309 | 76,80.80643766 310 | 44,48.59717283 311 | 10,13.93415049 312 | 21,27.3051179 313 | 16,14.00226297 314 | 32,33.67416 315 | 13,13.11612884 316 | 26,24.76649193 317 | 70,73.68477876 318 | 77,77.53149541 319 | 77,76.24503196 320 | 88,88.0578931 321 | 35,35.02445799 322 | 24,21.65857739 323 | 17,17.33681562 324 | 91,94.36778957 325 | 32,33.43396307 326 | 36,32.52179399 327 | 89,90.57741298 328 | 69,71.25634126 329 | 30,31.23212856 330 | 6,5.398840061 331 | 22,18.56241391 332 | 67,71.97121038 333 | 9,5.225759566 334 | 74,73.5964342 335 | 50,49.76948983 336 | 85,82.69087513 337 | 3,1.652309089 338 | 0,-3.836652144 339 | 59,62.03811556 340 | 62,61.26514581 341 | 17,13.24991628 342 | 90,88.61672694 343 | 23,21.13655528 344 | 19,23.85017475 345 | 93,92.01203405 346 | 14,10.26712261 347 | 58,54.14681616 348 | 87,87.00645713 349 | 37,37.69447352 350 | 20,19.62278654 351 | 35,34.78561007 352 | 63,62.03190983 353 | 56,52.67003801 354 | 62,58.09031476 355 | 98,97.19448821 356 | 90,90.50155298 357 | 51,50.5123462 358 | 93,94.45211871 359 | 22,21.10794636 360 | 38,37.36298431 361 | 13,10.28574844 362 | 98,96.04932416 363 | 99,100.0953697 364 | 31,30.6063167 365 | 94,96.19000542 366 | 73,71.30828034 367 | 37,34.59311043 368 | 23,19.02332876 369 | 11,10.76669688 370 | 88,90.5799868 371 | 47,48.71787679 372 | 79,78.74139764 373 | 91,85.23492274 374 | 71,71.65789964 375 | 10,8.938990554 376 | 39,39.89606046 377 | 92,91.85091116 378 | 99,99.11200375 379 | 28,26.22196486 380 | 32,33.21584226 381 | 32,35.72392691 382 | 75,76.88604495 383 | 99,99.30874567 384 | 27,25.77161074 385 | 64,67.85169407 386 | 98,98.50371084 387 | 38,31.11331895 388 | 46,45.51171028 389 | 13,12.65537808 390 | 96,95.56065366 391 | 9,9.526431641 392 | 34,36.10893209 393 | 49,46.43628318 394 | 1,-3.83998112 395 | 50,48.97302037 396 | 94,93.25305499 397 | 27,23.47650968 398 | 20,17.13551132 399 | 12,14.55896144 400 | 45,41.53992729 401 | 91,91.64730552 402 | 61,66.16652565 403 | 10,9.230857489 404 | 47,47.41377893 405 | 33,34.76441561 406 | 84,86.10796637 407 | 24,21.81267954 408 | 48,48.89963951 409 | 48,46.78108638 410 | 9,12.91328547 411 | 93,94.55203143 412 | 99,94.97068753 413 | 8,2.379172481 414 | 20,21.47982988 415 | 38,35.79795462 416 | 78,82.0763803 417 | 81,78.87097714 418 | 42,47.2492425 419 | 95,96.18852325 420 | 78,78.38491927 421 | 44,42.94274064 422 | 68,64.43231595 423 | 87,84.21191485 424 | 58,57.3069783 425 | 52,52.52101436 426 | 26,25.7440243 427 | 75,75.42283401 428 | 48,53.62523007 429 | 71,75.14466308 430 | 77,74.12151511 431 | 34,36.24807243 432 | 24,20.21665898 433 | 70,66.94758118 434 | 29,34.07278254 435 | 76,73.13850045 436 | 98,92.85929155 437 | 28,28.36793808 438 | 87,85.59308727 439 | 9,10.68453755 440 | 87,86.10708624 441 | 33,33.22031418 442 | 64,66.09563422 443 | 17,19.30486546 444 | 49,48.84542083 445 | 95,93.73176312 446 | 75,75.45758614 447 | 89,91.24239226 448 | 81,87.15690853 449 | 25,25.53752833 450 | 47,46.06629478 451 | 50,49.65277661 452 | 5,7.382244165 453 | 68,71.11189935 454 | 84,83.50570521 455 | 8,8.791139893 456 | 41,33.30638903 457 | 26,26.40362524 458 | 89,91.72960726 459 | 78,82.53030719 460 | 34,36.67762733 461 | 92,86.98450355 462 | 27,32.34784175 463 | 12,16.78353974 464 | 2,1.576584383 465 | 22,17.4618141 466 | 0,2.116113029 467 | 26,24.34804332 468 | 50,48.29491198 469 | 84,85.52145453 470 | 70,73.71434779 471 | 66,63.15189497 472 | 42,38.46213684 473 | 19,19.47100788 474 | 94,94.07428225 475 | 71,67.92051286 476 | 19,22.58096241 477 | 16,16.01629889 478 | 49,48.43307886 479 | 29,29.6673599 480 | 29,26.65566328 481 | 86,86.28206739 482 | 50,50.82304924 483 | 86,88.57251713 484 | 30,32.59980745 485 | 23,21.02469368 486 | 20,20.72894979 487 | 16,20.38051187 488 | 57,57.25180153 489 | 8,6.967537054 490 | 8,10.240085 491 | 62,64.94841088 492 | 55,55.35893915 493 | 30,31.24365589 494 | 86,90.72048818 495 | 62,58.750127 496 | 51,55.85003198 497 | 61,60.19925869 498 | 86,85.03295412 499 | 61,60.38823085 500 | 21,18.44679787 501 | 81,82.18839247 502 | 97,94.2963344 503 | 5,7.682024586 504 | 61,61.01858089 505 | 47,53.60562216 506 | 98,94.47728801 507 | 30,27.9645947 508 | 63,62.55662585 509 | 0,1.406254414 510 | 100,101.7003412 511 | 18,13.84973988 512 | 30,28.99769315 513 | 98,99.04315693 514 | 16,15.56135514 515 | 22,24.63528393 516 | 55,53.98393374 517 | 43,42.91449728 518 | 75,74.29662112 519 | 91,91.17012883 520 | 46,49.42440876 521 | 85,82.47683519 522 | 55,56.15303953 523 | 36,37.17063131 524 | 49,46.36928662 525 | 94,97.02383456 526 | 43,40.83182104 527 | 22,24.08498313 528 | 37,41.14386358 529 | 24,21.97388066 530 | 95,100.740897 531 | 61,61.19971596 532 | 75,74.39517002 533 | 68,69.04377173 534 | 58,56.68718792 535 | 5,5.860391715 536 | 53,55.72021356 537 | 80,79.22021816 538 | 83,86.30177517 539 | 25,25.26971886 540 | 34,36.33294447 541 | 26,27.65574228 542 | 90,94.79690531 543 | 60,58.67366671 544 | 49,56.15934471 545 | 19,18.40919388 546 | 92,86.26936988 547 | 29,26.59436195 548 | 8,8.452520159 549 | 57,56.18131518 550 | 29,27.65452669 551 | 19,20.87391785 552 | 81,77.83354439 553 | 50,50.01787825 554 | 15,9.290856256 555 | 70,75.0284725 556 | 39,38.3037698 557 | 43,44.70786405 558 | 21,22.51016575 559 | 98,102.4959452 560 | 86,86.76845244 561 | 16,13.89748578 562 | 25,24.81824269 563 | 31,33.94224862 564 | 93,92.26970059 565 | 67,68.73365081 566 | 49,47.38516883 567 | 25,32.37576914 568 | 88,87.67388681 569 | 54,54.57648371 570 | 21,18.06450222 571 | 8,7.896539841 572 | 32,35.00341078 573 | 35,36.72823317 574 | 67,65.84975426 575 | 90,89.59295492 576 | 59,61.69026202 577 | 15,11.60499315 578 | 67,71.0826803 579 | 42,43.71901164 580 | 44,41.57421008 581 | 77,74.25552425 582 | 68,66.28310437 583 | 36,36.62438077 584 | 11,10.32374866 585 | 10,7.156457657 586 | 65,67.88603132 587 | 98,101.1097591 588 | 98,98.6132033 589 | 49,50.19083844 590 | 31,27.83896261 591 | 56,55.9249564 592 | 70,76.47340872 593 | 91,92.05756378 594 | 25,27.35245439 595 | 54,55.32083476 596 | 39,41.39990349 597 | 91,93.59057024 598 | 3,5.297054029 599 | 22,21.01429422 600 | 2,2.267059451 601 | 2,-0.121860502 602 | 65,66.49546208 603 | 71,73.83637687 604 | 42,42.10140878 605 | 76,77.35135732 606 | 43,41.02251779 607 | 8,14.75305272 608 | 86,83.28199022 609 | 87,89.93374342 610 | 3,2.286571686 611 | 58,55.61421297 612 | 62,62.15313408 613 | 89,89.55803528 614 | 95,94.00291863 615 | 28,26.78023848 616 | 0,-0.764537626 617 | 1,0.282866003 618 | 49,44.26800515 619 | 21,19.85174138 620 | 46,47.15960005 621 | 11,8.359366572 622 | 89,92.08157084 623 | 37,41.88734051 624 | 29,30.5413129 625 | 44,46.87654473 626 | 96,96.35659485 627 | 16,17.9170699 628 | 74,71.67949917 629 | 35,32.64997554 630 | 42,39.34482965 631 | 16,17.03401999 632 | 56,52.87524074 633 | 18,15.85414849 634 | 100,108.8716183 635 | 54,49.30477253 636 | 92,89.4749477 637 | 63,63.67348242 638 | 81,83.78410946 639 | 73,73.51136922 640 | 48,46.80297244 641 | 1,5.809946802 642 | 85,85.23027975 643 | 14,10.58213964 644 | 25,21.37698317 645 | 45,46.0537745 646 | 98,95.2389253 647 | 97,94.15149206 648 | 58,54.54868046 649 | 93,87.36260449 650 | 88,88.47741598 651 | 89,84.48045678 652 | 47,48.79647071 653 | 6,10.76675683 654 | 34,30.48882921 655 | 30,29.76846185 656 | 16,13.51574749 657 | 86,86.12955884 658 | 40,43.30022747 659 | 52,51.92110232 660 | 15,16.49185287 661 | 4,7.998073432 662 | 95,97.66689567 663 | 99,89.80545367 664 | 35,38.07166567 665 | 58,60.27852322 666 | 10,6.709195759 667 | 16,18.35488924 668 | 53,56.37058203 669 | 58,62.80064204 670 | 42,41.25155632 671 | 24,19.42637541 672 | 84,82.88935804 673 | 64,63.61364981 674 | 12,11.29627199 675 | 61,60.02274882 676 | 75,72.60339326 677 | 15,11.87964573 678 | 100,100.7012737 679 | 43,45.12420809 680 | 13,14.81106804 681 | 48,48.09368034 682 | 45,42.29145672 683 | 52,52.73389794 684 | 34,36.72396986 685 | 30,28.64535198 686 | 65,62.16675273 687 | 100,95.58459518 688 | 67,66.04325304 689 | 99,99.9566225 690 | 45,46.14941984 691 | 87,89.13754963 692 | 73,69.71787806 693 | 9,12.31736648 694 | 81,78.20296268 695 | 72,71.30995371 696 | 81,81.45544709 697 | 58,58.59500642 698 | 93,94.62509374 699 | 82,88.60376995 700 | 66,63.64868529 701 | 97,94.9752655 --------------------------------------------------------------------------------